{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":81933,"databundleVersionId":9643020,"sourceType":"competition"}],"dockerImageVersionId":30822,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Reason for choosing this approach","metadata":{}},{"cell_type":"markdown","source":"The goal of this competition is to predict from this data a participant's Severity Impairment Index (sii), a standard measure of problematic internet use.  According to the problem description, this is a multiclass categorical classification task with outputs ranging from 0 to 3: 0 for None, 1 for Mild, 2 for Moderate, and 3 for Severe. Given this structure, our first approach was to use a classifier method to predict the test data, specifically implementing a Random Forest Classifier.","metadata":{}},{"cell_type":"markdown","source":"### Import required libraries","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import make_scorer, cohen_kappa_score\nfrom sklearn.model_selection import GridSearchCV, cross_val_score","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:29.028981Z","iopub.execute_input":"2024-12-23T06:05:29.029368Z","iopub.status.idle":"2024-12-23T06:05:30.188355Z","shell.execute_reply.started":"2024-12-23T06:05:29.029332Z","shell.execute_reply":"2024-12-23T06:05:30