{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceType":"competition","sourceId":81933,"databundleVersionId":9643020},{"sourceType":"datasetVersion","sourceId":7453542,"datasetId":921302,"databundleVersionId":7545479}],"dockerImageVersionId":31329,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true},"papermill":{"default_parameters":{},"duration":194.051082,"end_time":"2025-05-23T02:59:22.896257","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2025-05-23T02:56:08.845175","version":"2.6.0"}},"nbformat_minor":5,"nbformat":4,"cells":[{"id":"3571ab3b","cell_type":"markdown","source":"# Overview\n\nBài toán là dự đoán mức độ nghiện Internet (SII) của một nhóm người, đo lường mức độ sử dụng Internet có vấn đề ở trẻ em và thanh thiếu niên, dựa trên dữ liệu hoạt động thể chất và các đặc trưng khác. Trong tập dữ liệu huấn luyện, các người tham gia trả lời 20 câu hỏi (PCIAT-PCIAT_1, PCIAT-PCIAT_2, ..., PCIAT-PCIAT_20), trong đó mỗi câu hỏi được chấm điểm từ 0 đến 3, với 0 là không có mức độ nghiêm trọng và 3 là mức độ nghiêm trọng cao nhất. Tổng điểm của 20 câu hỏi này được tính trong cột PCIAT-PCIAT_Total và được ánh xạ sang cột mục tiêu SII, có giá trị từ 0 đến 3. Đây là cột cần được dự đoán.","metadata":{"papermill":{"duration":0.025459,"end_time":"2025-05-23T02:56:11.375406","exception":false,"start_time":"2025-05-23T02:56:11.349947","status":"completed"},"tags":[]}},{"id":"4dadc109","cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport seaborn as sns\nimport warnings\nfrom xgboost import XGBRegressor\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import Pipeline\nfrom xgboost import XGBClassifier\n\nimport os\nimport re\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MaxNLocator, FormatStrFormatter, PercentFormatter\nimport seaborn as sns\nimport plotly.subplots as sp\nimport plotly.express as px\nfrom concurrent.futures import ThreadPoolExecutor\nfrom colorama import Fore, Style\nfrom IPython.display import clear_output\nfrom IPython.display import display\nimport warnings\nwarnings.filterwarnings('ignore')\npd.options.display.max_columns = None\n\nfrom sklearn.base import clone, BaseEstimator, RegressorMixin\nfrom sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\nfrom sklearn.ensemble import StackingRegressor\nfrom sklearn.linear_model import Ridge\nfrom sklearn.experimental import enable_iterative_imputer\nfrom sklearn.impute import IterativeImputer\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc\nfrom sklearn.metrics import cohen_kappa_score\nfrom sklearn.model_selection import StratifiedKFold\nfrom scipy.optimize import minimize\nfrom sklearn.ensemble import VotingRegressor, RandomForestRegressor, GradientBoostingRegressor, HistGradientBoostingRegressor, ExtraTreesRegressor\nfrom sklearn.impute import SimpleImputer, KNNImputer\nfrom sklearn.pipeline import Pipeline\nfrom lightgbm import LGBMRegressor\nfrom xgboost import XGBRegressor\nfrom catboost import CatBoostRegressor\nfrom sklearn.model_selection import GridSearchCV\n\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.impute import SimpleImputer, KNNImputer\nfrom sklearn.model_selection import train_test_split\nimport os\nimport torch\n\nfrom keras.models import Model\nfrom keras.layers import Input, Dense\nfrom keras.optimizers import Adam\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2026-04-24T07:59:14.110618Z","iopub.execute_input":"2026-04-24T07:59:14.110982Z","iopub.status.idle":"2026-04-24T07:59:14.122826Z","shell.execute_reply.started":"2026-04-24T07:59:14.110952Z","shell.execute_reply":"2026-04-24T07:59:14.122027Z"},"papermill":{"duration":20.169928,"end_time":"2025-05-23T02:56:31.56921","exception":false,"start_time":"2025-05-23T02:56:11.399282","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":430},{"id":"52f43148","cell_type":"markdown","source":"## Load data","metadata":{"papermill":{"duration":0.023053,"end_time":"2025-05-23T02:56:31.616367","exception":false,"start_time":"2025-05-23T02:56:31.593314","status":"completed"},"tags":[]}},{"id":"f9a67fc6","cell_type":"code","source":"train = pd.read_csv('/kaggle/input/competitions/child-mind-institute-problematic-internet-use/train.csv')\ntest = pd.read_csv('/kaggle/input/competitions/child-mind-institute-problematic-internet-use/test.csv')\ndata_dict = pd.read_csv('/kaggle/input/competitions/child-mind-institute-problematic-internet-use/data_dictionary.csv')\ntrain.head(5)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:14.124307Z","iopub.execute_input":"2026-04-24T07:59:14.124609Z","iopub.status.idle":"2026-04-24T07:59:14.255632Z","shell.execute_reply.started":"2026-04-24T07:59:14.124583Z","shell.execute_reply":"2026-04-24T07:59:14.254729Z"},"papermill":{"duration":0.155993,"end_time":"2025-05-23T02:56:31.795784","exception":false,"start_time":"2025-05-23T02:56:31.639791","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":431,"output_type":"execute_result","data":{"text/plain":"         id Basic_Demos-Enroll_Season  Basic_Demos-Age  Basic_Demos-Sex  \\\n0  00008ff9                      Fall                5                0   \n1  000fd460                    Summer                9                0   \n2  00105258                    Summer               10                1   \n3  00115b9f                    Winter                9                0   \n4  0016bb22                    Spring               18                1   \n\n  CGAS-Season  CGAS-CGAS_Score Physical-Season  Physical-BMI  Physical-Height  \\\n0      Winter             51.0            Fall     16.877316             46.0   \n1         NaN              NaN            Fall     14.035590             48.0   \n2        Fall             71.0            Fall     16.648696             56.5   \n3        Fall             71.0          Summer     18.292347             56.0   \n4      Summer              NaN             NaN           NaN              NaN   \n\n   Physical-Weight  Physical-Waist_Circumference  Physical-Diastolic_BP  \\\n0             50.8                           NaN                    NaN   \n1             46.0                          22.0                   75.0   \n2             75.6                           NaN                   65.0   \n3             81.6                           NaN                   60.0   \n4              NaN                           NaN                    NaN   \n\n   Physical-HeartRate  Physical-Systolic_BP Fitness_Endurance-Season  \\\n0                 NaN                   NaN                      NaN   \n1                70.0                 122.0                      NaN   \n2                94.0                 117.0                     Fall   \n3                97.0                 117.0                   Summer   \n4                 NaN                   NaN                      NaN   \n\n   Fitness_Endurance-Max_Stage  Fitness_Endurance-Time_Mins  \\\n0                          NaN                          NaN   \n1                          NaN                          NaN   \n2                          5.0                          7.0   \n3                          6.0                          9.0   \n4                          NaN                          NaN   \n\n   Fitness_Endurance-Time_Sec FGC-Season  FGC-FGC_CU  FGC-FGC_CU_Zone  \\\n0                         NaN       Fall         0.0              0.0   \n1                         NaN       Fall         3.0              0.0   \n2                        33.0       Fall        20.0              1.0   \n3                        37.0     Summer        18.0              1.0   \n4                         NaN        NaN         NaN              NaN   \n\n   FGC-FGC_GSND  FGC-FGC_GSND_Zone  FGC-FGC_GSD  FGC-FGC_GSD_Zone  FGC-FGC_PU  \\\n0           NaN                NaN          NaN               NaN         0.0   \n1           NaN                NaN          NaN               NaN         5.0   \n2          10.2                1.0         14.7               2.0         7.0   \n3           NaN                NaN          NaN               NaN         5.0   \n4           NaN                NaN          NaN               NaN         NaN   \n\n   FGC-FGC_PU_Zone  FGC-FGC_SRL  FGC-FGC_SRL_Zone  FGC-FGC_SRR  \\\n0              0.0          7.0               0.0          6.0   \n1              0.0         11.0               1.0         11.0   \n2              1.0         10.0               1.0         10.0   \n3              0.0          7.0               0.0          7.0   \n4              NaN          NaN               NaN          NaN   \n\n   FGC-FGC_SRR_Zone  FGC-FGC_TL  FGC-FGC_TL_Zone BIA-Season  \\\n0               0.0         6.0              1.0       Fall   \n1               1.0         3.0              0.0     Winter   \n2               1.0         5.0              0.0        NaN   \n3               0.0         7.0              1.0     Summer   \n4               NaN         NaN              NaN        NaN   \n\n   BIA-BIA_Activity_Level_num  BIA-BIA_BMC  BIA-BIA_BMI  BIA-BIA_BMR  \\\n0                         2.0      2.66855      16.8792      932.498   \n1                         2.0      2.57949      14.0371      936.656   \n2                         NaN          NaN          NaN          NaN   \n3                         3.0      3.84191      18.2943     1131.430   \n4                         NaN          NaN          NaN          NaN   \n\n   BIA-BIA_DEE  BIA-BIA_ECW  BIA-BIA_FFM  BIA-BIA_FFMI  BIA-BIA_FMI  \\\n0      1492.00      8.25598      41.5862       13.8177      3.06143   \n1      1498.65      6.01993      42.0291       12.8254      1.21172   \n2          NaN          NaN          NaN           NaN          NaN   \n3      1923.44     15.59250      62.7757       14.0740      4.22033   \n4          NaN          NaN          NaN           NaN          NaN   \n\n   BIA-BIA_Fat  BIA-BIA_Frame_num  BIA-BIA_ICW  BIA-BIA_LDM  BIA-BIA_LST  \\\n0      9.21377                1.0      24.4349      8.89536      38.9177   \n1      3.97085                1.0      21.0352     14.97400      39.4497   \n2          NaN                NaN          NaN          NaN          NaN   \n3     18.82430                2.0      30.4041     16.77900      58.9338   \n4          NaN                NaN          NaN          NaN          NaN   \n\n   BIA-BIA_SMM  BIA-BIA_TBW PAQ_A-Season  PAQ_A-PAQ_A_Total PAQ_C-Season  \\\n0      19.5413      32.6909          NaN                NaN          NaN   \n1      15.4107      27.0552          NaN                NaN         Fall   \n2          NaN          NaN          NaN                NaN       Summer   \n3      26.4798      45.9966          NaN                NaN       Winter   \n4          NaN          NaN       Summer               1.04          NaN   \n\n   PAQ_C-PAQ_C_Total PCIAT-Season  PCIAT-PCIAT_01  PCIAT-PCIAT_02  \\\n0                NaN         Fall             5.0             4.0   \n1              2.340         Fall             0.0             0.0   \n2              2.170         Fall             5.0             2.0   \n3              2.451       Summer             4.0             2.0   \n4                NaN          NaN             NaN             NaN   \n\n   PCIAT-PCIAT_03  PCIAT-PCIAT_04  PCIAT-PCIAT_05  PCIAT-PCIAT_06  \\\n0             4.0             0.0             4.0             0.0   \n1             0.0             0.0             0.0             0.0   \n2             2.0             1.0             2.0             1.0   \n3             4.0             0.0             5.0             1.0   \n4             NaN             NaN             NaN             NaN   \n\n   PCIAT-PCIAT_07  PCIAT-PCIAT_08  PCIAT-PCIAT_09  PCIAT-PCIAT_10  \\\n0             0.0             4.0             0.0             0.0   \n1             0.0             0.0             0.0             0.0   \n2             1.0             2.0             1.0             1.0   \n3             0.0             3.0             2.0             2.0   \n4             NaN             NaN             NaN             NaN   \n\n   PCIAT-PCIAT_11  PCIAT-PCIAT_12  PCIAT-PCIAT_13  PCIAT-PCIAT_14  \\\n0             4.0             0.0             4.0             4.0   \n1             0.0             0.0             0.0             0.0   \n2             1.0             0.0             1.0             1.0   \n3             3.0             0.0             3.0             0.0   \n4             NaN             NaN             NaN             NaN   \n\n   PCIAT-PCIAT_15  PCIAT-PCIAT_16  PCIAT-PCIAT_17  PCIAT-PCIAT_18  \\\n0             4.0             4.0             4.0             4.0   \n1             0.0             0.0             0.0             0.0   \n2             1.0             0.0             2.0             2.0   \n3             0.0             3.0             4.0             3.0   \n4             NaN             NaN             NaN             NaN   \n\n   PCIAT-PCIAT_19  PCIAT-PCIAT_20  PCIAT-PCIAT_Total SDS-Season  \\\n0             2.0             4.0               55.0        NaN   \n1             0.0             0.0                0.0       Fall   \n2             1.0             1.0               28.0       Fall   \n3             4.0             1.0               44.0     Summer   \n4             NaN             NaN                NaN        NaN   \n\n   SDS-SDS_Total_Raw  SDS-SDS_Total_T PreInt_EduHx-Season  \\\n0                NaN              NaN                Fall   \n1               46.0             64.0              Summer   \n2               38.0             54.0              Summer   \n3               31.0             45.0              Winter   \n4                NaN              NaN                 NaN   \n\n   PreInt_EduHx-computerinternet_hoursday  sii  \n0                                     3.0  2.0  \n1                                     0.0  0.0  \n2                                     2.0  0.0  \n3                                     0.0  1.0  \n4                                     NaN  NaN  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>Basic_Demos-Enroll_Season</th>\n      <th>Basic_Demos-Age</th>\n      <th>Basic_Demos-Sex</th>\n      <th>CGAS-Season</th>\n      <th>CGAS-CGAS_Score</th>\n      <th>Physical-Season</th>\n      <th>Physical-BMI</th>\n      <th>Physical-Height</th>\n      <th>Physical-Weight</th>\n      <th>Physical-Waist_Circumference</th>\n      <th>Physical-Diastolic_BP</th>\n      <th>Physical-HeartRate</th>\n      <th>Physical-Systolic_BP</th>\n      <th>Fitness_Endurance-Season</th>\n      <th>Fitness_Endurance-Max_Stage</th>\n      <th>Fitness_Endurance-Time_Mins</th>\n      <th>Fitness_Endurance-Time_Sec</th>\n      <th>FGC-Season</th>\n      <th>FGC-FGC_CU</th>\n      <th>FGC-FGC_CU_Zone</th>\n      <th>FGC-FGC_GSND</th>\n      <th>FGC-FGC_GSND_Zone</th>\n      <th>FGC-FGC_GSD</th>\n      <th>FGC-FGC_GSD_Zone</th>\n      <th>FGC-FGC_PU</th>\n      <th>FGC-FGC_PU_Zone</th>\n      <th>FGC-FGC_SRL</th>\n      <th>FGC-FGC_SRL_Zone</th>\n      <th>FGC-FGC_SRR</th>\n      <th>FGC-FGC_SRR_Zone</th>\n      <th>FGC-FGC_TL</th>\n      <th>FGC-FGC_TL_Zone</th>\n      <th>BIA-Season</th>\n      <th>BIA-BIA_Activity_Level_num</th>\n      <th>BIA-BIA_BMC</th>\n      <th>BIA-BIA_BMI</th>\n      <th>BIA-BIA_BMR</th>\n      <th>BIA-BIA_DEE</th>\n      <th>BIA-BIA_ECW</th>\n      <th>BIA-BIA_FFM</th>\n      <th>BIA-BIA_FFMI</th>\n      <th>BIA-BIA_FMI</th>\n      <th>BIA-BIA_Fat</th>\n      <th>BIA-BIA_Frame_num</th>\n      <th>BIA-BIA_ICW</th>\n      <th>BIA-BIA_LDM</th>\n      <th>BIA-BIA_LST</th>\n      <th>BIA-BIA_SMM</th>\n      <th>BIA-BIA_TBW</th>\n      <th>PAQ_A-Season</th>\n      <th>PAQ_A-PAQ_A_Total</th>\n      <th>PAQ_C-Season</th>\n      <th>PAQ_C-PAQ_C_Total</th>\n      <th>PCIAT-Season</th>\n      <th>PCIAT-PCIAT_01</th>\n      <th>PCIAT-PCIAT_02</th>\n      <th>PCIAT-PCIAT_03</th>\n      <th>PCIAT-PCIAT_04</th>\n      <th>PCIAT-PCIAT_05</th>\n      <th>PCIAT-PCIAT_06</th>\n      <th>PCIAT-PCIAT_07</th>\n      <th>PCIAT-PCIAT_08</th>\n      <th>PCIAT-PCIAT_09</th>\n      <th>PCIAT-PCIAT_10</th>\n      <th>PCIAT-PCIAT_11</th>\n      <th>PCIAT-PCIAT_12</th>\n      <th>PCIAT-PCIAT_13</th>\n      <th>PCIAT-PCIAT_14</th>\n      <th>PCIAT-PCIAT_15</th>\n      <th>PCIAT-PCIAT_16</th>\n      <th>PCIAT-PCIAT_17</th>\n      <th>PCIAT-PCIAT_18</th>\n      <th>PCIAT-PCIAT_19</th>\n      <th>PCIAT-PCIAT_20</th>\n      <th>PCIAT-PCIAT_Total</th>\n      <th>SDS-Season</th>\n      <th>SDS-SDS_Total_Raw</th>\n      <th>SDS-SDS_Total_T</th>\n      <th>PreInt_EduHx-Season</th>\n      <th>PreInt_EduHx-computerinternet_hoursday</th>\n      <th>sii</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00008ff9</td>\n      <td>Fall</td>\n      <td>5</td>\n      <td>0</td>\n      <td>Winter</td>\n      <td>51.0</td>\n      <td>Fall</td>\n      <td>16.877316</td>\n      <td>46.0</td>\n      <td>50.8</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>Fall</td>\n      <td>2.0</td>\n      <td>2.66855</td>\n      <td>16.8792</td>\n      <td>932.498</td>\n      <td>1492.00</td>\n      <td>8.25598</td>\n      <td>41.5862</td>\n      <td>13.8177</td>\n      <td>3.06143</td>\n      <td>9.21377</td>\n      <td>1.0</td>\n      <td>24.4349</td>\n      <td>8.89536</td>\n      <td>38.9177</td>\n      <td>19.5413</td>\n      <td>32.6909</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>5.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>55.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>3.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000fd460</td>\n      <td>Summer</td>\n      <td>9</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>14.035590</td>\n      <td>48.0</td>\n      <td>46.0</td>\n      <td>22.0</td>\n      <td>75.0</td>\n      <td>70.0</td>\n      <td>122.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>Winter</td>\n      <td>2.0</td>\n      <td>2.57949</td>\n      <td>14.0371</td>\n      <td>936.656</td>\n      <td>1498.65</td>\n      <td>6.01993</td>\n      <td>42.0291</td>\n      <td>12.8254</td>\n      <td>1.21172</td>\n      <td>3.97085</td>\n      <td>1.0</td>\n      <td>21.0352</td>\n      <td>14.97400</td>\n      <td>39.4497</td>\n      <td>15.4107</td>\n      <td>27.0552</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>2.340</td>\n      <td>Fall</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Fall</td>\n      <td>46.0</td>\n      <td>64.0</td>\n      <td>Summer</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00105258</td>\n      <td>Summer</td>\n      <td>10</td>\n      <td>1</td>\n      <td>Fall</td>\n      <td>71.0</td>\n      <td>Fall</td>\n      <td>16.648696</td>\n      <td>56.5</td>\n      <td>75.6</td>\n      <td>NaN</td>\n      <td>65.0</td>\n      <td>94.0</td>\n      <td>117.0</td>\n      <td>Fall</td>\n      <td>5.0</td>\n      <td>7.0</td>\n      <td>33.0</td>\n      <td>Fall</td>\n      <td>20.0</td>\n      <td>1.0</td>\n      <td>10.2</td>\n      <td>1.0</td>\n      <td>14.7</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>2.170</td>\n      <td>Fall</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>28.0</td>\n      <td>Fall</td>\n      <td>38.0</td>\n      <td>54.0</td>\n      <td>Summer</td>\n      <td>2.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00115b9f</td>\n      <td>Winter</td>\n      <td>9</td>\n      <td>0</td>\n      <td>Fall</td>\n      <td>71.0</td>\n      <td>Summer</td>\n      <td>18.292347</td>\n      <td>56.0</td>\n      <td>81.6</td>\n      <td>NaN</td>\n      <td>60.0</td>\n      <td>97.0</td>\n      <td>117.0</td>\n      <td>Summer</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>37.0</td>\n      <td>Summer</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>Summer</td>\n      <td>3.0</td>\n      <td>3.84191</td>\n      <td>18.2943</td>\n      <td>1131.430</td>\n      <td>1923.44</td>\n      <td>15.59250</td>\n      <td>62.7757</td>\n      <td>14.0740</td>\n      <td>4.22033</td>\n      <td>18.82430</td>\n      <td>2.0</td>\n      <td>30.4041</td>\n      <td>16.77900</td>\n      <td>58.9338</td>\n      <td>26.4798</td>\n      <td>45.9966</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Winter</td>\n      <td>2.451</td>\n      <td>Summer</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>44.0</td>\n      <td>Summer</td>\n      <td>31.0</td>\n      <td>45.0</td>\n      <td>Winter</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0016bb22</td>\n      <td>Spring</td>\n      <td>18</td>\n      <td>1</td>\n      <td>Summer</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>1.04</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":431},{"id":"540191cb","cell_type":"code","source":"test.head(5)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:14.257191Z","iopub.execute_input":"2026-04-24T07:59:14.257496Z","iopub.status.idle":"2026-04-24T07:59:14.309473Z","shell.execute_reply.started":"2026-04-24T07:59:14.257469Z","shell.execute_reply":"2026-04-24T07:59:14.308726Z"},"papermill":{"duration":0.065571,"end_time":"2025-05-23T02:56:31.88585","exception":false,"start_time":"2025-05-23T02:56:31.820279","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":432,"output_type":"execute_result","data":{"text/plain":"         id Basic_Demos-Enroll_Season  Basic_Demos-Age  Basic_Demos-Sex  \\\n0  00008ff9                      Fall                5                0   \n1  000fd460                    Summer                9                0   \n2  00105258                    Summer               10                1   \n3  00115b9f                    Winter                9                0   \n4  0016bb22                    Spring               18                1   \n\n  CGAS-Season  CGAS-CGAS_Score Physical-Season  Physical-BMI  Physical-Height  \\\n0      Winter             51.0            Fall     16.877316             46.0   \n1         NaN              NaN            Fall     14.035590             48.0   \n2        Fall             71.0            Fall     16.648696             56.5   \n3        Fall             71.0          Summer     18.292347             56.0   \n4      Summer              NaN             NaN           NaN              NaN   \n\n   Physical-Weight  Physical-Waist_Circumference  Physical-Diastolic_BP  \\\n0             50.8                           NaN                    NaN   \n1             46.0                          22.0                   75.0   \n2             75.6                           NaN                   65.0   \n3             81.6                           NaN                   60.0   \n4              NaN                           NaN                    NaN   \n\n   Physical-HeartRate  Physical-Systolic_BP Fitness_Endurance-Season  \\\n0                 NaN                   NaN                      NaN   \n1                70.0                 122.0                      NaN   \n2                94.0                 117.0                     Fall   \n3                97.0                 117.0                   Summer   \n4                 NaN                   NaN                      NaN   \n\n   Fitness_Endurance-Max_Stage  Fitness_Endurance-Time_Mins  \\\n0                          NaN                          NaN   \n1                          NaN                          NaN   \n2                          5.0                          7.0   \n3                          6.0                          9.0   \n4                          NaN                          NaN   \n\n   Fitness_Endurance-Time_Sec FGC-Season  FGC-FGC_CU  FGC-FGC_CU_Zone  \\\n0                         NaN       Fall         0.0              0.0   \n1                         NaN       Fall         3.0              0.0   \n2                        33.0       Fall        20.0              1.0   \n3                        37.0     Summer        18.0              1.0   \n4                         NaN        NaN         NaN              NaN   \n\n   FGC-FGC_GSND  FGC-FGC_GSND_Zone  FGC-FGC_GSD  FGC-FGC_GSD_Zone  FGC-FGC_PU  \\\n0           NaN                NaN          NaN               NaN         0.0   \n1           NaN                NaN          NaN               NaN         5.0   \n2          10.2                1.0         14.7               2.0         7.0   \n3           NaN                NaN          NaN               NaN         5.0   \n4           NaN                NaN          NaN               NaN         NaN   \n\n   FGC-FGC_PU_Zone  FGC-FGC_SRL  FGC-FGC_SRL_Zone  FGC-FGC_SRR  \\\n0              0.0          7.0               0.0          6.0   \n1              0.0         11.0               1.0         11.0   \n2              1.0         10.0               1.0         10.0   \n3              0.0          7.0               0.0          7.0   \n4              NaN          NaN               NaN          NaN   \n\n   FGC-FGC_SRR_Zone  FGC-FGC_TL  FGC-FGC_TL_Zone BIA-Season  \\\n0               0.0         6.0              1.0       Fall   \n1               1.0         3.0              0.0     Winter   \n2               1.0         5.0              0.0        NaN   \n3               0.0         7.0              1.0     Summer   \n4               NaN         NaN              NaN        NaN   \n\n   BIA-BIA_Activity_Level_num  BIA-BIA_BMC  BIA-BIA_BMI  BIA-BIA_BMR  \\\n0                         2.0      2.66855      16.8792      932.498   \n1                         2.0      2.57949      14.0371      936.656   \n2                         NaN          NaN          NaN          NaN   \n3                         3.0      3.84191      18.2943     1131.430   \n4                         NaN          NaN          NaN          NaN   \n\n   BIA-BIA_DEE  BIA-BIA_ECW  BIA-BIA_FFM  BIA-BIA_FFMI  BIA-BIA_FMI  \\\n0      1492.00      8.25598      41.5862       13.8177      3.06143   \n1      1498.65      6.01993      42.0291       12.8254      1.21172   \n2          NaN          NaN          NaN           NaN          NaN   \n3      1923.44     15.59250      62.7757       14.0740      4.22033   \n4          NaN          NaN          NaN           NaN          NaN   \n\n   BIA-BIA_Fat  BIA-BIA_Frame_num  BIA-BIA_ICW  BIA-BIA_LDM  BIA-BIA_LST  \\\n0      9.21377                1.0      24.4349      8.89536      38.9177   \n1      3.97085                1.0      21.0352     14.97400      39.4497   \n2          NaN                NaN          NaN          NaN          NaN   \n3     18.82430                2.0      30.4041     16.77900      58.9338   \n4          NaN                NaN          NaN          NaN          NaN   \n\n   BIA-BIA_SMM  BIA-BIA_TBW PAQ_A-Season  PAQ_A-PAQ_A_Total PAQ_C-Season  \\\n0      19.5413      32.6909          NaN                NaN          NaN   \n1      15.4107      27.0552          NaN                NaN         Fall   \n2          NaN          NaN          NaN                NaN       Summer   \n3      26.4798      45.9966          NaN                NaN       Winter   \n4          NaN          NaN       Summer               1.04          NaN   \n\n   PAQ_C-PAQ_C_Total SDS-Season  SDS-SDS_Total_Raw  SDS-SDS_Total_T  \\\n0                NaN        NaN                NaN              NaN   \n1              2.340       Fall               46.0             64.0   \n2              2.170       Fall               38.0             54.0   \n3              2.451     Summer               31.0             45.0   \n4                NaN        NaN                NaN              NaN   \n\n  PreInt_EduHx-Season  PreInt_EduHx-computerinternet_hoursday  \n0                Fall                                     3.0  \n1              Summer                                     0.0  \n2              Summer                                     2.0  \n3              Winter                                     0.0  \n4                 NaN                                     NaN  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>Basic_Demos-Enroll_Season</th>\n      <th>Basic_Demos-Age</th>\n      <th>Basic_Demos-Sex</th>\n      <th>CGAS-Season</th>\n      <th>CGAS-CGAS_Score</th>\n      <th>Physical-Season</th>\n      <th>Physical-BMI</th>\n      <th>Physical-Height</th>\n      <th>Physical-Weight</th>\n      <th>Physical-Waist_Circumference</th>\n      <th>Physical-Diastolic_BP</th>\n      <th>Physical-HeartRate</th>\n      <th>Physical-Systolic_BP</th>\n      <th>Fitness_Endurance-Season</th>\n      <th>Fitness_Endurance-Max_Stage</th>\n      <th>Fitness_Endurance-Time_Mins</th>\n      <th>Fitness_Endurance-Time_Sec</th>\n      <th>FGC-Season</th>\n      <th>FGC-FGC_CU</th>\n      <th>FGC-FGC_CU_Zone</th>\n      <th>FGC-FGC_GSND</th>\n      <th>FGC-FGC_GSND_Zone</th>\n      <th>FGC-FGC_GSD</th>\n      <th>FGC-FGC_GSD_Zone</th>\n      <th>FGC-FGC_PU</th>\n      <th>FGC-FGC_PU_Zone</th>\n      <th>FGC-FGC_SRL</th>\n      <th>FGC-FGC_SRL_Zone</th>\n      <th>FGC-FGC_SRR</th>\n      <th>FGC-FGC_SRR_Zone</th>\n      <th>FGC-FGC_TL</th>\n      <th>FGC-FGC_TL_Zone</th>\n      <th>BIA-Season</th>\n      <th>BIA-BIA_Activity_Level_num</th>\n      <th>BIA-BIA_BMC</th>\n      <th>BIA-BIA_BMI</th>\n      <th>BIA-BIA_BMR</th>\n      <th>BIA-BIA_DEE</th>\n      <th>BIA-BIA_ECW</th>\n      <th>BIA-BIA_FFM</th>\n      <th>BIA-BIA_FFMI</th>\n      <th>BIA-BIA_FMI</th>\n      <th>BIA-BIA_Fat</th>\n      <th>BIA-BIA_Frame_num</th>\n      <th>BIA-BIA_ICW</th>\n      <th>BIA-BIA_LDM</th>\n      <th>BIA-BIA_LST</th>\n      <th>BIA-BIA_SMM</th>\n      <th>BIA-BIA_TBW</th>\n      <th>PAQ_A-Season</th>\n      <th>PAQ_A-PAQ_A_Total</th>\n      <th>PAQ_C-Season</th>\n      <th>PAQ_C-PAQ_C_Total</th>\n      <th>SDS-Season</th>\n      <th>SDS-SDS_Total_Raw</th>\n      <th>SDS-SDS_Total_T</th>\n      <th>PreInt_EduHx-Season</th>\n      <th>PreInt_EduHx-computerinternet_hoursday</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00008ff9</td>\n      <td>Fall</td>\n      <td>5</td>\n      <td>0</td>\n      <td>Winter</td>\n      <td>51.0</td>\n      <td>Fall</td>\n      <td>16.877316</td>\n      <td>46.0</td>\n      <td>50.8</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>Fall</td>\n      <td>2.0</td>\n      <td>2.66855</td>\n      <td>16.8792</td>\n      <td>932.498</td>\n      <td>1492.00</td>\n      <td>8.25598</td>\n      <td>41.5862</td>\n      <td>13.8177</td>\n      <td>3.06143</td>\n      <td>9.21377</td>\n      <td>1.0</td>\n      <td>24.4349</td>\n      <td>8.89536</td>\n      <td>38.9177</td>\n      <td>19.5413</td>\n      <td>32.6909</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000fd460</td>\n      <td>Summer</td>\n      <td>9</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>14.035590</td>\n      <td>48.0</td>\n      <td>46.0</td>\n      <td>22.0</td>\n      <td>75.0</td>\n      <td>70.0</td>\n      <td>122.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>Winter</td>\n      <td>2.0</td>\n      <td>2.57949</td>\n      <td>14.0371</td>\n      <td>936.656</td>\n      <td>1498.65</td>\n      <td>6.01993</td>\n      <td>42.0291</td>\n      <td>12.8254</td>\n      <td>1.21172</td>\n      <td>3.97085</td>\n      <td>1.0</td>\n      <td>21.0352</td>\n      <td>14.97400</td>\n      <td>39.4497</td>\n      <td>15.4107</td>\n      <td>27.0552</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>2.340</td>\n      <td>Fall</td>\n      <td>46.0</td>\n      <td>64.0</td>\n      <td>Summer</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00105258</td>\n      <td>Summer</td>\n      <td>10</td>\n      <td>1</td>\n      <td>Fall</td>\n      <td>71.0</td>\n      <td>Fall</td>\n      <td>16.648696</td>\n      <td>56.5</td>\n      <td>75.6</td>\n      <td>NaN</td>\n      <td>65.0</td>\n      <td>94.0</td>\n      <td>117.0</td>\n      <td>Fall</td>\n      <td>5.0</td>\n      <td>7.0</td>\n      <td>33.0</td>\n      <td>Fall</td>\n      <td>20.0</td>\n      <td>1.0</td>\n      <td>10.2</td>\n      <td>1.0</td>\n      <td>14.7</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>2.170</td>\n      <td>Fall</td>\n      <td>38.0</td>\n      <td>54.0</td>\n      <td>Summer</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00115b9f</td>\n      <td>Winter</td>\n      <td>9</td>\n      <td>0</td>\n      <td>Fall</td>\n      <td>71.0</td>\n      <td>Summer</td>\n      <td>18.292347</td>\n      <td>56.0</td>\n      <td>81.6</td>\n      <td>NaN</td>\n      <td>60.0</td>\n      <td>97.0</td>\n      <td>117.0</td>\n      <td>Summer</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>37.0</td>\n      <td>Summer</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>Summer</td>\n      <td>3.0</td>\n      <td>3.84191</td>\n      <td>18.2943</td>\n      <td>1131.430</td>\n      <td>1923.44</td>\n      <td>15.59250</td>\n      <td>62.7757</td>\n      <td>14.0740</td>\n      <td>4.22033</td>\n      <td>18.82430</td>\n      <td>2.0</td>\n      <td>30.4041</td>\n      <td>16.77900</td>\n      <td>58.9338</td>\n      <td>26.4798</td>\n      <td>45.9966</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Winter</td>\n      <td>2.451</td>\n      <td>Summer</td>\n      <td>31.0</td>\n      <td>45.0</td>\n      <td>Winter</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0016bb22</td>\n      <td>Spring</td>\n      <td>18</td>\n      <td>1</td>\n      <td>Summer</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>1.04</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":432},{"id":"8e36d615","cell_type":"markdown","source":"## Target","metadata":{"papermill":{"duration":0.023902,"end_time":"2025-05-23T02:56:31.934636","exception":false,"start_time":"2025-05-23T02:56:31.910734","status":"completed"},"tags":[]}},{"id":"fb7cbd43","cell_type":"code","source":"train_cols = set(train.columns)\ntest_cols = set(test.columns)\ncolumns_not_in_test = sorted(list(train_cols - test_cols))\n\ncolumns_to_exclude = ['PCIAT-PCIAT_Total', 'PCIAT-Season', 'sii']\nquestion_columns = [\n    col for col in columns_not_in_test if col not in columns_to_exclude\n]\n\ndata_dict[data_dict['Field'].isin(columns_not_in_test)]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:14.310465Z","iopub.execute_input":"2026-04-24T07:59:14.310747Z","iopub.status.idle":"2026-04-24T07:59:14.327685Z","shell.execute_reply.started":"2026-04-24T07:59:14.310723Z","shell.execute_reply":"2026-04-24T07:59:14.326878Z"},"papermill":{"duration":0.032581,"end_time":"2025-05-23T02:56:31.991536","exception":false,"start_time":"2025-05-23T02:56:31.958955","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":433,"output_type":"execute_result","data":{"text/plain":"                              Instrument              Field  \\\n54  Parent-Child Internet Addiction Test       PCIAT-Season   \n55  Parent-Child Internet Addiction Test     PCIAT-PCIAT_01   \n56  Parent-Child Internet Addiction Test     PCIAT-PCIAT_02   \n57  Parent-Child Internet Addiction Test     PCIAT-PCIAT_03   \n58  Parent-Child Internet Addiction Test     PCIAT-PCIAT_04   \n59  Parent-Child Internet Addiction Test     PCIAT-PCIAT_05   \n60  Parent-Child Internet Addiction Test     PCIAT-PCIAT_06   \n61  Parent-Child Internet Addiction Test     PCIAT-PCIAT_07   \n62  Parent-Child Internet Addiction Test     PCIAT-PCIAT_08   \n63  Parent-Child Internet Addiction Test     PCIAT-PCIAT_09   \n64  Parent-Child Internet Addiction Test     PCIAT-PCIAT_10   \n65  Parent-Child Internet Addiction Test     PCIAT-PCIAT_11   \n66  Parent-Child Internet Addiction Test     PCIAT-PCIAT_12   \n67  Parent-Child Internet Addiction Test     PCIAT-PCIAT_13   \n68  Parent-Child Internet Addiction Test     PCIAT-PCIAT_14   \n69  Parent-Child Internet Addiction Test     PCIAT-PCIAT_15   \n70  Parent-Child Internet Addiction Test     PCIAT-PCIAT_16   \n71  Parent-Child Internet Addiction Test     PCIAT-PCIAT_17   \n72  Parent-Child Internet Addiction Test     PCIAT-PCIAT_18   \n73  Parent-Child Internet Addiction Test     PCIAT-PCIAT_19   \n74  Parent-Child Internet Addiction Test     PCIAT-PCIAT_20   \n75  Parent-Child Internet Addiction Test  PCIAT-PCIAT_Total   \n\n                                          Description             Type  \\\n54                            Season of participation              str   \n55  How often does your child disobey time limits ...  categorical int   \n56  How often does your child neglect household ch...  categorical int   \n57  How often does your child prefer to spend time...  categorical int   \n58  How often does your child form new relationshi...  categorical int   \n59  How often do you complain about the amount of ...  categorical int   \n60  How often do your child's grades suffer becaus...  categorical int   \n61  How often does your child check his or her e-m...  categorical int   \n62  How often does your child seem withdrawn from ...  categorical int   \n63  How often does your child become defensive or ...  categorical int   \n64  How often have you caught your child sneaking ...  categorical int   \n65  How often does your child spend time along in ...  categorical int   \n66  How often does your child receive strange phon...  categorical int   \n67  How often does your child snap, yell, or act a...  categorical int   \n68  How often does your child seem more tired and ...  categorical int   \n69  How often does your child seem preoccupied wit...  categorical int   \n70  How often does your child throw tantrums with ...  categorical int   \n71  How often does your child choose to spend time...  categorical int   \n72  How often does your child become angry or bell...  categorical int   \n73  How often does your child choose to spend more...  categorical int   \n74  How often does your child feel depressed, mood...  categorical int   \n75                                        Total Score              int   \n\n                          Values  \\\n54  Spring, Summer, Fall, Winter   \n55                   0,1,2,3,4,5   \n56                   0,1,2,3,4,5   \n57                   0,1,2,3,4,5   \n58                   0,1,2,3,4,5   \n59                   0,1,2,3,4,5   \n60                   0,1,2,3,4,5   \n61                   0,1,2,3,4,5   \n62                   0,1,2,3,4,5   \n63                   0,1,2,3,4,5   \n64                   0,1,2,3,4,5   \n65                   0,1,2,3,4,5   \n66                   0,1,2,3,4,5   \n67                   0,1,2,3,4,5   \n68                   0,1,2,3,4,5   \n69                   0,1,2,3,4,5   \n70                   0,1,2,3,4,5   \n71                   0,1,2,3,4,5   \n72                   0,1,2,3,4,5   \n73                   0,1,2,3,4,5   \n74                   0,1,2,3,4,5   \n75                           NaN   \n\n                                         Value Labels  \n54                                                NaN  \n55  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n56  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n57  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n58  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n59  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n60  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n61  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n62  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n63  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n64  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n65  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n66  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n67  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n68  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n69  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n70  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n71  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n72  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n73  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n74  0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...  \n75  Severity Impairment Index: 0-30=None; 31-49=Mi...  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Instrument</th>\n      <th>Field</th>\n      <th>Description</th>\n      <th>Type</th>\n      <th>Values</th>\n      <th>Value Labels</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>54</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-Season</td>\n      <td>Season of participation</td>\n      <td>str</td>\n      <td>Spring, Summer, Fall, Winter</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_01</td>\n      <td>How often does your child disobey time limits ...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_02</td>\n      <td>How often does your child neglect household ch...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_03</td>\n      <td>How often does your child prefer to spend time...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_04</td>\n      <td>How often does your child form new relationshi...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_05</td>\n      <td>How often do you complain about the amount of ...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_06</td>\n      <td>How often do your child's grades suffer becaus...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_07</td>\n      <td>How often does your child check his or her e-m...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_08</td>\n      <td>How often does your child seem withdrawn from ...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_09</td>\n      <td>How often does your child become defensive or ...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_10</td>\n      <td>How often have you caught your child sneaking ...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_11</td>\n      <td>How often does your child spend time along in ...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_12</td>\n      <td>How often does your child receive strange phon...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_13</td>\n      <td>How often does your child snap, yell, or act a...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_14</td>\n      <td>How often does your child seem more tired and ...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_15</td>\n      <td>How often does your child seem preoccupied wit...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_16</td>\n      <td>How often does your child throw tantrums with ...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_17</td>\n      <td>How often does your child choose to spend time...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_18</td>\n      <td>How often does your child become angry or bell...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_19</td>\n      <td>How often does your child choose to spend more...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_20</td>\n      <td>How often does your child feel depressed, mood...</td>\n      <td>categorical int</td>\n      <td>0,1,2,3,4,5</td>\n      <td>0=Does Not Apply, 1=Rarely, 2=Occasionally, 3=...</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>Parent-Child Internet Addiction Test</td>\n      <td>PCIAT-PCIAT_Total</td>\n      <td>Total Score</td>\n      <td>int</td>\n      <td>NaN</td>\n      <td>Severity Impairment Index: 0-30=None; 31-49=Mi...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":433},{"id":"25961016-9d9e-43e5-b721-3b63e1f78e8c","cell_type":"code","source":"data_dict[data_dict['Field'] == 'PCIAT-PCIAT_Total']['Value Labels'].iloc[0]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T07:59:14.329902Z","iopub.execute_input":"2026-04-24T07:59:14.330191Z","iopub.status.idle":"2026-04-24T07:59:14.345125Z","shell.execute_reply.started":"2026-04-24T07:59:14.330166Z","shell.execute_reply":"2026-04-24T07:59:14.344248Z"}},"outputs":[{"execution_count":434,"output_type":"execute_result","data":{"text/plain":"'Severity Impairment Index: 0-30=None; 31-49=Mild; 50-79=Moderate; 80-100=Severe'"},"metadata":{}}],"execution_count":434},{"id":"79f9b9ec-5cac-4299-9e24-8bd634b036dd","cell_type":"markdown","source":"Biến mục tiêu (sii) được định nghĩa như sau:\n- 0: Không có (PCIAT-PCIAT_Total từ 0 đến 30)\n- 1: Nhẹ (PCIAT-PCIAT_Total từ 31 đến 49)\n- 2: Trung bình (PCIAT-PCIAT_Total từ 50 đến 79)\n- 3: Nặng (PCIAT-PCIAT_Total từ 80 trở lên)\n\n=> Các dạng bài toán ML có thể sử dụng với sii:\n- Phân loại có thứ tự (ordinal classification) (ví dụ: ordinal logistic regression)\n- Hồi quy (regression) (bỏ qua tính rời rạc của các mức, coi sii là biến liên tục rồi làm tròn kết quả dự đoán","metadata":{}},{"id":"55bd5ea6-9ddc-446a-9e2b-f0db2ec8c259","cell_type":"code","source":"pciat_min_max = train.groupby('sii')['PCIAT-PCIAT_Total'].agg(['min', 'max'])\npciat_min_max = pciat_min_max.rename(\n    columns={'min': 'Minimum PCIAT total Score', 'max': 'Maximum total PCIAT Score'}\n)\npciat_min_max","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T07:59:14.346156Z","iopub.execute_input":"2026-04-24T07:59:14.346542Z","iopub.status.idle":"2026-04-24T07:59:14.371471Z","shell.execute_reply.started":"2026-04-24T07:59:14.346479Z","shell.execute_reply":"2026-04-24T07:59:14.370564Z"}},"outputs":[{"execution_count":435,"output_type":"execute_result","data":{"text/plain":"     Minimum PCIAT total Score  Maximum total PCIAT Score\nsii                                                      \n0.0                        0.0                       30.0\n1.0                       31.0                       49.0\n2.0                       50.0                       79.0\n3.0                       80.0                       93.0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Minimum PCIAT total Score</th>\n      <th>Maximum total PCIAT Score</th>\n    </tr>\n    <tr>\n      <th>sii</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0.0</th>\n      <td>0.0</td>\n      <td>30.0</td>\n    </tr>\n    <tr>\n      <th>1.0</th>\n      <td>31.0</td>\n      <td>49.0</td>\n    </tr>\n    <tr>\n      <th>2.0</th>\n      <td>50.0</td>\n      <td>79.0</td>\n    </tr>\n    <tr>\n      <th>3.0</th>\n      <td>80.0</td>\n      <td>93.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":435},{"id":"33d81b07","cell_type":"markdown","source":"## Filter sii","metadata":{"papermill":{"duration":0.058463,"end_time":"2025-05-23T02:56:32.122269","exception":false,"start_time":"2025-05-23T02:56:32.063806","status":"completed"},"tags":[]}},{"id":"16836bb2","cell_type":"code","source":"def recalculate_sii(row):\n    if pd.isna(row['PCIAT-PCIAT_Total']):\n        return np.nan\n    max_possible = row['PCIAT-PCIAT_Total'] + row[question_columns].isna().sum() * 5\n    if row['PCIAT-PCIAT_Total'] <= 30 and max_possible <= 30:\n        return 0\n    elif 31 <= row['PCIAT-PCIAT_Total'] <= 49 and max_possible <= 49:\n        return 1\n    elif 50 <= row['PCIAT-PCIAT_Total'] <= 79 and max_possible <= 79:\n        return 2\n    elif row['PCIAT-PCIAT_Total'] >= 80 and max_possible >= 80:\n        return 3\n    return np.nan\n\ntrain['recalc_sii'] = train.apply(recalculate_sii, axis=1)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:14.372817Z","iopub.execute_input":"2026-04-24T07:59:14.373271Z","iopub.status.idle":"2026-04-24T07:59:15.666213Z","shell.execute_reply.started":"2026-04-24T07:59:14.373225Z","shell.execute_reply":"2026-04-24T07:59:15.66542Z"},"papermill":{"duration":1.183,"end_time":"2025-05-23T02:56:33.37843","exception":false,"start_time":"2025-05-23T02:56:32.19543","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":436},{"id":"5d632236","cell_type":"code","source":"mismatch_rows = train[\n    (train['recalc_sii'] != train['sii']) & train['sii'].notna()\n]\n\nmismatch_rows[question_columns + ['sii','recalc_sii']].style.map(\n    lambda x: 'background-color: #FFC0CB' if pd.isna(x) else ''\n)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:15.667448Z","iopub.execute_input":"2026-04-24T07:59:15.667786Z","iopub.status.idle":"2026-04-24T07:59:15.70963Z","shell.execute_reply.started":"2026-04-24T07:59:15.66776Z","shell.execute_reply":"2026-04-24T07:59:15.70871Z"},"papermill":{"duration":0.079798,"end_time":"2025-05-23T02:56:33.483322","exception":false,"start_time":"2025-05-23T02:56:33.403524","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":437,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7fba3c66aa80>","text/html":"<style type=\"text/css\">\n#T_65009_row0_col18, #T_65009_row0_col21, #T_65009_row1_col0, #T_65009_row1_col1, #T_65009_row1_col2, #T_65009_row1_col3, #T_65009_row1_col4, #T_65009_row1_col5, #T_65009_row1_col6, #T_65009_row1_col7, #T_65009_row1_col8, #T_65009_row1_col9, #T_65009_row1_col10, #T_65009_row1_col11, #T_65009_row1_col12, #T_65009_row1_col13, #T_65009_row1_col14, #T_65009_row1_col15, #T_65009_row1_col16, #T_65009_row1_col17, #T_65009_row1_col18, #T_65009_row1_col19, #T_65009_row1_col21, #T_65009_row2_col4, #T_65009_row2_col21, #T_65009_row3_col16, #T_65009_row3_col21, #T_65009_row4_col18, #T_65009_row4_col21, #T_65009_row5_col15, #T_65009_row5_col21, #T_65009_row6_col7, #T_65009_row6_col8, #T_65009_row6_col9, #T_65009_row6_col18, #T_65009_row6_col19, #T_65009_row6_col21, #T_65009_row7_col13, #T_65009_row7_col14, #T_65009_row7_col21, #T_65009_row8_col7, #T_65009_row8_col21, #T_65009_row9_col11, #T_65009_row9_col21, #T_65009_row10_col0, #T_65009_row10_col1, #T_65009_row10_col2, #T_65009_row10_col3, #T_65009_row10_col4, #T_65009_row10_col5, #T_65009_row10_col6, #T_65009_row10_col7, #T_65009_row10_col8, #T_65009_row10_col9, #T_65009_row10_col21, #T_65009_row11_col0, #T_65009_row11_col3, #T_65009_row11_col16, #T_65009_row11_col21, #T_65009_row12_col14, #T_65009_row12_col17, #T_65009_row12_col21, #T_65009_row13_col17, #T_65009_row13_col18, #T_65009_row13_col21, #T_65009_row14_col15, #T_65009_row14_col21, #T_65009_row15_col4, #T_65009_row15_col21, #T_65009_row16_col6, #T_65009_row16_col8, #T_65009_row16_col21 {\n  background-color: #FFC0CB;\n}\n</style>\n<table id=\"T_65009\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_65009_level0_col0\" class=\"col_heading level0 col0\" >PCIAT-PCIAT_01</th>\n      <th id=\"T_65009_level0_col1\" class=\"col_heading level0 col1\" >PCIAT-PCIAT_02</th>\n      <th id=\"T_65009_level0_col2\" class=\"col_heading level0 col2\" >PCIAT-PCIAT_03</th>\n      <th id=\"T_65009_level0_col3\" class=\"col_heading level0 col3\" >PCIAT-PCIAT_04</th>\n      <th id=\"T_65009_level0_col4\" class=\"col_heading level0 col4\" >PCIAT-PCIAT_05</th>\n      <th id=\"T_65009_level0_col5\" class=\"col_heading level0 col5\" >PCIAT-PCIAT_06</th>\n      <th id=\"T_65009_level0_col6\" class=\"col_heading level0 col6\" >PCIAT-PCIAT_07</th>\n      <th id=\"T_65009_level0_col7\" class=\"col_heading level0 col7\" >PCIAT-PCIAT_08</th>\n      <th id=\"T_65009_level0_col8\" class=\"col_heading level0 col8\" >PCIAT-PCIAT_09</th>\n      <th id=\"T_65009_level0_col9\" class=\"col_heading level0 col9\" >PCIAT-PCIAT_10</th>\n      <th id=\"T_65009_level0_col10\" class=\"col_heading level0 col10\" >PCIAT-PCIAT_11</th>\n      <th id=\"T_65009_level0_col11\" class=\"col_heading level0 col11\" >PCIAT-PCIAT_12</th>\n      <th id=\"T_65009_level0_col12\" class=\"col_heading level0 col12\" >PCIAT-PCIAT_13</th>\n      <th id=\"T_65009_level0_col13\" class=\"col_heading level0 col13\" >PCIAT-PCIAT_14</th>\n      <th id=\"T_65009_level0_col14\" class=\"col_heading level0 col14\" >PCIAT-PCIAT_15</th>\n      <th id=\"T_65009_level0_col15\" class=\"col_heading level0 col15\" >PCIAT-PCIAT_16</th>\n      <th id=\"T_65009_level0_col16\" class=\"col_heading level0 col16\" >PCIAT-PCIAT_17</th>\n      <th id=\"T_65009_level0_col17\" class=\"col_heading level0 col17\" >PCIAT-PCIAT_18</th>\n      <th id=\"T_65009_level0_col18\" class=\"col_heading level0 col18\" >PCIAT-PCIAT_19</th>\n      <th id=\"T_65009_level0_col19\" class=\"col_heading level0 col19\" >PCIAT-PCIAT_20</th>\n      <th id=\"T_65009_level0_col20\" class=\"col_heading level0 col20\" >sii</th>\n      <th id=\"T_65009_level0_col21\" class=\"col_heading level0 col21\" >recalc_sii</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_65009_level0_row0\" class=\"row_heading level0 row0\" >24</th>\n      <td id=\"T_65009_row0_col0\" class=\"data row0 col0\" >2.000000</td>\n      <td id=\"T_65009_row0_col1\" class=\"data row0 col1\" >2.000000</td>\n      <td id=\"T_65009_row0_col2\" class=\"data row0 col2\" >3.000000</td>\n      <td id=\"T_65009_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n      <td id=\"T_65009_row0_col4\" class=\"data row0 col4\" >2.000000</td>\n      <td id=\"T_65009_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n      <td id=\"T_65009_row0_col6\" class=\"data row0 col6\" >1.000000</td>\n      <td id=\"T_65009_row0_col7\" class=\"data row0 col7\" >1.000000</td>\n      <td id=\"T_65009_row0_col8\" class=\"data row0 col8\" >2.000000</td>\n      <td id=\"T_65009_row0_col9\" class=\"data row0 col9\" >1.000000</td>\n      <td id=\"T_65009_row0_col10\" class=\"data row0 col10\" >2.000000</td>\n      <td id=\"T_65009_row0_col11\" class=\"data row0 col11\" >1.000000</td>\n      <td id=\"T_65009_row0_col12\" class=\"data row0 col12\" >2.000000</td>\n      <td id=\"T_65009_row0_col13\" class=\"data row0 col13\" >1.000000</td>\n      <td id=\"T_65009_row0_col14\" class=\"data row0 col14\" >1.000000</td>\n      <td id=\"T_65009_row0_col15\" class=\"data row0 col15\" >2.000000</td>\n      <td id=\"T_65009_row0_col16\" class=\"data row0 col16\" >1.000000</td>\n      <td id=\"T_65009_row0_col17\" class=\"data row0 col17\" >2.000000</td>\n      <td id=\"T_65009_row0_col18\" class=\"data row0 col18\" >nan</td>\n      <td id=\"T_65009_row0_col19\" class=\"data row0 col19\" >2.000000</td>\n      <td id=\"T_65009_row0_col20\" class=\"data row0 col20\" >0.000000</td>\n      <td id=\"T_65009_row0_col21\" class=\"data row0 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row1\" class=\"row_heading level0 row1\" >93</th>\n      <td id=\"T_65009_row1_col0\" class=\"data row1 col0\" >nan</td>\n      <td id=\"T_65009_row1_col1\" class=\"data row1 col1\" >nan</td>\n      <td id=\"T_65009_row1_col2\" class=\"data row1 col2\" >nan</td>\n      <td id=\"T_65009_row1_col3\" class=\"data row1 col3\" >nan</td>\n      <td id=\"T_65009_row1_col4\" class=\"data row1 col4\" >nan</td>\n      <td id=\"T_65009_row1_col5\" class=\"data row1 col5\" >nan</td>\n      <td id=\"T_65009_row1_col6\" class=\"data row1 col6\" >nan</td>\n      <td id=\"T_65009_row1_col7\" class=\"data row1 col7\" >nan</td>\n      <td id=\"T_65009_row1_col8\" class=\"data row1 col8\" >nan</td>\n      <td id=\"T_65009_row1_col9\" class=\"data row1 col9\" >nan</td>\n      <td id=\"T_65009_row1_col10\" class=\"data row1 col10\" >nan</td>\n      <td id=\"T_65009_row1_col11\" class=\"data row1 col11\" >nan</td>\n      <td id=\"T_65009_row1_col12\" class=\"data row1 col12\" >nan</td>\n      <td id=\"T_65009_row1_col13\" class=\"data row1 col13\" >nan</td>\n      <td id=\"T_65009_row1_col14\" class=\"data row1 col14\" >nan</td>\n      <td id=\"T_65009_row1_col15\" class=\"data row1 col15\" >nan</td>\n      <td id=\"T_65009_row1_col16\" class=\"data row1 col16\" >nan</td>\n      <td id=\"T_65009_row1_col17\" class=\"data row1 col17\" >nan</td>\n      <td id=\"T_65009_row1_col18\" class=\"data row1 col18\" >nan</td>\n      <td id=\"T_65009_row1_col19\" class=\"data row1 col19\" >nan</td>\n      <td id=\"T_65009_row1_col20\" class=\"data row1 col20\" >0.000000</td>\n      <td id=\"T_65009_row1_col21\" class=\"data row1 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row2\" class=\"row_heading level0 row2\" >104</th>\n      <td id=\"T_65009_row2_col0\" class=\"data row2 col0\" >5.000000</td>\n      <td id=\"T_65009_row2_col1\" class=\"data row2 col1\" >2.000000</td>\n      <td id=\"T_65009_row2_col2\" class=\"data row2 col2\" >4.000000</td>\n      <td id=\"T_65009_row2_col3\" class=\"data row2 col3\" >2.000000</td>\n      <td id=\"T_65009_row2_col4\" class=\"data row2 col4\" >nan</td>\n      <td id=\"T_65009_row2_col5\" class=\"data row2 col5\" >2.000000</td>\n      <td id=\"T_65009_row2_col6\" class=\"data row2 col6\" >2.000000</td>\n      <td id=\"T_65009_row2_col7\" class=\"data row2 col7\" >2.000000</td>\n      <td id=\"T_65009_row2_col8\" class=\"data row2 col8\" >1.000000</td>\n      <td id=\"T_65009_row2_col9\" class=\"data row2 col9\" >2.000000</td>\n      <td id=\"T_65009_row2_col10\" class=\"data row2 col10\" >1.000000</td>\n      <td id=\"T_65009_row2_col11\" class=\"data row2 col11\" >1.000000</td>\n      <td id=\"T_65009_row2_col12\" class=\"data row2 col12\" >2.000000</td>\n      <td id=\"T_65009_row2_col13\" class=\"data row2 col13\" >2.000000</td>\n      <td id=\"T_65009_row2_col14\" class=\"data row2 col14\" >3.000000</td>\n      <td id=\"T_65009_row2_col15\" class=\"data row2 col15\" >3.000000</td>\n      <td id=\"T_65009_row2_col16\" class=\"data row2 col16\" >3.000000</td>\n      <td id=\"T_65009_row2_col17\" class=\"data row2 col17\" >3.000000</td>\n      <td id=\"T_65009_row2_col18\" class=\"data row2 col18\" >3.000000</td>\n      <td id=\"T_65009_row2_col19\" class=\"data row2 col19\" >2.000000</td>\n      <td id=\"T_65009_row2_col20\" class=\"data row2 col20\" >1.000000</td>\n      <td id=\"T_65009_row2_col21\" class=\"data row2 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row3\" class=\"row_heading level0 row3\" >141</th>\n      <td id=\"T_65009_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n      <td id=\"T_65009_row3_col1\" class=\"data row3 col1\" >2.000000</td>\n      <td id=\"T_65009_row3_col2\" class=\"data row3 col2\" >4.000000</td>\n      <td id=\"T_65009_row3_col3\" class=\"data row3 col3\" >2.000000</td>\n      <td id=\"T_65009_row3_col4\" class=\"data row3 col4\" >2.000000</td>\n      <td id=\"T_65009_row3_col5\" class=\"data row3 col5\" >2.000000</td>\n      <td id=\"T_65009_row3_col6\" class=\"data row3 col6\" >1.000000</td>\n      <td id=\"T_65009_row3_col7\" class=\"data row3 col7\" >3.000000</td>\n      <td id=\"T_65009_row3_col8\" class=\"data row3 col8\" >1.000000</td>\n      <td id=\"T_65009_row3_col9\" class=\"data row3 col9\" >1.000000</td>\n      <td id=\"T_65009_row3_col10\" class=\"data row3 col10\" >2.000000</td>\n      <td id=\"T_65009_row3_col11\" class=\"data row3 col11\" >0.000000</td>\n      <td id=\"T_65009_row3_col12\" class=\"data row3 col12\" >0.000000</td>\n      <td id=\"T_65009_row3_col13\" class=\"data row3 col13\" >0.000000</td>\n      <td id=\"T_65009_row3_col14\" class=\"data row3 col14\" >3.000000</td>\n      <td id=\"T_65009_row3_col15\" class=\"data row3 col15\" >0.000000</td>\n      <td id=\"T_65009_row3_col16\" class=\"data row3 col16\" >nan</td>\n      <td id=\"T_65009_row3_col17\" class=\"data row3 col17\" >0.000000</td>\n      <td id=\"T_65009_row3_col18\" class=\"data row3 col18\" >2.000000</td>\n      <td id=\"T_65009_row3_col19\" class=\"data row3 col19\" >0.000000</td>\n      <td id=\"T_65009_row3_col20\" class=\"data row3 col20\" >0.000000</td>\n      <td id=\"T_65009_row3_col21\" class=\"data row3 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row4\" class=\"row_heading level0 row4\" >142</th>\n      <td id=\"T_65009_row4_col0\" class=\"data row4 col0\" >2.000000</td>\n      <td id=\"T_65009_row4_col1\" class=\"data row4 col1\" >2.000000</td>\n      <td id=\"T_65009_row4_col2\" class=\"data row4 col2\" >2.000000</td>\n      <td id=\"T_65009_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n      <td id=\"T_65009_row4_col4\" class=\"data row4 col4\" >2.000000</td>\n      <td id=\"T_65009_row4_col5\" class=\"data row4 col5\" >1.000000</td>\n      <td id=\"T_65009_row4_col6\" class=\"data row4 col6\" >2.000000</td>\n      <td id=\"T_65009_row4_col7\" class=\"data row4 col7\" >1.000000</td>\n      <td id=\"T_65009_row4_col8\" class=\"data row4 col8\" >1.000000</td>\n      <td id=\"T_65009_row4_col9\" class=\"data row4 col9\" >1.000000</td>\n      <td id=\"T_65009_row4_col10\" class=\"data row4 col10\" >3.000000</td>\n      <td id=\"T_65009_row4_col11\" class=\"data row4 col11\" >0.000000</td>\n      <td id=\"T_65009_row4_col12\" class=\"data row4 col12\" >2.000000</td>\n      <td id=\"T_65009_row4_col13\" class=\"data row4 col13\" >1.000000</td>\n      <td id=\"T_65009_row4_col14\" class=\"data row4 col14\" >1.000000</td>\n      <td id=\"T_65009_row4_col15\" class=\"data row4 col15\" >1.000000</td>\n      <td id=\"T_65009_row4_col16\" class=\"data row4 col16\" >1.000000</td>\n      <td id=\"T_65009_row4_col17\" class=\"data row4 col17\" >1.000000</td>\n      <td id=\"T_65009_row4_col18\" class=\"data row4 col18\" >nan</td>\n      <td id=\"T_65009_row4_col19\" class=\"data row4 col19\" >1.000000</td>\n      <td id=\"T_65009_row4_col20\" class=\"data row4 col20\" >0.000000</td>\n      <td id=\"T_65009_row4_col21\" class=\"data row4 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row5\" class=\"row_heading level0 row5\" >270</th>\n      <td id=\"T_65009_row5_col0\" class=\"data row5 col0\" >3.000000</td>\n      <td id=\"T_65009_row5_col1\" class=\"data row5 col1\" >3.000000</td>\n      <td id=\"T_65009_row5_col2\" class=\"data row5 col2\" >4.000000</td>\n      <td id=\"T_65009_row5_col3\" class=\"data row5 col3\" >2.000000</td>\n      <td id=\"T_65009_row5_col4\" class=\"data row5 col4\" >4.000000</td>\n      <td id=\"T_65009_row5_col5\" class=\"data row5 col5\" >2.000000</td>\n      <td id=\"T_65009_row5_col6\" class=\"data row5 col6\" >1.000000</td>\n      <td id=\"T_65009_row5_col7\" class=\"data row5 col7\" >3.000000</td>\n      <td id=\"T_65009_row5_col8\" class=\"data row5 col8\" >2.000000</td>\n      <td id=\"T_65009_row5_col9\" class=\"data row5 col9\" >2.000000</td>\n      <td id=\"T_65009_row5_col10\" class=\"data row5 col10\" >4.000000</td>\n      <td id=\"T_65009_row5_col11\" class=\"data row5 col11\" >0.000000</td>\n      <td id=\"T_65009_row5_col12\" class=\"data row5 col12\" >2.000000</td>\n      <td id=\"T_65009_row5_col13\" class=\"data row5 col13\" >1.000000</td>\n      <td id=\"T_65009_row5_col14\" class=\"data row5 col14\" >4.000000</td>\n      <td id=\"T_65009_row5_col15\" class=\"data row5 col15\" >nan</td>\n      <td id=\"T_65009_row5_col16\" class=\"data row5 col16\" >2.000000</td>\n      <td id=\"T_65009_row5_col17\" class=\"data row5 col17\" >3.000000</td>\n      <td id=\"T_65009_row5_col18\" class=\"data row5 col18\" >4.000000</td>\n      <td id=\"T_65009_row5_col19\" class=\"data row5 col19\" >2.000000</td>\n      <td id=\"T_65009_row5_col20\" class=\"data row5 col20\" >1.000000</td>\n      <td id=\"T_65009_row5_col21\" class=\"data row5 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row6\" class=\"row_heading level0 row6\" >368</th>\n      <td id=\"T_65009_row6_col0\" class=\"data row6 col0\" >2.000000</td>\n      <td id=\"T_65009_row6_col1\" class=\"data row6 col1\" >3.000000</td>\n      <td id=\"T_65009_row6_col2\" class=\"data row6 col2\" >4.000000</td>\n      <td id=\"T_65009_row6_col3\" class=\"data row6 col3\" >2.000000</td>\n      <td id=\"T_65009_row6_col4\" class=\"data row6 col4\" >5.000000</td>\n      <td id=\"T_65009_row6_col5\" class=\"data row6 col5\" >1.000000</td>\n      <td id=\"T_65009_row6_col6\" class=\"data row6 col6\" >2.000000</td>\n      <td id=\"T_65009_row6_col7\" class=\"data row6 col7\" >nan</td>\n      <td id=\"T_65009_row6_col8\" class=\"data row6 col8\" >nan</td>\n      <td id=\"T_65009_row6_col9\" class=\"data row6 col9\" >nan</td>\n      <td id=\"T_65009_row6_col10\" class=\"data row6 col10\" >2.000000</td>\n      <td id=\"T_65009_row6_col11\" class=\"data row6 col11\" >1.000000</td>\n      <td id=\"T_65009_row6_col12\" class=\"data row6 col12\" >1.000000</td>\n      <td id=\"T_65009_row6_col13\" class=\"data row6 col13\" >2.000000</td>\n      <td id=\"T_65009_row6_col14\" class=\"data row6 col14\" >2.000000</td>\n      <td id=\"T_65009_row6_col15\" class=\"data row6 col15\" >1.000000</td>\n      <td id=\"T_65009_row6_col16\" class=\"data row6 col16\" >2.000000</td>\n      <td id=\"T_65009_row6_col17\" class=\"data row6 col17\" >1.000000</td>\n      <td id=\"T_65009_row6_col18\" class=\"data row6 col18\" >nan</td>\n      <td id=\"T_65009_row6_col19\" class=\"data row6 col19\" >nan</td>\n      <td id=\"T_65009_row6_col20\" class=\"data row6 col20\" >1.000000</td>\n      <td id=\"T_65009_row6_col21\" class=\"data row6 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row7\" class=\"row_heading level0 row7\" >592</th>\n      <td id=\"T_65009_row7_col0\" class=\"data row7 col0\" >3.000000</td>\n      <td id=\"T_65009_row7_col1\" class=\"data row7 col1\" >0.000000</td>\n      <td id=\"T_65009_row7_col2\" class=\"data row7 col2\" >3.000000</td>\n      <td id=\"T_65009_row7_col3\" class=\"data row7 col3\" >0.000000</td>\n      <td id=\"T_65009_row7_col4\" class=\"data row7 col4\" >3.000000</td>\n      <td id=\"T_65009_row7_col5\" class=\"data row7 col5\" >1.000000</td>\n      <td id=\"T_65009_row7_col6\" class=\"data row7 col6\" >0.000000</td>\n      <td id=\"T_65009_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n      <td id=\"T_65009_row7_col8\" class=\"data row7 col8\" >1.000000</td>\n      <td id=\"T_65009_row7_col9\" class=\"data row7 col9\" >1.000000</td>\n      <td id=\"T_65009_row7_col10\" class=\"data row7 col10\" >2.000000</td>\n      <td id=\"T_65009_row7_col11\" class=\"data row7 col11\" >0.000000</td>\n      <td id=\"T_65009_row7_col12\" class=\"data row7 col12\" >1.000000</td>\n      <td id=\"T_65009_row7_col13\" class=\"data row7 col13\" >nan</td>\n      <td id=\"T_65009_row7_col14\" class=\"data row7 col14\" >nan</td>\n      <td id=\"T_65009_row7_col15\" class=\"data row7 col15\" >1.000000</td>\n      <td id=\"T_65009_row7_col16\" class=\"data row7 col16\" >2.000000</td>\n      <td id=\"T_65009_row7_col17\" class=\"data row7 col17\" >1.000000</td>\n      <td id=\"T_65009_row7_col18\" class=\"data row7 col18\" >1.000000</td>\n      <td id=\"T_65009_row7_col19\" class=\"data row7 col19\" >0.000000</td>\n      <td id=\"T_65009_row7_col20\" class=\"data row7 col20\" >0.000000</td>\n      <td id=\"T_65009_row7_col21\" class=\"data row7 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row8\" class=\"row_heading level0 row8\" >724</th>\n      <td id=\"T_65009_row8_col0\" class=\"data row8 col0\" >3.000000</td>\n      <td id=\"T_65009_row8_col1\" class=\"data row8 col1\" >2.000000</td>\n      <td id=\"T_65009_row8_col2\" class=\"data row8 col2\" >4.000000</td>\n      <td id=\"T_65009_row8_col3\" class=\"data row8 col3\" >2.000000</td>\n      <td id=\"T_65009_row8_col4\" class=\"data row8 col4\" >2.000000</td>\n      <td id=\"T_65009_row8_col5\" class=\"data row8 col5\" >1.000000</td>\n      <td id=\"T_65009_row8_col6\" class=\"data row8 col6\" >0.000000</td>\n      <td id=\"T_65009_row8_col7\" class=\"data row8 col7\" >nan</td>\n      <td id=\"T_65009_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n      <td id=\"T_65009_row8_col9\" class=\"data row8 col9\" >1.000000</td>\n      <td id=\"T_65009_row8_col10\" class=\"data row8 col10\" >1.000000</td>\n      <td id=\"T_65009_row8_col11\" class=\"data row8 col11\" >1.000000</td>\n      <td id=\"T_65009_row8_col12\" class=\"data row8 col12\" >2.000000</td>\n      <td id=\"T_65009_row8_col13\" class=\"data row8 col13\" >1.000000</td>\n      <td id=\"T_65009_row8_col14\" class=\"data row8 col14\" >2.000000</td>\n      <td id=\"T_65009_row8_col15\" class=\"data row8 col15\" >1.000000</td>\n      <td id=\"T_65009_row8_col16\" class=\"data row8 col16\" >1.000000</td>\n      <td id=\"T_65009_row8_col17\" class=\"data row8 col17\" >3.000000</td>\n      <td id=\"T_65009_row8_col18\" class=\"data row8 col18\" >0.000000</td>\n      <td id=\"T_65009_row8_col19\" class=\"data row8 col19\" >1.000000</td>\n      <td id=\"T_65009_row8_col20\" class=\"data row8 col20\" >0.000000</td>\n      <td id=\"T_65009_row8_col21\" class=\"data row8 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row9\" class=\"row_heading level0 row9\" >877</th>\n      <td id=\"T_65009_row9_col0\" class=\"data row9 col0\" >5.000000</td>\n      <td id=\"T_65009_row9_col1\" class=\"data row9 col1\" >5.000000</td>\n      <td id=\"T_65009_row9_col2\" class=\"data row9 col2\" >5.000000</td>\n      <td id=\"T_65009_row9_col3\" class=\"data row9 col3\" >4.000000</td>\n      <td id=\"T_65009_row9_col4\" class=\"data row9 col4\" >5.000000</td>\n      <td id=\"T_65009_row9_col5\" class=\"data row9 col5\" >0.000000</td>\n      <td id=\"T_65009_row9_col6\" class=\"data row9 col6\" >5.000000</td>\n      <td id=\"T_65009_row9_col7\" class=\"data row9 col7\" >5.000000</td>\n      <td id=\"T_65009_row9_col8\" class=\"data row9 col8\" >5.000000</td>\n      <td id=\"T_65009_row9_col9\" class=\"data row9 col9\" >5.000000</td>\n      <td id=\"T_65009_row9_col10\" class=\"data row9 col10\" >4.000000</td>\n      <td id=\"T_65009_row9_col11\" class=\"data row9 col11\" >nan</td>\n      <td id=\"T_65009_row9_col12\" class=\"data row9 col12\" >4.000000</td>\n      <td id=\"T_65009_row9_col13\" class=\"data row9 col13\" >5.000000</td>\n      <td id=\"T_65009_row9_col14\" class=\"data row9 col14\" >5.000000</td>\n      <td id=\"T_65009_row9_col15\" class=\"data row9 col15\" >1.000000</td>\n      <td id=\"T_65009_row9_col16\" class=\"data row9 col16\" >5.000000</td>\n      <td id=\"T_65009_row9_col17\" class=\"data row9 col17\" >0.000000</td>\n      <td id=\"T_65009_row9_col18\" class=\"data row9 col18\" >5.000000</td>\n      <td id=\"T_65009_row9_col19\" class=\"data row9 col19\" >5.000000</td>\n      <td id=\"T_65009_row9_col20\" class=\"data row9 col20\" >2.000000</td>\n      <td id=\"T_65009_row9_col21\" class=\"data row9 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row10\" class=\"row_heading level0 row10\" >1706</th>\n      <td id=\"T_65009_row10_col0\" class=\"data row10 col0\" >nan</td>\n      <td id=\"T_65009_row10_col1\" class=\"data row10 col1\" >nan</td>\n      <td id=\"T_65009_row10_col2\" class=\"data row10 col2\" >nan</td>\n      <td id=\"T_65009_row10_col3\" class=\"data row10 col3\" >nan</td>\n      <td id=\"T_65009_row10_col4\" class=\"data row10 col4\" >nan</td>\n      <td id=\"T_65009_row10_col5\" class=\"data row10 col5\" >nan</td>\n      <td id=\"T_65009_row10_col6\" class=\"data row10 col6\" >nan</td>\n      <td id=\"T_65009_row10_col7\" class=\"data row10 col7\" >nan</td>\n      <td id=\"T_65009_row10_col8\" class=\"data row10 col8\" >nan</td>\n      <td id=\"T_65009_row10_col9\" class=\"data row10 col9\" >nan</td>\n      <td id=\"T_65009_row10_col10\" class=\"data row10 col10\" >0.000000</td>\n      <td id=\"T_65009_row10_col11\" class=\"data row10 col11\" >0.000000</td>\n      <td id=\"T_65009_row10_col12\" class=\"data row10 col12\" >0.000000</td>\n      <td id=\"T_65009_row10_col13\" class=\"data row10 col13\" >0.000000</td>\n      <td id=\"T_65009_row10_col14\" class=\"data row10 col14\" >0.000000</td>\n      <td id=\"T_65009_row10_col15\" class=\"data row10 col15\" >0.000000</td>\n      <td id=\"T_65009_row10_col16\" class=\"data row10 col16\" >0.000000</td>\n      <td id=\"T_65009_row10_col17\" class=\"data row10 col17\" >0.000000</td>\n      <td id=\"T_65009_row10_col18\" class=\"data row10 col18\" >0.000000</td>\n      <td id=\"T_65009_row10_col19\" class=\"data row10 col19\" >0.000000</td>\n      <td id=\"T_65009_row10_col20\" class=\"data row10 col20\" >0.000000</td>\n      <td id=\"T_65009_row10_col21\" class=\"data row10 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row11\" class=\"row_heading level0 row11\" >1911</th>\n      <td id=\"T_65009_row11_col0\" class=\"data row11 col0\" >nan</td>\n      <td id=\"T_65009_row11_col1\" class=\"data row11 col1\" >5.000000</td>\n      <td id=\"T_65009_row11_col2\" class=\"data row11 col2\" >5.000000</td>\n      <td id=\"T_65009_row11_col3\" class=\"data row11 col3\" >nan</td>\n      <td id=\"T_65009_row11_col4\" class=\"data row11 col4\" >3.000000</td>\n      <td id=\"T_65009_row11_col5\" class=\"data row11 col5\" >4.000000</td>\n      <td id=\"T_65009_row11_col6\" class=\"data row11 col6\" >0.000000</td>\n      <td id=\"T_65009_row11_col7\" class=\"data row11 col7\" >1.000000</td>\n      <td id=\"T_65009_row11_col8\" class=\"data row11 col8\" >5.000000</td>\n      <td id=\"T_65009_row11_col9\" class=\"data row11 col9\" >0.000000</td>\n      <td id=\"T_65009_row11_col10\" class=\"data row11 col10\" >4.000000</td>\n      <td id=\"T_65009_row11_col11\" class=\"data row11 col11\" >0.000000</td>\n      <td id=\"T_65009_row11_col12\" class=\"data row11 col12\" >0.000000</td>\n      <td id=\"T_65009_row11_col13\" class=\"data row11 col13\" >0.000000</td>\n      <td id=\"T_65009_row11_col14\" class=\"data row11 col14\" >5.000000</td>\n      <td id=\"T_65009_row11_col15\" class=\"data row11 col15\" >3.000000</td>\n      <td id=\"T_65009_row11_col16\" class=\"data row11 col16\" >nan</td>\n      <td id=\"T_65009_row11_col17\" class=\"data row11 col17\" >3.000000</td>\n      <td id=\"T_65009_row11_col18\" class=\"data row11 col18\" >0.000000</td>\n      <td id=\"T_65009_row11_col19\" class=\"data row11 col19\" >0.000000</td>\n      <td id=\"T_65009_row11_col20\" class=\"data row11 col20\" >1.000000</td>\n      <td id=\"T_65009_row11_col21\" class=\"data row11 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row12\" class=\"row_heading level0 row12\" >2285</th>\n      <td id=\"T_65009_row12_col0\" class=\"data row12 col0\" >2.000000</td>\n      <td id=\"T_65009_row12_col1\" class=\"data row12 col1\" >2.000000</td>\n      <td id=\"T_65009_row12_col2\" class=\"data row12 col2\" >2.000000</td>\n      <td id=\"T_65009_row12_col3\" class=\"data row12 col3\" >1.000000</td>\n      <td id=\"T_65009_row12_col4\" class=\"data row12 col4\" >2.000000</td>\n      <td id=\"T_65009_row12_col5\" class=\"data row12 col5\" >1.000000</td>\n      <td id=\"T_65009_row12_col6\" class=\"data row12 col6\" >0.000000</td>\n      <td id=\"T_65009_row12_col7\" class=\"data row12 col7\" >2.000000</td>\n      <td id=\"T_65009_row12_col8\" class=\"data row12 col8\" >2.000000</td>\n      <td id=\"T_65009_row12_col9\" class=\"data row12 col9\" >2.000000</td>\n      <td id=\"T_65009_row12_col10\" class=\"data row12 col10\" >2.000000</td>\n      <td id=\"T_65009_row12_col11\" class=\"data row12 col11\" >0.000000</td>\n      <td id=\"T_65009_row12_col12\" class=\"data row12 col12\" >2.000000</td>\n      <td id=\"T_65009_row12_col13\" class=\"data row12 col13\" >2.000000</td>\n      <td id=\"T_65009_row12_col14\" class=\"data row12 col14\" >nan</td>\n      <td id=\"T_65009_row12_col15\" class=\"data row12 col15\" >1.000000</td>\n      <td id=\"T_65009_row12_col16\" class=\"data row12 col16\" >2.000000</td>\n      <td id=\"T_65009_row12_col17\" class=\"data row12 col17\" >nan</td>\n      <td id=\"T_65009_row12_col18\" class=\"data row12 col18\" >1.000000</td>\n      <td id=\"T_65009_row12_col19\" class=\"data row12 col19\" >1.000000</td>\n      <td id=\"T_65009_row12_col20\" class=\"data row12 col20\" >0.000000</td>\n      <td id=\"T_65009_row12_col21\" class=\"data row12 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row13\" class=\"row_heading level0 row13\" >3037</th>\n      <td id=\"T_65009_row13_col0\" class=\"data row13 col0\" >3.000000</td>\n      <td id=\"T_65009_row13_col1\" class=\"data row13 col1\" >4.000000</td>\n      <td id=\"T_65009_row13_col2\" class=\"data row13 col2\" >4.000000</td>\n      <td id=\"T_65009_row13_col3\" class=\"data row13 col3\" >0.000000</td>\n      <td id=\"T_65009_row13_col4\" class=\"data row13 col4\" >4.000000</td>\n      <td id=\"T_65009_row13_col5\" class=\"data row13 col5\" >0.000000</td>\n      <td id=\"T_65009_row13_col6\" class=\"data row13 col6\" >0.000000</td>\n      <td id=\"T_65009_row13_col7\" class=\"data row13 col7\" >4.000000</td>\n      <td id=\"T_65009_row13_col8\" class=\"data row13 col8\" >2.000000</td>\n      <td id=\"T_65009_row13_col9\" class=\"data row13 col9\" >2.000000</td>\n      <td id=\"T_65009_row13_col10\" class=\"data row13 col10\" >4.000000</td>\n      <td id=\"T_65009_row13_col11\" class=\"data row13 col11\" >0.000000</td>\n      <td id=\"T_65009_row13_col12\" class=\"data row13 col12\" >4.000000</td>\n      <td id=\"T_65009_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n      <td id=\"T_65009_row13_col14\" class=\"data row13 col14\" >4.000000</td>\n      <td id=\"T_65009_row13_col15\" class=\"data row13 col15\" >4.000000</td>\n      <td id=\"T_65009_row13_col16\" class=\"data row13 col16\" >4.000000</td>\n      <td id=\"T_65009_row13_col17\" class=\"data row13 col17\" >nan</td>\n      <td id=\"T_65009_row13_col18\" class=\"data row13 col18\" >nan</td>\n      <td id=\"T_65009_row13_col19\" class=\"data row13 col19\" >1.000000</td>\n      <td id=\"T_65009_row13_col20\" class=\"data row13 col20\" >1.000000</td>\n      <td id=\"T_65009_row13_col21\" class=\"data row13 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row14\" class=\"row_heading level0 row14\" >3500</th>\n      <td id=\"T_65009_row14_col0\" class=\"data row14 col0\" >3.000000</td>\n      <td id=\"T_65009_row14_col1\" class=\"data row14 col1\" >3.000000</td>\n      <td id=\"T_65009_row14_col2\" class=\"data row14 col2\" >2.000000</td>\n      <td id=\"T_65009_row14_col3\" class=\"data row14 col3\" >3.000000</td>\n      <td id=\"T_65009_row14_col4\" class=\"data row14 col4\" >4.000000</td>\n      <td id=\"T_65009_row14_col5\" class=\"data row14 col5\" >4.000000</td>\n      <td id=\"T_65009_row14_col6\" class=\"data row14 col6\" >4.000000</td>\n      <td id=\"T_65009_row14_col7\" class=\"data row14 col7\" >2.000000</td>\n      <td id=\"T_65009_row14_col8\" class=\"data row14 col8\" >3.000000</td>\n      <td id=\"T_65009_row14_col9\" class=\"data row14 col9\" >3.000000</td>\n      <td id=\"T_65009_row14_col10\" class=\"data row14 col10\" >2.000000</td>\n      <td id=\"T_65009_row14_col11\" class=\"data row14 col11\" >1.000000</td>\n      <td id=\"T_65009_row14_col12\" class=\"data row14 col12\" >2.000000</td>\n      <td id=\"T_65009_row14_col13\" class=\"data row14 col13\" >2.000000</td>\n      <td id=\"T_65009_row14_col14\" class=\"data row14 col14\" >2.000000</td>\n      <td id=\"T_65009_row14_col15\" class=\"data row14 col15\" >nan</td>\n      <td id=\"T_65009_row14_col16\" class=\"data row14 col16\" >2.000000</td>\n      <td id=\"T_65009_row14_col17\" class=\"data row14 col17\" >2.000000</td>\n      <td id=\"T_65009_row14_col18\" class=\"data row14 col18\" >2.000000</td>\n      <td id=\"T_65009_row14_col19\" class=\"data row14 col19\" >1.000000</td>\n      <td id=\"T_65009_row14_col20\" class=\"data row14 col20\" >1.000000</td>\n      <td id=\"T_65009_row14_col21\" class=\"data row14 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row15\" class=\"row_heading level0 row15\" >3672</th>\n      <td id=\"T_65009_row15_col0\" class=\"data row15 col0\" >5.000000</td>\n      <td id=\"T_65009_row15_col1\" class=\"data row15 col1\" >5.000000</td>\n      <td id=\"T_65009_row15_col2\" class=\"data row15 col2\" >1.000000</td>\n      <td id=\"T_65009_row15_col3\" class=\"data row15 col3\" >0.000000</td>\n      <td id=\"T_65009_row15_col4\" class=\"data row15 col4\" >nan</td>\n      <td id=\"T_65009_row15_col5\" class=\"data row15 col5\" >1.000000</td>\n      <td id=\"T_65009_row15_col6\" class=\"data row15 col6\" >0.000000</td>\n      <td id=\"T_65009_row15_col7\" class=\"data row15 col7\" >0.000000</td>\n      <td id=\"T_65009_row15_col8\" class=\"data row15 col8\" >0.000000</td>\n      <td id=\"T_65009_row15_col9\" class=\"data row15 col9\" >0.000000</td>\n      <td id=\"T_65009_row15_col10\" class=\"data row15 col10\" >5.000000</td>\n      <td id=\"T_65009_row15_col11\" class=\"data row15 col11\" >0.000000</td>\n      <td id=\"T_65009_row15_col12\" class=\"data row15 col12\" >5.000000</td>\n      <td id=\"T_65009_row15_col13\" class=\"data row15 col13\" >1.000000</td>\n      <td id=\"T_65009_row15_col14\" class=\"data row15 col14\" >5.000000</td>\n      <td id=\"T_65009_row15_col15\" class=\"data row15 col15\" >5.000000</td>\n      <td id=\"T_65009_row15_col16\" class=\"data row15 col16\" >3.000000</td>\n      <td id=\"T_65009_row15_col17\" class=\"data row15 col17\" >5.000000</td>\n      <td id=\"T_65009_row15_col18\" class=\"data row15 col18\" >5.000000</td>\n      <td id=\"T_65009_row15_col19\" class=\"data row15 col19\" >3.000000</td>\n      <td id=\"T_65009_row15_col20\" class=\"data row15 col20\" >1.000000</td>\n      <td id=\"T_65009_row15_col21\" class=\"data row15 col21\" >nan</td>\n    </tr>\n    <tr>\n      <th id=\"T_65009_level0_row16\" class=\"row_heading level0 row16\" >3757</th>\n      <td id=\"T_65009_row16_col0\" class=\"data row16 col0\" >2.000000</td>\n      <td id=\"T_65009_row16_col1\" class=\"data row16 col1\" >2.000000</td>\n      <td id=\"T_65009_row16_col2\" class=\"data row16 col2\" >4.000000</td>\n      <td id=\"T_65009_row16_col3\" class=\"data row16 col3\" >0.000000</td>\n      <td id=\"T_65009_row16_col4\" class=\"data row16 col4\" >5.000000</td>\n      <td id=\"T_65009_row16_col5\" class=\"data row16 col5\" >1.000000</td>\n      <td id=\"T_65009_row16_col6\" class=\"data row16 col6\" >nan</td>\n      <td id=\"T_65009_row16_col7\" class=\"data row16 col7\" >5.000000</td>\n      <td id=\"T_65009_row16_col8\" class=\"data row16 col8\" >nan</td>\n      <td id=\"T_65009_row16_col9\" class=\"data row16 col9\" >0.000000</td>\n      <td id=\"T_65009_row16_col10\" class=\"data row16 col10\" >0.000000</td>\n      <td id=\"T_65009_row16_col11\" class=\"data row16 col11\" >0.000000</td>\n      <td id=\"T_65009_row16_col12\" class=\"data row16 col12\" >0.000000</td>\n      <td id=\"T_65009_row16_col13\" class=\"data row16 col13\" >4.000000</td>\n      <td id=\"T_65009_row16_col14\" class=\"data row16 col14\" >4.000000</td>\n      <td id=\"T_65009_row16_col15\" class=\"data row16 col15\" >5.000000</td>\n      <td id=\"T_65009_row16_col16\" class=\"data row16 col16\" >2.000000</td>\n      <td id=\"T_65009_row16_col17\" class=\"data row16 col17\" >4.000000</td>\n      <td id=\"T_65009_row16_col18\" class=\"data row16 col18\" >0.000000</td>\n      <td id=\"T_65009_row16_col19\" class=\"data row16 col19\" >4.000000</td>\n      <td id=\"T_65009_row16_col20\" class=\"data row16 col20\" >1.000000</td>\n      <td id=\"T_65009_row16_col21\" class=\"data row16 col21\" >nan</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}],"execution_count":437},{"id":"c66f0a47","cell_type":"markdown","source":"These rows have ambigious sii values => Drop","metadata":{"papermill":{"duration":0.024743,"end_time":"2025-05-23T02:56:33.533206","exception":false,"start_time":"2025-05-23T02:56:33.508463","status":"completed"},"tags":[]}},{"id":"ffbfc69b","cell_type":"code","source":"train['sii'] = train['recalc_sii']\ntrain = train.drop(mismatch_rows.index)\nprint(train.shape)\ntrain[columns_not_in_test + ['recalc_sii']]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:15.711033Z","iopub.execute_input":"2026-04-24T07:59:15.711852Z","iopub.status.idle":"2026-04-24T07:59:15.760607Z","shell.execute_reply.started":"2026-04-24T07:59:15.711824Z","shell.execute_reply":"2026-04-24T07:59:15.759725Z"},"papermill":{"duration":0.059762,"end_time":"2025-05-23T02:56:33.617813","exception":false,"start_time":"2025-05-23T02:56:33.558051","status":"completed"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","text":"(3943, 83)\n","output_type":"stream"},{"execution_count":438,"output_type":"execute_result","data":{"text/plain":"      PCIAT-PCIAT_01  PCIAT-PCIAT_02  PCIAT-PCIAT_03  PCIAT-PCIAT_04  \\\n0                5.0             4.0             4.0             0.0   \n1                0.0             0.0             0.0             0.0   \n2                5.0             2.0             2.0             1.0   \n3                4.0             2.0             4.0             0.0   \n4                NaN             NaN             NaN             NaN   \n...              ...             ...             ...             ...   \n3955             3.0             3.0             3.0             2.0   \n3956             NaN             NaN             NaN             NaN   \n3957             5.0             5.0             3.0             0.0   \n3958             2.0             1.0             1.0             1.0   \n3959             NaN             NaN             NaN             NaN   \n\n      PCIAT-PCIAT_05  PCIAT-PCIAT_06  PCIAT-PCIAT_07  PCIAT-PCIAT_08  \\\n0                4.0             0.0             0.0             4.0   \n1                0.0             0.0             0.0             0.0   \n2                2.0             1.0             1.0             2.0   \n3                5.0             1.0             0.0             3.0   \n4                NaN             NaN             NaN             NaN   \n...              ...             ...             ...             ...   \n3955             3.0             2.0             2.0             2.0   \n3956             NaN             NaN             NaN             NaN   \n3957             5.0             1.0             0.0             2.0   \n3958             0.0             0.0             0.0             1.0   \n3959             NaN             NaN             NaN             NaN   \n\n      PCIAT-PCIAT_09  PCIAT-PCIAT_10  PCIAT-PCIAT_11  PCIAT-PCIAT_12  \\\n0                0.0             0.0             4.0             0.0   \n1                0.0             0.0             0.0             0.0   \n2                1.0             1.0             1.0             0.0   \n3                2.0             2.0             3.0             0.0   \n4                NaN             NaN             NaN             NaN   \n...              ...             ...             ...             ...   \n3955             2.0             1.0             2.0             0.0   \n3956             NaN             NaN             NaN             NaN   \n3957             0.0             2.0             1.0             0.0   \n3958             1.0             1.0             2.0             0.0   \n3959             NaN             NaN             NaN             NaN   \n\n      PCIAT-PCIAT_13  PCIAT-PCIAT_14  PCIAT-PCIAT_15  PCIAT-PCIAT_16  \\\n0                4.0             4.0             4.0             4.0   \n1                0.0             0.0             0.0             0.0   \n2                1.0             1.0             1.0             0.0   \n3                3.0             0.0             0.0             3.0   \n4                NaN             NaN             NaN             NaN   \n...              ...             ...             ...             ...   \n3955             2.0             0.0             1.0             0.0   \n3956             NaN             NaN             NaN             NaN   \n3957             1.0             3.0             0.0             0.0   \n3958             1.0             1.0             2.0             1.0   \n3959             NaN             NaN             NaN             NaN   \n\n      PCIAT-PCIAT_17  PCIAT-PCIAT_18  PCIAT-PCIAT_19  PCIAT-PCIAT_20  \\\n0                4.0             4.0             2.0             4.0   \n1                0.0             0.0             0.0             0.0   \n2                2.0             2.0             1.0             1.0   \n3                4.0             3.0             4.0             1.0   \n4                NaN             NaN             NaN             NaN   \n...              ...             ...             ...             ...   \n3955             2.0             1.0             1.0             0.0   \n3956             NaN             NaN             NaN             NaN   \n3957             1.0             1.0             0.0             1.0   \n3958             1.0             1.0             1.0             1.0   \n3959             NaN             NaN             NaN             NaN   \n\n      PCIAT-PCIAT_Total PCIAT-Season  sii  recalc_sii  \n0                  55.0         Fall  2.0         2.0  \n1                   0.0         Fall  0.0         0.0  \n2                  28.0         Fall  0.0         0.0  \n3                  44.0       Summer  1.0         1.0  \n4                   NaN          NaN  NaN         NaN  \n...                 ...          ...  ...         ...  \n3955               32.0       Winter  1.0         1.0  \n3956                NaN          NaN  NaN         NaN  \n3957               31.0       Winter  1.0         1.0  \n3958               19.0       Spring  0.0         0.0  \n3959                NaN          NaN  NaN         NaN  \n\n[3943 rows x 24 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PCIAT-PCIAT_01</th>\n      <th>PCIAT-PCIAT_02</th>\n      <th>PCIAT-PCIAT_03</th>\n      <th>PCIAT-PCIAT_04</th>\n      <th>PCIAT-PCIAT_05</th>\n      <th>PCIAT-PCIAT_06</th>\n      <th>PCIAT-PCIAT_07</th>\n      <th>PCIAT-PCIAT_08</th>\n      <th>PCIAT-PCIAT_09</th>\n      <th>PCIAT-PCIAT_10</th>\n      <th>PCIAT-PCIAT_11</th>\n      <th>PCIAT-PCIAT_12</th>\n      <th>PCIAT-PCIAT_13</th>\n      <th>PCIAT-PCIAT_14</th>\n      <th>PCIAT-PCIAT_15</th>\n      <th>PCIAT-PCIAT_16</th>\n      <th>PCIAT-PCIAT_17</th>\n      <th>PCIAT-PCIAT_18</th>\n      <th>PCIAT-PCIAT_19</th>\n      <th>PCIAT-PCIAT_20</th>\n      <th>PCIAT-PCIAT_Total</th>\n      <th>PCIAT-Season</th>\n      <th>sii</th>\n      <th>recalc_sii</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>55.0</td>\n      <td>Fall</td>\n      <td>2.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Fall</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>28.0</td>\n      <td>Fall</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>44.0</td>\n      <td>Summer</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3955</th>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>32.0</td>\n      <td>Winter</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3956</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3957</th>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>31.0</td>\n      <td>Winter</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3958</th>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>19.0</td>\n      <td>Spring</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3959</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>3943 rows × 24 columns</p>\n</div>"},"metadata":{}}],"execution_count":438},{"id":"c0e4d304","cell_type":"code","source":"na_total_rows = train[train['sii'].isna()]\nna_total_rows","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:15.76181Z","iopub.execute_input":"2026-04-24T07:59:15.762125Z","iopub.status.idle":"2026-04-24T07:59:15.835795Z","shell.execute_reply.started":"2026-04-24T07:59:15.7621Z","shell.execute_reply":"2026-04-24T07:59:15.834789Z"},"papermill":{"duration":0.087043,"end_time":"2025-05-23T02:56:33.730524","exception":false,"start_time":"2025-05-23T02:56:33.643481","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":439,"output_type":"execute_result","data":{"text/plain":"            id Basic_Demos-Enroll_Season  Basic_Demos-Age  Basic_Demos-Sex  \\\n4     0016bb22                    Spring               18                1   \n7     0068a485                      Fall               10                1   \n8     0069fbed                    Summer               15                0   \n9     0083e397                    Summer               19                1   \n10    0087dd65                    Spring               11                1   \n...        ...                       ...              ...              ...   \n3943  fe7c87e2                    Spring               13                0   \n3944  fe7f68a7                    Spring               15                1   \n3950  ff0ab367                    Spring                9                0   \n3956  ffa9794a                    Winter               10                0   \n3959  ffef538e                    Spring               11                0   \n\n     CGAS-Season  CGAS-CGAS_Score Physical-Season  Physical-BMI  \\\n4         Summer              NaN             NaN           NaN   \n7            NaN              NaN            Fall     16.861286   \n8            NaN              NaN          Spring           NaN   \n9         Summer              NaN             NaN           NaN   \n10           NaN              NaN             NaN           NaN   \n...          ...              ...             ...           ...   \n3943      Summer              NaN          Summer           NaN   \n3944         NaN              NaN          Spring     22.457960   \n3950         NaN              NaN          Spring     20.200490   \n3956         NaN              NaN          Spring     18.764678   \n3959         NaN              NaN          Winter           NaN   \n\n      Physical-Height  Physical-Weight  Physical-Waist_Circumference  \\\n4                 NaN              NaN                           NaN   \n7               59.25             84.2                          27.0   \n8                 NaN              NaN                           NaN   \n9                 NaN              NaN                           NaN   \n10                NaN              NaN                           NaN   \n...               ...              ...                           ...   \n3943              NaN              NaN                           NaN   \n3944            62.00            122.8                          30.0   \n3950            52.50             79.2                          28.0   \n3956            53.50             76.4                          27.0   \n3959              NaN              NaN                           NaN   \n\n      Physical-Diastolic_BP  Physical-HeartRate  Physical-Systolic_BP  \\\n4                       NaN                 NaN                   NaN   \n7                      71.0                90.0                 116.0   \n8                       NaN                 NaN                   NaN   \n9                       NaN                 NaN                   NaN   \n10                      NaN                 NaN                   NaN   \n...                     ...                 ...                   ...   \n3943                    NaN                 NaN                   NaN   \n3944                   64.0                60.0                 106.0   \n3950                   75.0                92.0                 142.0   \n3956                   60.0                78.0                 118.0   \n3959                    NaN                 NaN                   NaN   \n\n     Fitness_Endurance-Season  Fitness_Endurance-Max_Stage  \\\n4                         NaN                          NaN   \n7                         NaN                          NaN   \n8                         NaN                          NaN   \n9                         NaN                          NaN   \n10                        NaN                          NaN   \n...                       ...                          ...   \n3943                      NaN                          NaN   \n3944                      NaN                          NaN   \n3950                      NaN                          NaN   \n3956                      NaN                          NaN   \n3959                      NaN                          NaN   \n\n      Fitness_Endurance-Time_Mins  Fitness_Endurance-Time_Sec FGC-Season  \\\n4                             NaN                         NaN        NaN   \n7                             NaN                         NaN       Fall   \n8                             NaN                         NaN     Spring   \n9                             NaN                         NaN        NaN   \n10                            NaN                         NaN        NaN   \n...                           ...                         ...        ...   \n3943                          NaN                         NaN        NaN   \n3944                          NaN                         NaN     Summer   \n3950                          NaN                         NaN     Spring   \n3956                          NaN                         NaN     Spring   \n3959                          NaN                         NaN     Winter   \n\n      FGC-FGC_CU  FGC-FGC_CU_Zone  FGC-FGC_GSND  FGC-FGC_GSND_Zone  \\\n4            NaN              NaN           NaN                NaN   \n7            0.0              0.0          12.6                2.0   \n8            NaN              NaN           NaN                NaN   \n9            NaN              NaN           NaN                NaN   \n10           NaN              NaN           NaN                NaN   \n...          ...              ...           ...                ...   \n3943         NaN              NaN           NaN                NaN   \n3944         9.0              0.0          18.5                2.0   \n3950         0.0              0.0           NaN                NaN   \n3956         0.0              0.0           NaN                NaN   \n3959         NaN              NaN           NaN                NaN   \n\n      FGC-FGC_GSD  FGC-FGC_GSD_Zone  FGC-FGC_PU  FGC-FGC_PU_Zone  FGC-FGC_SRL  \\\n4             NaN               NaN         NaN              NaN          NaN   \n7            11.1               1.0         0.0              0.0          0.0   \n8             NaN               NaN         NaN              NaN          NaN   \n9             NaN               NaN         NaN              NaN          NaN   \n10            NaN               NaN         NaN              NaN          NaN   \n...           ...               ...         ...              ...          ...   \n3943          NaN               NaN         NaN              NaN          NaN   \n3944         21.8               2.0         0.0              0.0          8.0   \n3950          NaN               NaN         0.0              0.0          9.5   \n3956          NaN               NaN         4.0              0.0          0.0   \n3959          NaN               NaN         NaN              NaN          NaN   \n\n      FGC-FGC_SRL_Zone  FGC-FGC_SRR  FGC-FGC_SRR_Zone  FGC-FGC_TL  \\\n4                  NaN          NaN               NaN         NaN   \n7                  0.0         0.00               0.0         4.0   \n8                  NaN          NaN               NaN         NaN   \n9                  NaN          NaN               NaN         NaN   \n10                 NaN          NaN               NaN         NaN   \n...                ...          ...               ...         ...   \n3943               NaN          NaN               NaN         NaN   \n3944               0.0         8.75               0.0         9.5   \n3950               1.0         9.00               1.0        12.0   \n3956               0.0         0.00               0.0        12.0   \n3959               NaN          NaN               NaN         NaN   \n\n      FGC-FGC_TL_Zone BIA-Season  BIA-BIA_Activity_Level_num  BIA-BIA_BMC  \\\n4                 NaN        NaN                         NaN          NaN   \n7                 0.0       Fall                         3.0      4.05726   \n8                 NaN        NaN                         NaN          NaN   \n9                 NaN        NaN                         NaN          NaN   \n10                NaN        NaN                         NaN          NaN   \n...               ...        ...                         ...          ...   \n3943              NaN        NaN                         NaN          NaN   \n3944              1.0     Summer                         NaN          NaN   \n3950              1.0     Spring                         NaN          NaN   \n3956              1.0     Spring                         NaN          NaN   \n3959              NaN        NaN                         NaN          NaN   \n\n      BIA-BIA_BMI  BIA-BIA_BMR  BIA-BIA_DEE  BIA-BIA_ECW  BIA-BIA_FFM  \\\n4             NaN          NaN          NaN          NaN          NaN   \n7         16.8631      1180.04      1888.06        21.94      67.9527   \n8             NaN          NaN          NaN          NaN          NaN   \n9             NaN          NaN          NaN          NaN          NaN   \n10            NaN          NaN          NaN          NaN          NaN   \n...           ...          ...          ...          ...          ...   \n3943          NaN          NaN          NaN          NaN          NaN   \n3944          NaN          NaN          NaN          NaN          NaN   \n3950          NaN          NaN          NaN          NaN          NaN   \n3956          NaN          NaN          NaN          NaN          NaN   \n3959          NaN          NaN          NaN          NaN          NaN   \n\n      BIA-BIA_FFMI  BIA-BIA_FMI  BIA-BIA_Fat  BIA-BIA_Frame_num  BIA-BIA_ICW  \\\n4              NaN          NaN          NaN                NaN          NaN   \n7          13.6092      3.25395      16.2474                2.0      28.5367   \n8              NaN          NaN          NaN                NaN          NaN   \n9              NaN          NaN          NaN                NaN          NaN   \n10             NaN          NaN          NaN                NaN          NaN   \n...            ...          ...          ...                ...          ...   \n3943           NaN          NaN          NaN                NaN          NaN   \n3944           NaN          NaN          NaN                NaN          NaN   \n3950           NaN          NaN          NaN                NaN          NaN   \n3956           NaN          NaN          NaN                NaN          NaN   \n3959           NaN          NaN          NaN                NaN          NaN   \n\n      BIA-BIA_LDM  BIA-BIA_LST  BIA-BIA_SMM  BIA-BIA_TBW PAQ_A-Season  \\\n4             NaN          NaN          NaN          NaN       Summer   \n7          17.476      63.8954       28.768      50.4767          NaN   \n8             NaN          NaN          NaN          NaN          NaN   \n9             NaN          NaN          NaN          NaN          NaN   \n10            NaN          NaN          NaN          NaN          NaN   \n...           ...          ...          ...          ...          ...   \n3943          NaN          NaN          NaN          NaN          NaN   \n3944          NaN          NaN          NaN          NaN       Spring   \n3950          NaN          NaN          NaN          NaN          NaN   \n3956          NaN          NaN          NaN          NaN          NaN   \n3959          NaN          NaN          NaN          NaN          NaN   \n\n      PAQ_A-PAQ_A_Total PAQ_C-Season  PAQ_C-PAQ_C_Total PCIAT-Season  \\\n4                  1.04          NaN                NaN          NaN   \n7                   NaN         Fall               1.27          NaN   \n8                   NaN          NaN                NaN          NaN   \n9                   NaN          NaN                NaN          NaN   \n10                  NaN          NaN                NaN          NaN   \n...                 ...          ...                ...          ...   \n3943                NaN       Summer               1.88          NaN   \n3944               2.32          NaN                NaN          NaN   \n3950                NaN       Spring               1.99          NaN   \n3956                NaN       Winter               2.34          NaN   \n3959                NaN          NaN                NaN          NaN   \n\n      PCIAT-PCIAT_01  PCIAT-PCIAT_02  PCIAT-PCIAT_03  PCIAT-PCIAT_04  \\\n4                NaN             NaN             NaN             NaN   \n7                NaN             NaN             NaN             NaN   \n8                NaN             NaN             NaN             NaN   \n9                NaN             NaN             NaN             NaN   \n10               NaN             NaN             NaN             NaN   \n...              ...             ...             ...             ...   \n3943             NaN             NaN             NaN             NaN   \n3944             NaN             NaN             NaN             NaN   \n3950             NaN             NaN             NaN             NaN   \n3956             NaN             NaN             NaN             NaN   \n3959             NaN             NaN             NaN             NaN   \n\n      PCIAT-PCIAT_05  PCIAT-PCIAT_06  PCIAT-PCIAT_07  PCIAT-PCIAT_08  \\\n4                NaN             NaN             NaN             NaN   \n7                NaN             NaN             NaN             NaN   \n8                NaN             NaN             NaN             NaN   \n9                NaN             NaN             NaN             NaN   \n10               NaN             NaN             NaN             NaN   \n...              ...             ...             ...             ...   \n3943             NaN             NaN             NaN             NaN   \n3944             NaN             NaN             NaN             NaN   \n3950             NaN             NaN             NaN             NaN   \n3956             NaN             NaN             NaN             NaN   \n3959             NaN             NaN             NaN             NaN   \n\n      PCIAT-PCIAT_09  PCIAT-PCIAT_10  PCIAT-PCIAT_11  PCIAT-PCIAT_12  \\\n4                NaN             NaN             NaN             NaN   \n7                NaN             NaN             NaN             NaN   \n8                NaN             NaN             NaN             NaN   \n9                NaN             NaN             NaN             NaN   \n10               NaN             NaN             NaN             NaN   \n...              ...             ...             ...             ...   \n3943             NaN             NaN             NaN             NaN   \n3944             NaN             NaN             NaN             NaN   \n3950             NaN             NaN             NaN             NaN   \n3956             NaN             NaN             NaN             NaN   \n3959             NaN             NaN             NaN             NaN   \n\n      PCIAT-PCIAT_13  PCIAT-PCIAT_14  PCIAT-PCIAT_15  PCIAT-PCIAT_16  \\\n4                NaN             NaN             NaN             NaN   \n7                NaN             NaN             NaN             NaN   \n8                NaN             NaN             NaN             NaN   \n9                NaN             NaN             NaN             NaN   \n10               NaN             NaN             NaN             NaN   \n...              ...             ...             ...             ...   \n3943             NaN             NaN             NaN             NaN   \n3944             NaN             NaN             NaN             NaN   \n3950             NaN             NaN             NaN             NaN   \n3956             NaN             NaN             NaN             NaN   \n3959             NaN             NaN             NaN             NaN   \n\n      PCIAT-PCIAT_17  PCIAT-PCIAT_18  PCIAT-PCIAT_19  PCIAT-PCIAT_20  \\\n4                NaN             NaN             NaN             NaN   \n7                NaN             NaN             NaN             NaN   \n8                NaN             NaN             NaN             NaN   \n9                NaN             NaN             NaN             NaN   \n10               NaN             NaN             NaN             NaN   \n...              ...             ...             ...             ...   \n3943             NaN             NaN             NaN             NaN   \n3944             NaN             NaN             NaN             NaN   \n3950             NaN             NaN             NaN             NaN   \n3956             NaN             NaN             NaN             NaN   \n3959             NaN             NaN             NaN             NaN   \n\n      PCIAT-PCIAT_Total SDS-Season  SDS-SDS_Total_Raw  SDS-SDS_Total_T  \\\n4                   NaN        NaN                NaN              NaN   \n7                   NaN        NaN                NaN              NaN   \n8                   NaN        NaN                NaN              NaN   \n9                   NaN        NaN                NaN              NaN   \n10                  NaN        NaN                NaN              NaN   \n...                 ...        ...                ...              ...   \n3943                NaN        NaN                NaN              NaN   \n3944                NaN     Spring               49.0             68.0   \n3950                NaN        NaN                NaN              NaN   \n3956                NaN        NaN                NaN              NaN   \n3959                NaN        NaN                NaN              NaN   \n\n     PreInt_EduHx-Season  PreInt_EduHx-computerinternet_hoursday  sii  \\\n4                    NaN                                     NaN  NaN   \n7                   Fall                                     2.0  NaN   \n8                 Summer                                     2.0  NaN   \n9                    NaN                                     NaN  NaN   \n10                Spring                                     NaN  NaN   \n...                  ...                                     ...  ...   \n3943                 NaN                                     NaN  NaN   \n3944              Spring                                     2.0  NaN   \n3950              Spring                                     0.0  NaN   \n3956              Winter                                     0.0  NaN   \n3959              Spring                                     1.0  NaN   \n\n      recalc_sii  \n4            NaN  \n7            NaN  \n8            NaN  \n9            NaN  \n10           NaN  \n...          ...  \n3943         NaN  \n3944         NaN  \n3950         NaN  \n3956         NaN  \n3959         NaN  \n\n[1224 rows x 83 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>Basic_Demos-Enroll_Season</th>\n      <th>Basic_Demos-Age</th>\n      <th>Basic_Demos-Sex</th>\n      <th>CGAS-Season</th>\n      <th>CGAS-CGAS_Score</th>\n      <th>Physical-Season</th>\n      <th>Physical-BMI</th>\n      <th>Physical-Height</th>\n      <th>Physical-Weight</th>\n      <th>Physical-Waist_Circumference</th>\n      <th>Physical-Diastolic_BP</th>\n      <th>Physical-HeartRate</th>\n      <th>Physical-Systolic_BP</th>\n      <th>Fitness_Endurance-Season</th>\n      <th>Fitness_Endurance-Max_Stage</th>\n      <th>Fitness_Endurance-Time_Mins</th>\n      <th>Fitness_Endurance-Time_Sec</th>\n      <th>FGC-Season</th>\n      <th>FGC-FGC_CU</th>\n      <th>FGC-FGC_CU_Zone</th>\n      <th>FGC-FGC_GSND</th>\n      <th>FGC-FGC_GSND_Zone</th>\n      <th>FGC-FGC_GSD</th>\n      <th>FGC-FGC_GSD_Zone</th>\n      <th>FGC-FGC_PU</th>\n      <th>FGC-FGC_PU_Zone</th>\n      <th>FGC-FGC_SRL</th>\n      <th>FGC-FGC_SRL_Zone</th>\n      <th>FGC-FGC_SRR</th>\n      <th>FGC-FGC_SRR_Zone</th>\n      <th>FGC-FGC_TL</th>\n      <th>FGC-FGC_TL_Zone</th>\n      <th>BIA-Season</th>\n      <th>BIA-BIA_Activity_Level_num</th>\n      <th>BIA-BIA_BMC</th>\n      <th>BIA-BIA_BMI</th>\n      <th>BIA-BIA_BMR</th>\n      <th>BIA-BIA_DEE</th>\n      <th>BIA-BIA_ECW</th>\n      <th>BIA-BIA_FFM</th>\n      <th>BIA-BIA_FFMI</th>\n      <th>BIA-BIA_FMI</th>\n      <th>BIA-BIA_Fat</th>\n      <th>BIA-BIA_Frame_num</th>\n      <th>BIA-BIA_ICW</th>\n      <th>BIA-BIA_LDM</th>\n      <th>BIA-BIA_LST</th>\n      <th>BIA-BIA_SMM</th>\n      <th>BIA-BIA_TBW</th>\n      <th>PAQ_A-Season</th>\n      <th>PAQ_A-PAQ_A_Total</th>\n      <th>PAQ_C-Season</th>\n      <th>PAQ_C-PAQ_C_Total</th>\n      <th>PCIAT-Season</th>\n      <th>PCIAT-PCIAT_01</th>\n      <th>PCIAT-PCIAT_02</th>\n      <th>PCIAT-PCIAT_03</th>\n      <th>PCIAT-PCIAT_04</th>\n      <th>PCIAT-PCIAT_05</th>\n      <th>PCIAT-PCIAT_06</th>\n      <th>PCIAT-PCIAT_07</th>\n      <th>PCIAT-PCIAT_08</th>\n      <th>PCIAT-PCIAT_09</th>\n      <th>PCIAT-PCIAT_10</th>\n      <th>PCIAT-PCIAT_11</th>\n      <th>PCIAT-PCIAT_12</th>\n      <th>PCIAT-PCIAT_13</th>\n      <th>PCIAT-PCIAT_14</th>\n      <th>PCIAT-PCIAT_15</th>\n      <th>PCIAT-PCIAT_16</th>\n      <th>PCIAT-PCIAT_17</th>\n      <th>PCIAT-PCIAT_18</th>\n      <th>PCIAT-PCIAT_19</th>\n      <th>PCIAT-PCIAT_20</th>\n      <th>PCIAT-PCIAT_Total</th>\n      <th>SDS-Season</th>\n      <th>SDS-SDS_Total_Raw</th>\n      <th>SDS-SDS_Total_T</th>\n      <th>PreInt_EduHx-Season</th>\n      <th>PreInt_EduHx-computerinternet_hoursday</th>\n      <th>sii</th>\n      <th>recalc_sii</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4</th>\n      <td>0016bb22</td>\n      <td>Spring</td>\n      <td>18</td>\n      <td>1</td>\n      <td>Summer</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>1.04</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0068a485</td>\n      <td>Fall</td>\n      <td>10</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>16.861286</td>\n      <td>59.25</td>\n      <td>84.2</td>\n      <td>27.0</td>\n      <td>71.0</td>\n      <td>90.0</td>\n      <td>116.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>12.6</td>\n      <td>2.0</td>\n      <td>11.1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>Fall</td>\n      <td>3.0</td>\n      <td>4.05726</td>\n      <td>16.8631</td>\n      <td>1180.04</td>\n      <td>1888.06</td>\n      <td>21.94</td>\n      <td>67.9527</td>\n      <td>13.6092</td>\n      <td>3.25395</td>\n      <td>16.2474</td>\n      <td>2.0</td>\n      <td>28.5367</td>\n      <td>17.476</td>\n      <td>63.8954</td>\n      <td>28.768</td>\n      <td>50.4767</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>1.27</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0069fbed</td>\n      <td>Summer</td>\n      <td>15</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0083e397</td>\n      <td>Summer</td>\n      <td>19</td>\n      <td>1</td>\n      <td>Summer</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0087dd65</td>\n      <td>Spring</td>\n      <td>11</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3943</th>\n      <td>fe7c87e2</td>\n      <td>Spring</td>\n      <td>13</td>\n      <td>0</td>\n      <td>Summer</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>1.88</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3944</th>\n      <td>fe7f68a7</td>\n      <td>Spring</td>\n      <td>15</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>22.457960</td>\n      <td>62.00</td>\n      <td>122.8</td>\n      <td>30.0</td>\n      <td>64.0</td>\n      <td>60.0</td>\n      <td>106.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>18.5</td>\n      <td>2.0</td>\n      <td>21.8</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>8.75</td>\n      <td>0.0</td>\n      <td>9.5</td>\n      <td>1.0</td>\n      <td>Summer</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>2.32</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>49.0</td>\n      <td>68.0</td>\n      <td>Spring</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3950</th>\n      <td>ff0ab367</td>\n      <td>Spring</td>\n      <td>9</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>20.200490</td>\n      <td>52.50</td>\n      <td>79.2</td>\n      <td>28.0</td>\n      <td>75.0</td>\n      <td>92.0</td>\n      <td>142.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>9.5</td>\n      <td>1.0</td>\n      <td>9.00</td>\n      <td>1.0</td>\n      <td>12.0</td>\n      <td>1.0</td>\n      <td>Spring</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>1.99</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3956</th>\n      <td>ffa9794a</td>\n      <td>Winter</td>\n      <td>10</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>18.764678</td>\n      <td>53.50</td>\n      <td>76.4</td>\n      <td>27.0</td>\n      <td>60.0</td>\n      <td>78.0</td>\n      <td>118.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>1.0</td>\n      <td>Spring</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Winter</td>\n      <td>2.34</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Winter</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3959</th>\n      <td>ffef538e</td>\n      <td>Spring</td>\n      <td>11</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Winter</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Winter</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>1224 rows × 83 columns</p>\n</div>"},"metadata":{}}],"execution_count":439},{"id":"e563df90","cell_type":"code","source":"train = train.dropna(subset=['PCIAT-PCIAT_Total'])\ntrain","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:15.838743Z","iopub.execute_input":"2026-04-24T07:59:15.839076Z","iopub.status.idle":"2026-04-24T07:59:15.931415Z","shell.execute_reply.started":"2026-04-24T07:59:15.839046Z","shell.execute_reply":"2026-04-24T07:59:15.930556Z"},"papermill":{"duration":0.101084,"end_time":"2025-05-23T02:56:33.91071","exception":false,"start_time":"2025-05-23T02:56:33.809626","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":440,"output_type":"execute_result","data":{"text/plain":"            id Basic_Demos-Enroll_Season  Basic_Demos-Age  Basic_Demos-Sex  \\\n0     00008ff9                      Fall                5                0   \n1     000fd460                    Summer                9                0   \n2     00105258                    Summer               10                1   \n3     00115b9f                    Winter                9                0   \n5     001f3379                    Spring               13                1   \n...        ...                       ...              ...              ...   \n3953  ff6c2bb8                      Fall                8                0   \n3954  ff759544                    Summer                7                1   \n3955  ff8a2de4                      Fall               13                0   \n3957  ffcd4dbd                      Fall               11                0   \n3958  ffed1dd5                    Spring               13                0   \n\n     CGAS-Season  CGAS-CGAS_Score Physical-Season  Physical-BMI  \\\n0         Winter             51.0            Fall     16.877316   \n1            NaN              NaN            Fall     14.035590   \n2           Fall             71.0            Fall     16.648696   \n3           Fall             71.0          Summer     18.292347   \n5         Winter             50.0          Summer     22.279952   \n...          ...              ...             ...           ...   \n3953         NaN              NaN            Fall     17.139810   \n3954         NaN              NaN          Summer     13.927006   \n3955      Spring             60.0            Fall     16.362460   \n3957      Spring             68.0          Winter     21.441500   \n3958      Spring             70.0          Winter     12.235895   \n\n      Physical-Height  Physical-Weight  Physical-Waist_Circumference  \\\n0                46.0             50.8                           NaN   \n1                48.0             46.0                          22.0   \n2                56.5             75.6                           NaN   \n3                56.0             81.6                           NaN   \n5                59.5            112.2                           NaN   \n...               ...              ...                           ...   \n3953             52.5             67.2                          25.0   \n3954             48.5             46.6                          23.0   \n3955             59.5             82.4                           NaN   \n3957             60.0            109.8                           NaN   \n3958             70.7             87.0                           NaN   \n\n      Physical-Diastolic_BP  Physical-HeartRate  Physical-Systolic_BP  \\\n0                       NaN                 NaN                   NaN   \n1                      75.0                70.0                 122.0   \n2                      65.0                94.0                 117.0   \n3                      60.0                97.0                 117.0   \n5                      60.0                73.0                 102.0   \n...                     ...                 ...                   ...   \n3953                   60.0                65.0                 112.0   \n3954                   65.0                75.0                 105.0   \n3955                   71.0                70.0                 104.0   \n3957                   79.0                99.0                 116.0   \n3958                   59.0                61.0                 113.0   \n\n     Fitness_Endurance-Season  Fitness_Endurance-Max_Stage  \\\n0                         NaN                          NaN   \n1                         NaN                          NaN   \n2                        Fall                          5.0   \n3                      Summer                          6.0   \n5                         NaN                          NaN   \n...                       ...                          ...   \n3953                      NaN                          NaN   \n3954                      NaN                          NaN   \n3955                      NaN                          NaN   \n3957                      NaN                          NaN   \n3958                      NaN                          NaN   \n\n      Fitness_Endurance-Time_Mins  Fitness_Endurance-Time_Sec FGC-Season  \\\n0                             NaN                         NaN       Fall   \n1                             NaN                         NaN       Fall   \n2                             7.0                        33.0       Fall   \n3                             9.0                        37.0     Summer   \n5                             NaN                         NaN     Summer   \n...                           ...                         ...        ...   \n3953                          NaN                         NaN       Fall   \n3954                          NaN                         NaN     Summer   \n3955                          NaN                         NaN       Fall   \n3957                          NaN                         NaN     Winter   \n3958                          NaN                         NaN     Spring   \n\n      FGC-FGC_CU  FGC-FGC_CU_Zone  FGC-FGC_GSND  FGC-FGC_GSND_Zone  \\\n0            0.0              0.0           NaN                NaN   \n1            3.0              0.0           NaN                NaN   \n2           20.0              1.0          10.2                1.0   \n3           18.0              1.0           NaN                NaN   \n5           12.0              0.0          16.5                2.0   \n...          ...              ...           ...                ...   \n3953         0.0              0.0           NaN                NaN   \n3954         0.0              0.0           NaN                NaN   \n3955        16.0              0.0          18.0                1.0   \n3957        15.0              1.0          18.5                2.0   \n3958         NaN              NaN           NaN                NaN   \n\n      FGC-FGC_GSD  FGC-FGC_GSD_Zone  FGC-FGC_PU  FGC-FGC_PU_Zone  FGC-FGC_SRL  \\\n0             NaN               NaN         0.0              0.0          7.0   \n1             NaN               NaN         5.0              0.0         11.0   \n2            14.7               2.0         7.0              1.0         10.0   \n3             NaN               NaN         5.0              0.0          7.0   \n5            17.9               2.0         6.0              0.0         10.0   \n...           ...               ...         ...              ...          ...   \n3953          NaN               NaN         0.0              0.0          8.0   \n3954          NaN               NaN         0.0              0.0          9.0   \n3955         19.9               2.0        10.0              1.0          8.0   \n3957         15.8               2.0         0.0              0.0         10.0   \n3958          NaN               NaN         NaN              NaN          NaN   \n\n      FGC-FGC_SRL_Zone  FGC-FGC_SRR  FGC-FGC_SRR_Zone  FGC-FGC_TL  \\\n0                  0.0          6.0               0.0         6.0   \n1                  1.0         11.0               1.0         3.0   \n2                  1.0         10.0               1.0         5.0   \n3                  0.0          7.0               0.0         7.0   \n5                  1.0         11.0               1.0         8.0   \n...                ...          ...               ...         ...   \n3953               1.0         10.0               1.0        12.0   \n3954               0.0          8.5               0.0         4.5   \n3955               1.0          9.0               1.0        12.0   \n3957               1.0         10.0               1.0        14.0   \n3958               NaN          NaN               NaN         NaN   \n\n      FGC-FGC_TL_Zone BIA-Season  BIA-BIA_Activity_Level_num  BIA-BIA_BMC  \\\n0                 1.0       Fall                         2.0      2.66855   \n1                 0.0     Winter                         2.0      2.57949   \n2                 0.0        NaN                         NaN          NaN   \n3                 1.0     Summer                         3.0      3.84191   \n5                 0.0     Summer                         2.0      4.33036   \n...               ...        ...                         ...          ...   \n3953              1.0       Fall                         3.0      3.20303   \n3954              0.0       Fall                         1.0      2.36680   \n3955              1.0       Fall                         3.0      4.52277   \n3957              1.0     Winter                         2.0      4.41305   \n3958              NaN     Summer                         4.0      6.66168   \n\n      BIA-BIA_BMI  BIA-BIA_BMR  BIA-BIA_DEE  BIA-BIA_ECW  BIA-BIA_FFM  \\\n0         16.8792      932.498      1492.00      8.25598      41.5862   \n1         14.0371      936.656      1498.65      6.01993      42.0291   \n2             NaN          NaN          NaN          NaN          NaN   \n3         18.2943     1131.430      1923.44     15.59250      62.7757   \n5         30.1865     1330.970      1996.45     30.21240      84.0285   \n...           ...          ...          ...          ...          ...   \n3953      17.1417     1035.270      1759.96     11.00630      52.5331   \n3954      13.6457      966.287      1256.17      9.98802      45.1853   \n3955      16.3642     1206.880      2051.70     19.46110      70.8117   \n3957      21.4438     1253.740      2005.99     20.48250      75.8033   \n3958      12.2372     1414.340      2970.12     26.53230      92.9092   \n\n      BIA-BIA_FFMI  BIA-BIA_FMI  BIA-BIA_Fat  BIA-BIA_Frame_num  BIA-BIA_ICW  \\\n0          13.8177     3.061430      9.21377                1.0      24.4349   \n1          12.8254     1.211720      3.97085                1.0      21.0352   \n2              NaN          NaN          NaN                NaN          NaN   \n3          14.0740     4.220330     18.82430                2.0      30.4041   \n5          16.6877    13.498800     67.97150                2.0      32.9141   \n...            ...          ...          ...                ...          ...   \n3953       13.4004     3.741300     14.66690                1.0      25.7118   \n3954       13.2315     0.414263      1.41470                1.0      20.0572   \n3955       14.0629     2.301380     11.58830                1.0      33.3709   \n3957       14.8043     6.639520     33.99670                2.0      33.9805   \n3958       13.0684    -0.831170     -5.90917                2.0      41.3715   \n\n      BIA-BIA_LDM  BIA-BIA_LST  BIA-BIA_SMM  BIA-BIA_TBW PAQ_A-Season  \\\n0         8.89536      38.9177      19.5413      32.6909          NaN   \n1        14.97400      39.4497      15.4107      27.0552          NaN   \n2             NaN          NaN          NaN          NaN          NaN   \n3        16.77900      58.9338      26.4798      45.9966          NaN   \n5        20.90200      79.6982      35.3804      63.1265          NaN   \n...           ...          ...          ...          ...          ...   \n3953     15.81500      49.3301      20.2645      36.7181          NaN   \n3954     15.14000      42.8185      18.0937      30.0453          NaN   \n3955     17.97970      66.2889      29.7790      52.8320          NaN   \n3957     21.34030      71.3903      28.7792      54.4630          NaN   \n3958     25.00540      86.2475      45.4340      67.9038          NaN   \n\n      PAQ_A-PAQ_A_Total PAQ_C-Season  PAQ_C-PAQ_C_Total PCIAT-Season  \\\n0                   NaN          NaN                NaN         Fall   \n1                   NaN         Fall              2.340         Fall   \n2                   NaN       Summer              2.170         Fall   \n3                   NaN       Winter              2.451       Summer   \n5                   NaN       Spring              4.110       Summer   \n...                 ...          ...                ...          ...   \n3953                NaN         Fall              3.440         Fall   \n3954                NaN          NaN                NaN       Summer   \n3955                NaN       Winter              3.260       Winter   \n3957                NaN       Winter              2.729       Winter   \n3958                NaN       Spring              3.300       Spring   \n\n      PCIAT-PCIAT_01  PCIAT-PCIAT_02  PCIAT-PCIAT_03  PCIAT-PCIAT_04  \\\n0                5.0             4.0             4.0             0.0   \n1                0.0             0.0             0.0             0.0   \n2                5.0             2.0             2.0             1.0   \n3                4.0             2.0             4.0             0.0   \n5                3.0             3.0             3.0             0.0   \n...              ...             ...             ...             ...   \n3953             3.0             3.0             3.0             0.0   \n3954             1.0             3.0             3.0             0.0   \n3955             3.0             3.0             3.0             2.0   \n3957             5.0             5.0             3.0             0.0   \n3958             2.0             1.0             1.0             1.0   \n\n      PCIAT-PCIAT_05  PCIAT-PCIAT_06  PCIAT-PCIAT_07  PCIAT-PCIAT_08  \\\n0                4.0             0.0             0.0             4.0   \n1                0.0             0.0             0.0             0.0   \n2                2.0             1.0             1.0             2.0   \n3                5.0             1.0             0.0             3.0   \n5                2.0             1.0             0.0             2.0   \n...              ...             ...             ...             ...   \n3953             0.0             0.0             0.0             3.0   \n3954             3.0             0.0             0.0             0.0   \n3955             3.0             2.0             2.0             2.0   \n3957             5.0             1.0             0.0             2.0   \n3958             0.0             0.0             0.0             1.0   \n\n      PCIAT-PCIAT_09  PCIAT-PCIAT_10  PCIAT-PCIAT_11  PCIAT-PCIAT_12  \\\n0                0.0             0.0             4.0             0.0   \n1                0.0             0.0             0.0             0.0   \n2                1.0             1.0             1.0             0.0   \n3                2.0             2.0             3.0             0.0   \n5                2.0             1.0             0.0             1.0   \n...              ...             ...             ...             ...   \n3953             0.0             0.0             0.0             0.0   \n3954             0.0             0.0             3.0             0.0   \n3955             2.0             1.0             2.0             0.0   \n3957             0.0             2.0             1.0             0.0   \n3958             1.0             1.0             2.0             0.0   \n\n      PCIAT-PCIAT_13  PCIAT-PCIAT_14  PCIAT-PCIAT_15  PCIAT-PCIAT_16  \\\n0                4.0             4.0             4.0             4.0   \n1                0.0             0.0             0.0             0.0   \n2                1.0             1.0             1.0             0.0   \n3                3.0             0.0             0.0             3.0   \n5                3.0             3.0             2.0             1.0   \n...              ...             ...             ...             ...   \n3953             2.0             0.0             0.0             3.0   \n3954             5.0             1.0             0.0             5.0   \n3955             2.0             0.0             1.0             0.0   \n3957             1.0             3.0             0.0             0.0   \n3958             1.0             1.0             2.0             1.0   \n\n      PCIAT-PCIAT_17  PCIAT-PCIAT_18  PCIAT-PCIAT_19  PCIAT-PCIAT_20  \\\n0                4.0             4.0             2.0             4.0   \n1                0.0             0.0             0.0             0.0   \n2                2.0             2.0             1.0             1.0   \n3                4.0             3.0             4.0             1.0   \n5                3.0             1.0             2.0             1.0   \n...              ...             ...             ...             ...   \n3953             0.0             2.0             2.0             1.0   \n3954             3.0             3.0             3.0             0.0   \n3955             2.0             1.0             1.0             0.0   \n3957             1.0             1.0             0.0             1.0   \n3958             1.0             1.0             1.0             1.0   \n\n      PCIAT-PCIAT_Total SDS-Season  SDS-SDS_Total_Raw  SDS-SDS_Total_T  \\\n0                  55.0        NaN                NaN              NaN   \n1                   0.0       Fall               46.0             64.0   \n2                  28.0       Fall               38.0             54.0   \n3                  44.0     Summer               31.0             45.0   \n5                  34.0     Summer               40.0             56.0   \n...                 ...        ...                ...              ...   \n3953               22.0       Fall               41.0             58.0   \n3954               33.0     Summer               48.0             67.0   \n3955               32.0     Winter               35.0             50.0   \n3957               31.0     Winter               56.0             77.0   \n3958               19.0     Spring               33.0             47.0   \n\n     PreInt_EduHx-Season  PreInt_EduHx-computerinternet_hoursday  sii  \\\n0                   Fall                                     3.0  2.0   \n1                 Summer                                     0.0  0.0   \n2                 Summer                                     2.0  0.0   \n3                 Winter                                     0.0  1.0   \n5                 Spring                                     0.0  1.0   \n...                  ...                                     ...  ...   \n3953                Fall                                     2.0  0.0   \n3954              Summer                                     0.0  1.0   \n3955                Fall                                     1.0  1.0   \n3957                Fall                                     0.0  1.0   \n3958              Spring                                     1.0  0.0   \n\n      recalc_sii  \n0            2.0  \n1            0.0  \n2            0.0  \n3            1.0  \n5            1.0  \n...          ...  \n3953         0.0  \n3954         1.0  \n3955         1.0  \n3957         1.0  \n3958         0.0  \n\n[2719 rows x 83 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>Basic_Demos-Enroll_Season</th>\n      <th>Basic_Demos-Age</th>\n      <th>Basic_Demos-Sex</th>\n      <th>CGAS-Season</th>\n      <th>CGAS-CGAS_Score</th>\n      <th>Physical-Season</th>\n      <th>Physical-BMI</th>\n      <th>Physical-Height</th>\n      <th>Physical-Weight</th>\n      <th>Physical-Waist_Circumference</th>\n      <th>Physical-Diastolic_BP</th>\n      <th>Physical-HeartRate</th>\n      <th>Physical-Systolic_BP</th>\n      <th>Fitness_Endurance-Season</th>\n      <th>Fitness_Endurance-Max_Stage</th>\n      <th>Fitness_Endurance-Time_Mins</th>\n      <th>Fitness_Endurance-Time_Sec</th>\n      <th>FGC-Season</th>\n      <th>FGC-FGC_CU</th>\n      <th>FGC-FGC_CU_Zone</th>\n      <th>FGC-FGC_GSND</th>\n      <th>FGC-FGC_GSND_Zone</th>\n      <th>FGC-FGC_GSD</th>\n      <th>FGC-FGC_GSD_Zone</th>\n      <th>FGC-FGC_PU</th>\n      <th>FGC-FGC_PU_Zone</th>\n      <th>FGC-FGC_SRL</th>\n      <th>FGC-FGC_SRL_Zone</th>\n      <th>FGC-FGC_SRR</th>\n      <th>FGC-FGC_SRR_Zone</th>\n      <th>FGC-FGC_TL</th>\n      <th>FGC-FGC_TL_Zone</th>\n      <th>BIA-Season</th>\n      <th>BIA-BIA_Activity_Level_num</th>\n      <th>BIA-BIA_BMC</th>\n      <th>BIA-BIA_BMI</th>\n      <th>BIA-BIA_BMR</th>\n      <th>BIA-BIA_DEE</th>\n      <th>BIA-BIA_ECW</th>\n      <th>BIA-BIA_FFM</th>\n      <th>BIA-BIA_FFMI</th>\n      <th>BIA-BIA_FMI</th>\n      <th>BIA-BIA_Fat</th>\n      <th>BIA-BIA_Frame_num</th>\n      <th>BIA-BIA_ICW</th>\n      <th>BIA-BIA_LDM</th>\n      <th>BIA-BIA_LST</th>\n      <th>BIA-BIA_SMM</th>\n      <th>BIA-BIA_TBW</th>\n      <th>PAQ_A-Season</th>\n      <th>PAQ_A-PAQ_A_Total</th>\n      <th>PAQ_C-Season</th>\n      <th>PAQ_C-PAQ_C_Total</th>\n      <th>PCIAT-Season</th>\n      <th>PCIAT-PCIAT_01</th>\n      <th>PCIAT-PCIAT_02</th>\n      <th>PCIAT-PCIAT_03</th>\n      <th>PCIAT-PCIAT_04</th>\n      <th>PCIAT-PCIAT_05</th>\n      <th>PCIAT-PCIAT_06</th>\n      <th>PCIAT-PCIAT_07</th>\n      <th>PCIAT-PCIAT_08</th>\n      <th>PCIAT-PCIAT_09</th>\n      <th>PCIAT-PCIAT_10</th>\n      <th>PCIAT-PCIAT_11</th>\n      <th>PCIAT-PCIAT_12</th>\n      <th>PCIAT-PCIAT_13</th>\n      <th>PCIAT-PCIAT_14</th>\n      <th>PCIAT-PCIAT_15</th>\n      <th>PCIAT-PCIAT_16</th>\n      <th>PCIAT-PCIAT_17</th>\n      <th>PCIAT-PCIAT_18</th>\n      <th>PCIAT-PCIAT_19</th>\n      <th>PCIAT-PCIAT_20</th>\n      <th>PCIAT-PCIAT_Total</th>\n      <th>SDS-Season</th>\n      <th>SDS-SDS_Total_Raw</th>\n      <th>SDS-SDS_Total_T</th>\n      <th>PreInt_EduHx-Season</th>\n      <th>PreInt_EduHx-computerinternet_hoursday</th>\n      <th>sii</th>\n      <th>recalc_sii</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00008ff9</td>\n      <td>Fall</td>\n      <td>5</td>\n      <td>0</td>\n      <td>Winter</td>\n      <td>51.0</td>\n      <td>Fall</td>\n      <td>16.877316</td>\n      <td>46.0</td>\n      <td>50.8</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>Fall</td>\n      <td>2.0</td>\n      <td>2.66855</td>\n      <td>16.8792</td>\n      <td>932.498</td>\n      <td>1492.00</td>\n      <td>8.25598</td>\n      <td>41.5862</td>\n      <td>13.8177</td>\n      <td>3.061430</td>\n      <td>9.21377</td>\n      <td>1.0</td>\n      <td>24.4349</td>\n      <td>8.89536</td>\n      <td>38.9177</td>\n      <td>19.5413</td>\n      <td>32.6909</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>5.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>55.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000fd460</td>\n      <td>Summer</td>\n      <td>9</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>14.035590</td>\n      <td>48.0</td>\n      <td>46.0</td>\n      <td>22.0</td>\n      <td>75.0</td>\n      <td>70.0</td>\n      <td>122.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>Winter</td>\n      <td>2.0</td>\n      <td>2.57949</td>\n      <td>14.0371</td>\n      <td>936.656</td>\n      <td>1498.65</td>\n      <td>6.01993</td>\n      <td>42.0291</td>\n      <td>12.8254</td>\n      <td>1.211720</td>\n      <td>3.97085</td>\n      <td>1.0</td>\n      <td>21.0352</td>\n      <td>14.97400</td>\n      <td>39.4497</td>\n      <td>15.4107</td>\n      <td>27.0552</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>2.340</td>\n      <td>Fall</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Fall</td>\n      <td>46.0</td>\n      <td>64.0</td>\n      <td>Summer</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00105258</td>\n      <td>Summer</td>\n      <td>10</td>\n      <td>1</td>\n      <td>Fall</td>\n      <td>71.0</td>\n      <td>Fall</td>\n      <td>16.648696</td>\n      <td>56.5</td>\n      <td>75.6</td>\n      <td>NaN</td>\n      <td>65.0</td>\n      <td>94.0</td>\n      <td>117.0</td>\n      <td>Fall</td>\n      <td>5.0</td>\n      <td>7.0</td>\n      <td>33.0</td>\n      <td>Fall</td>\n      <td>20.0</td>\n      <td>1.0</td>\n      <td>10.2</td>\n      <td>1.0</td>\n      <td>14.7</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>2.170</td>\n      <td>Fall</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>28.0</td>\n      <td>Fall</td>\n      <td>38.0</td>\n      <td>54.0</td>\n      <td>Summer</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00115b9f</td>\n      <td>Winter</td>\n      <td>9</td>\n      <td>0</td>\n      <td>Fall</td>\n      <td>71.0</td>\n      <td>Summer</td>\n      <td>18.292347</td>\n      <td>56.0</td>\n      <td>81.6</td>\n      <td>NaN</td>\n      <td>60.0</td>\n      <td>97.0</td>\n      <td>117.0</td>\n      <td>Summer</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>37.0</td>\n      <td>Summer</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>Summer</td>\n      <td>3.0</td>\n      <td>3.84191</td>\n      <td>18.2943</td>\n      <td>1131.430</td>\n      <td>1923.44</td>\n      <td>15.59250</td>\n      <td>62.7757</td>\n      <td>14.0740</td>\n      <td>4.220330</td>\n      <td>18.82430</td>\n      <td>2.0</td>\n      <td>30.4041</td>\n      <td>16.77900</td>\n      <td>58.9338</td>\n      <td>26.4798</td>\n      <td>45.9966</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Winter</td>\n      <td>2.451</td>\n      <td>Summer</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>44.0</td>\n      <td>Summer</td>\n      <td>31.0</td>\n      <td>45.0</td>\n      <td>Winter</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>001f3379</td>\n      <td>Spring</td>\n      <td>13</td>\n      <td>1</td>\n      <td>Winter</td>\n      <td>50.0</td>\n      <td>Summer</td>\n      <td>22.279952</td>\n      <td>59.5</td>\n      <td>112.2</td>\n      <td>NaN</td>\n      <td>60.0</td>\n      <td>73.0</td>\n      <td>102.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>16.5</td>\n      <td>2.0</td>\n      <td>17.9</td>\n      <td>2.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>Summer</td>\n      <td>2.0</td>\n      <td>4.33036</td>\n      <td>30.1865</td>\n      <td>1330.970</td>\n      <td>1996.45</td>\n      <td>30.21240</td>\n      <td>84.0285</td>\n      <td>16.6877</td>\n      <td>13.498800</td>\n      <td>67.97150</td>\n      <td>2.0</td>\n      <td>32.9141</td>\n      <td>20.90200</td>\n      <td>79.6982</td>\n      <td>35.3804</td>\n      <td>63.1265</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>4.110</td>\n      <td>Summer</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>34.0</td>\n      <td>Summer</td>\n      <td>40.0</td>\n      <td>56.0</td>\n      <td>Spring</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3953</th>\n      <td>ff6c2bb8</td>\n      <td>Fall</td>\n      <td>8</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>17.139810</td>\n      <td>52.5</td>\n      <td>67.2</td>\n      <td>25.0</td>\n      <td>60.0</td>\n      <td>65.0</td>\n      <td>112.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>12.0</td>\n      <td>1.0</td>\n      <td>Fall</td>\n      <td>3.0</td>\n      <td>3.20303</td>\n      <td>17.1417</td>\n      <td>1035.270</td>\n      <td>1759.96</td>\n      <td>11.00630</td>\n      <td>52.5331</td>\n      <td>13.4004</td>\n      <td>3.741300</td>\n      <td>14.66690</td>\n      <td>1.0</td>\n      <td>25.7118</td>\n      <td>15.81500</td>\n      <td>49.3301</td>\n      <td>20.2645</td>\n      <td>36.7181</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>3.440</td>\n      <td>Fall</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>22.0</td>\n      <td>Fall</td>\n      <td>41.0</td>\n      <td>58.0</td>\n      <td>Fall</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3954</th>\n      <td>ff759544</td>\n      <td>Summer</td>\n      <td>7</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>13.927006</td>\n      <td>48.5</td>\n      <td>46.6</td>\n      <td>23.0</td>\n      <td>65.0</td>\n      <td>75.0</td>\n      <td>105.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>8.5</td>\n      <td>0.0</td>\n      <td>4.5</td>\n      <td>0.0</td>\n      <td>Fall</td>\n      <td>1.0</td>\n      <td>2.36680</td>\n      <td>13.6457</td>\n      <td>966.287</td>\n      <td>1256.17</td>\n      <td>9.98802</td>\n      <td>45.1853</td>\n      <td>13.2315</td>\n      <td>0.414263</td>\n      <td>1.41470</td>\n      <td>1.0</td>\n      <td>20.0572</td>\n      <td>15.14000</td>\n      <td>42.8185</td>\n      <td>18.0937</td>\n      <td>30.0453</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>33.0</td>\n      <td>Summer</td>\n      <td>48.0</td>\n      <td>67.0</td>\n      <td>Summer</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3955</th>\n      <td>ff8a2de4</td>\n      <td>Fall</td>\n      <td>13</td>\n      <td>0</td>\n      <td>Spring</td>\n      <td>60.0</td>\n      <td>Fall</td>\n      <td>16.362460</td>\n      <td>59.5</td>\n      <td>82.4</td>\n      <td>NaN</td>\n      <td>71.0</td>\n      <td>70.0</td>\n      <td>104.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Fall</td>\n      <td>16.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>19.9</td>\n      <td>2.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>8.0</td>\n      <td>1.0</td>\n      <td>9.0</td>\n      <td>1.0</td>\n      <td>12.0</td>\n      <td>1.0</td>\n      <td>Fall</td>\n      <td>3.0</td>\n      <td>4.52277</td>\n      <td>16.3642</td>\n      <td>1206.880</td>\n      <td>2051.70</td>\n      <td>19.46110</td>\n      <td>70.8117</td>\n      <td>14.0629</td>\n      <td>2.301380</td>\n      <td>11.58830</td>\n      <td>1.0</td>\n      <td>33.3709</td>\n      <td>17.97970</td>\n      <td>66.2889</td>\n      <td>29.7790</td>\n      <td>52.8320</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Winter</td>\n      <td>3.260</td>\n      <td>Winter</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>32.0</td>\n      <td>Winter</td>\n      <td>35.0</td>\n      <td>50.0</td>\n      <td>Fall</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3957</th>\n      <td>ffcd4dbd</td>\n      <td>Fall</td>\n      <td>11</td>\n      <td>0</td>\n      <td>Spring</td>\n      <td>68.0</td>\n      <td>Winter</td>\n      <td>21.441500</td>\n      <td>60.0</td>\n      <td>109.8</td>\n      <td>NaN</td>\n      <td>79.0</td>\n      <td>99.0</td>\n      <td>116.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Winter</td>\n      <td>15.0</td>\n      <td>1.0</td>\n      <td>18.5</td>\n      <td>2.0</td>\n      <td>15.8</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>14.0</td>\n      <td>1.0</td>\n      <td>Winter</td>\n      <td>2.0</td>\n      <td>4.41305</td>\n      <td>21.4438</td>\n      <td>1253.740</td>\n      <td>2005.99</td>\n      <td>20.48250</td>\n      <td>75.8033</td>\n      <td>14.8043</td>\n      <td>6.639520</td>\n      <td>33.99670</td>\n      <td>2.0</td>\n      <td>33.9805</td>\n      <td>21.34030</td>\n      <td>71.3903</td>\n      <td>28.7792</td>\n      <td>54.4630</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Winter</td>\n      <td>2.729</td>\n      <td>Winter</td>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>31.0</td>\n      <td>Winter</td>\n      <td>56.0</td>\n      <td>77.0</td>\n      <td>Fall</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3958</th>\n      <td>ffed1dd5</td>\n      <td>Spring</td>\n      <td>13</td>\n      <td>0</td>\n      <td>Spring</td>\n      <td>70.0</td>\n      <td>Winter</td>\n      <td>12.235895</td>\n      <td>70.7</td>\n      <td>87.0</td>\n      <td>NaN</td>\n      <td>59.0</td>\n      <td>61.0</td>\n      <td>113.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Summer</td>\n      <td>4.0</td>\n      <td>6.66168</td>\n      <td>12.2372</td>\n      <td>1414.340</td>\n      <td>2970.12</td>\n      <td>26.53230</td>\n      <td>92.9092</td>\n      <td>13.0684</td>\n      <td>-0.831170</td>\n      <td>-5.90917</td>\n      <td>2.0</td>\n      <td>41.3715</td>\n      <td>25.00540</td>\n      <td>86.2475</td>\n      <td>45.4340</td>\n      <td>67.9038</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spring</td>\n      <td>3.300</td>\n      <td>Spring</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>19.0</td>\n      <td>Spring</td>\n      <td>33.0</td>\n      <td>47.0</td>\n      <td>Spring</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>2719 rows × 83 columns</p>\n</div>"},"metadata":{}}],"execution_count":440},{"id":"ed609374","cell_type":"code","source":"# fill the unanswered questions with the mode of the corresponding column.\nfor column in question_columns:\n    if train[column].isna().any():\n        mode_value = train[column].mode()[0]\n        train[column] = train[column].fillna(mode_value)\n\ntrain[columns_not_in_test + ['recalc_sii']]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:15.932616Z","iopub.execute_input":"2026-04-24T07:59:15.932952Z","iopub.status.idle":"2026-04-24T07:59:15.985442Z","shell.execute_reply.started":"2026-04-24T07:59:15.932917Z","shell.execute_reply":"2026-04-24T07:59:15.984573Z"},"papermill":{"duration":0.070742,"end_time":"2025-05-23T02:56:34.063859","exception":false,"start_time":"2025-05-23T02:56:33.993117","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":441,"output_type":"execute_result","data":{"text/plain":"      PCIAT-PCIAT_01  PCIAT-PCIAT_02  PCIAT-PCIAT_03  PCIAT-PCIAT_04  \\\n0                5.0             4.0             4.0             0.0   \n1                0.0             0.0             0.0             0.0   \n2                5.0             2.0             2.0             1.0   \n3                4.0             2.0             4.0             0.0   \n5                3.0             3.0             3.0             0.0   \n...              ...             ...             ...             ...   \n3953             3.0             3.0             3.0             0.0   \n3954             1.0             3.0             3.0             0.0   \n3955             3.0             3.0             3.0             2.0   \n3957             5.0             5.0             3.0             0.0   \n3958             2.0             1.0             1.0             1.0   \n\n      PCIAT-PCIAT_05  PCIAT-PCIAT_06  PCIAT-PCIAT_07  PCIAT-PCIAT_08  \\\n0                4.0             0.0             0.0             4.0   \n1                0.0             0.0             0.0             0.0   \n2                2.0             1.0             1.0             2.0   \n3                5.0             1.0             0.0             3.0   \n5                2.0             1.0             0.0             2.0   \n...              ...             ...             ...             ...   \n3953             0.0             0.0             0.0             3.0   \n3954             3.0             0.0             0.0             0.0   \n3955             3.0             2.0             2.0             2.0   \n3957             5.0             1.0             0.0             2.0   \n3958             0.0             0.0             0.0             1.0   \n\n      PCIAT-PCIAT_09  PCIAT-PCIAT_10  PCIAT-PCIAT_11  PCIAT-PCIAT_12  \\\n0                0.0             0.0             4.0             0.0   \n1                0.0             0.0             0.0             0.0   \n2                1.0             1.0             1.0             0.0   \n3                2.0             2.0             3.0             0.0   \n5                2.0             1.0             0.0             1.0   \n...              ...             ...             ...             ...   \n3953             0.0             0.0             0.0             0.0   \n3954             0.0             0.0             3.0             0.0   \n3955             2.0             1.0             2.0             0.0   \n3957             0.0             2.0             1.0             0.0   \n3958             1.0             1.0             2.0             0.0   \n\n      PCIAT-PCIAT_13  PCIAT-PCIAT_14  PCIAT-PCIAT_15  PCIAT-PCIAT_16  \\\n0                4.0             4.0             4.0             4.0   \n1                0.0             0.0             0.0             0.0   \n2                1.0             1.0             1.0             0.0   \n3                3.0             0.0             0.0             3.0   \n5                3.0             3.0             2.0             1.0   \n...              ...             ...             ...             ...   \n3953             2.0             0.0             0.0             3.0   \n3954             5.0             1.0             0.0             5.0   \n3955             2.0             0.0             1.0             0.0   \n3957             1.0             3.0             0.0             0.0   \n3958             1.0             1.0             2.0             1.0   \n\n      PCIAT-PCIAT_17  PCIAT-PCIAT_18  PCIAT-PCIAT_19  PCIAT-PCIAT_20  \\\n0                4.0             4.0             2.0             4.0   \n1                0.0             0.0             0.0             0.0   \n2                2.0             2.0             1.0             1.0   \n3                4.0             3.0             4.0             1.0   \n5                3.0             1.0             2.0             1.0   \n...              ...             ...             ...             ...   \n3953             0.0             2.0             2.0             1.0   \n3954             3.0             3.0             3.0             0.0   \n3955             2.0             1.0             1.0             0.0   \n3957             1.0             1.0             0.0             1.0   \n3958             1.0             1.0             1.0             1.0   \n\n      PCIAT-PCIAT_Total PCIAT-Season  sii  recalc_sii  \n0                  55.0         Fall  2.0         2.0  \n1                   0.0         Fall  0.0         0.0  \n2                  28.0         Fall  0.0         0.0  \n3                  44.0       Summer  1.0         1.0  \n5                  34.0       Summer  1.0         1.0  \n...                 ...          ...  ...         ...  \n3953               22.0         Fall  0.0         0.0  \n3954               33.0       Summer  1.0         1.0  \n3955               32.0       Winter  1.0         1.0  \n3957               31.0       Winter  1.0         1.0  \n3958               19.0       Spring  0.0         0.0  \n\n[2719 rows x 24 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PCIAT-PCIAT_01</th>\n      <th>PCIAT-PCIAT_02</th>\n      <th>PCIAT-PCIAT_03</th>\n      <th>PCIAT-PCIAT_04</th>\n      <th>PCIAT-PCIAT_05</th>\n      <th>PCIAT-PCIAT_06</th>\n      <th>PCIAT-PCIAT_07</th>\n      <th>PCIAT-PCIAT_08</th>\n      <th>PCIAT-PCIAT_09</th>\n      <th>PCIAT-PCIAT_10</th>\n      <th>PCIAT-PCIAT_11</th>\n      <th>PCIAT-PCIAT_12</th>\n      <th>PCIAT-PCIAT_13</th>\n      <th>PCIAT-PCIAT_14</th>\n      <th>PCIAT-PCIAT_15</th>\n      <th>PCIAT-PCIAT_16</th>\n      <th>PCIAT-PCIAT_17</th>\n      <th>PCIAT-PCIAT_18</th>\n      <th>PCIAT-PCIAT_19</th>\n      <th>PCIAT-PCIAT_20</th>\n      <th>PCIAT-PCIAT_Total</th>\n      <th>PCIAT-Season</th>\n      <th>sii</th>\n      <th>recalc_sii</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>55.0</td>\n      <td>Fall</td>\n      <td>2.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Fall</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>28.0</td>\n      <td>Fall</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>44.0</td>\n      <td>Summer</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>34.0</td>\n      <td>Summer</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3953</th>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>22.0</td>\n      <td>Fall</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3954</th>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>33.0</td>\n      <td>Summer</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3955</th>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>32.0</td>\n      <td>Winter</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3957</th>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>31.0</td>\n      <td>Winter</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3958</th>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>19.0</td>\n      <td>Spring</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>2719 rows × 24 columns</p>\n</div>"},"metadata":{}}],"execution_count":441},{"id":"eaf67acf","cell_type":"code","source":"train.drop(columns='recalc_sii', inplace=True)\n\ntrain[columns_not_in_test]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:15.986663Z","iopub.execute_input":"2026-04-24T07:59:15.987323Z","iopub.status.idle":"2026-04-24T07:59:16.032081Z","shell.execute_reply.started":"2026-04-24T07:59:15.987286Z","shell.execute_reply":"2026-04-24T07:59:16.031347Z"},"papermill":{"duration":0.060679,"end_time":"2025-05-23T02:56:34.152965","exception":false,"start_time":"2025-05-23T02:56:34.092286","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":442,"output_type":"execute_result","data":{"text/plain":"      PCIAT-PCIAT_01  PCIAT-PCIAT_02  PCIAT-PCIAT_03  PCIAT-PCIAT_04  \\\n0                5.0             4.0             4.0             0.0   \n1                0.0             0.0             0.0             0.0   \n2                5.0             2.0             2.0             1.0   \n3                4.0             2.0             4.0             0.0   \n5                3.0             3.0             3.0             0.0   \n...              ...             ...             ...             ...   \n3953             3.0             3.0             3.0             0.0   \n3954             1.0             3.0             3.0             0.0   \n3955             3.0             3.0             3.0             2.0   \n3957             5.0             5.0             3.0             0.0   \n3958             2.0             1.0             1.0             1.0   \n\n      PCIAT-PCIAT_05  PCIAT-PCIAT_06  PCIAT-PCIAT_07  PCIAT-PCIAT_08  \\\n0                4.0             0.0             0.0             4.0   \n1                0.0             0.0             0.0             0.0   \n2                2.0             1.0             1.0             2.0   \n3                5.0             1.0             0.0             3.0   \n5                2.0             1.0             0.0             2.0   \n...              ...             ...             ...             ...   \n3953             0.0             0.0             0.0             3.0   \n3954             3.0             0.0             0.0             0.0   \n3955             3.0             2.0             2.0             2.0   \n3957             5.0             1.0             0.0             2.0   \n3958             0.0             0.0             0.0             1.0   \n\n      PCIAT-PCIAT_09  PCIAT-PCIAT_10  PCIAT-PCIAT_11  PCIAT-PCIAT_12  \\\n0                0.0             0.0             4.0             0.0   \n1                0.0             0.0             0.0             0.0   \n2                1.0             1.0             1.0             0.0   \n3                2.0             2.0             3.0             0.0   \n5                2.0             1.0             0.0             1.0   \n...              ...             ...             ...             ...   \n3953             0.0             0.0             0.0             0.0   \n3954             0.0             0.0             3.0             0.0   \n3955             2.0             1.0             2.0             0.0   \n3957             0.0             2.0             1.0             0.0   \n3958             1.0             1.0             2.0             0.0   \n\n      PCIAT-PCIAT_13  PCIAT-PCIAT_14  PCIAT-PCIAT_15  PCIAT-PCIAT_16  \\\n0                4.0             4.0             4.0             4.0   \n1                0.0             0.0             0.0             0.0   \n2                1.0             1.0             1.0             0.0   \n3                3.0             0.0             0.0             3.0   \n5                3.0             3.0             2.0             1.0   \n...              ...             ...             ...             ...   \n3953             2.0             0.0             0.0             3.0   \n3954             5.0             1.0             0.0             5.0   \n3955             2.0             0.0             1.0             0.0   \n3957             1.0             3.0             0.0             0.0   \n3958             1.0             1.0             2.0             1.0   \n\n      PCIAT-PCIAT_17  PCIAT-PCIAT_18  PCIAT-PCIAT_19  PCIAT-PCIAT_20  \\\n0                4.0             4.0             2.0             4.0   \n1                0.0             0.0             0.0             0.0   \n2                2.0             2.0             1.0             1.0   \n3                4.0             3.0             4.0             1.0   \n5                3.0             1.0             2.0             1.0   \n...              ...             ...             ...             ...   \n3953             0.0             2.0             2.0             1.0   \n3954             3.0             3.0             3.0             0.0   \n3955             2.0             1.0             1.0             0.0   \n3957             1.0             1.0             0.0             1.0   \n3958             1.0             1.0             1.0             1.0   \n\n      PCIAT-PCIAT_Total PCIAT-Season  sii  \n0                  55.0         Fall  2.0  \n1                   0.0         Fall  0.0  \n2                  28.0         Fall  0.0  \n3                  44.0       Summer  1.0  \n5                  34.0       Summer  1.0  \n...                 ...          ...  ...  \n3953               22.0         Fall  0.0  \n3954               33.0       Summer  1.0  \n3955               32.0       Winter  1.0  \n3957               31.0       Winter  1.0  \n3958               19.0       Spring  0.0  \n\n[2719 rows x 23 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PCIAT-PCIAT_01</th>\n      <th>PCIAT-PCIAT_02</th>\n      <th>PCIAT-PCIAT_03</th>\n      <th>PCIAT-PCIAT_04</th>\n      <th>PCIAT-PCIAT_05</th>\n      <th>PCIAT-PCIAT_06</th>\n      <th>PCIAT-PCIAT_07</th>\n      <th>PCIAT-PCIAT_08</th>\n      <th>PCIAT-PCIAT_09</th>\n      <th>PCIAT-PCIAT_10</th>\n      <th>PCIAT-PCIAT_11</th>\n      <th>PCIAT-PCIAT_12</th>\n      <th>PCIAT-PCIAT_13</th>\n      <th>PCIAT-PCIAT_14</th>\n      <th>PCIAT-PCIAT_15</th>\n      <th>PCIAT-PCIAT_16</th>\n      <th>PCIAT-PCIAT_17</th>\n      <th>PCIAT-PCIAT_18</th>\n      <th>PCIAT-PCIAT_19</th>\n      <th>PCIAT-PCIAT_20</th>\n      <th>PCIAT-PCIAT_Total</th>\n      <th>PCIAT-Season</th>\n      <th>sii</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>55.0</td>\n      <td>Fall</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Fall</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>28.0</td>\n      <td>Fall</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>44.0</td>\n      <td>Summer</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>34.0</td>\n      <td>Summer</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3953</th>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>22.0</td>\n      <td>Fall</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3954</th>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>33.0</td>\n      <td>Summer</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3955</th>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>32.0</td>\n      <td>Winter</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3957</th>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>31.0</td>\n      <td>Winter</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3958</th>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>19.0</td>\n      <td>Spring</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>2719 rows × 23 columns</p>\n</div>"},"metadata":{}}],"execution_count":442},{"id":"2ce66d09","cell_type":"markdown","source":"## Helper functions","metadata":{"papermill":{"duration":0.027856,"end_time":"2025-05-23T02:56:34.209033","exception":false,"start_time":"2025-05-23T02:56:34.181177","status":"completed"},"tags":[]}},{"id":"a4831c0f","cell_type":"code","source":"def calculate_stats(data, columns):\n    if isinstance(columns, str):\n        columns = [columns]\n\n    stats = []\n    for col in columns:\n        if data[col].dtype in ['object', 'category']:\n            counts = data[col].value_counts(dropna=False, sort=False)\n            percents = data[col].value_counts(normalize=True, dropna=False, sort=False) * 100\n            formatted = counts.astype(str) + ' (' + percents.round(2).astype(str) + '%)'\n            stats_col = pd.DataFrame({'count (%)': formatted})\n            stats.append(stats_col)\n        else:\n            stats_col = data[col].describe().to_frame().transpose()\n            stats_col['missing'] = data[col].isnull().sum()\n            stats_col.index.name = col\n            stats.append(stats_col)\n\n    return pd.concat(stats, axis=0)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.033218Z","iopub.execute_input":"2026-04-24T07:59:16.033545Z","iopub.status.idle":"2026-04-24T07:59:16.041346Z","shell.execute_reply.started":"2026-04-24T07:59:16.033491Z","shell.execute_reply":"2026-04-24T07:59:16.040365Z"},"papermill":{"duration":0.0396,"end_time":"2025-05-23T02:56:34.2773","exception":false,"start_time":"2025-05-23T02:56:34.2377","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":443},{"id":"420e7f2d","cell_type":"markdown","source":"- **quadratic_weigthed_kappa**: Hàm tính điểm có trọng số. Đây là hàm được dùng tính điểm trong yêu cầu đề bài\n- **threshold_Rounder**: Hàm làm tròn dựa trên thresholds đã được customize. Thay vì làm tròn ở 0.5, 1.5 và 2.5, chúng ta có thể tự tính ngưỡng làm tròn sao cho mô hình hoạt động tốt nhất.\n- **evaluate_predictions**: Hàm này đánh giá điểm của mô hình dựa trên thresholds, sử dụng hàm threshold_Rounder để làm tròn giá trị dự đoán, sau đó tính điểm Kappa. Dùng để tối ưu hóa trong quá trình cải thiện mô hình.\n- **trainML**: Hàm huấn luyện mô hình với StratifiedKFold. Fit từng fold, tính ngưỡng làm tròn tối ưu cho sau khi huấn luyện qua tất cả các fold, và dự đoán trên tập test, trả về kết quả dự đoán và ô hình đã fit","metadata":{"papermill":{"duration":0.027724,"end_time":"2025-05-23T02:56:34.332809","exception":false,"start_time":"2025-05-23T02:56:34.305085","status":"completed"},"tags":[]}},{"id":"18deb21f","cell_type":"code","source":"def quadratic_weighted_kappa(y_true, y_pred):\n    return cohen_kappa_score(y_true, y_pred, weights='quadratic')\n\ndef threshold_Rounder(oof_non_rounded, thresholds):\n    return np.where(oof_non_rounded < thresholds[0], 0,\n                    np.where(oof_non_rounded < thresholds[1], 1,\n                             np.where(oof_non_rounded < thresholds[2], 2, 3)))\n\ndef evaluate_predictions(thresholds, y_true, oof_non_rounded):\n    rounded_p = threshold_Rounder(oof_non_rounded, thresholds)\n    return -quadratic_weighted_kappa(y_true, rounded_p)\n\ndef TrainML(model_class, train_data, test_data):\n    \n    X = train_data.drop(['sii'], axis=1)\n    y = train_data['sii']\n\n    SKF = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED)\n    \n    train_S = []\n    test_S = []\n    \n    oof_non_rounded = np.zeros(len(y), dtype=float) \n    oof_rounded = np.zeros(len(y), dtype=int) \n    test_preds = np.zeros((len(test_data), n_splits))\n\n    for fold, (train_idx, test_idx) in enumerate(tqdm(SKF.split(X, y), desc=\"Training Folds\", total=n_splits)):\n        X_train, X_val = X.iloc[train_idx], X.iloc[test_idx]\n        y_train, y_val = y.iloc[train_idx], y.iloc[test_idx]\n\n        model = clone(model_class)\n        model.fit(X_train, y_train)\n\n        y_train_pred = model.predict(X_train)\n        y_val_pred = model.predict(X_val)\n\n        oof_non_rounded[test_idx] = y_val_pred\n        y_val_pred_rounded = y_val_pred.round(0).astype(int)\n        oof_rounded[test_idx] = y_val_pred_rounded\n\n        train_kappa = quadratic_weighted_kappa(y_train, y_train_pred.round(0).astype(int))\n        val_kappa = quadratic_weighted_kappa(y_val, y_val_pred_rounded)\n\n        train_S.append(train_kappa)\n        test_S.append(val_kappa)\n        \n        test_preds[:, fold] = model.predict(test_data)\n        \n        print(f\"Fold {fold+1} - Train QWK: {train_kappa:.4f}, Validation QWK: {val_kappa:.4f}\")\n        clear_output(wait=True)\n\n    print(f\"Mean Train QWK --> {np.mean(train_S):.4f}\")\n    print(f\"Mean Validation QWK ---> {np.mean(test_S):.4f}\")\n\n    KappaOPtimizer = minimize(evaluate_predictions,\n                              x0=[0.5, 1.5, 2.5], args=(y, oof_non_rounded), \n                              method='Nelder-Mead') # Nelder-Mead | # Powell\n    assert KappaOPtimizer.success, \"Optimization did not converge.\"\n    \n    oof_tuned = threshold_Rounder(oof_non_rounded, KappaOPtimizer.x)\n    tKappa = quadratic_weighted_kappa(y, oof_tuned)\n\n    print(f\"----> || Optimized QWK SCORE :: {Fore.CYAN}{Style.BRIGHT} {tKappa:.3f}{Style.RESET_ALL}\")\n\n    tpm = test_preds.mean(axis=1)\n    tpTuned = threshold_Rounder(tpm, KappaOPtimizer.x)\n    \n    submission = pd.DataFrame({\n        'id': sample['id'],\n        'sii': tpTuned\n    })\n\n    return submission,model","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.042673Z","iopub.execute_input":"2026-04-24T07:59:16.043062Z","iopub.status.idle":"2026-04-24T07:59:16.065599Z","shell.execute_reply.started":"2026-04-24T07:59:16.043024Z","shell.execute_reply":"2026-04-24T07:59:16.064841Z"},"papermill":{"duration":0.040642,"end_time":"2025-05-23T02:56:34.402357","exception":false,"start_time":"2025-05-23T02:56:34.361715","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":444},{"id":"c280f3f2","cell_type":"markdown","source":"#  Data cleaning","metadata":{"papermill":{"duration":0.027832,"end_time":"2025-05-23T02:56:34.458166","exception":false,"start_time":"2025-05-23T02:56:34.430334","status":"completed"},"tags":[]}},{"id":"474ec5ea","cell_type":"markdown","source":"## Age","metadata":{"papermill":{"duration":0.028417,"end_time":"2025-05-23T02:56:34.62676","exception":false,"start_time":"2025-05-23T02:56:34.598343","status":"completed"},"tags":[]}},{"id":"f0415bf4","cell_type":"markdown","source":"Add Age Group columns","metadata":{"papermill":{"duration":0.028207,"end_time":"2025-05-23T02:56:34.683122","exception":false,"start_time":"2025-05-23T02:56:34.654915","status":"completed"},"tags":[]}},{"id":"49c8bf1c","cell_type":"code","source":"import pandas as pd\n\ndef apply_age_group(df):\n    df['Age Group'] = pd.cut(\n        df['Basic_Demos-Age'],\n        bins=[4, 12, 18, 22],\n        labels=['Children', 'Adolescents', 'Adults'],\n    )\n    return df\n\ntrain = apply_age_group(train)\ntest = apply_age_group(test)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.066781Z","iopub.execute_input":"2026-04-24T07:59:16.067135Z","iopub.status.idle":"2026-04-24T07:59:16.090414Z","shell.execute_reply.started":"2026-04-24T07:59:16.0671Z","shell.execute_reply":"2026-04-24T07:59:16.089594Z"},"papermill":{"duration":0.044515,"end_time":"2025-05-23T02:56:34.756199","exception":false,"start_time":"2025-05-23T02:56:34.711684","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":445},{"id":"3bbe6c40","cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\n\nle = LabelEncoder()\ntrain['Age_Group_Label'] = le.fit_transform(train['Age Group'])\ntest['Age_Group_Label'] = le.fit_transform(test['Age Group'])\ntrain[['Age Group', 'Age_Group_Label']]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.09186Z","iopub.execute_input":"2026-04-24T07:59:16.092424Z","iopub.status.idle":"2026-04-24T07:59:16.107582Z","shell.execute_reply.started":"2026-04-24T07:59:16.092383Z","shell.execute_reply":"2026-04-24T07:59:16.10666Z"},"papermill":{"duration":0.042292,"end_time":"2025-05-23T02:56:34.831122","exception":false,"start_time":"2025-05-23T02:56:34.78883","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":446,"output_type":"execute_result","data":{"text/plain":"        Age Group  Age_Group_Label\n0        Children                2\n1        Children                2\n2        Children                2\n3        Children                2\n5     Adolescents                0\n...           ...              ...\n3953     Children                2\n3954     Children                2\n3955  Adolescents                0\n3957     Children                2\n3958  Adolescents                0\n\n[2719 rows x 2 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age Group</th>\n      <th>Age_Group_Label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Children</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Children</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Children</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Children</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Adolescents</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3953</th>\n      <td>Children</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3954</th>\n      <td>Children</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3955</th>\n      <td>Adolescents</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3957</th>\n      <td>Children</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3958</th>\n      <td>Adolescents</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>2719 rows × 2 columns</p>\n</div>"},"metadata":{}}],"execution_count":446},{"id":"47b4b562","cell_type":"code","source":"calculate_stats(train, ['Basic_Demos-Age'])","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.108816Z","iopub.execute_input":"2026-04-24T07:59:16.109573Z","iopub.status.idle":"2026-04-24T07:59:16.139011Z","shell.execute_reply.started":"2026-04-24T07:59:16.109533Z","shell.execute_reply":"2026-04-24T07:59:16.138134Z"},"papermill":{"duration":0.044421,"end_time":"2025-05-23T02:56:34.904269","exception":false,"start_time":"2025-05-23T02:56:34.859848","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":447,"output_type":"execute_result","data":{"text/plain":"                  count       mean       std  min  25%   50%   75%   max  \\\nBasic_Demos-Age                                                            \nBasic_Demos-Age  2719.0  10.225451  3.419461  5.0  8.0  10.0  12.0  22.0   \n\n                 missing  \nBasic_Demos-Age           \nBasic_Demos-Age        0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n    <tr>\n      <th>Basic_Demos-Age</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Basic_Demos-Age</th>\n      <td>2719.0</td>\n      <td>10.225451</td>\n      <td>3.419461</td>\n      <td>5.0</td>\n      <td>8.0</td>\n      <td>10.0</td>\n      <td>12.0</td>\n      <td>22.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":447},{"id":"3f78a3a2","cell_type":"markdown","source":"## CGAS score\n\nThang đánh giá toàn diện trẻ em (Children's Global Assessment Scale - CGA) là thang điểm số được sử dụng để đánh giá chức năng tổng quát của thanh thiếu niên dưới 18 tuổi.","metadata":{"papermill":{"duration":0.028579,"end_time":"2025-05-23T02:56:34.961445","exception":false,"start_time":"2025-05-23T02:56:34.932866","status":"completed"},"tags":[]}},{"id":"a8d676aa","cell_type":"markdown","source":"###  CGAS-Season","metadata":{"papermill":{"duration":0.028203,"end_time":"2025-05-23T02:56:35.017898","exception":false,"start_time":"2025-05-23T02:56:34.989695","status":"completed"},"tags":[]}},{"id":"fdfab871","cell_type":"code","source":"calculate_stats(train, 'CGAS-Season')","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.140032Z","iopub.execute_input":"2026-04-24T07:59:16.140281Z","iopub.status.idle":"2026-04-24T07:59:16.151182Z","shell.execute_reply.started":"2026-04-24T07:59:16.140257Z","shell.execute_reply":"2026-04-24T07:59:16.150381Z"},"papermill":{"duration":0.039571,"end_time":"2025-05-23T02:56:35.085952","exception":false,"start_time":"2025-05-23T02:56:35.046381","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":448,"output_type":"execute_result","data":{"text/plain":"                count (%)\nCGAS-Season              \nWinter       535 (19.68%)\nNaN          391 (14.38%)\nFall         575 (21.15%)\nSummer       555 (20.41%)\nSpring       663 (24.38%)","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count (%)</th>\n    </tr>\n    <tr>\n      <th>CGAS-Season</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Winter</th>\n      <td>535 (19.68%)</td>\n    </tr>\n    <tr>\n      <th>NaN</th>\n      <td>391 (14.38%)</td>\n    </tr>\n    <tr>\n      <th>Fall</th>\n      <td>575 (21.15%)</td>\n    </tr>\n    <tr>\n      <th>Summer</th>\n      <td>555 (20.41%)</td>\n    </tr>\n    <tr>\n      <th>Spring</th>\n      <td>663 (24.38%)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":448},{"id":"06c77ff2","cell_type":"markdown","source":"Fill missing season values by ratio of each season's occurences in train data","metadata":{"papermill":{"duration":0.028339,"end_time":"2025-05-23T02:56:35.14293","exception":false,"start_time":"2025-05-23T02:56:35.114591","status":"completed"},"tags":[]}},{"id":"4f4b194b","cell_type":"code","source":"season_counts = train['CGAS-Season'].value_counts(normalize=True)\n\ntrain['CGAS-Season'] = train['CGAS-Season'].apply(\n    lambda x: np.random.choice(season_counts.index, p=season_counts.values) if pd.isna(x) else x\n)\ntest['CGAS-Season'] = test['CGAS-Season'].apply(\n    lambda x: np.random.choice(season_counts.index, p=season_counts.values) if pd.isna(x) else x\n)\ncalculate_stats(train, 'CGAS-Season')","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.152292Z","iopub.execute_input":"2026-04-24T07:59:16.152783Z","iopub.status.idle":"2026-04-24T07:59:16.191951Z","shell.execute_reply.started":"2026-04-24T07:59:16.15274Z","shell.execute_reply":"2026-04-24T07:59:16.19098Z"},"papermill":{"duration":0.058635,"end_time":"2025-05-23T02:56:35.233827","exception":false,"start_time":"2025-05-23T02:56:35.175192","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":449,"output_type":"execute_result","data":{"text/plain":"                count (%)\nCGAS-Season              \nWinter       624 (22.95%)\nSummer        647 (23.8%)\nFall         673 (24.75%)\nSpring        775 (28.5%)","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count (%)</th>\n    </tr>\n    <tr>\n      <th>CGAS-Season</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Winter</th>\n      <td>624 (22.95%)</td>\n    </tr>\n    <tr>\n      <th>Summer</th>\n      <td>647 (23.8%)</td>\n    </tr>\n    <tr>\n      <th>Fall</th>\n      <td>673 (24.75%)</td>\n    </tr>\n    <tr>\n      <th>Spring</th>\n      <td>775 (28.5%)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":449},{"id":"a00e6a5b","cell_type":"markdown","source":"### CGAS Score","metadata":{"papermill":{"duration":0.028376,"end_time":"2025-05-23T02:56:35.297159","exception":false,"start_time":"2025-05-23T02:56:35.268783","status":"completed"},"tags":[]}},{"id":"07b949bf","cell_type":"code","source":"calculate_stats(train, 'CGAS-CGAS_Score')","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.193222Z","iopub.execute_input":"2026-04-24T07:59:16.193903Z","iopub.status.idle":"2026-04-24T07:59:16.210254Z","shell.execute_reply.started":"2026-04-24T07:59:16.193862Z","shell.execute_reply":"2026-04-24T07:59:16.209294Z"},"papermill":{"duration":0.06122,"end_time":"2025-05-23T02:56:35.393117","exception":false,"start_time":"2025-05-23T02:56:35.331897","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":450,"output_type":"execute_result","data":{"text/plain":"                  count       mean       std   min   25%   50%   75%   max  \\\nCGAS-CGAS_Score                                                              \nCGAS-CGAS_Score  2328.0  65.145189  11.81724  25.0  59.0  65.0  75.0  95.0   \n\n                 missing  \nCGAS-CGAS_Score           \nCGAS-CGAS_Score      391  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n    <tr>\n      <th>CGAS-CGAS_Score</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>CGAS-CGAS_Score</th>\n      <td>2328.0</td>\n      <td>65.145189</td>\n      <td>11.81724</td>\n      <td>25.0</td>\n      <td>59.0</td>\n      <td>65.0</td>\n      <td>75.0</td>\n      <td>95.0</td>\n      <td>391</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":450},{"id":"54ec618f","cell_type":"markdown","source":"Kiểm tra mối quan hệ của CGAS-CGAS_Score với age, sex","metadata":{"papermill":{"duration":0.029471,"end_time":"2025-05-23T02:56:35.652894","exception":false,"start_time":"2025-05-23T02:56:35.623423","status":"completed"},"tags":[]}},{"id":"b0324c76","cell_type":"code","source":"sns.scatterplot(data=train, x='Basic_Demos-Age', y='CGAS-CGAS_Score', palette='viridis')\nplt.title(\"Relationship between Age and CGAS Score\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.211273Z","iopub.execute_input":"2026-04-24T07:59:16.21163Z","iopub.status.idle":"2026-04-24T07:59:16.398284Z","shell.execute_reply.started":"2026-04-24T07:59:16.211597Z","shell.execute_reply":"2026-04-24T07:59:16.397264Z"},"papermill":{"duration":0.308474,"end_time":"2025-05-23T02:56:35.990911","exception":false,"start_time":"2025-05-23T02:56:35.682437","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":451},{"id":"92703590","cell_type":"code","source":"sns.boxplot(data=train, x='Age Group', y='CGAS-CGAS_Score')\nplt.title(\"CGAS Score Distribution by Age Group\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.399402Z","iopub.execute_input":"2026-04-24T07:59:16.399777Z","iopub.status.idle":"2026-04-24T07:59:16.566384Z","shell.execute_reply.started":"2026-04-24T07:59:16.399749Z","shell.execute_reply":"2026-04-24T07:59:16.565479Z"},"papermill":{"duration":0.26073,"end_time":"2025-05-23T02:56:36.290537","exception":false,"start_time":"2025-05-23T02:56:36.029807","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{}}],"execution_count":452},{"id":"633fcf46","cell_type":"code","source":"sns.boxplot(data=train, x='Basic_Demos-Sex', y='CGAS-CGAS_Score')\nplt.title(\"CGAS Score Distribution by Sex\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.567725Z","iopub.execute_input":"2026-04-24T07:59:16.568468Z","iopub.status.idle":"2026-04-24T07:59:16.722595Z","shell.execute_reply.started":"2026-04-24T07:59:16.568438Z","shell.execute_reply":"2026-04-24T07:59:16.721762Z"},"papermill":{"duration":0.245995,"end_time":"2025-05-23T02:56:36.627765","exception":false,"start_time":"2025-05-23T02:56:36.38177","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{}}],"execution_count":453},{"id":"d9a91f3f","cell_type":"code","source":"age_group_cgas_stats = train.groupby('Age Group')['CGAS-CGAS_Score'].describe()\nage_group_cgas_stats","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.723645Z","iopub.execute_input":"2026-04-24T07:59:16.723961Z","iopub.status.idle":"2026-04-24T07:59:16.745329Z","shell.execute_reply.started":"2026-04-24T07:59:16.723934Z","shell.execute_reply":"2026-04-24T07:59:16.744597Z"},"papermill":{"duration":0.061083,"end_time":"2025-05-23T02:56:36.72138","exception":false,"start_time":"2025-05-23T02:56:36.660297","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":454,"output_type":"execute_result","data":{"text/plain":"              count       mean        std   min   25%   50%    75%   max\nAge Group                                                               \nChildren     1782.0  65.457912  11.644084  25.0  60.0  65.0  75.00  95.0\nAdolescents   516.0  64.488372  12.262100  30.0  55.0  65.0  72.25  95.0\nAdults         30.0  57.866667  11.834297  39.0  50.0  57.0  65.75  85.0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>Age Group</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Children</th>\n      <td>1782.0</td>\n      <td>65.457912</td>\n      <td>11.644084</td>\n      <td>25.0</td>\n      <td>60.0</td>\n      <td>65.0</td>\n      <td>75.00</td>\n      <td>95.0</td>\n    </tr>\n    <tr>\n      <th>Adolescents</th>\n      <td>516.0</td>\n      <td>64.488372</td>\n      <td>12.262100</td>\n      <td>30.0</td>\n      <td>55.0</td>\n      <td>65.0</td>\n      <td>72.25</td>\n      <td>95.0</td>\n    </tr>\n    <tr>\n      <th>Adults</th>\n      <td>30.0</td>\n      <td>57.866667</td>\n      <td>11.834297</td>\n      <td>39.0</td>\n      <td>50.0</td>\n      <td>57.0</td>\n      <td>65.75</td>\n      <td>85.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":454},{"id":"2a7a27f4","cell_type":"markdown","source":"Phân phối CGAS khác nhau giữa nhóm người trưởng thành và nhóm trẻ em/thanh thiếu niên. Tuy nhiên, nhóm người trưởng thành có rất ít mẫu, và bài kiểm tra CGAS không được thiết kế cho người lớn => không dùng\n\nPhân phối điểm CGAS tương tự nhau giữa nhóm trẻ em và thanh thiếu niên\n\n=> Kết luận: Không thể sử dụng tuổi để dự đoán điểm CGAS","metadata":{"papermill":{"duration":0.030783,"end_time":"2025-05-23T02:56:36.782802","exception":false,"start_time":"2025-05-23T02:56:36.752019","status":"completed"},"tags":[]}},{"id":"c5c941d4","cell_type":"code","source":"sex_cgas_stats = train.groupby('Basic_Demos-Sex')['CGAS-CGAS_Score'].describe()\nsex_cgas_stats","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.746496Z","iopub.execute_input":"2026-04-24T07:59:16.746807Z","iopub.status.idle":"2026-04-24T07:59:16.767321Z","shell.execute_reply.started":"2026-04-24T07:59:16.746781Z","shell.execute_reply":"2026-04-24T07:59:16.766394Z"},"papermill":{"duration":0.049251,"end_time":"2025-05-23T02:56:36.86304","exception":false,"start_time":"2025-05-23T02:56:36.813789","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":455,"output_type":"execute_result","data":{"text/plain":"                  count       mean        std   min   25%   50%   75%   max\nBasic_Demos-Sex                                                            \n0                1494.0  64.254351  11.417116  25.0  55.0  65.0  72.0  95.0\n1                 834.0  66.741007  12.349262  30.0  60.0  65.5  75.0  95.0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>Basic_Demos-Sex</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1494.0</td>\n      <td>64.254351</td>\n      <td>11.417116</td>\n      <td>25.0</td>\n      <td>55.0</td>\n      <td>65.0</td>\n      <td>72.0</td>\n      <td>95.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>834.0</td>\n      <td>66.741007</td>\n      <td>12.349262</td>\n      <td>30.0</td>\n      <td>60.0</td>\n      <td>65.5</td>\n      <td>75.0</td>\n      <td>95.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":455},{"id":"9b80fa71","cell_type":"markdown","source":"Tương tự với age","metadata":{"papermill":{"duration":0.0309,"end_time":"2025-05-23T02:56:36.924859","exception":false,"start_time":"2025-05-23T02:56:36.893959","status":"completed"},"tags":[]}},{"id":"101bcf7d","cell_type":"markdown","source":"### => không nên xử lý GGAS score","metadata":{"papermill":{"duration":0.030886,"end_time":"2025-05-23T02:56:36.98641","exception":false,"start_time":"2025-05-23T02:56:36.955524","status":"completed"},"tags":[]}},{"id":"02ea394d","cell_type":"markdown","source":"Thử với PCIAT Total:","metadata":{"papermill":{"duration":0.030819,"end_time":"2025-05-23T02:56:37.048205","exception":false,"start_time":"2025-05-23T02:56:37.017386","status":"completed"},"tags":[]}},{"id":"71738d21","cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\n\nvalid_data = train.dropna(subset=['CGAS-CGAS_Score', 'PCIAT-PCIAT_Total'])\n\nplt.figure(figsize=(10, 6))\nsns.scatterplot(\n    data=valid_data, \n    x='CGAS-CGAS_Score', \n    y='PCIAT-PCIAT_Total',\n    hue=valid_data['PCIAT-PCIAT_Total'] > 80,  # PCIAT severe group\n    alpha=0.3\n)\n\nplt.title(\"Scatter Plot of CGAS Score vs PCIAT Total\", fontsize=16)\nplt.xlabel(\"CGAS-CGAS_Score\", fontsize=12)\nplt.ylabel(\"PCIAT-PCIAT_Total (PIU Severity)\", fontsize=12)\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:16.768619Z","iopub.execute_input":"2026-04-24T07:59:16.769268Z","iopub.status.idle":"2026-04-24T07:59:17.060871Z","shell.execute_reply.started":"2026-04-24T07:59:16.769226Z","shell.execute_reply":"2026-04-24T07:59:17.059905Z"},"papermill":{"duration":0.392279,"end_time":"2025-05-23T02:56:37.471437","exception":false,"start_time":"2025-05-23T02:56:37.079158","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 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\n"},"metadata":{}}],"execution_count":456},{"id":"23d672bc","cell_type":"markdown","source":"Không có người nào có điểm sii mức 3 – mức nghiêm trọng mà lại có điểm CGAS tốt (81–100). => Xây dựng một hàm có trọng số tập trung vào nhóm CGAS > 80","metadata":{"papermill":{"duration":0.033842,"end_time":"2025-05-23T02:56:37.540668","exception":false,"start_time":"2025-05-23T02:56:37.506826","status":"completed"},"tags":[]}},{"id":"69d147c9","cell_type":"code","source":"def sigmoid_weight_cgas_high(cgas, a=0.2, b=80):\n    return 1 / (1 + np.exp(-a * (cgas - b)))\n\n# Add below\n# train['CGAS_Weight'] = train['CGAS-CGAS_Score'].apply(sigmoid_weight_cgas_high)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.062016Z","iopub.execute_input":"2026-04-24T07:59:17.062297Z","iopub.status.idle":"2026-04-24T07:59:17.06715Z","shell.execute_reply.started":"2026-04-24T07:59:17.062271Z","shell.execute_reply":"2026-04-24T07:59:17.066487Z"},"papermill":{"duration":0.040555,"end_time":"2025-05-23T02:56:37.614607","exception":false,"start_time":"2025-05-23T02:56:37.574052","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":457},{"id":"9a432dc8","cell_type":"markdown","source":"## Physical Measures","metadata":{"papermill":{"duration":0.033248,"end_time":"2025-05-23T02:56:37.748733","exception":false,"start_time":"2025-05-23T02:56:37.715485","status":"completed"},"tags":[]}},{"id":"1ee98496","cell_type":"code","source":"physical_columns = [\n 'Physical-BMI',\n 'Physical-Height',\n 'Physical-Weight',\n 'Physical-Waist_Circumference',\n 'Physical-Diastolic_BP',\n 'Physical-HeartRate',\n 'Physical-Systolic_BP'\n]\n\nwh_cols = [\n    'Physical-BMI', 'Physical-Height',\n    'Physical-Weight', 'Physical-Waist_Circumference'\n]\n\nheart_cols = [\n 'Physical-Diastolic_BP',\n 'Physical-HeartRate',\n 'Physical-Systolic_BP'\n]\n\ncalculate_stats(train, wh_cols)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.072623Z","iopub.execute_input":"2026-04-24T07:59:17.07297Z","iopub.status.idle":"2026-04-24T07:59:17.107694Z","shell.execute_reply.started":"2026-04-24T07:59:17.072943Z","shell.execute_reply":"2026-04-24T07:59:17.106893Z"},"papermill":{"duration":0.057254,"end_time":"2025-05-23T02:56:37.839091","exception":false,"start_time":"2025-05-23T02:56:37.781837","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":458,"output_type":"execute_result","data":{"text/plain":"                               count       mean        std   min        25%  \\\nPhysical-BMI                  2513.0  19.131766   4.914565   0.0  15.780022   \nPhysical-Height               2516.0  55.885688   7.389588  36.0  50.000000   \nPhysical-Weight               2557.0  87.859378  43.356403   0.0  57.200000   \nPhysical-Waist_Circumference   480.0  26.631250   5.225146  19.0  23.000000   \n\n                                    50%         75%         max  missing  \nPhysical-BMI                  17.823815   21.173921   46.102914      206  \nPhysical-Height               55.000000   61.637500   78.500000      203  \nPhysical-Weight               75.800000  111.400000  315.000000      162  \nPhysical-Waist_Circumference  26.000000   29.000000   50.000000     2239  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Physical-BMI</th>\n      <td>2513.0</td>\n      <td>19.131766</td>\n      <td>4.914565</td>\n      <td>0.0</td>\n      <td>15.780022</td>\n      <td>17.823815</td>\n      <td>21.173921</td>\n      <td>46.102914</td>\n      <td>206</td>\n    </tr>\n    <tr>\n      <th>Physical-Height</th>\n      <td>2516.0</td>\n      <td>55.885688</td>\n      <td>7.389588</td>\n      <td>36.0</td>\n      <td>50.000000</td>\n      <td>55.000000</td>\n      <td>61.637500</td>\n      <td>78.500000</td>\n      <td>203</td>\n    </tr>\n    <tr>\n      <th>Physical-Weight</th>\n      <td>2557.0</td>\n      <td>87.859378</td>\n      <td>43.356403</td>\n      <td>0.0</td>\n      <td>57.200000</td>\n      <td>75.800000</td>\n      <td>111.400000</td>\n      <td>315.000000</td>\n      <td>162</td>\n    </tr>\n    <tr>\n      <th>Physical-Waist_Circumference</th>\n      <td>480.0</td>\n      <td>26.631250</td>\n      <td>5.225146</td>\n      <td>19.0</td>\n      <td>23.000000</td>\n      <td>26.000000</td>\n      <td>29.000000</td>\n      <td>50.000000</td>\n      <td>2239</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":458},{"id":"3ed45903","cell_type":"code","source":"(train[wh_cols] == 0).sum()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.108731Z","iopub.execute_input":"2026-04-24T07:59:17.109024Z","iopub.status.idle":"2026-04-24T07:59:17.116783Z","shell.execute_reply.started":"2026-04-24T07:59:17.108999Z","shell.execute_reply":"2026-04-24T07:59:17.115984Z"},"papermill":{"duration":0.043042,"end_time":"2025-05-23T02:56:37.916029","exception":false,"start_time":"2025-05-23T02:56:37.872987","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":459,"output_type":"execute_result","data":{"text/plain":"Physical-BMI                     7\nPhysical-Height                  0\nPhysical-Weight                 51\nPhysical-Waist_Circumference     0\ndtype: int64"},"metadata":{}}],"execution_count":459},{"id":"ee129b24","cell_type":"markdown","source":"Filter zero values","metadata":{"papermill":{"duration":0.033615,"end_time":"2025-05-23T02:56:37.983775","exception":false,"start_time":"2025-05-23T02:56:37.95016","status":"completed"},"tags":[]}},{"id":"9f0ab6cb","cell_type":"code","source":"train[wh_cols] = train[wh_cols].replace(0, np.nan)\ntest[wh_cols] = test[wh_cols].replace(0, np.nan)\ncalculate_stats(train, wh_cols)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.1178Z","iopub.execute_input":"2026-04-24T07:59:17.118061Z","iopub.status.idle":"2026-04-24T07:59:17.153176Z","shell.execute_reply.started":"2026-04-24T07:59:17.118038Z","shell.execute_reply":"2026-04-24T07:59:17.152443Z"},"papermill":{"duration":0.057996,"end_time":"2025-05-23T02:56:38.075165","exception":false,"start_time":"2025-05-23T02:56:38.017169","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":460,"output_type":"execute_result","data":{"text/plain":"                               count       mean        std        min  \\\nPhysical-BMI                  2506.0  19.185206   4.816095   8.522436   \nPhysical-Height               2516.0  55.885688   7.389588  36.000000   \nPhysical-Weight               2506.0  89.647418  41.924831  32.800000   \nPhysical-Waist_Circumference   480.0  26.631250   5.225146  19.000000   \n\n                                    25%        50%         75%         max  \\\nPhysical-BMI                  15.799058  17.842121   21.206149   46.102914   \nPhysical-Height               50.000000  55.000000   61.637500   78.500000   \nPhysical-Weight               58.200000  76.800000  112.200000  315.000000   \nPhysical-Waist_Circumference  23.000000  26.000000   29.000000   50.000000   \n\n                              missing  \nPhysical-BMI                      213  \nPhysical-Height                   203  \nPhysical-Weight                   213  \nPhysical-Waist_Circumference     2239  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Physical-BMI</th>\n      <td>2506.0</td>\n      <td>19.185206</td>\n      <td>4.816095</td>\n      <td>8.522436</td>\n      <td>15.799058</td>\n      <td>17.842121</td>\n      <td>21.206149</td>\n      <td>46.102914</td>\n      <td>213</td>\n    </tr>\n    <tr>\n      <th>Physical-Height</th>\n      <td>2516.0</td>\n      <td>55.885688</td>\n      <td>7.389588</td>\n      <td>36.000000</td>\n      <td>50.000000</td>\n      <td>55.000000</td>\n      <td>61.637500</td>\n      <td>78.500000</td>\n      <td>203</td>\n    </tr>\n    <tr>\n      <th>Physical-Weight</th>\n      <td>2506.0</td>\n      <td>89.647418</td>\n      <td>41.924831</td>\n      <td>32.800000</td>\n      <td>58.200000</td>\n      <td>76.800000</td>\n      <td>112.200000</td>\n      <td>315.000000</td>\n      <td>213</td>\n    </tr>\n    <tr>\n      <th>Physical-Waist_Circumference</th>\n      <td>480.0</td>\n      <td>26.631250</td>\n      <td>5.225146</td>\n      <td>19.000000</td>\n      <td>23.000000</td>\n      <td>26.000000</td>\n      <td>29.000000</td>\n      <td>50.000000</td>\n      <td>2239</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":460},{"id":"9110ffd7","cell_type":"markdown","source":"Encode physical seasons:","metadata":{"papermill":{"duration":0.033709,"end_time":"2025-05-23T02:56:38.210441","exception":false,"start_time":"2025-05-23T02:56:38.176732","status":"completed"},"tags":[]}},{"id":"9458fd95","cell_type":"code","source":"encoded_season_train = pd.get_dummies(train, columns=['Basic_Demos-Enroll_Season'], prefix='Season', drop_first=False)\nencoded_season_test = pd.get_dummies(test, columns=['Basic_Demos-Enroll_Season'], prefix='Season', drop_first=False)\ntrain = train.join(encoded_season_train[['Season_Fall', 'Season_Spring', 'Season_Summer', 'Season_Winter']])\ntest = test.join(encoded_season_test[['Season_Fall', 'Season_Spring', 'Season_Summer', 'Season_Winter']])\ntrain.columns","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.154098Z","iopub.execute_input":"2026-04-24T07:59:17.154739Z","iopub.status.idle":"2026-04-24T07:59:17.179138Z","shell.execute_reply.started":"2026-04-24T07:59:17.154712Z","shell.execute_reply":"2026-04-24T07:59:17.178353Z"},"papermill":{"duration":0.060368,"end_time":"2025-05-23T02:56:38.304821","exception":false,"start_time":"2025-05-23T02:56:38.244453","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":461,"output_type":"execute_result","data":{"text/plain":"Index(['id', 'Basic_Demos-Enroll_Season', 'Basic_Demos-Age', 'Basic_Demos-Sex',\n       'CGAS-Season', 'CGAS-CGAS_Score', 'Physical-Season', 'Physical-BMI',\n       'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n       'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n       'Fitness_Endurance-Season', 'Fitness_Endurance-Max_Stage',\n       'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n       'FGC-Season', 'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n       'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n       'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n       'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone', 'BIA-Season',\n       'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n       'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n       'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n       'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n       'BIA-BIA_TBW', 'PAQ_A-Season', 'PAQ_A-PAQ_A_Total', 'PAQ_C-Season',\n       'PAQ_C-PAQ_C_Total', 'PCIAT-Season', 'PCIAT-PCIAT_01', 'PCIAT-PCIAT_02',\n       'PCIAT-PCIAT_03', 'PCIAT-PCIAT_04', 'PCIAT-PCIAT_05', 'PCIAT-PCIAT_06',\n       'PCIAT-PCIAT_07', 'PCIAT-PCIAT_08', 'PCIAT-PCIAT_09', 'PCIAT-PCIAT_10',\n       'PCIAT-PCIAT_11', 'PCIAT-PCIAT_12', 'PCIAT-PCIAT_13', 'PCIAT-PCIAT_14',\n       'PCIAT-PCIAT_15', 'PCIAT-PCIAT_16', 'PCIAT-PCIAT_17', 'PCIAT-PCIAT_18',\n       'PCIAT-PCIAT_19', 'PCIAT-PCIAT_20', 'PCIAT-PCIAT_Total', 'SDS-Season',\n       'SDS-SDS_Total_Raw', 'SDS-SDS_Total_T', 'PreInt_EduHx-Season',\n       'PreInt_EduHx-computerinternet_hoursday', 'sii', 'Age Group',\n       'Age_Group_Label', 'Season_Fall', 'Season_Spring', 'Season_Summer',\n       'Season_Winter'],\n      dtype='object')"},"metadata":{}}],"execution_count":461},{"id":"4569acfa","cell_type":"code","source":"train.groupby('Basic_Demos-Enroll_Season')[['Physical-Weight', 'Physical-Height']].mean()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.180253Z","iopub.execute_input":"2026-04-24T07:59:17.180938Z","iopub.status.idle":"2026-04-24T07:59:17.19218Z","shell.execute_reply.started":"2026-04-24T07:59:17.180911Z","shell.execute_reply":"2026-04-24T07:59:17.191301Z"},"papermill":{"duration":0.048968,"end_time":"2025-05-23T02:56:38.464075","exception":false,"start_time":"2025-05-23T02:56:38.415107","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":462,"output_type":"execute_result","data":{"text/plain":"                           Physical-Weight  Physical-Height\nBasic_Demos-Enroll_Season                                  \nFall                             88.344491        55.502040\nSpring                           87.942814        55.812538\nSummer                           90.484020        56.010065\nWinter                           92.070618        56.250434","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Physical-Weight</th>\n      <th>Physical-Height</th>\n    </tr>\n    <tr>\n      <th>Basic_Demos-Enroll_Season</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Fall</th>\n      <td>88.344491</td>\n      <td>55.502040</td>\n    </tr>\n    <tr>\n      <th>Spring</th>\n      <td>87.942814</td>\n      <td>55.812538</td>\n    </tr>\n    <tr>\n      <th>Summer</th>\n      <td>90.484020</td>\n      <td>56.010065</td>\n    </tr>\n    <tr>\n      <th>Winter</th>\n      <td>92.070618</td>\n      <td>56.250434</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":462},{"id":"ceb792bd","cell_type":"code","source":"train.groupby(['Basic_Demos-Enroll_Season', 'Basic_Demos-Sex'])[['Physical-Weight', 'Physical-Height']].mean()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.193271Z","iopub.execute_input":"2026-04-24T07:59:17.193542Z","iopub.status.idle":"2026-04-24T07:59:17.209476Z","shell.execute_reply.started":"2026-04-24T07:59:17.193494Z","shell.execute_reply":"2026-04-24T07:59:17.208685Z"},"papermill":{"duration":0.047916,"end_time":"2025-05-23T02:56:38.54623","exception":false,"start_time":"2025-05-23T02:56:38.498314","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":463,"output_type":"execute_result","data":{"text/plain":"                                           Physical-Weight  Physical-Height\nBasic_Demos-Enroll_Season Basic_Demos-Sex                                  \nFall                      0                      87.211184        55.427422\n                          1                      90.429778        55.638458\nSpring                    0                      88.242691        56.150742\n                          1                      87.380870        55.181515\nSummer                    0                      92.000529        56.250316\n                          1                      87.980786        55.618240\nWinter                    0                      92.120366        56.166554\n                          1                      91.982407        56.399167","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>Physical-Weight</th>\n      <th>Physical-Height</th>\n    </tr>\n    <tr>\n      <th>Basic_Demos-Enroll_Season</th>\n      <th>Basic_Demos-Sex</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Fall</th>\n      <th>0</th>\n      <td>87.211184</td>\n      <td>55.427422</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>90.429778</td>\n      <td>55.638458</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Spring</th>\n      <th>0</th>\n      <td>88.242691</td>\n      <td>56.150742</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>87.380870</td>\n      <td>55.181515</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Summer</th>\n      <th>0</th>\n      <td>92.000529</td>\n      <td>56.250316</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>87.980786</td>\n      <td>55.618240</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Winter</th>\n      <th>0</th>\n      <td>92.120366</td>\n      <td>56.166554</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>91.982407</td>\n      <td>56.399167</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":463},{"id":"a2722166","cell_type":"code","source":"lbs_to_kg = 0.453592\ninches_to_cm = 2.54\n\ndef process_physical_BMI(df):\n    df['Physical-Weight'] = df['Physical-Weight'] * lbs_to_kg\n    df['Physical-Height'] = df['Physical-Height'] * inches_to_cm\n    df['Physical-Waist_Circumference'] = df['Physical-Waist_Circumference'] * inches_to_cm\n    \n    df['Physical-BMI'] = np.where(\n        df['Physical-Weight'].notna() & df['Physical-Height'].notna(),\n        df['Physical-Weight'] / ((df['Physical-Height'] / 100) ** 2),\n        np.nan\n    )\n    \n    return df\n\ntrain = process_physical_BMI(train)\ntest = process_physical_BMI(test)\n\ncalculate_stats(train, wh_cols)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.210645Z","iopub.execute_input":"2026-04-24T07:59:17.210967Z","iopub.status.idle":"2026-04-24T07:59:17.243187Z","shell.execute_reply.started":"2026-04-24T07:59:17.21094Z","shell.execute_reply":"2026-04-24T07:59:17.24236Z"},"papermill":{"duration":0.061893,"end_time":"2025-05-23T02:56:38.848618","exception":false,"start_time":"2025-05-23T02:56:38.786725","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":464,"output_type":"execute_result","data":{"text/plain":"                               count        mean        std        min  \\\nPhysical-BMI                  2506.0   19.187090   4.816567   8.523273   \nPhysical-Height               2516.0  141.949647  18.769553  91.440000   \nPhysical-Weight               2506.0   40.663352  19.016768  14.877818   \nPhysical-Waist_Circumference   480.0   67.643375  13.271871  48.260000   \n\n                                     25%         50%         75%         max  \\\nPhysical-BMI                   15.800609   17.843872   21.208231   46.107439   \nPhysical-Height               127.000000  139.700000  156.559250  199.390000   \nPhysical-Weight                26.399054   34.835866   50.893022  142.881480   \nPhysical-Waist_Circumference   58.420000   66.040000   73.660000  127.000000   \n\n                              missing  \nPhysical-BMI                      213  \nPhysical-Height                   203  \nPhysical-Weight                   213  \nPhysical-Waist_Circumference     2239  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Physical-BMI</th>\n      <td>2506.0</td>\n      <td>19.187090</td>\n      <td>4.816567</td>\n      <td>8.523273</td>\n      <td>15.800609</td>\n      <td>17.843872</td>\n      <td>21.208231</td>\n      <td>46.107439</td>\n      <td>213</td>\n    </tr>\n    <tr>\n      <th>Physical-Height</th>\n      <td>2516.0</td>\n      <td>141.949647</td>\n      <td>18.769553</td>\n      <td>91.440000</td>\n      <td>127.000000</td>\n      <td>139.700000</td>\n      <td>156.559250</td>\n      <td>199.390000</td>\n      <td>203</td>\n    </tr>\n    <tr>\n      <th>Physical-Weight</th>\n      <td>2506.0</td>\n      <td>40.663352</td>\n      <td>19.016768</td>\n      <td>14.877818</td>\n      <td>26.399054</td>\n      <td>34.835866</td>\n      <td>50.893022</td>\n      <td>142.881480</td>\n      <td>213</td>\n    </tr>\n    <tr>\n      <th>Physical-Waist_Circumference</th>\n      <td>480.0</td>\n      <td>67.643375</td>\n      <td>13.271871</td>\n      <td>48.260000</td>\n      <td>58.420000</td>\n      <td>66.040000</td>\n      <td>73.660000</td>\n      <td>127.000000</td>\n      <td>2239</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":464},{"id":"3ae91692","cell_type":"code","source":"from sklearn.impute import KNNImputer\n\n# Chọn 2 mùa có chiều cao cân nặng chênh nhau nhiều nhất\nimputer = KNNImputer(n_neighbors=10)\n\nselected_features = ['Basic_Demos-Age', 'Season_Fall', 'Season_Winter', 'Basic_Demos-Sex', 'Physical-Weight', 'Physical-Height']\n\nimputed_data = imputer.fit_transform(train[selected_features])\ntrain_imputed = pd.DataFrame(imputed_data, columns=selected_features)\ntrain = train.drop(columns=selected_features).reset_index()\n\nimputed_test_data = imputer.transform(test[selected_features])\ntest_imputed = pd.DataFrame(imputed_test_data, columns=selected_features)\ntest = test.drop(columns=selected_features).reset_index()\n\ntrain = pd.concat([train, train_imputed], axis=1)\ntest = pd.concat([test, test_imputed], axis=1)\n\ncalculate_stats(train, ['Physical-Weight', 'Physical-Height'])","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.244187Z","iopub.execute_input":"2026-04-24T07:59:17.244436Z","iopub.status.idle":"2026-04-24T07:59:17.331052Z","shell.execute_reply.started":"2026-04-24T07:59:17.244414Z","shell.execute_reply":"2026-04-24T07:59:17.330326Z"},"papermill":{"duration":0.162342,"end_time":"2025-05-23T02:56:39.114052","exception":false,"start_time":"2025-05-23T02:56:38.95171","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":465,"output_type":"execute_result","data":{"text/plain":"                  count        mean        std        min         25%  \\\nPhysical-Weight  2719.0   40.736849  18.788616  14.877818   26.716569   \nPhysical-Height  2719.0  141.924194  18.652195  91.440000  127.635000   \n\n                        50%         75%        max  missing  \nPhysical-Weight   34.835866   51.015492  142.88148        0  \nPhysical-Height  139.700000  156.210000  199.39000        0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Physical-Weight</th>\n      <td>2719.0</td>\n      <td>40.736849</td>\n      <td>18.788616</td>\n      <td>14.877818</td>\n      <td>26.716569</td>\n      <td>34.835866</td>\n      <td>51.015492</td>\n      <td>142.88148</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Physical-Height</th>\n      <td>2719.0</td>\n      <td>141.924194</td>\n      <td>18.652195</td>\n      <td>91.440000</td>\n      <td>127.635000</td>\n      <td>139.700000</td>\n      <td>156.210000</td>\n      <td>199.39000</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":465},{"id":"00711705","cell_type":"markdown","source":"Recalculate BMI from w and h","metadata":{"papermill":{"duration":0.034824,"end_time":"2025-05-23T02:56:39.205437","exception":false,"start_time":"2025-05-23T02:56:39.170613","status":"completed"},"tags":[]}},{"id":"f486a24d","cell_type":"code","source":"train['Physical-BMI'] = train.apply(\n    lambda row: row['Physical-Weight'] / (row['Physical-Height'] / 100) ** 2 \n    if pd.isnull(row['Physical-BMI']) else row['Physical-BMI'], axis=1\n)\ntest['Physical-BMI'] = test.apply(\n    lambda row: row['Physical-Weight'] / (row['Physical-Height'] / 100) ** 2 \n    if pd.isnull(row['Physical-BMI']) else row['Physical-BMI'], axis=1\n)\ncalculate_stats(train, ['Physical-BMI'])","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.331946Z","iopub.execute_input":"2026-04-24T07:59:17.332235Z","iopub.status.idle":"2026-04-24T07:59:17.406891Z","shell.execute_reply.started":"2026-04-24T07:59:17.332196Z","shell.execute_reply":"2026-04-24T07:59:17.405905Z"},"papermill":{"duration":0.089667,"end_time":"2025-05-23T02:56:39.330006","exception":false,"start_time":"2025-05-23T02:56:39.240339","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":466,"output_type":"execute_result","data":{"text/plain":"               count       mean       std       min        25%        50%  \\\nPhysical-BMI                                                                \nPhysical-BMI  2719.0  19.238855  4.699363  8.523273  15.913649  18.032014   \n\n                   75%        max  missing  \nPhysical-BMI                                \nPhysical-BMI  21.30508  46.107439        0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n    <tr>\n      <th>Physical-BMI</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Physical-BMI</th>\n      <td>2719.0</td>\n      <td>19.238855</td>\n      <td>4.699363</td>\n      <td>8.523273</td>\n      <td>15.913649</td>\n      <td>18.032014</td>\n      <td>21.30508</td>\n      <td>46.107439</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":466},{"id":"bf453809","cell_type":"markdown","source":"Compare with the BIA-BIA_BMI column, which is also the BMI index measured in the same year.","metadata":{"papermill":{"duration":0.036268,"end_time":"2025-05-23T02:56:39.403022","exception":false,"start_time":"2025-05-23T02:56:39.366754","status":"completed"},"tags":[]}},{"id":"76ad0a0f","cell_type":"code","source":"bmi_ratio = train['Physical-BMI'] / train['BIA-BIA_BMI']\ncolor = (bmi_ratio < 0.8) | (bmi_ratio > 1.2)  # red if difference > 20%\n\nplt.scatter(\n    train['Physical-BMI'],\n    train['BIA-BIA_BMI'],\n    s=6,\n    c=color,\n    cmap='coolwarm'\n)\nplt.gca().set_aspect('equal')\nplt.xlabel('Physical-BMI')\nplt.ylabel('BIA-BIA_BMI')\nplt.title('Physical-BMI vs VIA-BIA_BMI')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.408279Z","iopub.execute_input":"2026-04-24T07:59:17.408628Z","iopub.status.idle":"2026-04-24T07:59:17.56769Z","shell.execute_reply.started":"2026-04-24T07:59:17.408595Z","shell.execute_reply":"2026-04-24T07:59:17.566877Z"},"papermill":{"duration":0.269028,"end_time":"2025-05-23T02:56:39.707914","exception":false,"start_time":"2025-05-23T02:56:39.438886","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":467},{"id":"3ce7bd71","cell_type":"code","source":"train.loc[color, 'BIA-BIA_BMI'] = train.loc[color, 'Physical-BMI']\nplt.scatter(\n    train['Physical-BMI'],\n    train['BIA-BIA_BMI'],\n    s=6,\n    c=color,\n    cmap='coolwarm'\n)\nplt.gca().set_aspect('equal')\nplt.xlabel('Physical-BMI')\nplt.ylabel('BIA-BIA_BMI')\nplt.title('Physical-BMI vs VIA-BIA_BMI')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.568695Z","iopub.execute_input":"2026-04-24T07:59:17.568994Z","iopub.status.idle":"2026-04-24T07:59:17.760123Z","shell.execute_reply.started":"2026-04-24T07:59:17.568956Z","shell.execute_reply":"2026-04-24T07:59:17.75924Z"},"papermill":{"duration":0.302053,"end_time":"2025-05-23T02:56:40.117959","exception":false,"start_time":"2025-05-23T02:56:39.815906","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":468},{"id":"3c2a80f6","cell_type":"markdown","source":"Waist Circumference (cm) = a × BMI + b × Height (cm) + c\n=> Using Linear Regression model => X = [BMI, Height] y = Waist","metadata":{"papermill":{"duration":0.036374,"end_time":"2025-05-23T02:56:40.192013","exception":false,"start_time":"2025-05-23T02:56:40.155639","status":"completed"},"tags":[]}},{"id":"6b32347d","cell_type":"code","source":"waist_data = train.dropna(subset=['Physical-Waist_Circumference'])\n\nX = waist_data[['Physical-BMI', 'Physical-Weight']]\ny = waist_data['Physical-Waist_Circumference']\n\nfrom sklearn.linear_model import LinearRegression\nwaist_model = LinearRegression()\nwaist_model.fit(X, y)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.761353Z","iopub.execute_input":"2026-04-24T07:59:17.761909Z","iopub.status.idle":"2026-04-24T07:59:17.776296Z","shell.execute_reply.started":"2026-04-24T07:59:17.76187Z","shell.execute_reply":"2026-04-24T07:59:17.775475Z"},"papermill":{"duration":0.067283,"end_time":"2025-05-23T02:56:40.295357","exception":false,"start_time":"2025-05-23T02:56:40.228074","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":469,"output_type":"execute_result","data":{"text/plain":"LinearRegression()","text/html":"<style>#sk-container-id-5 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: #000;\n  --sklearn-color-text-muted: #666;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-5 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-5 pre {\n  padding: 0;\n}\n\n#sk-container-id-5 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-5 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-5 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-5 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-5 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-5 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-5 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-5 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-5 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-5 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-5 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-5 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-5 label.sk-toggleable__label {\n  cursor: pointer;\n  display: flex;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n  align-items: start;\n  justify-content: space-between;\n  gap: 0.5em;\n}\n\n#sk-container-id-5 label.sk-toggleable__label .caption {\n  font-size: 0.6rem;\n  font-weight: lighter;\n  color: var(--sklearn-color-text-muted);\n}\n\n#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-5 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-5 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-5 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n#sk-container-id-5 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-5 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-5 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-5 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-5 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-5 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-5 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 0.5em;\n  text-align: center;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-5 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-5 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-5 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"},"metadata":{}}],"execution_count":469},{"id":"63ed9c4b","cell_type":"markdown","source":"Dự đoán các giá trị bị thiếu","metadata":{"papermill":{"duration":0.036032,"end_time":"2025-05-23T02:56:40.367275","exception":false,"start_time":"2025-05-23T02:56:40.331243","status":"completed"},"tags":[]}},{"id":"f1cb9330","cell_type":"code","source":"missing_waist = train['Physical-Waist_Circumference'].isnull()\ntrain.loc[missing_waist, 'Physical-Waist_Circumference'] = waist_model.predict(\n    train.loc[missing_waist, ['Physical-BMI', 'Physical-Weight']]\n)\n\nmissing_waist_test = test['Physical-Waist_Circumference'].isnull()\ntest.loc[missing_waist_test, 'Physical-Waist_Circumference'] = waist_model.predict(\n    test.loc[missing_waist_test, ['Physical-BMI', 'Physical-Weight']]\n)\n\ncalculate_stats(train, wh_cols)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.777417Z","iopub.execute_input":"2026-04-24T07:59:17.777774Z","iopub.status.idle":"2026-04-24T07:59:17.814923Z","shell.execute_reply.started":"2026-04-24T07:59:17.77774Z","shell.execute_reply":"2026-04-24T07:59:17.814072Z"},"papermill":{"duration":0.063664,"end_time":"2025-05-23T02:56:40.467694","exception":false,"start_time":"2025-05-23T02:56:40.40403","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":470,"output_type":"execute_result","data":{"text/plain":"                               count        mean        std        min  \\\nPhysical-BMI                  2719.0   19.238855   4.699363   8.523273   \nPhysical-Height               2719.0  141.924194  18.652195  91.440000   \nPhysical-Weight               2719.0   40.736849  18.788616  14.877818   \nPhysical-Waist_Circumference  2719.0   67.988307  12.096264  47.399382   \n\n                                     25%         50%         75%         max  \\\nPhysical-BMI                   15.913649   18.032014   21.305080   46.107439   \nPhysical-Height               127.635000  139.700000  156.210000  199.390000   \nPhysical-Weight                26.716569   34.835866   51.015492  142.881480   \nPhysical-Waist_Circumference   59.026096   64.527076   73.870130  133.742947   \n\n                              missing  \nPhysical-BMI                        0  \nPhysical-Height                     0  \nPhysical-Weight                     0  \nPhysical-Waist_Circumference        0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Physical-BMI</th>\n      <td>2719.0</td>\n      <td>19.238855</td>\n      <td>4.699363</td>\n      <td>8.523273</td>\n      <td>15.913649</td>\n      <td>18.032014</td>\n      <td>21.305080</td>\n      <td>46.107439</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Physical-Height</th>\n      <td>2719.0</td>\n      <td>141.924194</td>\n      <td>18.652195</td>\n      <td>91.440000</td>\n      <td>127.635000</td>\n      <td>139.700000</td>\n      <td>156.210000</td>\n      <td>199.390000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Physical-Weight</th>\n      <td>2719.0</td>\n      <td>40.736849</td>\n      <td>18.788616</td>\n      <td>14.877818</td>\n      <td>26.716569</td>\n      <td>34.835866</td>\n      <td>51.015492</td>\n      <td>142.881480</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Physical-Waist_Circumference</th>\n      <td>2719.0</td>\n      <td>67.988307</td>\n      <td>12.096264</td>\n      <td>47.399382</td>\n      <td>59.026096</td>\n      <td>64.527076</td>\n      <td>73.870130</td>\n      <td>133.742947</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":470},{"id":"6bcdad8c","cell_type":"markdown","source":"## Rối loạn Giấc ngủ - Sleep Disturbance Scale","metadata":{"papermill":{"duration":0.035971,"end_time":"2025-05-23T02:56:41.097576","exception":false,"start_time":"2025-05-23T02:56:41.061605","status":"completed"},"tags":[]}},{"id":"7aa5a1a6","cell_type":"code","source":"SDS_columns = ['SDS-Season', 'SDS-SDS_Total_Raw', 'SDS-SDS_Total_T']\n\nSDS_number_columns = ['SDS-SDS_Total_Raw', 'SDS-SDS_Total_T']","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.816119Z","iopub.execute_input":"2026-04-24T07:59:17.816763Z","iopub.status.idle":"2026-04-24T07:59:17.820637Z","shell.execute_reply.started":"2026-04-24T07:59:17.816727Z","shell.execute_reply":"2026-04-24T07:59:17.819752Z"},"papermill":{"duration":0.042626,"end_time":"2025-05-23T02:56:41.247958","exception":false,"start_time":"2025-05-23T02:56:41.205332","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":471},{"id":"81bf4ad8","cell_type":"code","source":"calculate_stats(train, 'SDS-Season')","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.821853Z","iopub.execute_input":"2026-04-24T07:59:17.822135Z","iopub.status.idle":"2026-04-24T07:59:17.841739Z","shell.execute_reply.started":"2026-04-24T07:59:17.82211Z","shell.execute_reply":"2026-04-24T07:59:17.840864Z"},"papermill":{"duration":0.047494,"end_time":"2025-05-23T02:56:41.33303","exception":false,"start_time":"2025-05-23T02:56:41.285536","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":472,"output_type":"execute_result","data":{"text/plain":"               count (%)\nSDS-Season              \nNaN          206 (7.58%)\nFall        602 (22.14%)\nSummer      598 (21.99%)\nWinter      625 (22.99%)\nSpring       688 (25.3%)","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count (%)</th>\n    </tr>\n    <tr>\n      <th>SDS-Season</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>NaN</th>\n      <td>206 (7.58%)</td>\n    </tr>\n    <tr>\n      <th>Fall</th>\n      <td>602 (22.14%)</td>\n    </tr>\n    <tr>\n      <th>Summer</th>\n      <td>598 (21.99%)</td>\n    </tr>\n    <tr>\n      <th>Winter</th>\n      <td>625 (22.99%)</td>\n    </tr>\n    <tr>\n      <th>Spring</th>\n      <td>688 (25.3%)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":472},{"id":"1e75ecca","cell_type":"code","source":"calculate_stats(train, SDS_number_columns)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.842872Z","iopub.execute_input":"2026-04-24T07:59:17.843216Z","iopub.status.idle":"2026-04-24T07:59:17.870671Z","shell.execute_reply.started":"2026-04-24T07:59:17.84318Z","shell.execute_reply":"2026-04-24T07:59:17.869893Z"},"papermill":{"duration":0.057809,"end_time":"2025-05-23T02:56:41.427076","exception":false,"start_time":"2025-05-23T02:56:41.369267","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":473,"output_type":"execute_result","data":{"text/plain":"                    count       mean        std   min   25%   50%   75%  \\\nSDS-SDS_Total_Raw  2513.0  40.987266  10.235631  17.0  33.0  39.0  46.0   \nSDS-SDS_Total_T    2511.0  57.658702  13.047179  38.0  47.0  55.0  64.0   \n\n                     max  missing  \nSDS-SDS_Total_Raw   96.0      206  \nSDS-SDS_Total_T    100.0      208  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>SDS-SDS_Total_Raw</th>\n      <td>2513.0</td>\n      <td>40.987266</td>\n      <td>10.235631</td>\n      <td>17.0</td>\n      <td>33.0</td>\n      <td>39.0</td>\n      <td>46.0</td>\n      <td>96.0</td>\n      <td>206</td>\n    </tr>\n    <tr>\n      <th>SDS-SDS_Total_T</th>\n      <td>2511.0</td>\n      <td>57.658702</td>\n      <td>13.047179</td>\n      <td>38.0</td>\n      <td>47.0</td>\n      <td>55.0</td>\n      <td>64.0</td>\n      <td>100.0</td>\n      <td>208</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":473},{"id":"1b063da9","cell_type":"code","source":"plt.figure(figsize=(18, 5))\n\n# SDS-SDS_Total_Raw\nplt.subplot(1, 3, 2)\nsns.histplot(train['SDS-SDS_Total_Raw'].dropna(), bins=20, kde=True)\nplt.title('SDS-SDS_Total_Raw')\nplt.xlabel('Value')\n\n# SDS-SDS_Total_T\nplt.subplot(1, 3, 3)\nsns.histplot(train['SDS-SDS_Total_T'].dropna(), bins=20, kde=True)\nplt.title('SDS-SDS_Total_T')\nplt.xlabel('Value')\n\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:17.871615Z","iopub.execute_input":"2026-04-24T07:59:17.871909Z","iopub.status.idle":"2026-04-24T07:59:18.366112Z","shell.execute_reply.started":"2026-04-24T07:59:17.871885Z","shell.execute_reply":"2026-04-24T07:59:18.365258Z"},"papermill":{"duration":0.630573,"end_time":"2025-05-23T02:56:42.099828","exception":false,"start_time":"2025-05-23T02:56:41.469255","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1800x500 with 2 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":474},{"id":"d74e4e3c","cell_type":"code","source":"plt.title('Sleep disturbance scale: conversion from raw to t score')\nplt.scatter(train['SDS-SDS_Total_Raw'],\n            train['SDS-SDS_Total_T'],\n            color='brown')\nplt.xlabel('SDS-SDS_Total_Raw')\nplt.ylabel('SDS-SDS_Total_T')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:18.36714Z","iopub.execute_input":"2026-04-24T07:59:18.36746Z","iopub.status.idle":"2026-04-24T07:59:18.538386Z","shell.execute_reply.started":"2026-04-24T07:59:18.367427Z","shell.execute_reply":"2026-04-24T07:59:18.537417Z"},"papermill":{"duration":0.222497,"end_time":"2025-05-23T02:56:42.36254","exception":false,"start_time":"2025-05-23T02:56:42.140043","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":475},{"id":"97531a34","cell_type":"code","source":"train = train.drop(columns=['SDS-SDS_Total_T'])\ntest = test.drop(columns=['SDS-SDS_Total_T'])","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:18.539796Z","iopub.execute_input":"2026-04-24T07:59:18.540369Z","iopub.status.idle":"2026-04-24T07:59:18.547901Z","shell.execute_reply.started":"2026-04-24T07:59:18.540316Z","shell.execute_reply":"2026-04-24T07:59:18.547159Z"},"papermill":{"duration":0.046772,"end_time":"2025-05-23T02:56:42.524632","exception":false,"start_time":"2025-05-23T02:56:42.47786","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":476},{"id":"9ded50c0","cell_type":"code","source":"cols=['Basic_Demos-Age', 'Age Group', 'SDS-SDS_Total_Raw', 'PCIAT-PCIAT_Total']\ntrain[train['SDS-SDS_Total_Raw'] > 70][cols]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:18.549146Z","iopub.execute_input":"2026-04-24T07:59:18.549648Z","iopub.status.idle":"2026-04-24T07:59:18.583096Z","shell.execute_reply.started":"2026-04-24T07:59:18.549609Z","shell.execute_reply":"2026-04-24T07:59:18.582119Z"},"papermill":{"duration":0.058506,"end_time":"2025-05-23T02:56:42.699512","exception":false,"start_time":"2025-05-23T02:56:42.641006","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":477,"output_type":"execute_result","data":{"text/plain":"      Basic_Demos-Age    Age Group  SDS-SDS_Total_Raw  PCIAT-PCIAT_Total\n29               12.0     Children               82.0               46.0\n174               8.0     Children               84.0               49.0\n276              10.0     Children               71.0               74.0\n355               8.0     Children               75.0               35.0\n433               7.0     Children               71.0               29.0\n440              10.0     Children               77.0               23.0\n511               8.0     Children               73.0                0.0\n541              10.0     Children               74.0               20.0\n585               5.0     Children               72.0               32.0\n664               6.0     Children               71.0               27.0\n732              18.0  Adolescents               75.0               92.0\n1061             10.0     Children               80.0                9.0\n1163              9.0     Children               76.0               48.0\n1164              6.0     Children               72.0                0.0\n1332             15.0  Adolescents               77.0               60.0\n1483              8.0     Children               82.0               27.0\n1531              6.0     Children               74.0               58.0\n1541             10.0     Children               75.0               26.0\n1672              6.0     Children               84.0               31.0\n1752             10.0     Children               80.0               30.0\n1758              8.0     Children               76.0               23.0\n1768              9.0     Children               71.0               43.0\n1855              7.0     Children               75.0                6.0\n1885             11.0     Children               72.0               50.0\n2031             12.0     Children               73.0               10.0\n2047              8.0     Children               82.0                0.0\n2077             17.0  Adolescents               79.0               53.0\n2079              7.0     Children               76.0               22.0\n2185             14.0  Adolescents               74.0               36.0\n2308              6.0     Children               96.0                1.0\n2430              7.0     Children               82.0               57.0\n2604              7.0     Children               76.0               67.0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Basic_Demos-Age</th>\n      <th>Age Group</th>\n      <th>SDS-SDS_Total_Raw</th>\n      <th>PCIAT-PCIAT_Total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>29</th>\n      <td>12.0</td>\n      <td>Children</td>\n      <td>82.0</td>\n      <td>46.0</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>8.0</td>\n      <td>Children</td>\n      <td>84.0</td>\n      <td>49.0</td>\n    </tr>\n    <tr>\n      <th>276</th>\n      <td>10.0</td>\n      <td>Children</td>\n      <td>71.0</td>\n      <td>74.0</td>\n    </tr>\n    <tr>\n      <th>355</th>\n      <td>8.0</td>\n      <td>Children</td>\n      <td>75.0</td>\n      <td>35.0</td>\n    </tr>\n    <tr>\n      <th>433</th>\n      <td>7.0</td>\n      <td>Children</td>\n      <td>71.0</td>\n      <td>29.0</td>\n    </tr>\n    <tr>\n      <th>440</th>\n      <td>10.0</td>\n      <td>Children</td>\n      <td>77.0</td>\n      <td>23.0</td>\n    </tr>\n    <tr>\n      <th>511</th>\n      <td>8.0</td>\n      <td>Children</td>\n      <td>73.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>541</th>\n      <td>10.0</td>\n      <td>Children</td>\n      <td>74.0</td>\n      <td>20.0</td>\n    </tr>\n    <tr>\n      <th>585</th>\n      <td>5.0</td>\n      <td>Children</td>\n      <td>72.0</td>\n      <td>32.0</td>\n    </tr>\n    <tr>\n      <th>664</th>\n      <td>6.0</td>\n      <td>Children</td>\n      <td>71.0</td>\n      <td>27.0</td>\n    </tr>\n    <tr>\n      <th>732</th>\n      <td>18.0</td>\n      <td>Adolescents</td>\n      <td>75.0</td>\n      <td>92.0</td>\n    </tr>\n    <tr>\n      <th>1061</th>\n      <td>10.0</td>\n      <td>Children</td>\n      <td>80.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>1163</th>\n      <td>9.0</td>\n      <td>Children</td>\n      <td>76.0</td>\n      <td>48.0</td>\n    </tr>\n    <tr>\n      <th>1164</th>\n      <td>6.0</td>\n      <td>Children</td>\n      <td>72.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1332</th>\n      <td>15.0</td>\n      <td>Adolescents</td>\n      <td>77.0</td>\n      <td>60.0</td>\n    </tr>\n    <tr>\n      <th>1483</th>\n      <td>8.0</td>\n      <td>Children</td>\n      <td>82.0</td>\n      <td>27.0</td>\n    </tr>\n    <tr>\n      <th>1531</th>\n      <td>6.0</td>\n      <td>Children</td>\n      <td>74.0</td>\n      <td>58.0</td>\n    </tr>\n    <tr>\n      <th>1541</th>\n      <td>10.0</td>\n      <td>Children</td>\n      <td>75.0</td>\n      <td>26.0</td>\n    </tr>\n    <tr>\n      <th>1672</th>\n      <td>6.0</td>\n      <td>Children</td>\n      <td>84.0</td>\n      <td>31.0</td>\n    </tr>\n    <tr>\n      <th>1752</th>\n      <td>10.0</td>\n      <td>Children</td>\n      <td>80.0</td>\n      <td>30.0</td>\n    </tr>\n    <tr>\n      <th>1758</th>\n      <td>8.0</td>\n      <td>Children</td>\n      <td>76.0</td>\n      <td>23.0</td>\n    </tr>\n    <tr>\n      <th>1768</th>\n      <td>9.0</td>\n      <td>Children</td>\n      <td>71.0</td>\n      <td>43.0</td>\n    </tr>\n    <tr>\n      <th>1855</th>\n      <td>7.0</td>\n      <td>Children</td>\n      <td>75.0</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>1885</th>\n      <td>11.0</td>\n      <td>Children</td>\n      <td>72.0</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>2031</th>\n      <td>12.0</td>\n      <td>Children</td>\n      <td>73.0</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>2047</th>\n      <td>8.0</td>\n      <td>Children</td>\n      <td>82.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2077</th>\n      <td>17.0</td>\n      <td>Adolescents</td>\n      <td>79.0</td>\n      <td>53.0</td>\n    </tr>\n    <tr>\n      <th>2079</th>\n      <td>7.0</td>\n      <td>Children</td>\n      <td>76.0</td>\n      <td>22.0</td>\n    </tr>\n    <tr>\n      <th>2185</th>\n      <td>14.0</td>\n      <td>Adolescents</td>\n      <td>74.0</td>\n      <td>36.0</td>\n    </tr>\n    <tr>\n      <th>2308</th>\n      <td>6.0</td>\n      <td>Children</td>\n      <td>96.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2430</th>\n      <td>7.0</td>\n      <td>Children</td>\n      <td>82.0</td>\n      <td>57.0</td>\n    </tr>\n    <tr>\n      <th>2604</th>\n      <td>7.0</td>\n      <td>Children</td>\n      <td>76.0</td>\n      <td>67.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":477},{"id":"fdb4c053","cell_type":"code","source":"train[train['SDS-SDS_Total_Raw'] > 70].groupby('Age Group').size()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:18.584662Z","iopub.execute_input":"2026-04-24T07:59:18.58495Z","iopub.status.idle":"2026-04-24T07:59:18.60577Z","shell.execute_reply.started":"2026-04-24T07:59:18.584924Z","shell.execute_reply":"2026-04-24T07:59:18.604595Z"},"papermill":{"duration":0.049969,"end_time":"2025-05-23T02:56:42.788009","exception":false,"start_time":"2025-05-23T02:56:42.73804","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":478,"output_type":"execute_result","data":{"text/plain":"Age Group\nChildren       28\nAdolescents     4\nAdults          0\ndtype: int64"},"metadata":{}}],"execution_count":478},{"id":"59e8e260","cell_type":"code","source":"train['SDS-SDS_Total_Raw'] = train.apply(\n    lambda row: np.nan if row['Age Group'] == 'Children' and row['SDS-SDS_Total_Raw'] > 70 else row['SDS-SDS_Total_Raw'], \n    axis=1\n)\ntrain[train['SDS-SDS_Total_Raw'] > 70][cols]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:18.607314Z","iopub.execute_input":"2026-04-24T07:59:18.607686Z","iopub.status.idle":"2026-04-24T07:59:18.666697Z","shell.execute_reply.started":"2026-04-24T07:59:18.607651Z","shell.execute_reply":"2026-04-24T07:59:18.665942Z"},"papermill":{"duration":0.085812,"end_time":"2025-05-23T02:56:42.988876","exception":false,"start_time":"2025-05-23T02:56:42.903064","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":479,"output_type":"execute_result","data":{"text/plain":"      Basic_Demos-Age    Age Group  SDS-SDS_Total_Raw  PCIAT-PCIAT_Total\n732              18.0  Adolescents               75.0               92.0\n1332             15.0  Adolescents               77.0               60.0\n2077             17.0  Adolescents               79.0               53.0\n2185             14.0  Adolescents               74.0               36.0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Basic_Demos-Age</th>\n      <th>Age Group</th>\n      <th>SDS-SDS_Total_Raw</th>\n      <th>PCIAT-PCIAT_Total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>732</th>\n      <td>18.0</td>\n      <td>Adolescents</td>\n      <td>75.0</td>\n      <td>92.0</td>\n    </tr>\n    <tr>\n      <th>1332</th>\n      <td>15.0</td>\n      <td>Adolescents</td>\n      <td>77.0</td>\n      <td>60.0</td>\n    </tr>\n    <tr>\n      <th>2077</th>\n      <td>17.0</td>\n      <td>Adolescents</td>\n      <td>79.0</td>\n      <td>53.0</td>\n    </tr>\n    <tr>\n      <th>2185</th>\n      <td>14.0</td>\n      <td>Adolescents</td>\n      <td>74.0</td>\n      <td>36.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":479},{"id":"db6b2af3","cell_type":"code","source":"valid_data = train.dropna(subset=['SDS-SDS_Total_Raw', 'PCIAT-PCIAT_Total'])\n\nplt.figure(figsize=(10, 6))\nsns.scatterplot(\n    data=valid_data, \n    x='SDS-SDS_Total_Raw', \n    y='PCIAT-PCIAT_Total',\n    hue=valid_data['PCIAT-PCIAT_Total'] > 80,  # PCIAT severe group\n    alpha=0.3\n)\n\nplt.title(\"Scatter Plot of SDS Total Raw vs PCIAT Total\", fontsize=16)\nplt.xlabel(\"SDS-SDS_Total_Raw (Sleep Disturbance Score)\", fontsize=12)\nplt.ylabel(\"PCIAT-PCIAT_Total (PIU Severity)\", fontsize=12)\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:18.667749Z","iopub.execute_input":"2026-04-24T07:59:18.668078Z","iopub.status.idle":"2026-04-24T07:59:18.969394Z","shell.execute_reply.started":"2026-04-24T07:59:18.668051Z","shell.execute_reply":"2026-04-24T07:59:18.968421Z"},"papermill":{"duration":0.347311,"end_time":"2025-05-23T02:56:43.454123","exception":false,"start_time":"2025-05-23T02:56:43.106812","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":480},{"id":"e4a4adbd","cell_type":"markdown","source":"đa số người tham gia khảo sát không bị rối loạn giấc ngủ nghiêm trọng hoặc nghiện Internet nặng.\n\nKhi SDS < 35, không có trường hợp nào có PCIAT > 80 (SII = 3). Điều này cho thấy những người có mức rối loạn giấc ngủ thấp thường không bị nghiện Internet nghiêm trọng. Điều này củng cố mối quan hệ giữa rối loạn giấc ngủ và nghiện Internet: rối loạn giấc ngủ có thể là một yếu tố dự đoán cho nghiện Internet nghiêm trọng.","metadata":{"papermill":{"duration":0.041821,"end_time":"2025-05-23T02:56:43.53993","exception":false,"start_time":"2025-05-23T02:56:43.498109","status":"completed"},"tags":[]}},{"id":"85b4fc0d","cell_type":"markdown","source":"SDS-Season","metadata":{"papermill":{"duration":0.041108,"end_time":"2025-05-23T02:56:43.622178","exception":false,"start_time":"2025-05-23T02:56:43.58107","status":"completed"},"tags":[]}},{"id":"e4b6feef","cell_type":"code","source":"season_counts = train['SDS-Season'].value_counts(normalize=True)\n\ntrain['SDS-Season'] = train['SDS-Season'].apply(\n    lambda x: np.random.choice(season_counts.index, p=season_counts.values) if pd.isna(x) else x\n)\ntest['SDS-Season'] = test['SDS-Season'].apply(\n    lambda x: np.random.choice(season_counts.index, p=season_counts.values) if pd.isna(x) else x\n)\n\ncalculate_stats(train, 'SDS-Season')","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:18.970386Z","iopub.execute_input":"2026-04-24T07:59:18.97066Z","iopub.status.idle":"2026-04-24T07:59:18.999851Z","shell.execute_reply.started":"2026-04-24T07:59:18.970636Z","shell.execute_reply":"2026-04-24T07:59:18.999099Z"},"papermill":{"duration":0.063993,"end_time":"2025-05-23T02:56:43.993108","exception":false,"start_time":"2025-05-23T02:56:43.929115","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":481,"output_type":"execute_result","data":{"text/plain":"               count (%)\nSDS-Season              \nSpring      750 (27.58%)\nFall         647 (23.8%)\nSummer      640 (23.54%)\nWinter      682 (25.08%)","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count (%)</th>\n    </tr>\n    <tr>\n      <th>SDS-Season</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Spring</th>\n      <td>750 (27.58%)</td>\n    </tr>\n    <tr>\n      <th>Fall</th>\n      <td>647 (23.8%)</td>\n    </tr>\n    <tr>\n      <th>Summer</th>\n      <td>640 (23.54%)</td>\n    </tr>\n    <tr>\n      <th>Winter</th>\n      <td>682 (25.08%)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":481},{"id":"10b62e27","cell_type":"code","source":"season_stats = train.groupby('SDS-Season')['SDS-SDS_Total_Raw'].agg(['mean', 'median'])\nseason_stats","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:19.000765Z","iopub.execute_input":"2026-04-24T07:59:19.000994Z","iopub.status.idle":"2026-04-24T07:59:19.012242Z","shell.execute_reply.started":"2026-04-24T07:59:19.000971Z","shell.execute_reply":"2026-04-24T07:59:19.011549Z"},"papermill":{"duration":0.057077,"end_time":"2025-05-23T02:56:43.721314","exception":false,"start_time":"2025-05-23T02:56:43.664237","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":482,"output_type":"execute_result","data":{"text/plain":"                 mean  median\nSDS-Season                   \nFall        40.350168    38.0\nSpring      40.454412    39.0\nSummer      40.683502    39.0\nWinter      40.852512    40.0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>mean</th>\n      <th>median</th>\n    </tr>\n    <tr>\n      <th>SDS-Season</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Fall</th>\n      <td>40.350168</td>\n      <td>38.0</td>\n    </tr>\n    <tr>\n      <th>Spring</th>\n      <td>40.454412</td>\n      <td>39.0</td>\n    </tr>\n    <tr>\n      <th>Summer</th>\n      <td>40.683502</td>\n      <td>39.0</td>\n    </tr>\n    <tr>\n      <th>Winter</th>\n      <td>40.852512</td>\n      <td>40.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":482},{"id":"044c39bc","cell_type":"markdown","source":"Check relationship between Age Sex and SDS score","metadata":{"papermill":{"duration":0.04089,"end_time":"2025-05-23T02:56:44.075409","exception":false,"start_time":"2025-05-23T02:56:44.034519","status":"completed"},"tags":[]}},{"id":"64eeec3f","cell_type":"code","source":"plt.figure(figsize=(8, 6))\nsns.boxplot(x='Basic_Demos-Sex', y='SDS-SDS_Total_Raw', data=train)\nplt.title('Distribution of SDS by Sex')\nplt.xlabel('Sex (0 = Male, 1 = Female)')\nplt.ylabel('SDS-SDS_Total_Raw')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:19.013367Z","iopub.execute_input":"2026-04-24T07:59:19.013659Z","iopub.status.idle":"2026-04-24T07:59:19.188784Z","shell.execute_reply.started":"2026-04-24T07:59:19.013632Z","shell.execute_reply":"2026-04-24T07:59:19.187799Z"},"papermill":{"duration":0.183524,"end_time":"2025-05-23T02:56:44.300417","exception":false,"start_time":"2025-05-23T02:56:44.116893","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}],"execution_count":483},{"id":"9e8447ca","cell_type":"code","source":"plt.figure(figsize=(8, 6))\nsns.boxplot(x='Age Group', y='SDS-SDS_Total_Raw', data=train)\nplt.title('Boxplot of SDS by Age Group')\nplt.xlabel('Age Group')\nplt.ylabel('SDS-SDS_Total_Raw')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:19.189837Z","iopub.execute_input":"2026-04-24T07:59:19.190173Z","iopub.status.idle":"2026-04-24T07:59:19.362338Z","shell.execute_reply.started":"2026-04-24T07:59:19.190145Z","shell.execute_reply":"2026-04-24T07:59:19.361404Z"},"papermill":{"duration":0.206962,"end_time":"2025-05-23T02:56:44.636508","exception":false,"start_time":"2025-05-23T02:56:44.429546","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 1 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\n"},"metadata":{}}],"execution_count":484},{"id":"d56ce53e","cell_type":"markdown","source":"\n=> Use Age and SDS-Season to predict the SDS score","metadata":{"papermill":{"duration":0.04159,"end_time":"2025-05-23T02:56:44.815737","exception":false,"start_time":"2025-05-23T02:56:44.774147","status":"completed"},"tags":[]}},{"id":"cad91b91","cell_type":"markdown","source":"Encode season columns","metadata":{"papermill":{"duration":0.041557,"end_time":"2025-05-23T02:56:44.89896","exception":false,"start_time":"2025-05-23T02:56:44.857403","status":"completed"},"tags":[]}},{"id":"1b9b9031","cell_type":"code","source":"encoded_season_df = pd.get_dummies(train, columns=['SDS-Season'], prefix='SDS-Season', drop_first=False)\ntrain = train.join(encoded_season_df[['SDS-Season_Fall', 'SDS-Season_Spring', 'SDS-Season_Summer', 'SDS-Season_Winter']])\n\nencoded_season_df = pd.get_dummies(test, columns=['SDS-Season'], prefix='SDS-Season', drop_first=False)\ntest = test.join(encoded_season_df[['SDS-Season_Fall', 'SDS-Season_Spring', 'SDS-Season_Summer', 'SDS-Season_Winter']])\n\ntrain.columns","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:19.3635Z","iopub.execute_input":"2026-04-24T07:59:19.363811Z","iopub.status.idle":"2026-04-24T07:59:19.38887Z","shell.execute_reply.started":"2026-04-24T07:59:19.363785Z","shell.execute_reply":"2026-04-24T07:59:19.388003Z"},"papermill":{"duration":0.067036,"end_time":"2025-05-23T02:56:45.007819","exception":false,"start_time":"2025-05-23T02:56:44.940783","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":485,"output_type":"execute_result","data":{"text/plain":"Index(['index', 'id', 'Basic_Demos-Enroll_Season', 'CGAS-Season',\n       'CGAS-CGAS_Score', 'Physical-Season', 'Physical-BMI',\n       'Physical-Waist_Circumference', 'Physical-Diastolic_BP',\n       'Physical-HeartRate', 'Physical-Systolic_BP',\n       'Fitness_Endurance-Season', 'Fitness_Endurance-Max_Stage',\n       'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n       'FGC-Season', 'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n       'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n       'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n       'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone', 'BIA-Season',\n       'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n       'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n       'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n       'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n       'BIA-BIA_TBW', 'PAQ_A-Season', 'PAQ_A-PAQ_A_Total', 'PAQ_C-Season',\n       'PAQ_C-PAQ_C_Total', 'PCIAT-Season', 'PCIAT-PCIAT_01', 'PCIAT-PCIAT_02',\n       'PCIAT-PCIAT_03', 'PCIAT-PCIAT_04', 'PCIAT-PCIAT_05', 'PCIAT-PCIAT_06',\n       'PCIAT-PCIAT_07', 'PCIAT-PCIAT_08', 'PCIAT-PCIAT_09', 'PCIAT-PCIAT_10',\n       'PCIAT-PCIAT_11', 'PCIAT-PCIAT_12', 'PCIAT-PCIAT_13', 'PCIAT-PCIAT_14',\n       'PCIAT-PCIAT_15', 'PCIAT-PCIAT_16', 'PCIAT-PCIAT_17', 'PCIAT-PCIAT_18',\n       'PCIAT-PCIAT_19', 'PCIAT-PCIAT_20', 'PCIAT-PCIAT_Total', 'SDS-Season',\n       'SDS-SDS_Total_Raw', 'PreInt_EduHx-Season',\n       'PreInt_EduHx-computerinternet_hoursday', 'sii', 'Age Group',\n       'Age_Group_Label', 'Season_Spring', 'Season_Summer', 'Basic_Demos-Age',\n       'Season_Fall', 'Season_Winter', 'Basic_Demos-Sex', 'Physical-Weight',\n       'Physical-Height', 'SDS-Season_Fall', 'SDS-Season_Spring',\n       'SDS-Season_Summer', 'SDS-Season_Winter'],\n      dtype='object')"},"metadata":{}}],"execution_count":485},{"id":"7c9f216e","cell_type":"markdown","source":"Fill missing SDS-SDS_Total_Raw values with Age SDS-season by KNN","metadata":{"papermill":{"duration":0.042485,"end_time":"2025-05-23T02:56:45.093932","exception":false,"start_time":"2025-05-23T02:56:45.051447","status":"completed"},"tags":[]}},{"id":"60bd5da0","cell_type":"code","source":"X_train_SDS_Score = train[['Basic_Demos-Age', 'SDS-Season_Fall', 'SDS-Season_Spring', 'SDS-Season_Winter']]\n\ny_train_SDS_Score = train['SDS-SDS_Total_Raw']","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:19.390041Z","iopub.execute_input":"2026-04-24T07:59:19.390417Z","iopub.status.idle":"2026-04-24T07:59:19.396018Z","shell.execute_reply.started":"2026-04-24T07:59:19.390375Z","shell.execute_reply":"2026-04-24T07:59:19.395106Z"},"papermill":{"duration":0.053764,"end_time":"2025-05-23T02:56:45.190777","exception":false,"start_time":"2025-05-23T02:56:45.137013","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":486},{"id":"5cce86ea","cell_type":"code","source":"sds_imputer = KNNImputer(n_neighbors=7)\n\ndata_with_sds = pd.concat([X_train_SDS_Score, y_train_SDS_Score], axis=1)\n\nfilled_data = sds_imputer.fit_transform(data_with_sds)\n\ncolumns = data_with_sds.columns.tolist()\nfilled_df = pd.DataFrame(filled_data, columns=columns)\n\ntrain['SDS-SDS_Total_Raw'] = filled_df['SDS-SDS_Total_Raw']\ncalculate_stats(train, ['SDS-SDS_Total_Raw'])","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:19.397299Z","iopub.execute_input":"2026-04-24T07:59:19.397692Z","iopub.status.idle":"2026-04-24T07:59:19.466651Z","shell.execute_reply.started":"2026-04-24T07:59:19.397652Z","shell.execute_reply":"2026-04-24T07:59:19.465604Z"},"papermill":{"duration":0.151896,"end_time":"2025-05-23T02:56:45.385084","exception":false,"start_time":"2025-05-23T02:56:45.233188","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":487,"output_type":"execute_result","data":{"text/plain":"                    count       mean      std   min   25%   50%   75%   max  \\\nSDS-SDS_Total_Raw                                                             \nSDS-SDS_Total_Raw  2719.0  40.601061  9.14364  17.0  34.0  39.0  45.0  79.0   \n\n                   missing  \nSDS-SDS_Total_Raw           \nSDS-SDS_Total_Raw        0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n    <tr>\n      <th>SDS-SDS_Total_Raw</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>SDS-SDS_Total_Raw</th>\n      <td>2719.0</td>\n      <td>40.601061</td>\n      <td>9.14364</td>\n      <td>17.0</td>\n      <td>34.0</td>\n      <td>39.0</td>\n      <td>45.0</td>\n      <td>79.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":487},{"id":"af16d846","cell_type":"code","source":"X_test_SDS_Score = test[['Basic_Demos-Age', 'SDS-Season_Fall', 'SDS-Season_Spring', 'SDS-Season_Winter']]\ny_test_SDS_Score = test['SDS-SDS_Total_Raw']","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:19.467744Z","iopub.execute_input":"2026-04-24T07:59:19.468047Z","iopub.status.idle":"2026-04-24T07:59:19.474197Z","shell.execute_reply.started":"2026-04-24T07:59:19.468016Z","shell.execute_reply":"2026-04-24T07:59:19.47329Z"},"papermill":{"duration":0.050601,"end_time":"2025-05-23T02:56:45.60677","exception":false,"start_time":"2025-05-23T02:56:45.556169","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":488},{"id":"66797719","cell_type":"code","source":"data_test_with_sds = pd.concat([X_test_SDS_Score, y_test_SDS_Score], axis=1)\nfilled_test_data = sds_imputer.transform(data_test_with_sds)\nfilled_test_df = pd.DataFrame(filled_test_data, columns=data_test_with_sds.columns)\ntest['SDS-SDS_Total_Raw'] = filled_test_df['SDS-SDS_Total_Raw']","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:19.475077Z","iopub.execute_input":"2026-04-24T07:59:19.475611Z","iopub.status.idle":"2026-04-24T07:59:19.507279Z","shell.execute_reply.started":"2026-04-24T07:59:19.475573Z","shell.execute_reply":"2026-04-24T07:59:19.506355Z"},"papermill":{"duration":0.055117,"end_time":"2025-05-23T02:56:45.705841","exception":false,"start_time":"2025-05-23T02:56:45.650724","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":489},{"id":"b4c338e3","cell_type":"markdown","source":"Add SDS_Weight","metadata":{"papermill":{"duration":0.042446,"end_time":"2025-05-23T02:56:45.790672","exception":false,"start_time":"2025-05-23T02:56:45.748226","status":"completed"},"tags":[]}},{"id":"a8b156a3","cell_type":"code","source":"def sigmoid_weight(sds, a=0.1, b=35):\n    return 1 / (1 + np.exp(a * (sds - b)))\n\ntrain['SDS_Weight'] = train['SDS-SDS_Total_Raw'].apply(sigmoid_weight)\ntest['SDS_Weight'] = test['SDS-SDS_Total_Raw'].apply(sigmoid_weight)\ntrain[['SDS_Weight', 'SDS-SDS_Total_Raw']]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:19.508217Z","iopub.execute_input":"2026-04-24T07:59:19.509035Z","iopub.status.idle":"2026-04-24T07:59:19.53994Z","shell.execute_reply.started":"2026-04-24T07:59:19.508997Z","shell.execute_reply":"2026-04-24T07:59:19.539181Z"},"papermill":{"duration":0.062214,"end_time":"2025-05-23T02:56:45.895457","exception":false,"start_time":"2025-05-23T02:56:45.833243","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":490,"output_type":"execute_result","data":{"text/plain":"      SDS_Weight  SDS-SDS_Total_Raw\n0       0.380904          39.857143\n1       0.249740          46.000000\n2       0.425557          38.000000\n3       0.598688          31.000000\n4       0.377541          40.000000\n...          ...                ...\n2714    0.354344          41.000000\n2715    0.214165          48.000000\n2716    0.500000          35.000000\n2717    0.109097          56.000000\n2718    0.549834          33.000000\n\n[2719 rows x 2 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SDS_Weight</th>\n      <th>SDS-SDS_Total_Raw</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.380904</td>\n      <td>39.857143</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.249740</td>\n      <td>46.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.425557</td>\n      <td>38.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.598688</td>\n      <td>31.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.377541</td>\n      <td>40.000000</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2714</th>\n      <td>0.354344</td>\n      <td>41.000000</td>\n    </tr>\n    <tr>\n      <th>2715</th>\n      <td>0.214165</td>\n      <td>48.000000</td>\n    </tr>\n    <tr>\n      <th>2716</th>\n      <td>0.500000</td>\n      <td>35.000000</td>\n    </tr>\n    <tr>\n      <th>2717</th>\n      <td>0.109097</td>\n      <td>56.000000</td>\n    </tr>\n    <tr>\n      <th>2718</th>\n      <td>0.549834</td>\n      <td>33.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>2719 rows × 2 columns</p>\n</div>"},"metadata":{}}],"execution_count":490},{"id":"b45abc69","cell_type":"markdown","source":"## Internet Use","metadata":{"papermill":{"duration":0.042823,"end_time":"2025-05-23T02:56:45.981913","exception":false,"start_time":"2025-05-23T02:56:45.93909","status":"completed"},"tags":[]}},{"id":"a522a7b3","cell_type":"code","source":"calculate_stats(train, 'PreInt_EduHx-Season')","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:19.540853Z","iopub.execute_input":"2026-04-24T07:59:19.541376Z","iopub.status.idle":"2026-04-24T07:59:19.556059Z","shell.execute_reply.started":"2026-04-24T07:59:19.541338Z","shell.execute_reply":"2026-04-24T07:59:19.554932Z"},"papermill":{"duration":0.05463,"end_time":"2025-05-23T02:56:46.164931","exception":false,"start_time":"2025-05-23T02:56:46.110301","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":491,"output_type":"execute_result","data":{"text/plain":"                        count (%)\nPreInt_EduHx-Season              \nFall                 683 (25.12%)\nSummer               649 (23.87%)\nWinter               648 (23.83%)\nSpring               722 (26.55%)\nNaN                    17 (0.63%)","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count (%)</th>\n    </tr>\n    <tr>\n      <th>PreInt_EduHx-Season</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Fall</th>\n      <td>683 (25.12%)</td>\n    </tr>\n    <tr>\n      <th>Summer</th>\n      <td>649 (23.87%)</td>\n    </tr>\n    <tr>\n      <th>Winter</th>\n      <td>648 (23.83%)</td>\n    </tr>\n    <tr>\n      <th>Spring</th>\n      <td>722 (26.55%)</td>\n    </tr>\n    <tr>\n      <th>NaN</th>\n      <td>17 (0.63%)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":491},{"id":"96a468bf","cell_type":"code","source":"calculate_stats(train, ['PreInt_EduHx-computerinternet_hoursday'])","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:19.557008Z","iopub.execute_input":"2026-04-24T07:59:19.557331Z","iopub.status.idle":"2026-04-24T07:59:19.582287Z","shell.execute_reply.started":"2026-04-24T07:59:19.557292Z","shell.execute_reply":"2026-04-24T07:59:19.581597Z"},"papermill":{"duration":0.058218,"end_time":"2025-05-23T02:56:46.265809","exception":false,"start_time":"2025-05-23T02:56:46.207591","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":492,"output_type":"execute_result","data":{"text/plain":"                                         count      mean       std  min  25%  \\\nPreInt_EduHx-computerinternet_hoursday                                         \nPreInt_EduHx-computerinternet_hoursday  2637.0  1.011756  1.080001  0.0  0.0   \n\n                                        50%  75%  max  missing  \nPreInt_EduHx-computerinternet_hoursday                          \nPreInt_EduHx-computerinternet_hoursday  1.0  2.0  3.0       82  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n    <tr>\n      <th>PreInt_EduHx-computerinternet_hoursday</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>PreInt_EduHx-computerinternet_hoursday</th>\n      <td>2637.0</td>\n      <td>1.011756</td>\n      <td>1.080001</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>82</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":492},{"id":"76cf4341","cell_type":"markdown","source":"Relationshiop with age and sex","metadata":{"papermill":{"duration":0.042603,"end_time":"2025-05-23T02:56:46.353605","exception":false,"start_time":"2025-05-23T02:56:46.311002","status":"completed"},"tags":[]}},{"id":"6ce43ccc","cell_type":"code","source":"fig, axes = plt.subplots(1, 2, figsize=(18, 5))\n\n# Hours of Internet Use by Age\nsns.boxplot(y=train['Basic_Demos-Age'], x=train['PreInt_EduHx-computerinternet_hoursday'], ax=axes[0], palette=\"Set3\")\naxes[0].set_title('Hours of Internet Use by Age')\naxes[0].set_ylabel('Age')\naxes[0].set_xlabel('Hours per Day Group')\n\n# Hours of Internet Use by Age Group\nsns.boxplot(y='PreInt_EduHx-computerinternet_hoursday', x='Age Group', data=train, ax=axes[1], palette=\"Set3\")\naxes[1].set_title('Internet Hours by Age Group')\naxes[1].set_ylabel('Hours per Day (Numeric)')\naxes[1].set_xlabel('Age Group')\n\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:19.583307Z","iopub.execute_input":"2026-04-24T07:59:19.583675Z","iopub.status.idle":"2026-04-24T07:59:20.018433Z","shell.execute_reply.started":"2026-04-24T07:59:19.583646Z","shell.execute_reply":"2026-04-24T07:59:20.017602Z"},"papermill":{"duration":0.509926,"end_time":"2025-05-23T02:56:46.907302","exception":false,"start_time":"2025-05-23T02:56:46.397376","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1800x500 with 2 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= train.groupby(['Basic_Demos-Sex', 'PreInt_EduHx-computerinternet_hoursday']\n).size().unstack(fill_value=0)\nstats_prop = stats.div(stats.sum(axis=1), axis=0) * 100\n\nstats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)'\nstats","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:20.019466Z","iopub.execute_input":"2026-04-24T07:59:20.019794Z","iopub.status.idle":"2026-04-24T07:59:20.035979Z","shell.execute_reply.started":"2026-04-24T07:59:20.019769Z","shell.execute_reply":"2026-04-24T07:59:20.035254Z"},"papermill":{"duration":0.061439,"end_time":"2025-05-23T02:56:47.015179","exception":false,"start_time":"2025-05-23T02:56:46.95374","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":494,"output_type":"execute_result","data":{"text/plain":"PreInt_EduHx-computerinternet_hoursday          0.0          1.0          2.0  \\\nBasic_Demos-Sex                                                                 \n0.0                                     793 (47.1%)  224 (13.3%)  505 (30.0%)   \n1.0                                     470 (49.2%)  115 (12.0%)  271 (28.4%)   \n\nPreInt_EduHx-computerinternet_hoursday         3.0  \nBasic_Demos-Sex                                     \n0.0                                     160 (9.5%)  \n1.0                                     99 (10.4%)  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>PreInt_EduHx-computerinternet_hoursday</th>\n      <th>0.0</th>\n      <th>1.0</th>\n      <th>2.0</th>\n      <th>3.0</th>\n    </tr>\n    <tr>\n      <th>Basic_Demos-Sex</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0.0</th>\n      <td>793 (47.1%)</td>\n      <td>224 (13.3%)</td>\n      <td>505 (30.0%)</td>\n      <td>160 (9.5%)</td>\n    </tr>\n    <tr>\n      <th>1.0</th>\n      <td>470 (49.2%)</td>\n      <td>115 (12.0%)</td>\n      <td>271 (28.4%)</td>\n      <td>99 (10.4%)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":494},{"id":"1ffdc30f","cell_type":"markdown","source":"- Older age leads to more internet use => create a new feature combining age and internet use.\n- Sex is not useful","metadata":{"papermill":{"duration":0.044239,"end_time":"2025-05-23T02:56:47.102894","exception":false,"start_time":"2025-05-23T02:56:47.058655","status":"completed"},"tags":[]}},{"id":"dd72bdd7","cell_type":"markdown","source":"Relationship with sii","metadata":{"papermill":{"duration":0.047904,"end_time":"2025-05-23T02:56:47.195066","exception":false,"start_time":"2025-05-23T02:56:47.147162","status":"completed"},"tags":[]}},{"id":"998a79bd","cell_type":"code","source":"stats = train.groupby(\n    ['sii', 'PreInt_EduHx-computerinternet_hoursday']\n).size().unstack(fill_value=0)\nstats_prop = stats.div(stats.sum(axis=1), axis=0) * 100\n\nstats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)'\nstats","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:20.036972Z","iopub.execute_input":"2026-04-24T07:59:20.037189Z","iopub.status.idle":"2026-04-24T07:59:20.068468Z","shell.execute_reply.started":"2026-04-24T07:59:20.037168Z","shell.execute_reply":"2026-04-24T07:59:20.067509Z"},"papermill":{"duration":0.063623,"end_time":"2025-05-23T02:56:47.30401","exception":false,"start_time":"2025-05-23T02:56:47.240387","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":495,"output_type":"execute_result","data":{"text/plain":"PreInt_EduHx-computerinternet_hoursday          0.0          1.0          2.0  \\\nsii                                                                             \n0.0                                     933 (60.8%)  160 (10.4%)  366 (23.9%)   \n1.0                                     247 (34.9%)  123 (17.4%)  251 (35.5%)   \n2.0                                      78 (21.5%)   54 (14.9%)  147 (40.6%)   \n3.0                                       5 (14.7%)     2 (5.9%)   12 (35.3%)   \n\nPreInt_EduHx-computerinternet_hoursday         3.0  \nsii                                                 \n0.0                                      75 (4.9%)  \n1.0                                     86 (12.2%)  \n2.0                                     83 (22.9%)  \n3.0                                     15 (44.1%)  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>PreInt_EduHx-computerinternet_hoursday</th>\n      <th>0.0</th>\n      <th>1.0</th>\n      <th>2.0</th>\n      <th>3.0</th>\n    </tr>\n    <tr>\n      <th>sii</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0.0</th>\n      <td>933 (60.8%)</td>\n      <td>160 (10.4%)</td>\n      <td>366 (23.9%)</td>\n      <td>75 (4.9%)</td>\n    </tr>\n    <tr>\n      <th>1.0</th>\n      <td>247 (34.9%)</td>\n      <td>123 (17.4%)</td>\n      <td>251 (35.5%)</td>\n      <td>86 (12.2%)</td>\n    </tr>\n    <tr>\n      <th>2.0</th>\n      <td>78 (21.5%)</td>\n      <td>54 (14.9%)</td>\n      <td>147 (40.6%)</td>\n      <td>83 (22.9%)</td>\n    </tr>\n    <tr>\n      <th>3.0</th>\n      <td>5 (14.7%)</td>\n      <td>2 (5.9%)</td>\n      <td>12 (35.3%)</td>\n      <td>15 (44.1%)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":495},{"id":"91e58f3d","cell_type":"code","source":"plt.figure(figsize=(8, 6))\nsns.boxplot(\n    x='PreInt_EduHx-computerinternet_hoursday', y='PCIAT-PCIAT_Total',\n    data=train,\n    hue='Age Group', palette=\"Set3\"\n)\nplt.title('PCIAT_Total vs Hours of Internet Use by Age Group')\nplt.ylabel('PCIAT_Total')\nplt.xlabel('Hours per Day Group')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:20.069814Z","iopub.execute_input":"2026-04-24T07:59:20.070786Z","iopub.status.idle":"2026-04-24T07:59:20.446592Z","shell.execute_reply.started":"2026-04-24T07:59:20.070746Z","shell.execute_reply":"2026-04-24T07:59:20.445691Z"},"papermill":{"duration":0.429365,"end_time":"2025-05-23T02:56:47.779369","exception":false,"start_time":"2025-05-23T02:56:47.350004","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 1 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\n"},"metadata":{}}],"execution_count":496},{"id":"05dfeaa4","cell_type":"markdown","source":"Nhiều người sử dụng Internet dưới 1 giờ/ngày nhưng vẫn có SII = 2 hoặc 3 => Kiểm tra các chỉ số khác để xem có yếu tố nào góp phần vào PIU mà không liên quan đến thời gian sử dụng Internet hay không.\n\nNhiều người sử dụng Internet trên 3 giờ/ngày nhưng vẫn có SII = 0 hoặc 1 => Kiểm tra hai nhóm này.","metadata":{"papermill":{"duration":0.045406,"end_time":"2025-05-23T02:56:47.870342","exception":false,"start_time":"2025-05-23T02:56:47.824936","status":"completed"},"tags":[]}},{"id":"b196de65","cell_type":"markdown","source":"High internet use low sii group:","metadata":{"papermill":{"duration":0.044055,"end_time":"2025-05-23T02:56:47.958976","exception":false,"start_time":"2025-05-23T02:56:47.914921","status":"completed"},"tags":[]}},{"id":"1c09559d","cell_type":"code","source":"high_sii_high_internet = train[(train['sii'] >= 2) & (train['PreInt_EduHx-computerinternet_hoursday'] >= 2)]\nlow_sii_high_internet = train[(train['sii'] < 2) & (train['PreInt_EduHx-computerinternet_hoursday'] >= 2)]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:20.447595Z","iopub.execute_input":"2026-04-24T07:59:20.447941Z","iopub.status.idle":"2026-04-24T07:59:20.456621Z","shell.execute_reply.started":"2026-04-24T07:59:20.447915Z","shell.execute_reply":"2026-04-24T07:59:20.455874Z"},"papermill":{"duration":0.055458,"end_time":"2025-05-23T02:56:48.059266","exception":false,"start_time":"2025-05-23T02:56:48.003808","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":497},{"id":"322203c2","cell_type":"code","source":"high_sii_means = [\n    high_sii_high_internet['Basic_Demos-Age'].mean(),\n    high_sii_high_internet['Basic_Demos-Sex'].mean(),\n    high_sii_high_internet['Physical-Weight'].mean(),\n    high_sii_high_internet['Physical-Height'].mean(),\n    high_sii_high_internet['SDS-SDS_Total_Raw'].mean(),\n    high_sii_high_internet['CGAS-CGAS_Score'].mean(),\n    len(high_sii_high_internet),\n]\nlow_sii_means = [\n    low_sii_high_internet['Basic_Demos-Age'].mean(),\n    low_sii_high_internet['Basic_Demos-Sex'].mean(),\n    low_sii_high_internet['Physical-Weight'].mean(),\n    low_sii_high_internet['Physical-Height'].mean(),\n    low_sii_high_internet['SDS-SDS_Total_Raw'].mean(),\n    low_sii_high_internet['CGAS-CGAS_Score'].mean(),\n    len(low_sii_high_internet),\n]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:20.457802Z","iopub.execute_input":"2026-04-24T07:59:20.458161Z","iopub.status.idle":"2026-04-24T07:59:20.471373Z","shell.execute_reply.started":"2026-04-24T07:59:20.458126Z","shell.execute_reply":"2026-04-24T07:59:20.470568Z"},"papermill":{"duration":0.052789,"end_time":"2025-05-23T02:56:48.155958","exception":false,"start_time":"2025-05-23T02:56:48.103169","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":498},{"id":"e5606c5d","cell_type":"code","source":"labels = ['Age', 'Sex (0=Male)', 'Weight', 'Height', 'SDS', 'CGAS', 'Total Individuals']\n\nfig, axes = plt.subplots(1, 7, figsize=(20, 5), sharey=False)\n\nfor i, ax in enumerate(axes):\n    ax.bar(['High SII', 'Low SII'], [high_sii_means[i], low_sii_means[i]], color=['blue', 'orange'])\n    ax.set_title(labels[i])\n    ax.set_ylabel('Value' if i != 6 else 'Count')\n\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:20.472543Z","iopub.execute_input":"2026-04-24T07:59:20.472854Z","iopub.status.idle":"2026-04-24T07:59:21.236022Z","shell.execute_reply.started":"2026-04-24T07:59:20.47283Z","shell.execute_reply":"2026-04-24T07:59:21.235187Z"},"papermill":{"duration":0.872115,"end_time":"2025-05-23T02:56:49.072594","exception":false,"start_time":"2025-05-23T02:56:48.200479","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x500 with 7 Axes>","image/png":"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There is a large difference in quantity between the groups.\n- There is a trend where males have higher SII => Create a new feature combining age and sex","metadata":{"papermill":{"duration":0.04471,"end_time":"2025-05-23T02:56:49.166323","exception":false,"start_time":"2025-05-23T02:56:49.121613","status":"completed"},"tags":[]}},{"id":"79d6ba2d","cell_type":"markdown","source":"Low internet high piu:","metadata":{"papermill":{"duration":0.044361,"end_time":"2025-05-23T02:56:49.254999","exception":false,"start_time":"2025-05-23T02:56:49.210638","status":"completed"},"tags":[]}},{"id":"69e87991","cell_type":"code","source":"high_sii_low_internet = train[(train['sii'] >= 2) & (train['PreInt_EduHx-computerinternet_hoursday'] < 2)]\nlow_sii_low_internet = train[(train['sii'] < 2) & (train['PreInt_EduHx-computerinternet_hoursday'] < 2)]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:21.237127Z","iopub.execute_input":"2026-04-24T07:59:21.237484Z","iopub.status.idle":"2026-04-24T07:59:21.246704Z","shell.execute_reply.started":"2026-04-24T07:59:21.237444Z","shell.execute_reply":"2026-04-24T07:59:21.245921Z"},"papermill":{"duration":0.055673,"end_time":"2025-05-23T02:56:49.356463","exception":false,"start_time":"2025-05-23T02:56:49.30079","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":500},{"id":"7a66dfc7","cell_type":"code","source":"high_sii_means = [\n    high_sii_low_internet['Basic_Demos-Age'].mean(),\n    high_sii_low_internet['Basic_Demos-Sex'].mean(),\n    high_sii_low_internet['Physical-Weight'].mean(),\n    high_sii_low_internet['Physical-Height'].mean(),\n    high_sii_low_internet['SDS-SDS_Total_Raw'].mean(),\n    high_sii_low_internet['CGAS-CGAS_Score'].mean(),\n    len(high_sii_low_internet)\n]\nlow_sii_means = [\n    low_sii_low_internet['Basic_Demos-Age'].mean(),\n    low_sii_low_internet['Basic_Demos-Sex'].mean(),\n    low_sii_low_internet['Physical-Weight'].mean(),\n    low_sii_low_internet['Physical-Height'].mean(),\n    low_sii_low_internet['SDS-SDS_Total_Raw'].mean(),\n    low_sii_low_internet['CGAS-CGAS_Score'].mean(),\n    len(low_sii_low_internet)\n]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:21.248066Z","iopub.execute_input":"2026-04-24T07:59:21.248582Z","iopub.status.idle":"2026-04-24T07:59:21.26116Z","shell.execute_reply.started":"2026-04-24T07:59:21.248548Z","shell.execute_reply":"2026-04-24T07:59:21.260242Z"},"papermill":{"duration":0.053838,"end_time":"2025-05-23T02:56:49.455188","exception":false,"start_time":"2025-05-23T02:56:49.40135","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":501},{"id":"0980f72b","cell_type":"code","source":"labels = ['Age', 'Sex (1=Male)', 'Weight', 'Height', 'SDS_Total_Raw', 'CGAS_Score', 'Total Individuals']\n\ngroups = ['High SII', 'Low SII']\ndata = [high_sii_means, low_sii_means]\n\nfig, axes = plt.subplots(1, 7, figsize=(24, 6), sharey=False)\n\nfor i, ax in enumerate(axes):\n    ax.bar(groups, [data[0][i], data[1][i]], color=['blue', 'orange'])\n    ax.set_title(labels[i])\n    ax.set_ylabel('Value' if i != 6 else 'Count')\n\nplt.title('Low Internet')\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:21.262261Z","iopub.execute_input":"2026-04-24T07:59:21.262644Z","iopub.status.idle":"2026-04-24T07:59:22.02762Z","shell.execute_reply.started":"2026-04-24T07:59:21.262608Z","shell.execute_reply":"2026-04-24T07:59:22.026722Z"},"papermill":{"duration":1.039706,"end_time":"2025-05-23T02:56:50.539579","exception":false,"start_time":"2025-05-23T02:56:49.499873","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2400x600 with 7 Axes>","image/png":"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cả hai trường hợp SII cao với mức sử dụng Internet thấp và SII cao với mức sử dụng Internet cao, đa số đều là nam. Một đặc trưng kết hợp giữa giới tính và mức sử dụng Internet; một đặc trưng kết hợp giữa tuổi và mức sử dụng Internet hữu ích","metadata":{"papermill":{"duration":0.046172,"end_time":"2025-05-23T02:56:50.744625","exception":false,"start_time":"2025-05-23T02:56:50.698453","status":"completed"},"tags":[]}},{"id":"a34d783b","cell_type":"markdown","source":"### PreInt_EduHx-Season","metadata":{"papermill":{"duration":0.04568,"end_time":"2025-05-23T02:56:50.835543","exception":false,"start_time":"2025-05-23T02:56:50.789863","status":"completed"},"tags":[]}},{"id":"0b2cb615","cell_type":"code","source":"season_counts = train['PreInt_EduHx-Season'].value_counts(normalize=True)\n\ntrain['PreInt_EduHx-Season'] = train['PreInt_EduHx-Season'].apply(lambda x: np.random.choice(season_counts.index, p=season_counts.values) if pd.isna(x) else x)\ntest['PreInt_EduHx-Season'] = test['PreInt_EduHx-Season'].apply(lambda x: np.random.choice(season_counts.index, p=season_counts.values) if pd.isna(x) else x)\n\ncalculate_stats(train, 'PreInt_EduHx-Season')","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:22.028846Z","iopub.execute_input":"2026-04-24T07:59:22.029219Z","iopub.status.idle":"2026-04-24T07:59:22.047206Z","shell.execute_reply.started":"2026-04-24T07:59:22.029177Z","shell.execute_reply":"2026-04-24T07:59:22.046285Z"},"papermill":{"duration":0.064837,"end_time":"2025-05-23T02:56:51.036656","exception":false,"start_time":"2025-05-23T02:56:50.971819","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":503,"output_type":"execute_result","data":{"text/plain":"                        count (%)\nPreInt_EduHx-Season              \nFall                 690 (25.38%)\nSummer               653 (24.02%)\nWinter               652 (23.98%)\nSpring               724 (26.63%)","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count (%)</th>\n    </tr>\n    <tr>\n      <th>PreInt_EduHx-Season</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Fall</th>\n      <td>690 (25.38%)</td>\n    </tr>\n    <tr>\n      <th>Summer</th>\n      <td>653 (24.02%)</td>\n    </tr>\n    <tr>\n      <th>Winter</th>\n      <td>652 (23.98%)</td>\n    </tr>\n    <tr>\n      <th>Spring</th>\n      <td>724 (26.63%)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":503},{"id":"2675c771","cell_type":"markdown","source":"###  PreInt_EduHx-computerinternet_hoursday","metadata":{"papermill":{"duration":0.046713,"end_time":"2025-05-23T02:56:51.130781","exception":false,"start_time":"2025-05-23T02:56:51.084068","status":"completed"},"tags":[]}},{"id":"2194032c","cell_type":"code","source":"features = ['Basic_Demos-Age', 'Basic_Demos-Sex', 'SDS-SDS_Total_Raw', 'PreInt_EduHx-Season']\ntarget = 'PreInt_EduHx-computerinternet_hoursday'\n\ntrain_data = train[train[target].notna()]\n\nX_train = train_data[features]\ny_train = train_data[target]\n\npreprocessor = ColumnTransformer(\n    transformers=[\n        ('season', OneHotEncoder(), ['PreInt_EduHx-Season']),  # Mùa\n        ('num', 'passthrough', ['Basic_Demos-Age', 'Basic_Demos-Sex', 'SDS-SDS_Total_Raw'])  # Các cột số\n    ])\n\nmodel = Pipeline(steps=[\n    ('preprocessor', preprocessor),\n    ('classifier', LogisticRegression(max_iter=1000, multi_class='ovr'))\n])\n\nmodel.fit(X_train, y_train)\n\nX_missing = train[train[target].isna()][features]\npredicted_values = model.predict(X_missing)\n\ntrain.loc[train[target].isna(), target] = predicted_values\n\nX_missing_test = test[test[target].isna()][features]\npredicted_values_test = model.predict(X_missing_test)\n\ntest.loc[test[target].isna(), target] = predicted_values_test\n\ncalculate_stats(train, ['PreInt_EduHx-computerinternet_hoursday'])","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:22.04964Z","iopub.execute_input":"2026-04-24T07:59:22.050024Z","iopub.status.idle":"2026-04-24T07:59:22.23887Z","shell.execute_reply.started":"2026-04-24T07:59:22.049982Z","shell.execute_reply":"2026-04-24T07:59:22.238084Z"},"papermill":{"duration":0.321082,"end_time":"2025-05-23T02:56:51.590857","exception":false,"start_time":"2025-05-23T02:56:51.269775","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":504,"output_type":"execute_result","data":{"text/plain":"                                         count      mean       std  min  25%  \\\nPreInt_EduHx-computerinternet_hoursday                                         \nPreInt_EduHx-computerinternet_hoursday  2719.0  1.007723  1.078732  0.0  0.0   \n\n                                        50%  75%  max  missing  \nPreInt_EduHx-computerinternet_hoursday                          \nPreInt_EduHx-computerinternet_hoursday  1.0  2.0  3.0        0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n    <tr>\n      <th>PreInt_EduHx-computerinternet_hoursday</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>PreInt_EduHx-computerinternet_hoursday</th>\n      <td>2719.0</td>\n      <td>1.007723</td>\n      <td>1.078732</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":504},{"id":"c6c29a2b","cell_type":"markdown","source":"## PAQ","metadata":{"papermill":{"duration":0.04584,"end_time":"2025-05-23T02:56:51.746621","exception":false,"start_time":"2025-05-23T02:56:51.700781","status":"completed"},"tags":[]}},{"id":"ced6a947","cell_type":"code","source":"PAQ_Adolescents_columns = ['PAQ_A-Season', 'PAQ_A-PAQ_A_Total']\ncalculate_stats(train, 'PAQ_A-Season')","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:22.239794Z","iopub.execute_input":"2026-04-24T07:59:22.24011Z","iopub.status.idle":"2026-04-24T07:59:22.252149Z","shell.execute_reply.started":"2026-04-24T07:59:22.240079Z","shell.execute_reply":"2026-04-24T07:59:22.251217Z"},"papermill":{"duration":0.058335,"end_time":"2025-05-23T02:56:51.850473","exception":false,"start_time":"2025-05-23T02:56:51.792138","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":505,"output_type":"execute_result","data":{"text/plain":"                  count (%)\nPAQ_A-Season               \nNaN           2362 (86.87%)\nSummer           95 (3.49%)\nSpring           90 (3.31%)\nFall             77 (2.83%)\nWinter           95 (3.49%)","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count (%)</th>\n    </tr>\n    <tr>\n      <th>PAQ_A-Season</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>NaN</th>\n      <td>2362 (86.87%)</td>\n    </tr>\n    <tr>\n      <th>Summer</th>\n      <td>95 (3.49%)</td>\n    </tr>\n    <tr>\n      <th>Spring</th>\n      <td>90 (3.31%)</td>\n    </tr>\n    <tr>\n      <th>Fall</th>\n      <td>77 (2.83%)</td>\n    </tr>\n    <tr>\n      <th>Winter</th>\n      <td>95 (3.49%)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":505},{"id":"98c0ea7d","cell_type":"code","source":"calculate_stats(train, 'PAQ_A-PAQ_A_Total')","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:22.253052Z","iopub.execute_input":"2026-04-24T07:59:22.253715Z","iopub.status.idle":"2026-04-24T07:59:22.289021Z","shell.execute_reply.started":"2026-04-24T07:59:22.25368Z","shell.execute_reply":"2026-04-24T07:59:22.288071Z"},"papermill":{"duration":0.061275,"end_time":"2025-05-23T02:56:51.95796","exception":false,"start_time":"2025-05-23T02:56:51.896685","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":506,"output_type":"execute_result","data":{"text/plain":"                   count      mean       std   min   25%   50%   75%   max  \\\nPAQ_A-PAQ_A_Total                                                            \nPAQ_A-PAQ_A_Total  357.0  2.184059  0.817494  0.66  1.52  2.08  2.78  4.54   \n\n                   missing  \nPAQ_A-PAQ_A_Total           \nPAQ_A-PAQ_A_Total     2362  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n    <tr>\n      <th>PAQ_A-PAQ_A_Total</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>PAQ_A-PAQ_A_Total</th>\n      <td>357.0</td>\n      <td>2.184059</td>\n      <td>0.817494</td>\n      <td>0.66</td>\n      <td>1.52</td>\n      <td>2.08</td>\n      <td>2.78</td>\n      <td>4.54</td>\n      <td>2362</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":506},{"id":"5dd7abfd","cell_type":"code","source":"PAQ_Children_columns = ['PAQ_C-Season', 'PAQ_C-PAQ_C_Total']\ncalculate_stats(train, 'PAQ_C-Season')","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:22.290507Z","iopub.execute_input":"2026-04-24T07:59:22.2911Z","iopub.status.idle":"2026-04-24T07:59:22.304549Z","shell.execute_reply.started":"2026-04-24T07:59:22.291064Z","shell.execute_reply":"2026-04-24T07:59:22.303795Z"},"papermill":{"duration":0.059057,"end_time":"2025-05-23T02:56:52.099675","exception":false,"start_time":"2025-05-23T02:56:52.040618","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":507,"output_type":"execute_result","data":{"text/plain":"                  count (%)\nPAQ_C-Season               \nNaN           1284 (47.22%)\nFall           307 (11.29%)\nSummer         341 (12.54%)\nWinter         383 (14.09%)\nSpring         404 (14.86%)","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count (%)</th>\n    </tr>\n    <tr>\n      <th>PAQ_C-Season</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>NaN</th>\n      <td>1284 (47.22%)</td>\n    </tr>\n    <tr>\n      <th>Fall</th>\n      <td>307 (11.29%)</td>\n    </tr>\n    <tr>\n      <th>Summer</th>\n      <td>341 (12.54%)</td>\n    </tr>\n    <tr>\n      <th>Winter</th>\n      <td>383 (14.09%)</td>\n    </tr>\n    <tr>\n      <th>Spring</th>\n      <td>404 (14.86%)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":507},{"id":"41bd8b8e","cell_type":"code","source":"calculate_stats(train, 'PAQ_C-PAQ_C_Total')","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:22.305414Z","iopub.execute_input":"2026-04-24T07:59:22.305752Z","iopub.status.idle":"2026-04-24T07:59:22.332568Z","shell.execute_reply.started":"2026-04-24T07:59:22.305719Z","shell.execute_reply":"2026-04-24T07:59:22.33094Z"},"papermill":{"duration":0.06817,"end_time":"2025-05-23T02:56:52.214591","exception":false,"start_time":"2025-05-23T02:56:52.146421","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":508,"output_type":"execute_result","data":{"text/plain":"                    count      mean       std   min   25%   50%   75%   max  \\\nPAQ_C-PAQ_C_Total                                                             \nPAQ_C-PAQ_C_Total  1435.0  2.591907  0.787015  0.58  2.02  2.55  3.16  4.79   \n\n                   missing  \nPAQ_C-PAQ_C_Total           \nPAQ_C-PAQ_C_Total     1284  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>missing</th>\n    </tr>\n    <tr>\n      <th>PAQ_C-PAQ_C_Total</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>PAQ_C-PAQ_C_Total</th>\n      <td>1435.0</td>\n      <td>2.591907</td>\n      <td>0.787015</td>\n      <td>0.58</td>\n      <td>2.02</td>\n      <td>2.55</td>\n      <td>3.16</td>\n      <td>4.79</td>\n      <td>1284</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":508},{"id":"d9cb0be2","cell_type":"markdown","source":"##  Bio-electric Impedance Analysis","metadata":{"papermill":{"duration":0.045778,"end_time":"2025-05-23T02:56:52.406323","exception":false,"start_time":"2025-05-23T02:56:52.360545","status":"completed"},"tags":[]}},{"id":"cea0b2c7","cell_type":"code","source":"bia_data_dict = data_dict[data_dict['Instrument'] == 'Bio-electric Impedance Analysis']\ncategorical_columns = bia_data_dict[bia_data_dict['Type'] == 'categorical int']['Field'].tolist()\ncontinuous_columns = bia_data_dict[bia_data_dict['Type'] == 'float']['Field'].tolist()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:22.333493Z","iopub.execute_input":"2026-04-24T07:59:22.334355Z","iopub.status.idle":"2026-04-24T07:59:22.343955Z","shell.execute_reply.started":"2026-04-24T07:59:22.334314Z","shell.execute_reply":"2026-04-24T07:59:22.343139Z"},"papermill":{"duration":0.054677,"end_time":"2025-05-23T02:56:52.5067","exception":false,"start_time":"2025-05-23T02:56:52.452023","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":509},{"id":"3c2303a5","cell_type":"code","source":"plt.figure(figsize=(24, 20))\n\nfor idx, col in enumerate(continuous_columns):\n    plt.subplot(4, 4, idx + 1)\n    sns.histplot(train[col].dropna(), bins=20, kde=True)\n    plt.title(data_dict[data_dict['Field'] == col]['Description'].values[0])\n    plt.xlabel('Value')\n\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:22.345066Z","iopub.execute_input":"2026-04-24T07:59:22.345437Z","iopub.status.idle":"2026-04-24T07:59:25.511708Z","shell.execute_reply.started":"2026-04-24T07:59:22.345403Z","shell.execute_reply":"2026-04-24T07:59:25.510622Z"},"papermill":{"duration":3.549285,"end_time":"2025-05-23T02:56:56.102495","exception":false,"start_time":"2025-05-23T02:56:52.55321","status":"completed"},"tags":[],"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2400x2000 with 14 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAACVUAAAfGCAYAAAAd7cy8AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XlcVHX7//H3zADD5oCigKYSaqW4ZGkpba5JRptZ3ample20aHfL7ffX4lJZVlreubR4q3dqpu2lpebWIpqZlreWuaMpkBuoyDZzfn/AHBlBBQRmRl/Px2MeyTmfc87nDD3y6nOuc10WwzAMAQAAAAAAAAAAAAAAAAAkSVZvTwAAAAAAAAAAAAAAAAAAfAlJVQAAAAAAAAAAAAAAAABQAklVAAAAAAAAAAAAAAAAAFACSVUAAAAAAAAAAAAAAAAAUAJJVQAAAAAAAAAAAAAAAABQAklVAAAAAAAAAAAAAAAAAFACSVUAAAAAAAAAAAAAAAAAUAJJVQAAAAAAAAAAAAAAAABQAklVAAAAAAAAAAAAAAAAAFACSVUA/NawYcNksVi8PY1T2r59uywWi6ZOnertqQAAgDOYxWLRsGHDvD2NM1bnzp3VuXNnb08DAABUgzMpjrJYLHr44Yer7HxLly6VxWLR0qVLzW133nmnzj333Cq7BgAAqJypU6fKYrFo+/bt5jbWL1ARxHrAqZFUBZwB3EFTyU90dLS6dOmir7/+2tvTO6Vzzz1XFotF3bt3L3P/u+++a97Xzz//XMOzq3kZGRl64okn1Lx5c4WGhiosLEzt2rXTCy+8oIMHD1bbdXfv3q1hw4Zp7dq11XaNkl566SV99tlnNXItAAC8wZ9jNPeCisVi0fTp08scc/nll8tisahVq1Y1PLtTq+qHiQAAoPL8OSYqyf3SnMVi0QsvvFDmmH79+slisSg8PLxS15g3b94Zk9xVXnfeeafHvxt2u13nn3++nnvuOeXm5lbqnBs2bNCwYcM8HjADAOAtx8dCwcHBatCggZKSkjRu3DgdOnTI21M8qePjuJKfBx54wNvTq1bHxynH/x7PFjk5ORo2bJhH4hVwNgnw9gQAVJ0RI0YoPj5ehmEoIyNDU6dO1bXXXqsvv/xS1113nbend1LBwcFasmSJ0tPTFRsb67FvxowZCg4OLrWQ8swzz+hf//pXTU6z2q1atUrXXnutDh8+rDvuuEPt2rWTJP388896+eWX9d1332nBggXVcu3du3dr+PDhOvfcc9W2bdtquUZJL730km655RbddNNN1X4tAAC8yd9jtJkzZ+qOO+7w2L59+3YtX778rFpAAgAAp8efY6KSgoOD9cEHH+iZZ57x2H7kyBF9/vnnpxUfzZs3T+PHj/f5xKp3331XLperys5nt9v13nvvSZKysrL0+eefa+TIkdqyZYtmzJhR4fNt2LBBw4cPV+fOnamyAADwGe5YqKCgQOnp6Vq6dKkGDx6sMWPG6IsvvlCbNm0qfM7+/fvr9ttvl91ur4YZH3P11VdrwIABpbaff/751XpdX1AyTinJZrN5YTY14/hYLycnR8OHD5ckqqDhrERSFXAG6dmzp9q3b2/+PGjQIMXExOiDDz7w+cWpyy+/XKtWrdKHH36oxx57zNy+a9cuff/99+rVq5c+/vhjj2MCAgIUEFDz/xnLzc1VUFCQrNaqLfZ38OBB9erVSzabTWvWrFHz5s099r/44ot69913q/SaAACg+vlzjHbttdfqiy++0N69e1W3bl1z+8yZMxUTE6PzzjtPBw4c8OIMAQCAv/DnmKika6+9Vp988ol+/fVXXXjhheb2zz//XPn5+brmmmu0ePFiL86w+gUGBlbp+QICAjyS+B966CFddtll+uCDDzRmzBjFxMRU6fUAAPCG42OhoUOHavHixbruuut0ww036Pfff1dISEiFzmmz2Wokuef8888v9cKdN7hcLuXn59foS37Hxylng6qO9U6ksLBQLpdLQUFBNXI9oLJo/wecwSIjIxUSElIq8ejIkSP65z//qUaNGslut+uCCy7Qa6+9JsMwPMa526Z89tlnatWqlex2u1q2bKlvvvmm1LX++usv3X333YqJiTHH/ec//yn3XIODg3XzzTdr5syZHts/+OAD1a5dW0lJSaWOGTZsmCwWS7XO2d36ZtasWXrmmWd0zjnnKDQ0VNnZ2dq/f7+eeOIJtW7dWuHh4XI4HOrZs6d+/fXXct93SW+//bb++usvjRkzplRClSTFxMSUegtywoQJatmypex2uxo0aKCUlJRSLQI7d+6sVq1aacOGDerSpYtCQ0N1zjnnaPTo0R73eckll0iS7rrrLrN86dSpU80xK1eu1DXXXKOIiAiFhoaqU6dO+vHHHz2u5f6dbN68WXfeeaciIyMVERGhu+66Szk5OeY4i8WiI0eOaNq0aea17rzzzkp9bwAA+JvTjdHy8vI0ZMgQ1atXT7Vq1dINN9ygXbt2eYxZsmSJLBaLPv3001LXnzlzpiwWi1JTU0851xtvvFF2u11z5swpdY7bbrutzIW7KVOmqGvXroqOjpbdbldCQoImTpxYatzPP/+spKQk1a1bVyEhIYqPj9fdd9/tMWbWrFlq166datWqJYfDodatW+vNN9885byP547pZs+erRdffFENGzZUcHCwunXrps2bN5ca/84776hp06YKCQnRpZdequ+//77M8+bl5en5559Xs2bNZLfb1ahRIz311FPKy8szxwwcOFDBwcH6/fffPY5NSkpS7dq1tXv37grfDwAAZ4ITxUSvvfaaLrvsMkVFRSkkJETt2rXTRx99VOr4hQsX6oorrlBkZKTCw8N1wQUX6P/+7//M/fn5+XruuefUrl07RUREKCwsTFdeeaWWLFlyWvNOTExUfHx8qTWsGTNm6JprrlGdOnXKPO7rr7/WlVdeqbCwMNWqVUvJyclav369uf/OO+/U+PHjJXm22ano91JyPhdccIGCg4PVrl07fffdd6XGrFmzRj179pTD4VB4eLi6deumFStWnPI7uPPOO0tVgHK5XHrzzTfVunVrBQcHq169errmmmv0888/n/J8x7NYLLriiitkGIa2bt1qbt+xY4ceeughXXDBBQoJCVFUVJRuvfVWjzZ/U6dO1a233ipJ6tKli/k9lmxXc6rfBQAANaVr16569tlntWPHDk2fPt3c/ttvv+nOO+9UkyZNFBwcrNjYWN19993at2+fx/Hu1oInanl7+PBhhYWFeRQzcNu1a5dsNptGjRpVJfdSnmdRbuVZT5GOPfObMWOG+SzM/bzvt99+U6dOnRQSEqKGDRvqhRde0JQpUzy+j4EDB6pu3boqKCgoNYcePXroggsuOO37NgxDXbp0Ub169ZSZmWluz8/PV+vWrdW0aVMdOXJE0rFnaH/88Yduu+02ORwORUVF6bHHHiuz7fH06dPVrl07hYSEqE6dOrr99tu1c+dOjzEV+d537dqlm266SWFhYYqOjtaQIUNKfeeSZ6y3fft21atXT5I0fPhwM7ZyV1bt3LlzmdWrjo8X3a20X3vtNb3xxhtq2rSp7Ha7NmzYIEn6448/dMstt6hOnToKDg5W+/bt9cUXX5z4iwdqEJWqgDNIVlaW9u7dK8MwlJmZqX//+99mGzk3wzB0ww03aMmSJRo0aJDatm2r+fPn68knn9Rff/2lsWPHepzzhx9+0CeffKKHHnpItWrV0rhx49S7d2+lpaUpKipKkpSRkaGOHTuawU29evX09ddfa9CgQcrOztbgwYPLNf++ffuqR48e2rJli5o2bSqp6IHdLbfcUqGs6OqY88iRIxUUFKQnnnhCeXl5CgoK0oYNG/TZZ5/p1ltvVXx8vDIyMvT222+rU6dO2rBhgxo0aFDuOUvSF198oZCQEN1yyy3lGj9s2DANHz5c3bt314MPPqiNGzdq4sSJWrVqlX788UeP7+zAgQO65pprdPPNN+u2227TRx99pKefflqtW7dWz5491aJFC40YMULPPfec7rvvPl155ZWSpMsuu0yStHjxYvXs2VPt2rXT888/L6vVaj4w/f7773XppZd6zO22225TfHy8Ro0apV9++UXvvfeeoqOj9corr0iS3n//fd1zzz269NJLdd9990mS+TsHAOBMU9Ux2j333KPp06erb9++uuyyy7R48WIlJyd7XLNz585q1KiRZsyYoV69ennsmzFjhpo2barExMRTzj00NFQ33nijPvjgAz344IOSpF9//VXr16/Xe++9p99++63UMRMnTlTLli11ww03KCAgQF9++aUeeughuVwupaSkSJIyMzPVo0cP1atXT//6178UGRmp7du365NPPjHPs3DhQvXp00fdunUzY4jff/9dP/74Y5mLgeXx8ssvy2q16oknnlBWVpZGjx6tfv36aeXKleaYyZMn6/7779dll12mwYMHa+vWrbrhhhtUp04dNWrUyBzncrl0ww036IcfftB9992nFi1aaN26dRo7dqz+/PNPffbZZ5KkN998U4sXL9bAgQOVmpoqm82mt99+WwsWLND7779f4ZgRAAB/VZ6YSCr6u/OGG25Qv379lJ+fr1mzZunWW2/VV199ZcY869ev13XXXac2bdpoxIgRstvt2rx5s8fLX9nZ2XrvvffUp08f3XvvvTp06JAmT56spKQk/fTTT2rbtm2l76VPnz6aPn26Xn75ZVksFu3du9f8u72sF/vef/99DRw4UElJSXrllVeUk5OjiRMn6oorrtCaNWt07rnn6v7779fu3bu1cOFCvf/++6XOUZ7vxW3ZsmX68MMP9eijj8put2vChAm65ppr9NNPP6lVq1bmd3jllVfK4XDoqaeeUmBgoN5++2117txZy5YtU4cOHSr0nQwaNEhTp05Vz549dc8996iwsFDff/+9VqxY4VGVo7zcD0Nr165tblu1apWWL1+u22+/XQ0bNtT27ds1ceJEde7cWRs2bFBoaKiuuuoqPfrooxo3bpz+7//+Ty1atJAk85/l+V0AAFCT+vfvr//7v//TggULdO+990oqWhPZunWr7rrrLsXGxmr9+vV65513tH79eq1YsaJUwYETCQ8PV69evfThhx9qzJgxHi/HffDBBzIMQ/369TvleXJzc7V3795S2x0Oh0eVoVM9i5LKv57itnjxYs2ePVsPP/yw6tatq3PPPVd//fWXmTw9dOhQhYWF6b333ivVBrF///7673//q/nz53tURk1PT9fixYv1/PPPl+t7LOveg4KC5HA4ZLFY9J///Edt2rTRAw88YK5tPf/881q/fr2WLl2qsLAwj2Nvu+02nXvuuRo1apRWrFihcePG6cCBA/rvf/9rjnnxxRf17LPP6rbbbtM999yjv//+W//+97911VVXac2aNYqMjKzQ93706FF169ZNaWlpevTRR9WgQQO9//77p6ywWq9ePU2cOFEPPvigevXqpZtvvlmSKtWuUip6GTM3N1f33Xef7Ha76tSpo/Xr1+vyyy/XOeeco3/9618KCwvT7NmzddNNN+njjz8utbYJ1DgDgN+bMmWKIanUx263G1OnTvUY+9lnnxmSjBdeeMFj+y233GJYLBZj8+bN5jZJRlBQkMe2X3/91ZBk/Pvf/za3DRo0yKhfv76xd+9ej3PefvvtRkREhJGTk3PS+cfFxRnJyclGYWGhERsba4wcOdIwDMPYsGGDIclYtmyZeY+rVq0yj3v++eeN4/8zVtVzXrJkiSHJaNKkSan7yM3NNZxOp8e2bdu2GXa73RgxYoTHNknGlClTTvo91K5d27jwwgtPOsYtMzPTCAoKMnr06OExh7feesuQZPznP/8xt3Xq1MmQZPz3v/81t+Xl5RmxsbFG7969zW2rVq0qc54ul8s477zzjKSkJMPlcpnbc3JyjPj4eOPqq682t7l/J3fffbfHOXr16mVERUV5bAsLCzMGDhxYrvsFAMAfVUeMtnbtWkOS8dBDD3mM69u3ryHJeP75581tQ4cONex2u3Hw4EFzW2ZmphEQEOAxrizuGGjOnDnGV199ZVgsFiMtLc0wDMN48sknjSZNmhiGURRntGzZ0uPYsmK/pKQk8xjDMIxPP/20VGx3vMcee8xwOBxGYWHhSedaFklGSkpKqftp0aKFkZeXZ25/8803DUnGunXrDMMwjPz8fCM6Otpo27atx7h33nnHkGR06tTJ3Pb+++8bVqvV+P777z2uPWnSJEOS8eOPP5rb5s+fb/5+t27daoSHhxs33XRThe8LAAB/VJGYyDBKxxL5+flGq1atjK5du5rbxo4da0gy/v777xNet7Cw0OPvc8MwjAMHDhgxMTGl1i2Oj6PK4l7fefXVV43//e9/hiQzDhg/frwRHh5uHDlyxBg4cKARFhZmHnfo0CEjMjLSuPfeez3Ol56ebkRERHhsT0lJKbXW5Vae78V9L5KMn3/+2dy2Y8cOIzg42OjVq5e57aabbjKCgoKMLVu2mNt2795t1KpVy7jqqqvMbe44asmSJea2gQMHGnFxcebPixcvNiQZjz76aKl5l1xLKov7+/r777+Nv//+29i8ebPx2muvGRaLxWjVqlWptajjpaamllr3mjNnTqk5G0bFfhcAAFSVsp5vHS8iIsK46KKLzJ/L+jvvgw8+MCQZ3333Xalzb9u2zdzWqVMnj/UL95rE119/7XG+Nm3aeIw7kbLiOPfngw8+8LhueZ5FVWQ9RZJhtVqN9evXe4x95JFHDIvFYqxZs8bctm/fPqNOnToe34fT6TQaNmxo/OMf//A4fsyYMYbFYjG2bt160nsfOHDgCe89KSnJY+zbb79tSDKmT59urFixwrDZbMbgwYM9xrifod1www0e2x966CFDkvHrr78ahmEY27dvN2w2m/Hiiy96jFu3bp0REBDgsb283/sbb7xhSDJmz55tbjty5IjRrFmzU8Z6f//99wnj5eP/fTvROdyxtMPhMDIzMz3GduvWzWjdurWRm5trbnO5XMZll11mnHfeeaXODdQ02v8BZ5Dx48dr4cKFWrhwoaZPn64uXbronnvu8Xjjf968ebLZbHr00Uc9jv3nP/8pwzD09ddfe2zv3r27RwWhNm3ayOFwmKW3DcPQxx9/rOuvv16GYWjv3r3mJykpSVlZWfrll1/KNX+bzabbbrtNH3zwgaSiKgqNGjUyqyaVV3XMeeDAgaV6WdvtdlmtRf8ZdTqd2rdvn1luvrz3XFJ2drZq1apVrrHffvut8vPzNXjwYHMOknTvvffK4XBo7ty5HuPDw8M93vwMCgrSpZde6lFC/UTWrl2rTZs2qW/fvtq3b5/5XR05ckTdunXTd999J5fL5XHMAw884PHzlVdeqX379ik7O7tc9wcAwJmkKmO0efPmSVKpcWVVBh0wYIDy8vI82sJ8+OGHKiwsLFUR4mR69OihOnXqaNasWTIMQ7NmzVKfPn1OOL5kzOSuSNGpUydt3bpVWVlZkmS+TffVV1+VWYLdPebIkSNauHBhued6KnfddZfHG5TuONMdE/3888/KzMzUAw884DHuzjvvVEREhMe55syZoxYtWqh58+Ye8WTXrl0lyaO1UI8ePXT//fdrxIgRuvnmmxUcHKy33367yu4LAAB/UJ6YSPKMJQ4cOKCsrCxdeeWVHmst7lji888/L7Um4Waz2cy/z10ul/bv36/CwkK1b9++Uus2JbVs2VJt2rQx17BmzpypG2+8UaGhoaXGLly4UAcPHlSfPn08YgabzaYOHTqUux1heb4Xt8TERLVr1878uXHjxrrxxhs1f/58OZ1OOZ1OLViwQDfddJOaNGlijqtfv7769u2rH374oUJrOB9//LEsFkuZ1R7KU0njyJEjqlevnurVq6dmzZrpiSee0OWXX67PP//c4/iS30FBQYH27dunZs2aKTIysly/06r6XQAAUNXCw8N16NAh8+eSf+e5q0R17NhRkiocx3Tv3l0NGjTQjBkzzG3/+9//9Ntvv5V7fejGG28047iSny5dupS6j1M9i6rIeookderUSQkJCR7bvvnmGyUmJnpUHq1Tp06pqltWq1X9+vXTF1984fH9zpgxQ5dddpni4+NPee/BwcFl3vvLL7/sMe6+++5TUlKSHnnkEfXv319NmzbVSy+9VOY53ZXc3R555BFJx9b9PvnkE7lcLt12220e31FsbKzOO++8Ut9Reb73efPmqX79+h7dckJDQ81uMjWld+/eZjtBSdq/f78WL16s2267TYcOHTLvdd++fUpKStKmTZv0119/1egcgePR/g84g1x66aUe5bT79Omjiy66SA8//LCuu+46BQUFaceOHWrQoEGp5B13CewdO3Z4bG/cuHGp69SuXVsHDhyQJP399986ePCg3nnnHb3zzjtlzqtkD+FT6du3r8aNG6dff/1VM2fO1O23317uMqbVOeeyAiuXy6U333xTEyZM0LZt2+R0Os197jaDFeFwODyCupNx/56O7/ccFBSkJk2alPo9NmzYsNT3WLt27TJb9hxv06ZNkooSy04kKyvLoxz78b8D974DBw7I4XCc8poAAJxJqjJG27Fjh6xWa6m2ucfHBJLUvHlzXXLJJZoxY4YGDRokqWjRqGPHjmrWrFm55x8YGKhbb71VM2fO1KWXXqqdO3eqb9++Jxz/448/6vnnn1dqaqpycnI89mVlZSkiIkKdOnVS7969NXz4cI0dO1adO3fWTTfdpL59+5ql2h966CHNnj1bPXv21DnnnKMePXrotttu0zXXXFPuuR/vZDGKdOx7Pu+880p9ByUfOEpFMdLvv//usRBU0vHx5GuvvabPP/9ca9eu1cyZMxUdHV3p+wAAwB+VJyaSipKuX3jhBa1du1Z5eXnm+JLrGv/4xz/03nvv6Z577tG//vUvdevWTTfffLNuueUWj5fPpk2bptdff11//PGHRyJ3eR6gnUrfvn31+uuva8iQIVq+fLn+7//+r8xx7nUV94PC45V3naQ834vb8bGMJJ1//vnKycnR33//LUnKyckpM4Zs0aKFXC6Xdu7cqZYtW5Zrblu2bFGDBg1Up06dco0/XnBwsL788ktJ0q5duzR69GhlZmaWesHx6NGjGjVqlKZMmaK//vpLhmGY+9zJ+ydTVb8LAACq2uHDhz3WCfbv36/hw4dr1qxZpdYXyvN3XknuxKKJEycqJydHoaGhmjFjhoKDg3XrrbeW6xwNGzZU9+7dyzXuVM+iKrqeUlbctmPHDiUmJpbaXtZ614ABA/TKK6/o008/1YABA7Rx40atXr1akyZNOuX9SEWJ+uW5d0maPHmymjZtqk2bNmn58uWlYhm342O1pk2bymq1mu2PN23aJMMwyozppKJ1qpLK873v2LFDzZo1KzWurHiwOh3/+9y8ebMMw9Czzz6rZ599tsxjMjMzdc4559TE9IAykVQFnMGsVqu6dOmiN998U5s2bSr3QkhJJfsrl+RetHC/DXjHHXecMOmmIn11O3TooKZNm2rw4MHatm3bSR/YnUh1zLmswOell17Ss88+q7vvvlsjR45UnTp1ZLVaNXjw4BO+JXkyzZs319q1a5Wfn+9RGaEqnOo7ORn3vbz66qseWf8lhYeHV9n1AAA401VFjFZeAwYM0GOPPaZdu3YpLy9PK1as0FtvvVXh8/Tt21eTJk3SsGHDdOGFF5Z6Q9Bty5Yt6tatm5o3b64xY8aoUaNGCgoK0rx58zR27FgzrrBYLProo4+0YsUKffnll5o/f77uvvtuvf7661qxYoXCw8MVHR2ttWvXav78+fr666/19ddfa8qUKRowYICmTZtWqe+jKmMUl8ul1q1ba8yYMWXub9SokcfPa9asMRcG161bd9JqXwAAnA3Kiom+//573XDDDbrqqqs0YcIE1a9fX4GBgZoyZYpmzpxpHhsSEqLvvvtOS5Ys0dy5c/XNN9/oww8/VNeuXbVgwQLZbDZNnz5dd955p2666SY9+eSTio6Ols1m06hRo7Rly5bTnn+fPn00dOhQ3XvvvYqKilKPHj3KHOeOf95//33FxsaW2h8QcOol+vJ+L/7q+IeVSUlJat68ue6//3598cUX5vZHHnlEU6ZM0eDBg5WYmKiIiAhZLBbdfvvt5VqLq4rfBQAAVW3Xrl3KysrySAi67bbbtHz5cj355JNq27atwsPD5XK5dM0111Tq+dOAAQP06quv6rPPPlOfPn00c+ZMXXfddaWqcp+u8qy7VHQ95USJSeWVkJCgdu3aafr06RowYICmT5+uoKAg3Xbbbad13rIsXbrUTH5ft25dmYlfZTk+0cnlcslisejrr78u8zv1pWdyFoulzOuULERR0vG/T/e/z0888YSSkpLKPKYiL4cC1YH/SwDOcIWFhZKKstwlKS4uTt9++60OHTrkUQnhjz/+MPdXRL169VSrVi05nc5yZ2qfSp8+ffTCCy+oRYsWJ0ziOR1VNeePPvpIXbp00eTJkz22Hzx4UHXr1q3w+a6//nqlpqbq448/PuVDNvfvaePGjR5VE/Lz87Vt27ZK3deJKoK5K2E4HI4q+x2f7HoAAJwNKhujxcXFyeVyacuWLR5vkm3cuLHM69x+++16/PHH9cEHH+jo0aMKDAzUP/7xjwrP94orrlDjxo21dOlSvfLKKycc9+WXXyovL09ffPGFR1WoE7VS6dixozp27KgXX3xRM2fOVL9+/TRr1izdc889koqqcF5//fW6/vrr5XK59NBDD+ntt9/Ws88+Wy0LKu7vedOmTR4VDAoKCrRt2zZdeOGF5ramTZvq119/Vbdu3U4Z1xw5ckR33XWXEhISdNlll2n06NHq1auXLrnkkiq/BwAA/MnxMdHHH3+s4OBgzZ8/36xeKUlTpkwpdazValW3bt3UrVs3jRkzRi+99JL+3//7f1qyZIm6d++ujz76SE2aNNEnn3zi8Xd1WS3qKqNx48a6/PLLtXTpUj344IMnTMhxr6tER0efcl3lRDFFRb4X6VhFppL+/PNPhYaGmlUhQkNDy4wh//jjD1mt1lIPNE+madOmmj9/vvbv31/palUl1a9fX0OGDNHw4cO1YsUKs93RRx99pIEDB+r11183x+bm5urgwYMex59qjas8vwsAAGrK+++/L0lmQsmBAwe0aNEiDR8+XM8995w5rqy/38urVatWuuiiizRjxgw1bNhQaWlp+ve//316E6+kiqynnEhcXJw2b95cantZ26SipLLHH39ce/bs0cyZM5WcnOzRfaUq7NmzR4888oh69OihoKAgM0morOeumzZt8qjYtHnzZrlcLp177rmSir4jwzAUHx+v888/v0rmFxcXp//9738yDMPjez/RmmJJJ/s91a5d26PNoNvxHXVOxP2MMzAwkPgMPst66iEA/FVBQYEWLFigoKAgs3XMtddeK6fTWapCwdixY2WxWNSzZ88KXcNms6l37976+OOP9b///a/UfndJ8Yq455579Pzzz3sskFSlqpqzzWYrlX09Z86cSvf2feCBB1S/fn3985//1J9//llqf2Zmpl544QVJRT2wg4KCNG7cOI85TJ48WVlZWUpOTq7w9cPCwiSp1EJUu3bt1LRpU7322mvmImdJlfkdu693/LUAADgbnE6M5v7nuHHjPMa98cYbZV6rbt266tmzp6ZPn64ZM2bommuuqVTyt8Vi0bhx4/T888+rf//+JxznfjPu+FYsxz/wO3DgQKk4yp1M736jb9++fR77rVarWU20ZMubqtS+fXvVq1dPkyZNUn5+vrl96tSppeKW2267TX/99ZfefffdUuc5evSojhw5Yv789NNPKy0tTdOmTdOYMWN07rnnauDAgdV2HwAA+IOyYiKbzSaLxeLxZvv27dv12WefeRy7f//+Uuc7PpYoKy5ZuXKlUlNTq+weXnjhBT3//PN65JFHTjgmKSlJDodDL730kkcLQreS6yonWpsp7/filpqaql9++cX8eefOnfr888/Vo0cP2Ww22Ww29ejRQ59//rnZZkaSMjIyNHPmTF1xxRUVaoXXu3dvGYah4cOHl9pX2QoJjzzyiEJDQ/Xyyy+b28pai/v3v/9dqhLCib7HivwuAACoCYsXL9bIkSMVHx+vfv36SSo7hpFOvPZTXv3799eCBQv0xhtvKCoqqsLPA6tKRdZTTiQpKUmpqalau3atuW3//v2aMWNGmeP79Okji8Wixx57TFu3btUdd9xR6fmfyL333iuXy6XJkyfrnXfeUUBAgAYNGlRmLDR+/HiPn90Jbu7fyc033yybzabhw4eXOt4wjFJrZuVx7bXXavfu3froo4/MbTk5OXrnnXdOeWxoaKik0rGVVJQA9scff3jEUb/++qt+/PHHcs0rOjpanTt31ttvv609e/aU2k98Bl9ApSrgDPL111+b1QwyMzM1c+ZMbdq0Sf/617/MhZDrr79eXbp00f/7f/9P27dv14UXXqgFCxbo888/1+DBg803tiri5Zdf1pIlS9ShQwfde++9SkhI0P79+/XLL7/o22+/LXOh62Ti4uI0bNiwCs+jIqpiztddd51GjBihu+66S5dddpnWrVunGTNmeFSOqojatWvr008/1bXXXqu2bdvqjjvuULt27SRJv/zyiz744AOzVGi9evU0dOhQDR8+XNdcc41uuOEGbdy4URMmTNAll1xSqYCwadOmioyM1KRJk1SrVi2FhYWpQ4cOio+P13vvvaeePXuqZcuWuuuuu3TOOefor7/+0pIlS+RwOPTll19W+Hrt2rXTt99+qzFjxqhBgwaKj49Xhw4dKnweAAB8XVXGaG3btlWfPn00YcIEZWVl6bLLLtOiRYtO+CaeVPQ23i233CJJGjlyZKXv48Ybb9SNN9540jHut/Guv/563X///Tp8+LDeffddRUdHeyyMTJs2TRMmTFCvXr3UtGlTHTp0SO+++64cDoeuvfZaSUWJ9vv371fXrl3VsGFD7dixQ//+97/Vtm1b88FrVQsMDNQLL7yg+++/X127dtU//vEPbdu2TVOmTCkV4/Xv31+zZ8/WAw88oCVLlujyyy+X0+nUH3/8odmzZ2v+/Plq3769Fi9erAkTJuj555/XxRdfLKmoqkTnzp317LPPavTo0dVyLwAA+JryxETJyckaM2aMrrnmGvXt21eZmZkaP368mjVrpt9++80814gRI/Tdd98pOTlZcXFxyszM1IQJE9SwYUNdccUVkorWbT755BP16tVLycnJ2rZtmyZNmqSEhIQyXxqrjE6dOqlTp04nHeNwODRx4kT1799fF198sW6//XbVq1dPaWlpmjt3ri6//HIzsd69DvToo48qKSlJNptNt99+e7m/F7dWrVopKSlJjz76qOx2uyZMmCBJHklPL7zwghYuXKgrrrhCDz30kAICAvT2228rLy+vwvFJly5d1L9/f40bN06bNm0yWxN9//336tKlix5++OEKnU+SoqKidNddd2nChAn6/fff1aJFC1133XV6//33FRERoYSEBKWmpurbb79VVFSUx7Ft27aVzWbTK6+8oqysLNntdnXt2lXR0dHl/l0AAFDV3LFQYWGhMjIytHjxYi1cuFBxcXH64osvFBwcLKkodrjqqqs0evRoFRQU6JxzztGCBQu0bdu207p+37599dRTT+nTTz/Vgw8+qMDAwHIf++eff2r69OmltsfExOjqq6+u0DzKu55yMk899ZSmT5+uq6++Wo888ojCwsL03nvvqXHjxtq/f3+pykr16tXTNddcozlz5igyMrJChQkKCwvLvHdJ6tWrl8LCwjRlyhTNnTtXU6dOVcOGDSUVJUrdcccdmjhxoh566CGP47Zt26YbbrhB11xzjVJTUzV9+nT17dvXrJDetGlTvfDCCxo6dKi2b9+um266SbVq1dK2bdv06aef6r777tMTTzxR7nuQipK+3nrrLQ0YMECrV69W/fr19f7775sJUycTEhKihIQEffjhhzr//PNVp04dtWrVSq1atdLdd9+tMWPGKCkpSYMGDVJmZqYmTZqkli1bKjs7u1xzGz9+vK644gq1bt1a9957r5o0aaKMjAylpqZq165d+vXXXyt0r0CVMwD4vSlTphiSPD7BwcFG27ZtjYkTJxoul8tj/KFDh4whQ4YYDRo0MAIDA43zzjvPePXVV0uNk2SkpKSUul5cXJwxcOBAj20ZGRlGSkqK0ahRIyMwMNCIjY01unXrZrzzzjunnH9cXJyRnJxcrntctWqVue355583jv/PWFXPecmSJYYkY86cOaXOmZuba/zzn/806tevb4SEhBiXX365kZqaanTq1Mno1KmTOW7btm2GJGPKlCknvUe33bt3G0OGDDHOP/98Izg42AgNDTXatWtnvPjii0ZWVpbH2Lfeesto3ry5ERgYaMTExBgPPvigceDAAY8xnTp1Mlq2bFnqOgMHDjTi4uI8tn3++edGQkKCERAQUGrOa9asMW6++WYjKirKsNvtRlxcnHHbbbcZixYtMse4fyd///23x3ndv79t27aZ2/744w/jqquuMkJCQgxJpX4/AAD4u+qK0Y4ePWo8+uijRlRUlBEWFmZcf/31xs6dOw1JxvPPP19qHnl5eUbt2rWNiIgI4+jRo+Wa+8lioJLKijO++OILo02bNkZwcLBx7rnnGq+88orxn//8xyMW+OWXX4w+ffoYjRs3Nux2uxEdHW1cd911xs8//2ye56OPPjJ69OhhREdHG0FBQUbjxo2N+++/39izZ88p5398THii+zlRnDZhwgQjPj7esNvtRvv27Y3vvvuuVIxnGIaRn59vvPLKK0bLli0Nu91u1K5d22jXrp0xfPhwIysry8jOzjbi4uKMiy++2CgoKPA4dsiQIYbVajVSU1NPeT8AAPizisZEkydPNs477zzDbrcbzZs3N6ZMmVJqDWjRokXGjTfeaDRo0MAICgoyGjRoYPTp08f4888/zTEul8t46aWXjLi4OMNutxsXXXSR8dVXX5W5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col in continuous_columns:\n    if (col != 'BIA-BIA_BMI'):\n        train = train.drop(columns=col)\n        test = test.drop(columns=col)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:25.512697Z","iopub.execute_input":"2026-04-24T07:59:25.512943Z","iopub.status.idle":"2026-04-24T07:59:25.5491Z","shell.execute_reply.started":"2026-04-24T07:59:25.51292Z","shell.execute_reply":"2026-04-24T07:59:25.548076Z"},"papermill":{"duration":0.077242,"end_time":"2025-05-23T02:56:56.335757","exception":false,"start_time":"2025-05-23T02:56:56.258515","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":511},{"id":"4f310cfe","cell_type":"markdown","source":"## Time series","metadata":{"papermill":{"duration":0.049339,"end_time":"2025-05-23T02:56:56.435092","exception":false,"start_time":"2025-05-23T02:56:56.385753","status":"completed"},"tags":[]}},{"id":"2888d50b","cell_type":"code","source":"def process_file(filename, dirname):\n    df = pd.read_parquet(os.path.join(dirname, filename, 'part-0.parquet'))\n    df.drop('step', axis=1, inplace=True)\n    return df.describe().values.reshape(-1), filename.split('=')[1]\n\ndef load_time_series(dirname) -> pd.DataFrame:\n    ids = os.listdir(dirname)\n\n    with ThreadPoolExecutor() as executor:\n        results = list(tqdm(executor.map(lambda fname: process_file(fname, dirname), ids), total=len(ids)))\n\n    stats, indexes = zip(*results)\n\n    df = pd.DataFrame(stats, columns=[f\"stat_{i}\" for i in range(len(stats[0]))])\n    df['id'] = indexes\n    return df\n\ntrain_ts = load_time_series(\"/kaggle/input/competitions/child-mind-institute-problematic-internet-use/series_train.parquet\")\ntest_ts = load_time_series(\"/kaggle/input/competitions/child-mind-institute-problematic-internet-use/series_test.parquet\")\n\ntime_series_cols = train_ts.columns.tolist()\ntime_series_cols.remove(\"id\")\n\ntrain = pd.merge(train, train_ts, how=\"left\", on='id')\ntest = pd.merge(test, test_ts, how=\"left\", on='id')\n\ntrain = train.drop('id', axis=1)\ntest = test.drop('id', axis=1)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T07:59:25.557021Z","iopub.execute_input":"2026-04-24T07:59:25.557499Z","iopub.status.idle":"2026-04-24T08:00:36.57392Z","shell.execute_reply.started":"2026-04-24T07:59:25.557463Z","shell.execute_reply":"2026-04-24T08:00:36.573046Z"},"papermill":{"duration":70.238521,"end_time":"2025-05-23T02:58:06.723059","exception":false,"start_time":"2025-05-23T02:56:56.484538","status":"completed"},"tags":[],"trusted":true},"outputs":[{"name":"stderr","text":"100%|██████████| 996/996 [01:10<00:00, 14.08it/s]\n100%|██████████| 2/2 [00:00<00:00, 10.70it/s]\n","output_type":"stream"}],"execution_count":512},{"id":"eab493d6","cell_type":"markdown","source":"## Other features","metadata":{"papermill":{"duration":0.062767,"end_time":"2025-05-23T02:58:06.852879","exception":false,"start_time":"2025-05-23T02:58:06.790112","status":"completed"},"tags":[]}},{"id":"ce65eb2a","cell_type":"code","source":"imputer = KNNImputer(n_neighbors=10)\n\nfeature_numeric_cols = train.select_dtypes(include=['float64', 'int64', 'bool']).columns\n\ncolumns_to_exclude = question_columns + ['PCIAT-PCIAT_Total', 'sii']\n\nfeature_numeric_cols = [col for col in feature_numeric_cols if col not in columns_to_exclude]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T08:00:36.575317Z","iopub.execute_input":"2026-04-24T08:00:36.575854Z","iopub.status.idle":"2026-04-24T08:00:36.583449Z","shell.execute_reply.started":"2026-04-24T08:00:36.575826Z","shell.execute_reply":"2026-04-24T08:00:36.58268Z"},"papermill":{"duration":0.075288,"end_time":"2025-05-23T02:58:07.115953","exception":false,"start_time":"2025-05-23T02:58:07.040665","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":513},{"id":"4e69aa83","cell_type":"markdown","source":"# Feature engineering","metadata":{"papermill":{"duration":0.061947,"end_time":"2025-05-23T02:58:07.238929","exception":false,"start_time":"2025-05-23T02:58:07.176982","status":"completed"},"tags":[]}},{"id":"afc6a9cd","cell_type":"code","source":"encoded_season_cols = ['Basic_Demos-Enroll_Season', 'SDS-Season']\nnon_encoded_season_cols = ['CGAS-Season', 'Physical-Season', 'Fitness_Endurance-Season', \n          'FGC-Season', 'BIA-Season', 'PAQ_A-Season', 'PAQ_C-Season', 'PreInt_EduHx-Season']\n\ndef remove_encoded_cols(df):\n    return df.drop(columns=encoded_season_cols + ['Age Group', 'index'])\n\ntrain = remove_encoded_cols(train)\ntest = remove_encoded_cols(test)\n\ndef fillna_season(df):\n    for c in non_encoded_season_cols: \n        df[c] = df[c].fillna('Missing')\n        df[c] = df[c].astype('category')\n    return df\n        \ntrain = fillna_season(train)\ntest = fillna_season(test)\n\ndef create_mapping(column, dataset):\n    unique_values = dataset[column].unique()\n    return {value: idx for idx, value in enumerate(unique_values)}\n\nfor col in non_encoded_season_cols:\n    mapping_train = create_mapping(col, train)\n    mapping_test = create_mapping(col, test)\n    \n    train[col] = train[col].replace(mapping_train).astype(int)\n    test[col] = test[col].replace(mapping_test).astype(int)\n\n\ntrain = train.drop(columns=question_columns + ['PCIAT-Season', 'PCIAT-PCIAT_Total'])\n\nprint(f'Train Shape : {train.shape} || Test Shape : {test.shape}')\ntrain.head()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T08:00:36.584479Z","iopub.execute_input":"2026-04-24T08:00:36.584787Z","iopub.status.idle":"2026-04-24T08:00:36.743287Z","shell.execute_reply.started":"2026-04-24T08:00:36.584761Z","shell.execute_reply":"2026-04-24T08:00:36.742398Z"},"papermill":{"duration":0.204123,"end_time":"2025-05-23T02:58:07.627342","exception":false,"start_time":"2025-05-23T02:58:07.423219","status":"completed"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","text":"Train Shape : (2719, 149) || Test Shape : (20, 148)\n","output_type":"stream"},{"execution_count":514,"output_type":"execute_result","data":{"text/plain":"   CGAS-Season  CGAS-CGAS_Score  Physical-Season  Physical-BMI  \\\n0            0             51.0                0     16.878972   \n1            1              NaN                0     14.036968   \n2            2             71.0                0     16.650330   \n3            2             71.0                1     18.294143   \n4            0             50.0                1     22.282139   \n\n   Physical-Waist_Circumference  Physical-Diastolic_BP  Physical-HeartRate  \\\n0                     58.837055                    NaN                 NaN   \n1                     55.880000                   75.0                70.0   \n2                     62.576299                   65.0                94.0   \n3                     65.523740                   60.0                97.0   \n4                     75.271245                   60.0                73.0   \n\n   Physical-Systolic_BP  Fitness_Endurance-Season  \\\n0                   NaN                         0   \n1                 122.0                         0   \n2                 117.0                         1   \n3                 117.0                         2   \n4                 102.0                         0   \n\n   Fitness_Endurance-Max_Stage  Fitness_Endurance-Time_Mins  \\\n0                          NaN                          NaN   \n1                          NaN                          NaN   \n2                          5.0                          7.0   \n3                          6.0                          9.0   \n4                          NaN                          NaN   \n\n   Fitness_Endurance-Time_Sec  FGC-Season  FGC-FGC_CU  FGC-FGC_CU_Zone  \\\n0                         NaN           0         0.0              0.0   \n1                         NaN           0         3.0              0.0   \n2                        33.0           0        20.0              1.0   \n3                        37.0           1        18.0              1.0   \n4                         NaN           1        12.0              0.0   \n\n   FGC-FGC_GSND  FGC-FGC_GSND_Zone  FGC-FGC_GSD  FGC-FGC_GSD_Zone  FGC-FGC_PU  \\\n0           NaN                NaN          NaN               NaN         0.0   \n1           NaN                NaN          NaN               NaN         5.0   \n2          10.2                1.0         14.7               2.0         7.0   \n3           NaN                NaN          NaN               NaN         5.0   \n4          16.5                2.0         17.9               2.0         6.0   \n\n   FGC-FGC_PU_Zone  FGC-FGC_SRL  FGC-FGC_SRL_Zone  FGC-FGC_SRR  \\\n0              0.0          7.0               0.0          6.0   \n1              0.0         11.0               1.0         11.0   \n2              1.0         10.0               1.0         10.0   \n3              0.0          7.0               0.0          7.0   \n4              0.0         10.0               1.0         11.0   \n\n   FGC-FGC_SRR_Zone  FGC-FGC_TL  FGC-FGC_TL_Zone  BIA-Season  \\\n0               0.0         6.0              1.0           0   \n1               1.0         3.0              0.0           1   \n2               1.0         5.0              0.0           2   \n3               0.0         7.0              1.0           3   \n4               1.0         8.0              0.0           3   \n\n   BIA-BIA_Activity_Level_num  BIA-BIA_BMI  BIA-BIA_Frame_num  PAQ_A-Season  \\\n0                         2.0    16.879200                1.0             0   \n1                         2.0    14.037100                1.0             0   \n2                         NaN          NaN                NaN             0   \n3                         3.0    18.294300                2.0             0   \n4                         2.0    22.282139                2.0             0   \n\n   PAQ_A-PAQ_A_Total  PAQ_C-Season  PAQ_C-PAQ_C_Total  SDS-SDS_Total_Raw  \\\n0                NaN             0                NaN          39.857143   \n1                NaN             1              2.340          46.000000   \n2                NaN             2              2.170          38.000000   \n3                NaN             3              2.451          31.000000   \n4                NaN             4              4.110          40.000000   \n\n   PreInt_EduHx-Season  PreInt_EduHx-computerinternet_hoursday  sii  \\\n0                    0                                     3.0  2.0   \n1                    1                                     0.0  0.0   \n2                    1                                     2.0  0.0   \n3                    2                                     0.0  1.0   \n4                    3                                     0.0  1.0   \n\n   Age_Group_Label  Season_Spring  Season_Summer  Basic_Demos-Age  \\\n0                2          False          False              5.0   \n1                2          False           True              9.0   \n2                2          False           True             10.0   \n3                2          False          False              9.0   \n4                0           True          False             13.0   \n\n   Season_Fall  Season_Winter  Basic_Demos-Sex  Physical-Weight  \\\n0          1.0            0.0              0.0        23.042474   \n1          0.0            0.0              0.0        20.865232   \n2          0.0            0.0              1.0        34.291555   \n3          0.0            1.0              0.0        37.013107   \n4          0.0            0.0              1.0        50.893022   \n\n   Physical-Height  SDS-Season_Fall  SDS-Season_Spring  SDS-Season_Summer  \\\n0           116.84            False               True              False   \n1           121.92             True              False              False   \n2           143.51             True              False              False   \n3           142.24            False              False               True   \n4           151.13            False              False               True   \n\n   SDS-Season_Winter  SDS_Weight    stat_0    stat_1    stat_2    stat_3  \\\n0              False    0.380904       NaN       NaN       NaN       NaN   \n1              False    0.249740       NaN       NaN       NaN       NaN   \n2              False    0.425557       NaN       NaN       NaN       NaN   \n3              False    0.598688   43330.0   43330.0   43330.0   43330.0   \n4              False    0.377541  396396.0  396396.0  396396.0  396396.0   \n\n     stat_4    stat_5    stat_6    stat_7    stat_8    stat_9   stat_10  \\\n0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n3   43330.0   43330.0   43330.0   43330.0   43330.0   43330.0   43330.0   \n4  396396.0  396396.0  396396.0  396396.0  396396.0  396396.0  396396.0   \n\n    stat_11   stat_12   stat_13   stat_14   stat_15    stat_16   stat_17  \\\n0       NaN       NaN       NaN       NaN       NaN        NaN       NaN   \n1       NaN       NaN       NaN       NaN       NaN        NaN       NaN   \n2       NaN       NaN       NaN       NaN       NaN        NaN       NaN   \n3   43330.0 -0.316384  0.016009 -0.167890  0.047388 -10.580416  0.000000   \n4  396396.0 -0.004272  0.016859 -0.631731  0.011926 -55.630768  0.655708   \n\n     stat_18      stat_19       stat_20   stat_21  stat_22    stat_23  \\\n0        NaN          NaN           NaN       NaN      NaN        NaN   \n1        NaN          NaN           NaN       NaN      NaN        NaN   \n2        NaN          NaN           NaN       NaN      NaN        NaN   \n3  42.296310  4053.579102  5.046215e+13  4.470182      3.0  53.201683   \n4  16.771982  3838.189453  4.321212e+13  3.909848      3.0  79.435593   \n\n    stat_24   stat_25   stat_26   stat_27    stat_28   stat_29     stat_30  \\\n0       NaN       NaN       NaN       NaN        NaN       NaN         NaN   \n1       NaN       NaN       NaN       NaN        NaN       NaN         NaN   \n2       NaN       NaN       NaN       NaN        NaN       NaN         NaN   \n3  0.453665  0.502698  0.585710  0.106353  42.947163  0.000000  208.168869   \n4  0.351582  0.303726  0.622458  0.024306  50.368004  0.467727   95.304085   \n\n      stat_31       stat_32   stat_33  stat_34    stat_35   stat_36   stat_37  \\\n0         NaN           NaN       NaN      NaN        NaN       NaN       NaN   \n1         NaN           NaN       NaN      NaN        NaN       NaN       NaN   \n2         NaN           NaN       NaN      NaN        NaN       NaN       NaN   \n3  112.401535  1.942842e+13  1.931421      0.0  14.245132 -1.746094 -2.905339   \n4  155.542389  2.497264e+13  1.946892      0.0   6.633580 -1.038711 -1.522690   \n\n    stat_38  stat_39    stat_40  stat_41  stat_42      stat_43       stat_44  \\\n0       NaN      NaN        NaN      NaN      NaN          NaN           NaN   \n1       NaN      NaN        NaN      NaN      NaN          NaN           NaN   \n2       NaN      NaN        NaN      NaN      NaN          NaN           NaN   \n3 -1.048372      0.0 -89.833092      0.0      0.0  3824.000000  5.500000e+10   \n4 -1.018787      0.0 -88.761833      0.0      0.0  3098.166748  0.000000e+00   \n\n   stat_45  stat_46  stat_47   stat_48   stat_49   stat_50   stat_51  \\\n0      NaN      NaN      NaN       NaN       NaN       NaN       NaN   \n1      NaN      NaN      NaN       NaN       NaN       NaN       NaN   \n2      NaN      NaN      NaN       NaN       NaN       NaN       NaN   \n3      1.0      3.0     41.0 -0.684180 -0.309863 -0.649974  0.006432   \n4      1.0      3.0     68.0 -0.052803 -0.044517 -1.009344  0.008622   \n\n     stat_52  stat_53   stat_54      stat_55       stat_56  stat_57  stat_58  \\\n0        NaN      NaN       NaN          NaN           NaN      NaN      NaN   \n1        NaN      NaN       NaN          NaN           NaN      NaN      NaN   \n2        NaN      NaN       NaN          NaN           NaN      NaN      NaN   \n3 -41.541863      0.0  2.392969  4028.666748  3.689000e+13      3.0      3.0   \n4 -88.386049      0.0  0.500000  3747.000000  2.154000e+13      2.0      3.0   \n\n   stat_59   stat_60   stat_61   stat_62   stat_63    stat_64  stat_65  \\\n0      NaN       NaN       NaN       NaN       NaN        NaN      NaN   \n1      NaN       NaN       NaN       NaN       NaN        NaN      NaN   \n2      NaN       NaN       NaN       NaN       NaN        NaN      NaN   \n3     42.0 -0.366849  0.024974 -0.245378  0.023637 -15.086617      0.0   \n4     74.0 -0.020622 -0.028179 -1.007728  0.009831 -86.119919      1.0   \n\n    stat_66  stat_67       stat_68  stat_69  stat_70  stat_71   stat_72  \\\n0       NaN      NaN           NaN      NaN      NaN      NaN       NaN   \n1       NaN      NaN           NaN      NaN      NaN      NaN       NaN   \n2       NaN      NaN           NaN      NaN      NaN      NaN       NaN   \n3  6.926828   4070.0  5.347750e+13      5.0      3.0     50.0 -0.010677   \n4  0.879005   3812.0  4.331000e+13      4.0      3.0     79.0 -0.019081   \n\n    stat_73   stat_74   stat_75    stat_76  stat_77    stat_78      stat_79  \\\n0       NaN       NaN       NaN        NaN      NaN        NaN          NaN   \n1       NaN       NaN       NaN        NaN      NaN        NaN          NaN   \n2       NaN       NaN       NaN        NaN      NaN        NaN          NaN   \n3  0.400677  0.204727  0.041420  12.220764      0.0  15.000000  4147.000000   \n4  0.020307 -0.294459  0.010668 -17.483364      1.0   6.141348  3951.187561   \n\n        stat_80  stat_81  stat_82  stat_83   stat_84   stat_85   stat_86  \\\n0           NaN      NaN      NaN      NaN       NaN       NaN       NaN   \n1           NaN      NaN      NaN      NaN       NaN       NaN       NaN   \n2           NaN      NaN      NaN      NaN       NaN       NaN       NaN   \n3  6.640875e+13      6.0      3.0     53.0  1.507865  1.666354  1.546979   \n4  6.485500e+13      6.0      3.0     85.0  1.034351  1.946303  1.146284   \n\n    stat_87    stat_88  stat_89      stat_90  stat_91       stat_92  stat_93  \\\n0       NaN        NaN      NaN          NaN      NaN           NaN      NaN   \n1       NaN        NaN      NaN          NaN      NaN           NaN      NaN   \n2       NaN        NaN      NaN          NaN      NaN           NaN      NaN   \n3  4.004276  89.751656      0.0  2633.250000   4188.5  8.611000e+13      7.0   \n4  2.952888  89.476036      1.0  2597.800049   4175.0  8.639500e+13      7.0   \n\n   stat_94  stat_95  \n0      NaN      NaN  \n1      NaN      NaN  \n2      NaN      NaN  \n3      3.0     85.0  \n4      3.0     91.0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CGAS-Season</th>\n      <th>CGAS-CGAS_Score</th>\n      <th>Physical-Season</th>\n      <th>Physical-BMI</th>\n      <th>Physical-Waist_Circumference</th>\n      <th>Physical-Diastolic_BP</th>\n      <th>Physical-HeartRate</th>\n      <th>Physical-Systolic_BP</th>\n      <th>Fitness_Endurance-Season</th>\n      <th>Fitness_Endurance-Max_Stage</th>\n      <th>Fitness_Endurance-Time_Mins</th>\n      <th>Fitness_Endurance-Time_Sec</th>\n      <th>FGC-Season</th>\n      <th>FGC-FGC_CU</th>\n      <th>FGC-FGC_CU_Zone</th>\n      <th>FGC-FGC_GSND</th>\n      <th>FGC-FGC_GSND_Zone</th>\n      <th>FGC-FGC_GSD</th>\n      <th>FGC-FGC_GSD_Zone</th>\n      <th>FGC-FGC_PU</th>\n      <th>FGC-FGC_PU_Zone</th>\n      <th>FGC-FGC_SRL</th>\n      <th>FGC-FGC_SRL_Zone</th>\n      <th>FGC-FGC_SRR</th>\n      <th>FGC-FGC_SRR_Zone</th>\n      <th>FGC-FGC_TL</th>\n      <th>FGC-FGC_TL_Zone</th>\n      <th>BIA-Season</th>\n      <th>BIA-BIA_Activity_Level_num</th>\n      <th>BIA-BIA_BMI</th>\n      <th>BIA-BIA_Frame_num</th>\n      <th>PAQ_A-Season</th>\n      <th>PAQ_A-PAQ_A_Total</th>\n      <th>PAQ_C-Season</th>\n      <th>PAQ_C-PAQ_C_Total</th>\n      <th>SDS-SDS_Total_Raw</th>\n      <th>PreInt_EduHx-Season</th>\n      <th>PreInt_EduHx-computerinternet_hoursday</th>\n      <th>sii</th>\n      <th>Age_Group_Label</th>\n      <th>Season_Spring</th>\n      <th>Season_Summer</th>\n      <th>Basic_Demos-Age</th>\n      <th>Season_Fall</th>\n      <th>Season_Winter</th>\n      <th>Basic_Demos-Sex</th>\n      <th>Physical-Weight</th>\n      <th>Physical-Height</th>\n      <th>SDS-Season_Fall</th>\n      <th>SDS-Season_Spring</th>\n      <th>SDS-Season_Summer</th>\n      <th>SDS-Season_Winter</th>\n      <th>SDS_Weight</th>\n      <th>stat_0</th>\n      <th>stat_1</th>\n      <th>stat_2</th>\n      <th>stat_3</th>\n      <th>stat_4</th>\n      <th>stat_5</th>\n      <th>stat_6</th>\n      <th>stat_7</th>\n      <th>stat_8</th>\n      <th>stat_9</th>\n      <th>stat_10</th>\n      <th>stat_11</th>\n      <th>stat_12</th>\n      <th>stat_13</th>\n      <th>stat_14</th>\n      <th>stat_15</th>\n      <th>stat_16</th>\n      <th>stat_17</th>\n      <th>stat_18</th>\n      <th>stat_19</th>\n      <th>stat_20</th>\n      <th>stat_21</th>\n      <th>stat_22</th>\n      <th>stat_23</th>\n      <th>stat_24</th>\n      <th>stat_25</th>\n      <th>stat_26</th>\n      <th>stat_27</th>\n      <th>stat_28</th>\n      <th>stat_29</th>\n      <th>stat_30</th>\n      <th>stat_31</th>\n      <th>stat_32</th>\n      <th>stat_33</th>\n      <th>stat_34</th>\n      <th>stat_35</th>\n      <th>stat_36</th>\n      <th>stat_37</th>\n      <th>stat_38</th>\n      <th>stat_39</th>\n      <th>stat_40</th>\n      <th>stat_41</th>\n      <th>stat_42</th>\n      <th>stat_43</th>\n      <th>stat_44</th>\n      <th>stat_45</th>\n      <th>stat_46</th>\n      <th>stat_47</th>\n      <th>stat_48</th>\n      <th>stat_49</th>\n      <th>stat_50</th>\n      <th>stat_51</th>\n      <th>stat_52</th>\n      <th>stat_53</th>\n      <th>stat_54</th>\n      <th>stat_55</th>\n      <th>stat_56</th>\n      <th>stat_57</th>\n      <th>stat_58</th>\n      <th>stat_59</th>\n      <th>stat_60</th>\n      <th>stat_61</th>\n      <th>stat_62</th>\n      <th>stat_63</th>\n      <th>stat_64</th>\n      <th>stat_65</th>\n      <th>stat_66</th>\n      <th>stat_67</th>\n      <th>stat_68</th>\n      <th>stat_69</th>\n      <th>stat_70</th>\n      <th>stat_71</th>\n      <th>stat_72</th>\n      <th>stat_73</th>\n      <th>stat_74</th>\n      <th>stat_75</th>\n      <th>stat_76</th>\n      <th>stat_77</th>\n      <th>stat_78</th>\n      <th>stat_79</th>\n      <th>stat_80</th>\n      <th>stat_81</th>\n      <th>stat_82</th>\n      <th>stat_83</th>\n      <th>stat_84</th>\n      <th>stat_85</th>\n      <th>stat_86</th>\n      <th>stat_87</th>\n      <th>stat_88</th>\n      <th>stat_89</th>\n      <th>stat_90</th>\n      <th>stat_91</th>\n      <th>stat_92</th>\n      <th>stat_93</th>\n      <th>stat_94</th>\n      <th>stat_95</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>51.0</td>\n      <td>0</td>\n      <td>16.878972</td>\n      <td>58.837055</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>2.0</td>\n      <td>16.879200</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>39.857143</td>\n      <td>0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>2</td>\n      <td>False</td>\n      <td>False</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>23.042474</td>\n      <td>116.84</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>0.380904</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>14.036968</td>\n      <td>55.880000</td>\n      <td>75.0</td>\n      <td>70.0</td>\n      <td>122.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>14.037100</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>2.340</td>\n      <td>46.000000</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>False</td>\n      <td>True</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>20.865232</td>\n      <td>121.92</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>0.249740</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>71.0</td>\n      <td>0</td>\n      <td>16.650330</td>\n      <td>62.576299</td>\n      <td>65.0</td>\n      <td>94.0</td>\n      <td>117.0</td>\n      <td>1</td>\n      <td>5.0</td>\n      <td>7.0</td>\n      <td>33.0</td>\n      <td>0</td>\n      <td>20.0</td>\n      <td>1.0</td>\n      <td>10.2</td>\n      <td>1.0</td>\n      <td>14.7</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>2.170</td>\n      <td>38.000000</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>False</td>\n      <td>True</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>34.291555</td>\n      <td>143.51</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>0.425557</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>71.0</td>\n      <td>1</td>\n      <td>18.294143</td>\n      <td>65.523740</td>\n      <td>60.0</td>\n      <td>97.0</td>\n      <td>117.0</td>\n      <td>2</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>37.0</td>\n      <td>1</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>3</td>\n      <td>3.0</td>\n      <td>18.294300</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>2.451</td>\n      <td>31.000000</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>False</td>\n      <td>False</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>37.013107</td>\n      <td>142.24</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>0.598688</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>-0.316384</td>\n      <td>0.016009</td>\n      <td>-0.167890</td>\n      <td>0.047388</td>\n      <td>-10.580416</td>\n      <td>0.000000</td>\n      <td>42.296310</td>\n      <td>4053.579102</td>\n      <td>5.046215e+13</td>\n      <td>4.470182</td>\n      <td>3.0</td>\n      <td>53.201683</td>\n      <td>0.453665</td>\n      <td>0.502698</td>\n      <td>0.585710</td>\n      <td>0.106353</td>\n      <td>42.947163</td>\n      <td>0.000000</td>\n      <td>208.168869</td>\n      <td>112.401535</td>\n      <td>1.942842e+13</td>\n      <td>1.931421</td>\n      <td>0.0</td>\n      <td>14.245132</td>\n      <td>-1.746094</td>\n      <td>-2.905339</td>\n      <td>-1.048372</td>\n      <td>0.0</td>\n      <td>-89.833092</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3824.000000</td>\n      <td>5.500000e+10</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>41.0</td>\n      <td>-0.684180</td>\n      <td>-0.309863</td>\n      <td>-0.649974</td>\n      <td>0.006432</td>\n      <td>-41.541863</td>\n      <td>0.0</td>\n      <td>2.392969</td>\n      <td>4028.666748</td>\n      <td>3.689000e+13</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>42.0</td>\n      <td>-0.366849</td>\n      <td>0.024974</td>\n      <td>-0.245378</td>\n      <td>0.023637</td>\n      <td>-15.086617</td>\n      <td>0.0</td>\n      <td>6.926828</td>\n      <td>4070.0</td>\n      <td>5.347750e+13</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>50.0</td>\n      <td>-0.010677</td>\n      <td>0.400677</td>\n      <td>0.204727</td>\n      <td>0.041420</td>\n      <td>12.220764</td>\n      <td>0.0</td>\n      <td>15.000000</td>\n      <td>4147.000000</td>\n      <td>6.640875e+13</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>53.0</td>\n      <td>1.507865</td>\n      <td>1.666354</td>\n      <td>1.546979</td>\n      <td>4.004276</td>\n      <td>89.751656</td>\n      <td>0.0</td>\n      <td>2633.250000</td>\n      <td>4188.5</td>\n      <td>8.611000e+13</td>\n      <td>7.0</td>\n      <td>3.0</td>\n      <td>85.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>50.0</td>\n      <td>1</td>\n      <td>22.282139</td>\n      <td>75.271245</td>\n      <td>60.0</td>\n      <td>73.0</td>\n      <td>102.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>16.5</td>\n      <td>2.0</td>\n      <td>17.9</td>\n      <td>2.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>2.0</td>\n      <td>22.282139</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>4</td>\n      <td>4.110</td>\n      <td>40.000000</td>\n      <td>3</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>True</td>\n      <td>False</td>\n      <td>13.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>50.893022</td>\n      <td>151.13</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>0.377541</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>-0.004272</td>\n      <td>0.016859</td>\n      <td>-0.631731</td>\n      <td>0.011926</td>\n      <td>-55.630768</td>\n      <td>0.655708</td>\n      <td>16.771982</td>\n      <td>3838.189453</td>\n      <td>4.321212e+13</td>\n      <td>3.909848</td>\n      <td>3.0</td>\n      <td>79.435593</td>\n      <td>0.351582</td>\n      <td>0.303726</td>\n      <td>0.622458</td>\n      <td>0.024306</td>\n      <td>50.368004</td>\n      <td>0.467727</td>\n      <td>95.304085</td>\n      <td>155.542389</td>\n      <td>2.497264e+13</td>\n      <td>1.946892</td>\n      <td>0.0</td>\n      <td>6.633580</td>\n      <td>-1.038711</td>\n      <td>-1.522690</td>\n      <td>-1.018787</td>\n      <td>0.0</td>\n      <td>-88.761833</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3098.166748</td>\n      <td>0.000000e+00</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>68.0</td>\n      <td>-0.052803</td>\n      <td>-0.044517</td>\n      <td>-1.009344</td>\n      <td>0.008622</td>\n      <td>-88.386049</td>\n      <td>0.0</td>\n      <td>0.500000</td>\n      <td>3747.000000</td>\n      <td>2.154000e+13</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>74.0</td>\n      <td>-0.020622</td>\n      <td>-0.028179</td>\n      <td>-1.007728</td>\n      <td>0.009831</td>\n      <td>-86.119919</td>\n      <td>1.0</td>\n      <td>0.879005</td>\n      <td>3812.0</td>\n      <td>4.331000e+13</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>79.0</td>\n      <td>-0.019081</td>\n      <td>0.020307</td>\n      <td>-0.294459</td>\n      <td>0.010668</td>\n      <td>-17.483364</td>\n      <td>1.0</td>\n      <td>6.141348</td>\n      <td>3951.187561</td>\n      <td>6.485500e+13</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>85.0</td>\n      <td>1.034351</td>\n      <td>1.946303</td>\n      <td>1.146284</td>\n      <td>2.952888</td>\n      <td>89.476036</td>\n      <td>1.0</td>\n      <td>2597.800049</td>\n      <td>4175.0</td>\n      <td>8.639500e+13</td>\n      <td>7.0</td>\n      <td>3.0</td>\n      <td>91.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":514},{"id":"23dbfc6e","cell_type":"markdown","source":"\nThis step is crucial as it creates new features by combining existing variables, capturing more complex relationships in the data. These engineered features, such as interactions between age, BMI, CGAS scores, and internet usage, are key for improving the predictive power of the model.","metadata":{"papermill":{"duration":0.063647,"end_time":"2025-05-23T02:58:07.756467","exception":false,"start_time":"2025-05-23T02:58:07.69282","status":"completed"},"tags":[]}},{"id":"1be1f950","cell_type":"code","source":"def feature_engineering(df):\n    #Weight\n    df['CGAS_Weight'] = df['CGAS-CGAS_Score'].apply(sigmoid_weight_cgas_high)\n    df['SDS_Score_Weighted'] = df['SDS-SDS_Total_Raw'] * df['SDS_Weight']\n    df['CGAS_Score_Weighted'] = df['CGAS-CGAS_Score'] * df['CGAS_Weight']\n    df = df.drop(columns=['SDS-SDS_Total_Raw', 'SDS_Weight', 'CGAS-CGAS_Score', 'CGAS_Weight', 'Age_Group_Label'])\n    \n    #Age\n    df['Internet_Hours_Age'] = df['PreInt_EduHx-computerinternet_hoursday'] * df['Basic_Demos-Age']\n    df['Physical-Waist_Age'] = df['Basic_Demos-Age'] * df['Physical-Waist_Circumference']\n    df['BMI_Age'] = df['Physical-BMI'] * df['Basic_Demos-Age']\n    df['Physical-Height_Age'] = df['Basic_Demos-Age'] * df['Physical-Height']\n\n    #SDS\n    df['SDS_BMI'] = df['Physical-BMI'] * df['SDS_Score_Weighted']\n    df['CGAS_SDS'] = df['CGAS_Score_Weighted'] * df['SDS_Score_Weighted']\n    df['CGAS_Endurance_Mins'] = df['CGAS_Score_Weighted'] * df['Fitness_Endurance-Time_Mins']\n    df['SDS_Activity'] = df['BIA-BIA_Activity_Level_num'] * df['SDS_Score_Weighted']\n    df['SDS_InternetHours'] = df['SDS_Score_Weighted'] * df['PreInt_EduHx-computerinternet_hoursday']\n\n    df['BMI_Systolic_BP'] = df['Physical-BMI'] * df['Physical-Systolic_BP']\n    df['Age_Systolic_BP'] = df['Basic_Demos-Age'] * df['Physical-Systolic_BP']\n    df['PreInt_Systolic_BP'] = df['Physical-Systolic_BP'] * df['PreInt_EduHx-computerinternet_hoursday']\n    df['PAQ_A_Activity'] = df['BIA-BIA_Activity_Level_num'] * df['PAQ_A-PAQ_A_Total']\n    df['Activity_CU_PU'] = df['BIA-BIA_Activity_Level_num'] * df['FGC-FGC_CU'] * df['FGC-FGC_PU']\n\n    #FGC\n    df['FGC_CU_PU'] = df['FGC-FGC_CU'] * df['FGC-FGC_PU']\n    df['FGC_CU_PU_Age'] = df['FGC-FGC_CU'] * df['FGC-FGC_PU'] * df['Basic_Demos-Age']\n    df['FGC_GSND_GSD'] = df['FGC-FGC_GSND'] * df['FGC-FGC_GSD']\n    df['FGC_GSND_GSD_Age'] = df['FGC-FGC_GSND'] * df['FGC-FGC_GSD'] * df['Basic_Demos-Age']\n    df['CGAS_CU_PU'] = df['CGAS_Score_Weighted'] * df['FGC-FGC_CU'] * df['FGC-FGC_PU']\n    df['PreInt_FGC_CU_PU'] = df['PreInt_EduHx-computerinternet_hoursday'] * df['FGC-FGC_CU'] * df['FGC-FGC_PU']\n    df['Endurance_CU_PU'] = df['Fitness_Endurance-Time_Mins'] * df['FGC-FGC_CU'] * df['FGC-FGC_PU']\n    return df\n\ntrain = feature_engineering(train)\ntest = feature_engineering(test)\n\nnew_features = ['Internet_Hours_Age', 'Physical-Waist_Age', 'BMI_Age', 'Physical-Height_Age', 'SDS_InternetHours', 'SDS_BMI', 'CGAS_SDS', 'CGAS_Endurance_Mins', 'SDS_Activity', 'BMI_Systolic_BP', 'Age_Systolic_BP', 'PreInt_Systolic_BP', 'PAQ_A_Activity', 'Activity_CU_PU', 'FGC_CU_PU', 'FGC_CU_PU_Age', 'FGC_GSND_GSD', 'FGC_GSND_GSD_Age', 'CGAS_CU_PU', 'PreInt_FGC_CU_PU', 'Endurance_CU_PU', 'CGAS_Weight', 'SDS_Score_Weighted', 'CGAS_Score_Weighted']","metadata":{"execution":{"iopub.status.busy":"2026-04-24T08:00:36.744656Z","iopub.execute_input":"2026-04-24T08:00:36.744953Z","iopub.status.idle":"2026-04-24T08:00:36.790416Z","shell.execute_reply.started":"2026-04-24T08:00:36.744926Z","shell.execute_reply":"2026-04-24T08:00:36.789725Z"},"papermill":{"duration":0.100546,"end_time":"2025-05-23T02:58:07.919665","exception":false,"start_time":"2025-05-23T02:58:07.819119","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":515},{"id":"41144f7e","cell_type":"markdown","source":"# Parameters Setting","metadata":{"papermill":{"duration":0.0634,"end_time":"2025-05-23T02:58:08.047024","exception":false,"start_time":"2025-05-23T02:58:07.983624","status":"completed"},"tags":[]}},{"id":"e54ce0bb","cell_type":"code","source":"from sklearn.linear_model import Ridge\nfrom sklearn.svm import SVR\n\nSEED = 42\nn_splits = 5","metadata":{"execution":{"iopub.status.busy":"2026-04-24T08:00:36.791431Z","iopub.execute_input":"2026-04-24T08:00:36.791877Z","iopub.status.idle":"2026-04-24T08:00:36.796025Z","shell.execute_reply.started":"2026-04-24T08:00:36.79185Z","shell.execute_reply":"2026-04-24T08:00:36.795259Z"},"papermill":{"duration":0.073372,"end_time":"2025-05-23T02:58:08.183905","exception":false,"start_time":"2025-05-23T02:58:08.110533","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":516},{"id":"b3738566","cell_type":"code","source":"LGB_Params = {\n    'learning_rate': 0.046,\n    'max_depth': 12,\n    'num_leaves': 478,\n    'min_data_in_leaf': 13,\n    'feature_fraction': 0.893,\n    'bagging_fraction': 0.784,\n    'bagging_freq': 4,\n    'lambda_l1': 10,\n    'lambda_l2': 0.01\n}\n\nCatBoost_Params = {\n    'learning_rate': 0.05,\n    'depth': 6,\n    'iterations': 200,\n    'random_seed': SEED,\n    'verbose': 0,\n    'l2_leaf_reg': 10,\n    'task_type': 'GPU'\n}\n\nXGB_Params = {\n    'learning_rate': 0.01,\n    'max_depth': 5,\n    'n_estimators': 500,\n    'subsample': 0.8,\n    'colsample_bytree': 0.8,\n    'reg_alpha': 5,  \n    'reg_lambda': 10,  \n    'random_state': SEED,\n    'tree_method': 'hist',\n    'device': 'cuda',\n}","metadata":{"execution":{"iopub.status.busy":"2026-04-24T08:00:36.797228Z","iopub.execute_input":"2026-04-24T08:00:36.797615Z","iopub.status.idle":"2026-04-24T08:00:36.814699Z","shell.execute_reply.started":"2026-04-24T08:00:36.797584Z","shell.execute_reply":"2026-04-24T08:00:36.813841Z"},"papermill":{"duration":0.070963,"end_time":"2025-05-23T02:58:08.317491","exception":false,"start_time":"2025-05-23T02:58:08.246528","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":517},{"id":"915d7019","cell_type":"markdown","source":"# Model definition","metadata":{"papermill":{"duration":0.062387,"end_time":"2025-05-23T02:58:08.442123","exception":false,"start_time":"2025-05-23T02:58:08.379736","status":"completed"},"tags":[]}},{"id":"48681ede","cell_type":"code","source":"LGB_Model = LGBMRegressor(**LGB_Params, random_state=SEED, verbose=-1, n_estimators=300)\nCatBoost_Model = CatBoostRegressor(**CatBoost_Params)\nXGB_Model = XGBRegressor(**XGB_Params)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T08:00:36.815998Z","iopub.execute_input":"2026-04-24T08:00:36.816634Z","iopub.status.idle":"2026-04-24T08:00:36.833742Z","shell.execute_reply.started":"2026-04-24T08:00:36.816596Z","shell.execute_reply":"2026-04-24T08:00:36.832939Z"},"papermill":{"duration":0.073444,"end_time":"2025-05-23T02:58:08.578249","exception":false,"start_time":"2025-05-23T02:58:08.504805","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":518},{"id":"c295e2f0","cell_type":"markdown","source":"# Train and predict","metadata":{"papermill":{"duration":0.062479,"end_time":"2025-05-23T02:58:08.703824","exception":false,"start_time":"2025-05-23T02:58:08.641345","status":"completed"},"tags":[]}},{"id":"8b274e96","cell_type":"code","source":"print(train.shape)\ntrain.head()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T08:00:36.834774Z","iopub.execute_input":"2026-04-24T08:00:36.835018Z","iopub.status.idle":"2026-04-24T08:00:36.961154Z","shell.execute_reply.started":"2026-04-24T08:00:36.834985Z","shell.execute_reply":"2026-04-24T08:00:36.960262Z"},"papermill":{"duration":0.162548,"end_time":"2025-05-23T02:58:08.92828","exception":false,"start_time":"2025-05-23T02:58:08.765732","status":"completed"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","text":"(2719, 168)\n","output_type":"stream"},{"execution_count":519,"output_type":"execute_result","data":{"text/plain":"   CGAS-Season  Physical-Season  Physical-BMI  Physical-Waist_Circumference  \\\n0            0                0     16.878972                     58.837055   \n1            1                0     14.036968                     55.880000   \n2            2                0     16.650330                     62.576299   \n3            2                1     18.294143                     65.523740   \n4            0                1     22.282139                     75.271245   \n\n   Physical-Diastolic_BP  Physical-HeartRate  Physical-Systolic_BP  \\\n0                    NaN                 NaN                   NaN   \n1                   75.0                70.0                 122.0   \n2                   65.0                94.0                 117.0   \n3                   60.0                97.0                 117.0   \n4                   60.0                73.0                 102.0   \n\n   Fitness_Endurance-Season  Fitness_Endurance-Max_Stage  \\\n0                         0                          NaN   \n1                         0                          NaN   \n2                         1                          5.0   \n3                         2                          6.0   \n4                         0                          NaN   \n\n   Fitness_Endurance-Time_Mins  Fitness_Endurance-Time_Sec  FGC-Season  \\\n0                          NaN                         NaN           0   \n1                          NaN                         NaN           0   \n2                          7.0                        33.0           0   \n3                          9.0                        37.0           1   \n4                          NaN                         NaN           1   \n\n   FGC-FGC_CU  FGC-FGC_CU_Zone  FGC-FGC_GSND  FGC-FGC_GSND_Zone  FGC-FGC_GSD  \\\n0         0.0              0.0           NaN                NaN          NaN   \n1         3.0              0.0           NaN                NaN          NaN   \n2        20.0              1.0          10.2                1.0         14.7   \n3        18.0              1.0           NaN                NaN          NaN   \n4        12.0              0.0          16.5                2.0         17.9   \n\n   FGC-FGC_GSD_Zone  FGC-FGC_PU  FGC-FGC_PU_Zone  FGC-FGC_SRL  \\\n0               NaN         0.0              0.0          7.0   \n1               NaN         5.0              0.0         11.0   \n2               2.0         7.0              1.0         10.0   \n3               NaN         5.0              0.0          7.0   \n4               2.0         6.0              0.0         10.0   \n\n   FGC-FGC_SRL_Zone  FGC-FGC_SRR  FGC-FGC_SRR_Zone  FGC-FGC_TL  \\\n0               0.0          6.0               0.0         6.0   \n1               1.0         11.0               1.0         3.0   \n2               1.0         10.0               1.0         5.0   \n3               0.0          7.0               0.0         7.0   \n4               1.0         11.0               1.0         8.0   \n\n   FGC-FGC_TL_Zone  BIA-Season  BIA-BIA_Activity_Level_num  BIA-BIA_BMI  \\\n0              1.0           0                         2.0    16.879200   \n1              0.0           1                         2.0    14.037100   \n2              0.0           2                         NaN          NaN   \n3              1.0           3                         3.0    18.294300   \n4              0.0           3                         2.0    22.282139   \n\n   BIA-BIA_Frame_num  PAQ_A-Season  PAQ_A-PAQ_A_Total  PAQ_C-Season  \\\n0                1.0             0                NaN             0   \n1                1.0             0                NaN             1   \n2                NaN             0                NaN             2   \n3                2.0             0                NaN             3   \n4                2.0             0                NaN             4   \n\n   PAQ_C-PAQ_C_Total  PreInt_EduHx-Season  \\\n0                NaN                    0   \n1              2.340                    1   \n2              2.170                    1   \n3              2.451                    2   \n4              4.110                    3   \n\n   PreInt_EduHx-computerinternet_hoursday  sii  Season_Spring  Season_Summer  \\\n0                                     3.0  2.0          False          False   \n1                                     0.0  0.0          False           True   \n2                                     2.0  0.0          False           True   \n3                                     0.0  1.0          False          False   \n4                                     0.0  1.0           True          False   \n\n   Basic_Demos-Age  Season_Fall  Season_Winter  Basic_Demos-Sex  \\\n0              5.0          1.0            0.0              0.0   \n1              9.0          0.0            0.0              0.0   \n2             10.0          0.0            0.0              1.0   \n3              9.0          0.0            1.0              0.0   \n4             13.0          0.0            0.0              1.0   \n\n   Physical-Weight  Physical-Height  SDS-Season_Fall  SDS-Season_Spring  \\\n0        23.042474           116.84            False               True   \n1        20.865232           121.92             True              False   \n2        34.291555           143.51             True              False   \n3        37.013107           142.24            False              False   \n4        50.893022           151.13            False              False   \n\n   SDS-Season_Summer  SDS-Season_Winter    stat_0    stat_1    stat_2  \\\n0              False              False       NaN       NaN       NaN   \n1              False              False       NaN       NaN       NaN   \n2              False              False       NaN       NaN       NaN   \n3               True              False   43330.0   43330.0   43330.0   \n4               True              False  396396.0  396396.0  396396.0   \n\n     stat_3    stat_4    stat_5    stat_6    stat_7    stat_8    stat_9  \\\n0       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n3   43330.0   43330.0   43330.0   43330.0   43330.0   43330.0   43330.0   \n4  396396.0  396396.0  396396.0  396396.0  396396.0  396396.0  396396.0   \n\n    stat_10   stat_11   stat_12   stat_13   stat_14   stat_15    stat_16  \\\n0       NaN       NaN       NaN       NaN       NaN       NaN        NaN   \n1       NaN       NaN       NaN       NaN       NaN       NaN        NaN   \n2       NaN       NaN       NaN       NaN       NaN       NaN        NaN   \n3   43330.0   43330.0 -0.316384  0.016009 -0.167890  0.047388 -10.580416   \n4  396396.0  396396.0 -0.004272  0.016859 -0.631731  0.011926 -55.630768   \n\n    stat_17    stat_18      stat_19       stat_20   stat_21  stat_22  \\\n0       NaN        NaN          NaN           NaN       NaN      NaN   \n1       NaN        NaN          NaN           NaN       NaN      NaN   \n2       NaN        NaN          NaN           NaN       NaN      NaN   \n3  0.000000  42.296310  4053.579102  5.046215e+13  4.470182      3.0   \n4  0.655708  16.771982  3838.189453  4.321212e+13  3.909848      3.0   \n\n     stat_23   stat_24   stat_25   stat_26   stat_27    stat_28   stat_29  \\\n0        NaN       NaN       NaN       NaN       NaN        NaN       NaN   \n1        NaN       NaN       NaN       NaN       NaN        NaN       NaN   \n2        NaN       NaN       NaN       NaN       NaN        NaN       NaN   \n3  53.201683  0.453665  0.502698  0.585710  0.106353  42.947163  0.000000   \n4  79.435593  0.351582  0.303726  0.622458  0.024306  50.368004  0.467727   \n\n      stat_30     stat_31       stat_32   stat_33  stat_34    stat_35  \\\n0         NaN         NaN           NaN       NaN      NaN        NaN   \n1         NaN         NaN           NaN       NaN      NaN        NaN   \n2         NaN         NaN           NaN       NaN      NaN        NaN   \n3  208.168869  112.401535  1.942842e+13  1.931421      0.0  14.245132   \n4   95.304085  155.542389  2.497264e+13  1.946892      0.0   6.633580   \n\n    stat_36   stat_37   stat_38  stat_39    stat_40  stat_41  stat_42  \\\n0       NaN       NaN       NaN      NaN        NaN      NaN      NaN   \n1       NaN       NaN       NaN      NaN        NaN      NaN      NaN   \n2       NaN       NaN       NaN      NaN        NaN      NaN      NaN   \n3 -1.746094 -2.905339 -1.048372      0.0 -89.833092      0.0      0.0   \n4 -1.038711 -1.522690 -1.018787      0.0 -88.761833      0.0      0.0   \n\n       stat_43       stat_44  stat_45  stat_46  stat_47   stat_48   stat_49  \\\n0          NaN           NaN      NaN      NaN      NaN       NaN       NaN   \n1          NaN           NaN      NaN      NaN      NaN       NaN       NaN   \n2          NaN           NaN      NaN      NaN      NaN       NaN       NaN   \n3  3824.000000  5.500000e+10      1.0      3.0     41.0 -0.684180 -0.309863   \n4  3098.166748  0.000000e+00      1.0      3.0     68.0 -0.052803 -0.044517   \n\n    stat_50   stat_51    stat_52  stat_53   stat_54      stat_55  \\\n0       NaN       NaN        NaN      NaN       NaN          NaN   \n1       NaN       NaN        NaN      NaN       NaN          NaN   \n2       NaN       NaN        NaN      NaN       NaN          NaN   \n3 -0.649974  0.006432 -41.541863      0.0  2.392969  4028.666748   \n4 -1.009344  0.008622 -88.386049      0.0  0.500000  3747.000000   \n\n        stat_56  stat_57  stat_58  stat_59   stat_60   stat_61   stat_62  \\\n0           NaN      NaN      NaN      NaN       NaN       NaN       NaN   \n1           NaN      NaN      NaN      NaN       NaN       NaN       NaN   \n2           NaN      NaN      NaN      NaN       NaN       NaN       NaN   \n3  3.689000e+13      3.0      3.0     42.0 -0.366849  0.024974 -0.245378   \n4  2.154000e+13      2.0      3.0     74.0 -0.020622 -0.028179 -1.007728   \n\n    stat_63    stat_64  stat_65   stat_66  stat_67       stat_68  stat_69  \\\n0       NaN        NaN      NaN       NaN      NaN           NaN      NaN   \n1       NaN        NaN      NaN       NaN      NaN           NaN      NaN   \n2       NaN        NaN      NaN       NaN      NaN           NaN      NaN   \n3  0.023637 -15.086617      0.0  6.926828   4070.0  5.347750e+13      5.0   \n4  0.009831 -86.119919      1.0  0.879005   3812.0  4.331000e+13      4.0   \n\n   stat_70  stat_71   stat_72   stat_73   stat_74   stat_75    stat_76  \\\n0      NaN      NaN       NaN       NaN       NaN       NaN        NaN   \n1      NaN      NaN       NaN       NaN       NaN       NaN        NaN   \n2      NaN      NaN       NaN       NaN       NaN       NaN        NaN   \n3      3.0     50.0 -0.010677  0.400677  0.204727  0.041420  12.220764   \n4      3.0     79.0 -0.019081  0.020307 -0.294459  0.010668 -17.483364   \n\n   stat_77    stat_78      stat_79       stat_80  stat_81  stat_82  stat_83  \\\n0      NaN        NaN          NaN           NaN      NaN      NaN      NaN   \n1      NaN        NaN          NaN           NaN      NaN      NaN      NaN   \n2      NaN        NaN          NaN           NaN      NaN      NaN      NaN   \n3      0.0  15.000000  4147.000000  6.640875e+13      6.0      3.0     53.0   \n4      1.0   6.141348  3951.187561  6.485500e+13      6.0      3.0     85.0   \n\n    stat_84   stat_85   stat_86   stat_87    stat_88  stat_89      stat_90  \\\n0       NaN       NaN       NaN       NaN        NaN      NaN          NaN   \n1       NaN       NaN       NaN       NaN        NaN      NaN          NaN   \n2       NaN       NaN       NaN       NaN        NaN      NaN          NaN   \n3  1.507865  1.666354  1.546979  4.004276  89.751656      0.0  2633.250000   \n4  1.034351  1.946303  1.146284  2.952888  89.476036      1.0  2597.800049   \n\n   stat_91       stat_92  stat_93  stat_94  stat_95  SDS_Score_Weighted  \\\n0      NaN           NaN      NaN      NaN      NaN           15.181733   \n1      NaN           NaN      NaN      NaN      NaN           11.488035   \n2      NaN           NaN      NaN      NaN      NaN           16.171184   \n3   4188.5  8.611000e+13      7.0      3.0     85.0           18.559317   \n4   4175.0  8.639500e+13      7.0      3.0     91.0           15.101627   \n\n   CGAS_Score_Weighted  Internet_Hours_Age  Physical-Waist_Age     BMI_Age  \\\n0             0.153939                15.0          294.185277   84.394862   \n1                  NaN                 0.0          502.920000  126.332712   \n2            10.071426                20.0          625.762995  166.503303   \n3            10.071426                 0.0          589.713663  164.647283   \n4             0.123631                 0.0          978.526184  289.667807   \n\n   Physical-Height_Age     SDS_BMI    CGAS_SDS  CGAS_Endurance_Mins  \\\n0               584.20  256.252049    2.337064                  NaN   \n1              1097.28  161.257182         NaN                  NaN   \n2              1435.10  269.255561  162.866880            70.499979   \n3              1280.16  339.526799  186.918785            90.642830   \n4              1964.69  336.496546    1.867032                  NaN   \n\n   SDS_Activity  SDS_InternetHours  BMI_Systolic_BP  Age_Systolic_BP  \\\n0     30.363466          45.545198              NaN              NaN   \n1     22.976070           0.000000      1712.510097           1098.0   \n2           NaN          32.342369      1948.088644           1170.0   \n3     55.677952           0.000000      2140.414673           1053.0   \n4     30.203254           0.000000      2272.778175           1326.0   \n\n   PreInt_Systolic_BP  PAQ_A_Activity  Activity_CU_PU  FGC_CU_PU  \\\n0                 NaN             NaN             0.0        0.0   \n1                 0.0             NaN            30.0       15.0   \n2               234.0             NaN             NaN      140.0   \n3                 0.0             NaN           270.0       90.0   \n4                 0.0             NaN           144.0       72.0   \n\n   FGC_CU_PU_Age  FGC_GSND_GSD  FGC_GSND_GSD_Age   CGAS_CU_PU  \\\n0            0.0           NaN               NaN     0.000000   \n1          135.0           NaN               NaN          NaN   \n2         1400.0        149.94           1499.40  1409.999585   \n3          810.0           NaN               NaN   906.428305   \n4          936.0        295.35           3839.55     8.901443   \n\n   PreInt_FGC_CU_PU  Endurance_CU_PU  \n0               0.0              NaN  \n1               0.0              NaN  \n2             280.0            980.0  \n3               0.0            810.0  \n4               0.0              NaN  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CGAS-Season</th>\n      <th>Physical-Season</th>\n      <th>Physical-BMI</th>\n      <th>Physical-Waist_Circumference</th>\n      <th>Physical-Diastolic_BP</th>\n      <th>Physical-HeartRate</th>\n      <th>Physical-Systolic_BP</th>\n      <th>Fitness_Endurance-Season</th>\n      <th>Fitness_Endurance-Max_Stage</th>\n      <th>Fitness_Endurance-Time_Mins</th>\n      <th>Fitness_Endurance-Time_Sec</th>\n      <th>FGC-Season</th>\n      <th>FGC-FGC_CU</th>\n      <th>FGC-FGC_CU_Zone</th>\n      <th>FGC-FGC_GSND</th>\n      <th>FGC-FGC_GSND_Zone</th>\n      <th>FGC-FGC_GSD</th>\n      <th>FGC-FGC_GSD_Zone</th>\n      <th>FGC-FGC_PU</th>\n      <th>FGC-FGC_PU_Zone</th>\n      <th>FGC-FGC_SRL</th>\n      <th>FGC-FGC_SRL_Zone</th>\n      <th>FGC-FGC_SRR</th>\n      <th>FGC-FGC_SRR_Zone</th>\n      <th>FGC-FGC_TL</th>\n      <th>FGC-FGC_TL_Zone</th>\n      <th>BIA-Season</th>\n      <th>BIA-BIA_Activity_Level_num</th>\n      <th>BIA-BIA_BMI</th>\n      <th>BIA-BIA_Frame_num</th>\n      <th>PAQ_A-Season</th>\n      <th>PAQ_A-PAQ_A_Total</th>\n      <th>PAQ_C-Season</th>\n      <th>PAQ_C-PAQ_C_Total</th>\n      <th>PreInt_EduHx-Season</th>\n      <th>PreInt_EduHx-computerinternet_hoursday</th>\n      <th>sii</th>\n      <th>Season_Spring</th>\n      <th>Season_Summer</th>\n      <th>Basic_Demos-Age</th>\n      <th>Season_Fall</th>\n      <th>Season_Winter</th>\n      <th>Basic_Demos-Sex</th>\n      <th>Physical-Weight</th>\n      <th>Physical-Height</th>\n      <th>SDS-Season_Fall</th>\n      <th>SDS-Season_Spring</th>\n      <th>SDS-Season_Summer</th>\n      <th>SDS-Season_Winter</th>\n      <th>stat_0</th>\n      <th>stat_1</th>\n      <th>stat_2</th>\n      <th>stat_3</th>\n      <th>stat_4</th>\n      <th>stat_5</th>\n      <th>stat_6</th>\n      <th>stat_7</th>\n      <th>stat_8</th>\n      <th>stat_9</th>\n      <th>stat_10</th>\n      <th>stat_11</th>\n      <th>stat_12</th>\n      <th>stat_13</th>\n      <th>stat_14</th>\n      <th>stat_15</th>\n      <th>stat_16</th>\n      <th>stat_17</th>\n      <th>stat_18</th>\n      <th>stat_19</th>\n      <th>stat_20</th>\n      <th>stat_21</th>\n      <th>stat_22</th>\n      <th>stat_23</th>\n      <th>stat_24</th>\n      <th>stat_25</th>\n      <th>stat_26</th>\n      <th>stat_27</th>\n      <th>stat_28</th>\n      <th>stat_29</th>\n      <th>stat_30</th>\n      <th>stat_31</th>\n      <th>stat_32</th>\n      <th>stat_33</th>\n      <th>stat_34</th>\n      <th>stat_35</th>\n      <th>stat_36</th>\n      <th>stat_37</th>\n      <th>stat_38</th>\n      <th>stat_39</th>\n      <th>stat_40</th>\n      <th>stat_41</th>\n      <th>stat_42</th>\n      <th>stat_43</th>\n      <th>stat_44</th>\n      <th>stat_45</th>\n      <th>stat_46</th>\n      <th>stat_47</th>\n      <th>stat_48</th>\n      <th>stat_49</th>\n      <th>stat_50</th>\n      <th>stat_51</th>\n      <th>stat_52</th>\n      <th>stat_53</th>\n      <th>stat_54</th>\n      <th>stat_55</th>\n      <th>stat_56</th>\n      <th>stat_57</th>\n      <th>stat_58</th>\n      <th>stat_59</th>\n      <th>stat_60</th>\n      <th>stat_61</th>\n      <th>stat_62</th>\n      <th>stat_63</th>\n      <th>stat_64</th>\n      <th>stat_65</th>\n      <th>stat_66</th>\n      <th>stat_67</th>\n      <th>stat_68</th>\n      <th>stat_69</th>\n      <th>stat_70</th>\n      <th>stat_71</th>\n      <th>stat_72</th>\n      <th>stat_73</th>\n      <th>stat_74</th>\n      <th>stat_75</th>\n      <th>stat_76</th>\n      <th>stat_77</th>\n      <th>stat_78</th>\n      <th>stat_79</th>\n      <th>stat_80</th>\n      <th>stat_81</th>\n      <th>stat_82</th>\n      <th>stat_83</th>\n      <th>stat_84</th>\n      <th>stat_85</th>\n      <th>stat_86</th>\n      <th>stat_87</th>\n      <th>stat_88</th>\n      <th>stat_89</th>\n      <th>stat_90</th>\n      <th>stat_91</th>\n      <th>stat_92</th>\n      <th>stat_93</th>\n      <th>stat_94</th>\n      <th>stat_95</th>\n      <th>SDS_Score_Weighted</th>\n      <th>CGAS_Score_Weighted</th>\n      <th>Internet_Hours_Age</th>\n      <th>Physical-Waist_Age</th>\n      <th>BMI_Age</th>\n      <th>Physical-Height_Age</th>\n      <th>SDS_BMI</th>\n      <th>CGAS_SDS</th>\n      <th>CGAS_Endurance_Mins</th>\n      <th>SDS_Activity</th>\n      <th>SDS_InternetHours</th>\n      <th>BMI_Systolic_BP</th>\n      <th>Age_Systolic_BP</th>\n      <th>PreInt_Systolic_BP</th>\n      <th>PAQ_A_Activity</th>\n      <th>Activity_CU_PU</th>\n      <th>FGC_CU_PU</th>\n      <th>FGC_CU_PU_Age</th>\n      <th>FGC_GSND_GSD</th>\n      <th>FGC_GSND_GSD_Age</th>\n      <th>CGAS_CU_PU</th>\n      <th>PreInt_FGC_CU_PU</th>\n      <th>Endurance_CU_PU</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>16.878972</td>\n      <td>58.837055</td>\n      <td>NaN</td>\n      <td>NaN</td>\n 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<td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>15.181733</td>\n      <td>0.153939</td>\n      <td>15.0</td>\n      <td>294.185277</td>\n      <td>84.394862</td>\n      <td>584.20</td>\n      <td>256.252049</td>\n      <td>2.337064</td>\n      <td>NaN</td>\n      <td>30.363466</td>\n      <td>45.545198</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>14.036968</td>\n      <td>55.880000</td>\n      <td>75.0</td>\n      <td>70.0</td>\n      <td>122.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>14.037100</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>2.340</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>False</td>\n      <td>True</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>20.865232</td>\n      <td>121.92</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>11.488035</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>502.920000</td>\n      <td>126.332712</td>\n      <td>1097.28</td>\n      <td>161.257182</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>22.976070</td>\n      <td>0.000000</td>\n      <td>1712.510097</td>\n      <td>1098.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>30.0</td>\n      <td>15.0</td>\n      <td>135.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>0</td>\n      <td>16.650330</td>\n      <td>62.576299</td>\n      <td>65.0</td>\n      <td>94.0</td>\n      <td>117.0</td>\n      <td>1</td>\n      <td>5.0</td>\n      <td>7.0</td>\n      <td>33.0</td>\n      <td>0</td>\n      <td>20.0</td>\n      <td>1.0</td>\n      <td>10.2</td>\n      <td>1.0</td>\n      <td>14.7</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>2.170</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>False</td>\n      <td>True</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>34.291555</td>\n      <td>143.51</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>16.171184</td>\n      <td>10.071426</td>\n      <td>20.0</td>\n      <td>625.762995</td>\n      <td>166.503303</td>\n      <td>1435.10</td>\n      <td>269.255561</td>\n      <td>162.866880</td>\n      <td>70.499979</td>\n      <td>NaN</td>\n      <td>32.342369</td>\n      <td>1948.088644</td>\n      <td>1170.0</td>\n      <td>234.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>140.0</td>\n      <td>1400.0</td>\n      <td>149.94</td>\n      <td>1499.40</td>\n      <td>1409.999585</td>\n      <td>280.0</td>\n      <td>980.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>1</td>\n      <td>18.294143</td>\n      <td>65.523740</td>\n      <td>60.0</td>\n      <td>97.0</td>\n      <td>117.0</td>\n      <td>2</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>37.0</td>\n      <td>1</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>3</td>\n      <td>3.0</td>\n      <td>18.294300</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>2.451</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>False</td>\n      <td>False</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>37.013107</td>\n      <td>142.24</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>-0.316384</td>\n      <td>0.016009</td>\n      <td>-0.167890</td>\n      <td>0.047388</td>\n      <td>-10.580416</td>\n      <td>0.000000</td>\n      <td>42.296310</td>\n      <td>4053.579102</td>\n      <td>5.046215e+13</td>\n      <td>4.470182</td>\n      <td>3.0</td>\n      <td>53.201683</td>\n      <td>0.453665</td>\n      <td>0.502698</td>\n      <td>0.585710</td>\n      <td>0.106353</td>\n      <td>42.947163</td>\n      <td>0.000000</td>\n      <td>208.168869</td>\n      <td>112.401535</td>\n      <td>1.942842e+13</td>\n      <td>1.931421</td>\n      <td>0.0</td>\n      <td>14.245132</td>\n      <td>-1.746094</td>\n      <td>-2.905339</td>\n      <td>-1.048372</td>\n      <td>0.0</td>\n      <td>-89.833092</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3824.000000</td>\n      <td>5.500000e+10</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>41.0</td>\n      <td>-0.684180</td>\n      <td>-0.309863</td>\n      <td>-0.649974</td>\n      <td>0.006432</td>\n      <td>-41.541863</td>\n      <td>0.0</td>\n      <td>2.392969</td>\n      <td>4028.666748</td>\n      <td>3.689000e+13</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>42.0</td>\n      <td>-0.366849</td>\n      <td>0.024974</td>\n      <td>-0.245378</td>\n      <td>0.023637</td>\n      <td>-15.086617</td>\n      <td>0.0</td>\n      <td>6.926828</td>\n      <td>4070.0</td>\n      <td>5.347750e+13</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>50.0</td>\n      <td>-0.010677</td>\n      <td>0.400677</td>\n      <td>0.204727</td>\n      <td>0.041420</td>\n      <td>12.220764</td>\n      <td>0.0</td>\n      <td>15.000000</td>\n      <td>4147.000000</td>\n      <td>6.640875e+13</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>53.0</td>\n      <td>1.507865</td>\n      <td>1.666354</td>\n      <td>1.546979</td>\n      <td>4.004276</td>\n      <td>89.751656</td>\n      <td>0.0</td>\n      <td>2633.250000</td>\n      <td>4188.5</td>\n      <td>8.611000e+13</td>\n      <td>7.0</td>\n      <td>3.0</td>\n      <td>85.0</td>\n      <td>18.559317</td>\n      <td>10.071426</td>\n      <td>0.0</td>\n      <td>589.713663</td>\n      <td>164.647283</td>\n      <td>1280.16</td>\n      <td>339.526799</td>\n      <td>186.918785</td>\n      <td>90.642830</td>\n      <td>55.677952</td>\n      <td>0.000000</td>\n      <td>2140.414673</td>\n      <td>1053.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>270.0</td>\n      <td>90.0</td>\n      <td>810.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>906.428305</td>\n      <td>0.0</td>\n      <td>810.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>1</td>\n      <td>22.282139</td>\n      <td>75.271245</td>\n      <td>60.0</td>\n      <td>73.0</td>\n      <td>102.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>16.5</td>\n      <td>2.0</td>\n      <td>17.9</td>\n      <td>2.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>2.0</td>\n      <td>22.282139</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>4</td>\n      <td>4.110</td>\n      <td>3</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>True</td>\n      <td>False</td>\n      <td>13.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>50.893022</td>\n      <td>151.13</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>396396.0</td>\n      <td>-0.004272</td>\n      <td>0.016859</td>\n      <td>-0.631731</td>\n      <td>0.011926</td>\n      <td>-55.630768</td>\n      <td>0.655708</td>\n      <td>16.771982</td>\n      <td>3838.189453</td>\n      <td>4.321212e+13</td>\n      <td>3.909848</td>\n      <td>3.0</td>\n      <td>79.435593</td>\n      <td>0.351582</td>\n      <td>0.303726</td>\n      <td>0.622458</td>\n      <td>0.024306</td>\n      <td>50.368004</td>\n      <td>0.467727</td>\n      <td>95.304085</td>\n      <td>155.542389</td>\n      <td>2.497264e+13</td>\n      <td>1.946892</td>\n      <td>0.0</td>\n      <td>6.633580</td>\n      <td>-1.038711</td>\n      <td>-1.522690</td>\n      <td>-1.018787</td>\n      <td>0.0</td>\n      <td>-88.761833</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3098.166748</td>\n      <td>0.000000e+00</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>68.0</td>\n      <td>-0.052803</td>\n      <td>-0.044517</td>\n      <td>-1.009344</td>\n      <td>0.008622</td>\n      <td>-88.386049</td>\n      <td>0.0</td>\n      <td>0.500000</td>\n      <td>3747.000000</td>\n      <td>2.154000e+13</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>74.0</td>\n      <td>-0.020622</td>\n      <td>-0.028179</td>\n      <td>-1.007728</td>\n      <td>0.009831</td>\n      <td>-86.119919</td>\n      <td>1.0</td>\n      <td>0.879005</td>\n      <td>3812.0</td>\n      <td>4.331000e+13</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>79.0</td>\n      <td>-0.019081</td>\n      <td>0.020307</td>\n      <td>-0.294459</td>\n      <td>0.010668</td>\n      <td>-17.483364</td>\n      <td>1.0</td>\n      <td>6.141348</td>\n      <td>3951.187561</td>\n      <td>6.485500e+13</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>85.0</td>\n      <td>1.034351</td>\n      <td>1.946303</td>\n      <td>1.146284</td>\n      <td>2.952888</td>\n      <td>89.476036</td>\n      <td>1.0</td>\n      <td>2597.800049</td>\n      <td>4175.0</td>\n      <td>8.639500e+13</td>\n      <td>7.0</td>\n      <td>3.0</td>\n      <td>91.0</td>\n      <td>15.101627</td>\n      <td>0.123631</td>\n      <td>0.0</td>\n      <td>978.526184</td>\n      <td>289.667807</td>\n      <td>1964.69</td>\n      <td>336.496546</td>\n      <td>1.867032</td>\n      <td>NaN</td>\n      <td>30.203254</td>\n      <td>0.000000</td>\n      <td>2272.778175</td>\n      <td>1326.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>144.0</td>\n      <td>72.0</td>\n      <td>936.0</td>\n      <td>295.35</td>\n      <td>3839.55</td>\n      <td>8.901443</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":519},{"id":"6551144c","cell_type":"code","source":"print(test.shape)\ntest.head()","metadata":{"execution":{"iopub.status.busy":"2026-04-24T08:00:36.962422Z","iopub.execute_input":"2026-04-24T08:00:36.962805Z","iopub.status.idle":"2026-04-24T08:00:37.078654Z","shell.execute_reply.started":"2026-04-24T08:00:36.962776Z","shell.execute_reply":"2026-04-24T08:00:37.077746Z"},"papermill":{"duration":0.165859,"end_time":"2025-05-23T02:58:09.161991","exception":false,"start_time":"2025-05-23T02:58:08.996132","status":"completed"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","text":"(20, 167)\n","output_type":"stream"},{"execution_count":520,"output_type":"execute_result","data":{"text/plain":"   CGAS-Season  Physical-Season  Physical-BMI  Physical-Waist_Circumference  \\\n0            0                0     16.878972                     58.837055   \n1            1                0     14.036968                     55.880000   \n2            1                0     16.650330                     62.576299   \n3            1                1     18.294143                     65.523740   \n4            2                2     24.799164                     82.865040   \n\n   Physical-Diastolic_BP  Physical-HeartRate  Physical-Systolic_BP  \\\n0                    NaN                 NaN                   NaN   \n1                   75.0                70.0                 122.0   \n2                   65.0                94.0                 117.0   \n3                   60.0                97.0                 117.0   \n4                    NaN                 NaN                   NaN   \n\n   Fitness_Endurance-Season  Fitness_Endurance-Max_Stage  \\\n0                         0                          NaN   \n1                         0                          NaN   \n2                         1                          5.0   \n3                         2                          6.0   \n4                         0                          NaN   \n\n   Fitness_Endurance-Time_Mins  Fitness_Endurance-Time_Sec  FGC-Season  \\\n0                          NaN                         NaN           0   \n1                          NaN                         NaN           0   \n2                          7.0                        33.0           0   \n3                          9.0                        37.0           1   \n4                          NaN                         NaN           2   \n\n   FGC-FGC_CU  FGC-FGC_CU_Zone  FGC-FGC_GSND  FGC-FGC_GSND_Zone  FGC-FGC_GSD  \\\n0         0.0              0.0           NaN                NaN          NaN   \n1         3.0              0.0           NaN                NaN          NaN   \n2        20.0              1.0          10.2                1.0         14.7   \n3        18.0              1.0           NaN                NaN          NaN   \n4         NaN              NaN           NaN                NaN          NaN   \n\n   FGC-FGC_GSD_Zone  FGC-FGC_PU  FGC-FGC_PU_Zone  FGC-FGC_SRL  \\\n0               NaN         0.0              0.0          7.0   \n1               NaN         5.0              0.0         11.0   \n2               2.0         7.0              1.0         10.0   \n3               NaN         5.0              0.0          7.0   \n4               NaN         NaN              NaN          NaN   \n\n   FGC-FGC_SRL_Zone  FGC-FGC_SRR  FGC-FGC_SRR_Zone  FGC-FGC_TL  \\\n0               0.0          6.0               0.0         6.0   \n1               1.0         11.0               1.0         3.0   \n2               1.0         10.0               1.0         5.0   \n3               0.0          7.0               0.0         7.0   \n4               NaN          NaN               NaN         NaN   \n\n   FGC-FGC_TL_Zone  BIA-Season  BIA-BIA_Activity_Level_num  BIA-BIA_BMI  \\\n0              1.0           0                         2.0      16.8792   \n1              0.0           1                         2.0      14.0371   \n2              0.0           2                         NaN          NaN   \n3              1.0           3                         3.0      18.2943   \n4              NaN           2                         NaN          NaN   \n\n   BIA-BIA_Frame_num  PAQ_A-Season  PAQ_A-PAQ_A_Total  PAQ_C-Season  \\\n0                1.0             0                NaN             0   \n1                1.0             0                NaN             1   \n2                NaN             0                NaN             2   \n3                2.0             0                NaN             3   \n4                NaN             1               1.04             0   \n\n   PAQ_C-PAQ_C_Total  PreInt_EduHx-Season  \\\n0                NaN                    0   \n1              2.340                    1   \n2              2.170                    1   \n3              2.451                    2   \n4                NaN                    0   \n\n   PreInt_EduHx-computerinternet_hoursday  Season_Spring  Season_Summer  \\\n0                                     3.0          False          False   \n1                                     0.0          False           True   \n2                                     2.0          False           True   \n3                                     0.0          False          False   \n4                                     2.0           True          False   \n\n   Basic_Demos-Age  Season_Fall  Season_Winter  Basic_Demos-Sex  \\\n0              5.0          1.0            0.0              0.0   \n1              9.0          0.0            0.0              0.0   \n2             10.0          0.0            0.0              1.0   \n3              9.0          0.0            1.0              0.0   \n4             18.0          0.0            0.0              1.0   \n\n   Physical-Weight  Physical-Height  SDS-Season_Fall  SDS-Season_Spring  \\\n0        23.042474         116.8400            False              False   \n1        20.865232         121.9200             True              False   \n2        34.291555         143.5100             True              False   \n3        37.013107         142.2400            False              False   \n4        63.693389         160.2613            False              False   \n\n   SDS-Season_Summer  SDS-Season_Winter   stat_0   stat_1   stat_2   stat_3  \\\n0              False               True      NaN      NaN      NaN      NaN   \n1              False              False      NaN      NaN      NaN      NaN   \n2              False              False      NaN      NaN      NaN      NaN   \n3               True              False  43330.0  43330.0  43330.0  43330.0   \n4               True              False      NaN      NaN      NaN      NaN   \n\n    stat_4   stat_5   stat_6   stat_7   stat_8   stat_9  stat_10  stat_11  \\\n0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n3  43330.0  43330.0  43330.0  43330.0  43330.0  43330.0  43330.0  43330.0   \n4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n\n    stat_12   stat_13  stat_14   stat_15    stat_16  stat_17   stat_18  \\\n0       NaN       NaN      NaN       NaN        NaN      NaN       NaN   \n1       NaN       NaN      NaN       NaN        NaN      NaN       NaN   \n2       NaN       NaN      NaN       NaN        NaN      NaN       NaN   \n3 -0.316384  0.016009 -0.16789  0.047388 -10.580416      0.0  42.29631   \n4       NaN       NaN      NaN       NaN        NaN      NaN       NaN   \n\n       stat_19       stat_20   stat_21  stat_22    stat_23   stat_24  \\\n0          NaN           NaN       NaN      NaN        NaN       NaN   \n1          NaN           NaN       NaN      NaN        NaN       NaN   \n2          NaN           NaN       NaN      NaN        NaN       NaN   \n3  4053.579102  5.046215e+13  4.470182      3.0  53.201683  0.453665   \n4          NaN           NaN       NaN      NaN        NaN       NaN   \n\n    stat_25  stat_26   stat_27    stat_28  stat_29     stat_30     stat_31  \\\n0       NaN      NaN       NaN        NaN      NaN         NaN         NaN   \n1       NaN      NaN       NaN        NaN      NaN         NaN         NaN   \n2       NaN      NaN       NaN        NaN      NaN         NaN         NaN   \n3  0.502698  0.58571  0.106353  42.947163      0.0  208.168869  112.401535   \n4       NaN      NaN       NaN        NaN      NaN         NaN         NaN   \n\n        stat_32   stat_33  stat_34    stat_35   stat_36   stat_37   stat_38  \\\n0           NaN       NaN      NaN        NaN       NaN       NaN       NaN   \n1           NaN       NaN      NaN        NaN       NaN       NaN       NaN   \n2           NaN       NaN      NaN        NaN       NaN       NaN       NaN   \n3  1.942842e+13  1.931421      0.0  14.245132 -1.746094 -2.905339 -1.048372   \n4           NaN       NaN      NaN        NaN       NaN       NaN       NaN   \n\n   stat_39    stat_40  stat_41  stat_42  stat_43       stat_44  stat_45  \\\n0      NaN        NaN      NaN      NaN      NaN           NaN      NaN   \n1      NaN        NaN      NaN      NaN      NaN           NaN      NaN   \n2      NaN        NaN      NaN      NaN      NaN           NaN      NaN   \n3      0.0 -89.833092      0.0      0.0   3824.0  5.500000e+10      1.0   \n4      NaN        NaN      NaN      NaN      NaN           NaN      NaN   \n\n   stat_46  stat_47  stat_48   stat_49   stat_50   stat_51    stat_52  \\\n0      NaN      NaN      NaN       NaN       NaN       NaN        NaN   \n1      NaN      NaN      NaN       NaN       NaN       NaN        NaN   \n2      NaN      NaN      NaN       NaN       NaN       NaN        NaN   \n3      3.0     41.0 -0.68418 -0.309863 -0.649974  0.006432 -41.541863   \n4      NaN      NaN      NaN       NaN       NaN       NaN        NaN   \n\n   stat_53   stat_54      stat_55       stat_56  stat_57  stat_58  stat_59  \\\n0      NaN       NaN          NaN           NaN      NaN      NaN      NaN   \n1      NaN       NaN          NaN           NaN      NaN      NaN      NaN   \n2      NaN       NaN          NaN           NaN      NaN      NaN      NaN   \n3      0.0  2.392969  4028.666748  3.689000e+13      3.0      3.0     42.0   \n4      NaN       NaN          NaN           NaN      NaN      NaN      NaN   \n\n    stat_60   stat_61   stat_62   stat_63    stat_64  stat_65   stat_66  \\\n0       NaN       NaN       NaN       NaN        NaN      NaN       NaN   \n1       NaN       NaN       NaN       NaN        NaN      NaN       NaN   \n2       NaN       NaN       NaN       NaN        NaN      NaN       NaN   \n3 -0.366849  0.024974 -0.245378  0.023637 -15.086617      0.0  6.926828   \n4       NaN       NaN       NaN       NaN        NaN      NaN       NaN   \n\n   stat_67       stat_68  stat_69  stat_70  stat_71   stat_72   stat_73  \\\n0      NaN           NaN      NaN      NaN      NaN       NaN       NaN   \n1      NaN           NaN      NaN      NaN      NaN       NaN       NaN   \n2      NaN           NaN      NaN      NaN      NaN       NaN       NaN   \n3   4070.0  5.347750e+13      5.0      3.0     50.0 -0.010677  0.400677   \n4      NaN           NaN      NaN      NaN      NaN       NaN       NaN   \n\n    stat_74  stat_75    stat_76  stat_77  stat_78  stat_79       stat_80  \\\n0       NaN      NaN        NaN      NaN      NaN      NaN           NaN   \n1       NaN      NaN        NaN      NaN      NaN      NaN           NaN   \n2       NaN      NaN        NaN      NaN      NaN      NaN           NaN   \n3  0.204727  0.04142  12.220764      0.0     15.0   4147.0  6.640875e+13   \n4       NaN      NaN        NaN      NaN      NaN      NaN           NaN   \n\n   stat_81  stat_82  stat_83   stat_84   stat_85   stat_86   stat_87  \\\n0      NaN      NaN      NaN       NaN       NaN       NaN       NaN   \n1      NaN      NaN      NaN       NaN       NaN       NaN       NaN   \n2      NaN      NaN      NaN       NaN       NaN       NaN       NaN   \n3      6.0      3.0     53.0  1.507865  1.666354  1.546979  4.004276   \n4      NaN      NaN      NaN       NaN       NaN       NaN       NaN   \n\n     stat_88  stat_89  stat_90  stat_91       stat_92  stat_93  stat_94  \\\n0        NaN      NaN      NaN      NaN           NaN      NaN      NaN   \n1        NaN      NaN      NaN      NaN           NaN      NaN      NaN   \n2        NaN      NaN      NaN      NaN           NaN      NaN      NaN   \n3  89.751656      0.0  2633.25   4188.5  8.611000e+13      7.0      3.0   \n4        NaN      NaN      NaN      NaN           NaN      NaN      NaN   \n\n   stat_95  SDS_Score_Weighted  CGAS_Score_Weighted  Internet_Hours_Age  \\\n0      NaN           16.313556             0.153939                15.0   \n1      NaN           11.488035                  NaN                 0.0   \n2      NaN           16.171184            10.071426                20.0   \n3     85.0           18.559317            10.071426                 0.0   \n4      NaN           12.366370                  NaN                36.0   \n\n   Physical-Waist_Age     BMI_Age  Physical-Height_Age     SDS_BMI  \\\n0          294.185277   84.394862             584.2000  275.356066   \n1          502.920000  126.332712            1097.2800  161.257182   \n2          625.762995  166.503303            1435.1000  269.255561   \n3          589.713663  164.647283            1280.1600  339.526799   \n4         1491.570723  446.384944            2884.7034  306.675630   \n\n     CGAS_SDS  CGAS_Endurance_Mins  SDS_Activity  SDS_InternetHours  \\\n0    2.511296                  NaN     32.627113          48.940669   \n1         NaN                  NaN     22.976070           0.000000   \n2  162.866880            70.499979           NaN          32.342369   \n3  186.918785            90.642830     55.677952           0.000000   \n4         NaN                  NaN           NaN          24.732740   \n\n   BMI_Systolic_BP  Age_Systolic_BP  PreInt_Systolic_BP  PAQ_A_Activity  \\\n0              NaN              NaN                 NaN             NaN   \n1      1712.510097           1098.0                 0.0             NaN   \n2      1948.088644           1170.0               234.0             NaN   \n3      2140.414673           1053.0                 0.0             NaN   \n4              NaN              NaN                 NaN             NaN   \n\n   Activity_CU_PU  FGC_CU_PU  FGC_CU_PU_Age  FGC_GSND_GSD  FGC_GSND_GSD_Age  \\\n0             0.0        0.0            0.0           NaN               NaN   \n1            30.0       15.0          135.0           NaN               NaN   \n2             NaN      140.0         1400.0        149.94            1499.4   \n3           270.0       90.0          810.0           NaN               NaN   \n4             NaN        NaN            NaN           NaN               NaN   \n\n    CGAS_CU_PU  PreInt_FGC_CU_PU  Endurance_CU_PU  \n0     0.000000               0.0              NaN  \n1          NaN               0.0              NaN  \n2  1409.999585             280.0            980.0  \n3   906.428305               0.0            810.0  \n4          NaN               NaN              NaN  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CGAS-Season</th>\n      <th>Physical-Season</th>\n      <th>Physical-BMI</th>\n      <th>Physical-Waist_Circumference</th>\n      <th>Physical-Diastolic_BP</th>\n      <th>Physical-HeartRate</th>\n      <th>Physical-Systolic_BP</th>\n      <th>Fitness_Endurance-Season</th>\n      <th>Fitness_Endurance-Max_Stage</th>\n      <th>Fitness_Endurance-Time_Mins</th>\n      <th>Fitness_Endurance-Time_Sec</th>\n      <th>FGC-Season</th>\n      <th>FGC-FGC_CU</th>\n      <th>FGC-FGC_CU_Zone</th>\n      <th>FGC-FGC_GSND</th>\n      <th>FGC-FGC_GSND_Zone</th>\n      <th>FGC-FGC_GSD</th>\n      <th>FGC-FGC_GSD_Zone</th>\n      <th>FGC-FGC_PU</th>\n      <th>FGC-FGC_PU_Zone</th>\n      <th>FGC-FGC_SRL</th>\n      <th>FGC-FGC_SRL_Zone</th>\n      <th>FGC-FGC_SRR</th>\n      <th>FGC-FGC_SRR_Zone</th>\n      <th>FGC-FGC_TL</th>\n      <th>FGC-FGC_TL_Zone</th>\n      <th>BIA-Season</th>\n      <th>BIA-BIA_Activity_Level_num</th>\n      <th>BIA-BIA_BMI</th>\n      <th>BIA-BIA_Frame_num</th>\n      <th>PAQ_A-Season</th>\n      <th>PAQ_A-PAQ_A_Total</th>\n      <th>PAQ_C-Season</th>\n      <th>PAQ_C-PAQ_C_Total</th>\n      <th>PreInt_EduHx-Season</th>\n      <th>PreInt_EduHx-computerinternet_hoursday</th>\n      <th>Season_Spring</th>\n      <th>Season_Summer</th>\n      <th>Basic_Demos-Age</th>\n      <th>Season_Fall</th>\n      <th>Season_Winter</th>\n      <th>Basic_Demos-Sex</th>\n      <th>Physical-Weight</th>\n      <th>Physical-Height</th>\n      <th>SDS-Season_Fall</th>\n      <th>SDS-Season_Spring</th>\n      <th>SDS-Season_Summer</th>\n      <th>SDS-Season_Winter</th>\n      <th>stat_0</th>\n      <th>stat_1</th>\n      <th>stat_2</th>\n      <th>stat_3</th>\n      <th>stat_4</th>\n      <th>stat_5</th>\n      <th>stat_6</th>\n      <th>stat_7</th>\n      <th>stat_8</th>\n      <th>stat_9</th>\n      <th>stat_10</th>\n      <th>stat_11</th>\n      <th>stat_12</th>\n      <th>stat_13</th>\n      <th>stat_14</th>\n      <th>stat_15</th>\n      <th>stat_16</th>\n      <th>stat_17</th>\n      <th>stat_18</th>\n      <th>stat_19</th>\n      <th>stat_20</th>\n      <th>stat_21</th>\n      <th>stat_22</th>\n      <th>stat_23</th>\n      <th>stat_24</th>\n      <th>stat_25</th>\n      <th>stat_26</th>\n      <th>stat_27</th>\n      <th>stat_28</th>\n      <th>stat_29</th>\n      <th>stat_30</th>\n      <th>stat_31</th>\n      <th>stat_32</th>\n      <th>stat_33</th>\n      <th>stat_34</th>\n      <th>stat_35</th>\n      <th>stat_36</th>\n      <th>stat_37</th>\n      <th>stat_38</th>\n      <th>stat_39</th>\n      <th>stat_40</th>\n      <th>stat_41</th>\n      <th>stat_42</th>\n      <th>stat_43</th>\n      <th>stat_44</th>\n      <th>stat_45</th>\n      <th>stat_46</th>\n      <th>stat_47</th>\n      <th>stat_48</th>\n      <th>stat_49</th>\n      <th>stat_50</th>\n      <th>stat_51</th>\n      <th>stat_52</th>\n      <th>stat_53</th>\n      <th>stat_54</th>\n      <th>stat_55</th>\n      <th>stat_56</th>\n      <th>stat_57</th>\n      <th>stat_58</th>\n      <th>stat_59</th>\n      <th>stat_60</th>\n      <th>stat_61</th>\n      <th>stat_62</th>\n      <th>stat_63</th>\n      <th>stat_64</th>\n      <th>stat_65</th>\n      <th>stat_66</th>\n      <th>stat_67</th>\n      <th>stat_68</th>\n      <th>stat_69</th>\n      <th>stat_70</th>\n      <th>stat_71</th>\n      <th>stat_72</th>\n      <th>stat_73</th>\n      <th>stat_74</th>\n      <th>stat_75</th>\n      <th>stat_76</th>\n      <th>stat_77</th>\n      <th>stat_78</th>\n      <th>stat_79</th>\n      <th>stat_80</th>\n      <th>stat_81</th>\n      <th>stat_82</th>\n      <th>stat_83</th>\n      <th>stat_84</th>\n      <th>stat_85</th>\n      <th>stat_86</th>\n      <th>stat_87</th>\n      <th>stat_88</th>\n      <th>stat_89</th>\n      <th>stat_90</th>\n      <th>stat_91</th>\n      <th>stat_92</th>\n      <th>stat_93</th>\n      <th>stat_94</th>\n      <th>stat_95</th>\n      <th>SDS_Score_Weighted</th>\n      <th>CGAS_Score_Weighted</th>\n      <th>Internet_Hours_Age</th>\n      <th>Physical-Waist_Age</th>\n      <th>BMI_Age</th>\n      <th>Physical-Height_Age</th>\n      <th>SDS_BMI</th>\n      <th>CGAS_SDS</th>\n      <th>CGAS_Endurance_Mins</th>\n      <th>SDS_Activity</th>\n      <th>SDS_InternetHours</th>\n      <th>BMI_Systolic_BP</th>\n      <th>Age_Systolic_BP</th>\n      <th>PreInt_Systolic_BP</th>\n      <th>PAQ_A_Activity</th>\n      <th>Activity_CU_PU</th>\n      <th>FGC_CU_PU</th>\n      <th>FGC_CU_PU_Age</th>\n      <th>FGC_GSND_GSD</th>\n      <th>FGC_GSND_GSD_Age</th>\n      <th>CGAS_CU_PU</th>\n      <th>PreInt_FGC_CU_PU</th>\n      <th>Endurance_CU_PU</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>16.878972</td>\n      <td>58.837055</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>2.0</td>\n      <td>16.8792</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>3.0</td>\n      <td>False</td>\n      <td>False</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>23.042474</td>\n      <td>116.8400</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>16.313556</td>\n      <td>0.153939</td>\n      <td>15.0</td>\n      <td>294.185277</td>\n      <td>84.394862</td>\n      <td>584.2000</td>\n      <td>275.356066</td>\n      <td>2.511296</td>\n      <td>NaN</td>\n      <td>32.627113</td>\n      <td>48.940669</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>14.036968</td>\n      <td>55.880000</td>\n      <td>75.0</td>\n      <td>70.0</td>\n      <td>122.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>11.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>14.0371</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>2.340</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>False</td>\n      <td>True</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>20.865232</td>\n      <td>121.9200</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>11.488035</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>502.920000</td>\n      <td>126.332712</td>\n      <td>1097.2800</td>\n      <td>161.257182</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>22.976070</td>\n      <td>0.000000</td>\n      <td>1712.510097</td>\n      <td>1098.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>30.0</td>\n      <td>15.0</td>\n      <td>135.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>16.650330</td>\n      <td>62.576299</td>\n      <td>65.0</td>\n      <td>94.0</td>\n      <td>117.0</td>\n      <td>1</td>\n      <td>5.0</td>\n      <td>7.0</td>\n      <td>33.0</td>\n      <td>0</td>\n      <td>20.0</td>\n      <td>1.0</td>\n      <td>10.2</td>\n      <td>1.0</td>\n      <td>14.7</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>2.170</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>False</td>\n      <td>True</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>34.291555</td>\n      <td>143.5100</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>16.171184</td>\n      <td>10.071426</td>\n      <td>20.0</td>\n      <td>625.762995</td>\n      <td>166.503303</td>\n      <td>1435.1000</td>\n      <td>269.255561</td>\n      <td>162.866880</td>\n      <td>70.499979</td>\n      <td>NaN</td>\n      <td>32.342369</td>\n      <td>1948.088644</td>\n      <td>1170.0</td>\n      <td>234.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>140.0</td>\n      <td>1400.0</td>\n      <td>149.94</td>\n      <td>1499.4</td>\n      <td>1409.999585</td>\n      <td>280.0</td>\n      <td>980.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>18.294143</td>\n      <td>65.523740</td>\n      <td>60.0</td>\n      <td>97.0</td>\n      <td>117.0</td>\n      <td>2</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>37.0</td>\n      <td>1</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>3</td>\n      <td>3.0</td>\n      <td>18.2943</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>2.451</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>False</td>\n      <td>False</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>37.013107</td>\n      <td>142.2400</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>43330.0</td>\n      <td>-0.316384</td>\n      <td>0.016009</td>\n      <td>-0.16789</td>\n      <td>0.047388</td>\n      <td>-10.580416</td>\n      <td>0.0</td>\n      <td>42.29631</td>\n      <td>4053.579102</td>\n      <td>5.046215e+13</td>\n      <td>4.470182</td>\n      <td>3.0</td>\n      <td>53.201683</td>\n      <td>0.453665</td>\n      <td>0.502698</td>\n      <td>0.58571</td>\n      <td>0.106353</td>\n      <td>42.947163</td>\n      <td>0.0</td>\n      <td>208.168869</td>\n      <td>112.401535</td>\n      <td>1.942842e+13</td>\n      <td>1.931421</td>\n      <td>0.0</td>\n      <td>14.245132</td>\n      <td>-1.746094</td>\n      <td>-2.905339</td>\n      <td>-1.048372</td>\n      <td>0.0</td>\n      <td>-89.833092</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3824.0</td>\n      <td>5.500000e+10</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>41.0</td>\n      <td>-0.68418</td>\n      <td>-0.309863</td>\n      <td>-0.649974</td>\n      <td>0.006432</td>\n      <td>-41.541863</td>\n      <td>0.0</td>\n      <td>2.392969</td>\n      <td>4028.666748</td>\n      <td>3.689000e+13</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>42.0</td>\n      <td>-0.366849</td>\n      <td>0.024974</td>\n      <td>-0.245378</td>\n      <td>0.023637</td>\n      <td>-15.086617</td>\n      <td>0.0</td>\n      <td>6.926828</td>\n      <td>4070.0</td>\n      <td>5.347750e+13</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>50.0</td>\n      <td>-0.010677</td>\n      <td>0.400677</td>\n      <td>0.204727</td>\n      <td>0.04142</td>\n      <td>12.220764</td>\n      <td>0.0</td>\n      <td>15.0</td>\n      <td>4147.0</td>\n      <td>6.640875e+13</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>53.0</td>\n      <td>1.507865</td>\n      <td>1.666354</td>\n      <td>1.546979</td>\n      <td>4.004276</td>\n      <td>89.751656</td>\n      <td>0.0</td>\n      <td>2633.25</td>\n      <td>4188.5</td>\n      <td>8.611000e+13</td>\n      <td>7.0</td>\n      <td>3.0</td>\n      <td>85.0</td>\n      <td>18.559317</td>\n      <td>10.071426</td>\n      <td>0.0</td>\n      <td>589.713663</td>\n      <td>164.647283</td>\n      <td>1280.1600</td>\n      <td>339.526799</td>\n      <td>186.918785</td>\n      <td>90.642830</td>\n      <td>55.677952</td>\n      <td>0.000000</td>\n      <td>2140.414673</td>\n      <td>1053.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>270.0</td>\n      <td>90.0</td>\n      <td>810.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>906.428305</td>\n      <td>0.0</td>\n      <td>810.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>2</td>\n      <td>24.799164</td>\n      <td>82.865040</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>1.04</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2.0</td>\n      <td>True</td>\n      <td>False</td>\n      <td>18.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>63.693389</td>\n      <td>160.2613</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.366370</td>\n      <td>NaN</td>\n      <td>36.0</td>\n      <td>1491.570723</td>\n      <td>446.384944</td>\n      <td>2884.7034</td>\n      <td>306.675630</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>24.732740</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":520},{"id":"209d8595","cell_type":"code","source":"sample = pd.read_csv('/kaggle/input/competitions/child-mind-institute-problematic-internet-use/sample_submission.csv')","metadata":{"execution":{"iopub.status.busy":"2026-04-24T08:00:37.079835Z","iopub.execute_input":"2026-04-24T08:00:37.080234Z","iopub.status.idle":"2026-04-24T08:00:37.089239Z","shell.execute_reply.started":"2026-04-24T08:00:37.080208Z","shell.execute_reply":"2026-04-24T08:00:37.088082Z"},"papermill":{"duration":0.075434,"end_time":"2025-05-23T02:58:09.84748","exception":false,"start_time":"2025-05-23T02:58:09.772046","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":521},{"id":"f7ec11bc-2864-438a-9bd6-dd1c34c3a851","cell_type":"code","source":"X = train.drop(columns=['sii'])\ny = train['sii']\n\nX_filled = X.fillna(X.median())\n\n# =========================\n# 2. TRAIN 3 MODELS FOR FEATURE IMPORTANCE\n# =========================\nxgb_model = XGBRegressor(**XGB_Params)\nlgb_model = LGBMRegressor(**LGB_Params, random_state=SEED, verbose=-1, n_estimators=300)\ncat_model = CatBoostRegressor(**CatBoost_Params)\n\nxgb_model.fit(X_filled, y)\nlgb_model.fit(X_filled, y)\ncat_model.fit(X_filled, y, verbose=0)\n\n# =========================\n# 3. GET FEATURE IMPORTANCE\n# =========================\nxgb_imp = xgb_model.feature_importances_\nlgb_imp = lgb_model.feature_importances_\ncat_imp = cat_model.get_feature_importance()\n\n# =========================\n# 4. NORMALIZE IMPORTANCE\n# =========================\nxgb_imp = xgb_imp / xgb_imp.sum()\nlgb_imp = lgb_imp / lgb_imp.sum()\ncat_imp = cat_imp / cat_imp.sum()\n\n# =========================\n# 5. COMBINE IMPORTANCE\n# =========================\nfinal_imp = (xgb_imp + lgb_imp + cat_imp) / 3\n\nfeat_imp_series = pd.Series(final_imp, index=X.columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T08:00:37.090496Z","iopub.execute_input":"2026-04-24T08:00:37.091813Z","iopub.status.idle":"2026-04-24T08:00:42.138281Z","shell.execute_reply.started":"2026-04-24T08:00:37.091772Z","shell.execute_reply":"2026-04-24T08:00:42.137417Z"}},"outputs":[],"execution_count":522},{"id":"d173ad3f-f1fa-4b64-9c74-f6cc77022d76","cell_type":"code","source":"imp_df = pd.DataFrame({\n    'Feature': X.columns,\n    'XGB': xgb_imp,\n    'LGB': lgb_imp,\n    'CAT': cat_imp,\n    'Combined': final_imp\n})\n\nimp_df = imp_df.sort_values(by='Combined', ascending=False).head(20)\n\nimp_df.set_index('Feature')[['XGB','LGB','CAT','Combined']].plot(\n    kind='barh', figsize=(12, 8)\n)\n\nplt.title(\"Feature Importance Comparison\")\nplt.xlabel(\"Normalized Importance\")\nplt.gca().invert_yaxis()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T08:00:42.139647Z","iopub.execute_input":"2026-04-24T08:00:42.140043Z","iopub.status.idle":"2026-04-24T08:00:42.737014Z","shell.execute_reply.started":"2026-04-24T08:00:42.14001Z","shell.execute_reply":"2026-04-24T08:00:42.736209Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x800 with 1 Axes>","image/png":"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= feat_imp_series.nlargest(20).index.tolist()\n\nprint(f\"Selected Features (Combined): {selected_features}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T08:00:42.738459Z","iopub.execute_input":"2026-04-24T08:00:42.739181Z","iopub.status.idle":"2026-04-24T08:00:42.748905Z","shell.execute_reply.started":"2026-04-24T08:00:42.739143Z","shell.execute_reply":"2026-04-24T08:00:42.745773Z"}},"outputs":[{"name":"stdout","text":"Selected Features (Combined): ['Physical-Height_Age', 'SDS_Score_Weighted', 'Internet_Hours_Age', 'PreInt_EduHx-computerinternet_hoursday', 'Physical-Height', 'SDS_BMI', 'CGAS_SDS', 'BMI_Age', 'Basic_Demos-Sex', 'Physical-HeartRate', 'Age_Systolic_BP', 'SDS_InternetHours', 'PreInt_Systolic_BP', 'Basic_Demos-Age', 'PreInt_FGC_CU_PU', 'PAQ_C-PAQ_C_Total', 'FGC-FGC_CU', 'Physical-Waist_Age', 'Physical-Systolic_BP', 'SDS_Activity']\n","output_type":"stream"}],"execution_count":524},{"id":"f2f6edd5-55cc-406e-9c58-d59af5c78fa2","cell_type":"code","source":"X_train_final = train[selected_features + ['sii']]\nX_test_final = test[selected_features]\n\n# 2. Huấn luyện các mô hình đơn lẻ để so sánh\nprint(\"\\n--- Training Individual Models ---\")\nresults = {}\n\n# XGBoost\nsub_xgb, model_xgb = TrainML(XGBRegressor(**XGB_Params), X_train_final, X_test_final)\nresults['XGBoost'] = quadratic_weighted_kappa(train['sii'], threshold_Rounder(model_xgb.predict(X_train_final.drop(columns='sii')), [0.5, 1.5, 2.5]))\n\n# LightGBM\nsub_lgb, model_lgb = TrainML(LGBMRegressor(**LGB_Params, random_state=SEED, verbose=-1, n_estimators=300), X_train_final, X_test_final)\nresults['LightGBM'] = quadratic_weighted_kappa(train['sii'], threshold_Rounder(model_lgb.predict(X_train_final.drop(columns='sii')), [0.5, 1.5, 2.5]))\n\n# CatBoost\nsub_cat, model_cat = TrainML(CatBoostRegressor(**CatBoost_Params), X_train_final, X_test_final)\nresults['CatBoost'] = quadratic_weighted_kappa(train['sii'], threshold_Rounder(model_cat.predict(X_train_final.drop(columns='sii')), [0.5, 1.5, 2.5]))\n\n# 3. So sánh với Ensemble hiện tại\nprint(\"\\n--- Training Ensemble Model ---\")\nestimators = [\n    ('catboost', CatBoost_Model),\n    ('lightgbm', LGB_Model),\n    ('xgboost', XGB_Model),\n    # ('tabnet', TabNet_Model),\n    # ('ridge', ridge_pipeline),\n    # ('svr', svr_pipeline)\n]\n\n# StackingRegressor\nensemble_model = StackingRegressor(\n    estimators=estimators,\n    # final_estimator=GradientBoostingRegressor(n_estimators=50, learning_rate=0.1),\n    # cv=5  \n)\n\n# final_submission,new_ensemble_model = TrainML(ensemble_model, train, test)\nsub_ens, model_ens = TrainML(ensemble_model, X_train_final, X_test_final)\nresults['Ensemble'] = quadratic_weighted_kappa(train['sii'], threshold_Rounder(model_ens.predict(X_train_final.drop(columns='sii')), [0.5, 1.5, 2.5]))\n\n# Gán lại submission cuối cùng\nfinal_submission = sub_ens","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T08:00:42.75048Z","iopub.execute_input":"2026-04-24T08:00:42.750924Z","iopub.status.idle":"2026-04-24T08:02:22.236599Z","shell.execute_reply.started":"2026-04-24T08:00:42.750887Z","shell.execute_reply":"2026-04-24T08:02:22.235452Z"}},"outputs":[{"name":"stderr","text":"Training Folds: 100%|██████████| 5/5 [01:23<00:00, 16.69s/it]","output_type":"stream"},{"name":"stdout","text":"Mean Train QWK --> 0.5601\nMean Validation QWK ---> 0.3924\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"----> || Optimized QWK SCORE :: \u001b[36m\u001b[1m 0.472\u001b[0m\n","output_type":"stream"}],"execution_count":525},{"id":"3a593d40-be21-41d1-9f34-e99a4f448208","cell_type":"code","source":"# Visualize Model Comparison\nplt.figure(figsize=(10, 5))\nsns.barplot(x=list(results.keys()), y=list(results.values()), palette='viridis')\nplt.title(\"Comparison of Optimized QWK Scores (Train Data)\")\nplt.ylabel(\"Quadratic Weighted Kappa\")\nplt.ylim(0, 1)\nfor i, v in enumerate(results.values()):\n    plt.text(i, v + 0.02, f\"{v:.3f}\", ha='center')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T08:02:22.237948Z","iopub.execute_input":"2026-04-24T08:02:22.238266Z","iopub.status.idle":"2026-04-24T08:02:22.434949Z","shell.execute_reply.started":"2026-04-24T08:02:22.238236Z","shell.execute_reply":"2026-04-24T08:02:22.4341Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x500 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":526},{"id":"621c686f","cell_type":"code","source":"# simple_features = ['SDS_Score_Weighted', 'Internet_Hours_Age', 'Physical-Height_Age', \n#                   'Basic_Demos-Sex', 'PreInt_FGC_CU_PU']\n\n# Selected Features: ['stat_31', 'stat_71', 'stat_59', \n#                     'Physical-Weight', 'PreInt_Systolic_BP',\n#                     'stat_95', 'stat_47', 'stat_77', 'SDS_BMI', \n#                     'SDS_InternetHours', 'stat_83', 'Physical-Height', \n#                     'Basic_Demos-Sex', 'PreInt_FGC_CU_PU', 'Physical-Waist_Age', 'SDS_Score_Weighted', \n#                     'PreInt_EduHx-computerinternet_hoursday', 'Basic_Demos-Age', 'Internet_Hours_Age', 'Physical-Height_Age']\n\n# train = train[simple_features + ['sii']]\n# test = test[simple_features]","metadata":{"execution":{"iopub.status.busy":"2026-04-24T08:02:22.436263Z","iopub.execute_input":"2026-04-24T08:02:22.436596Z","iopub.status.idle":"2026-04-24T08:02:22.441086Z","shell.execute_reply.started":"2026-04-24T08:02:22.436567Z","shell.execute_reply":"2026-04-24T08:02:22.440378Z"},"papermill":{"duration":0.074504,"end_time":"2025-05-23T02:58:09.435737","exception":false,"start_time":"2025-05-23T02:58:09.361233","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":527},{"id":"7e85f265","cell_type":"markdown","source":"Show features imporatance for XGB, LGB and CatBoost models","metadata":{"papermill":{"duration":0.066323,"end_time":"2025-05-23T02:58:09.566261","exception":false,"start_time":"2025-05-23T02:58:09.499938","status":"completed"},"tags":[]}},{"id":"0c5d2cd6","cell_type":"code","source":"final_submission","metadata":{"execution":{"iopub.status.busy":"2026-04-24T08:02:22.442109Z","iopub.execute_input":"2026-04-24T08:02:22.442438Z","iopub.status.idle":"2026-04-24T08:02:22.46286Z","shell.execute_reply.started":"2026-04-24T08:02:22.442401Z","shell.execute_reply":"2026-04-24T08:02:22.462017Z"},"papermill":{"duration":0.076572,"end_time":"2025-05-23T02:59:19.580008","exception":false,"start_time":"2025-05-23T02:59:19.503436","status":"completed"},"tags":[],"trusted":true},"outputs":[{"execution_count":528,"output_type":"execute_result","data":{"text/plain":"          id  sii\n0   00008ff9    1\n1   000fd460    0\n2   00105258    1\n3   00115b9f    0\n4   0016bb22    2\n5   001f3379    0\n6   0038ba98    1\n7   0068a485    0\n8   0069fbed    2\n9   0083e397    2\n10  0087dd65    0\n11  00abe655    1\n12  00ae59c9    2\n13  00af6387    1\n14  00bd4359    1\n15  00c0cd71    0\n16  00d56d4b    0\n17  00d9913d    0\n18  00e6167c    0\n19  00ebc35d    1","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>sii</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00008ff9</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000fd460</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00105258</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00115b9f</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0016bb22</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>001f3379</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0038ba98</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0068a485</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0069fbed</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0083e397</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0087dd65</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>00abe655</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>00ae59c9</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>00af6387</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>00bd4359</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>00c0cd71</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>00d56d4b</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>00d9913d</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>00e6167c</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>00ebc35d</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":528},{"id":"523afa7f","cell_type":"markdown","source":"# Submission","metadata":{"papermill":{"duration":0.067426,"end_time":"2025-05-23T02:59:19.713143","exception":false,"start_time":"2025-05-23T02:59:19.645717","status":"completed"},"tags":[]}},{"id":"43bb3d76","cell_type":"code","source":"final_submission[['id', 'sii']].to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2026-04-24T08:02:22.463904Z","iopub.execute_input":"2026-04-24T08:02:22.465418Z","iopub.status.idle":"2026-04-24T08:02:22.481479Z","shell.execute_reply.started":"2026-04-24T08:02:22.46539Z","shell.execute_reply":"2026-04-24T08:02:22.480734Z"},"papermill":{"duration":0.077421,"end_time":"2025-05-23T02:59:19.85807","exception":false,"start_time":"2025-05-23T02:59:19.780649","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":529}]}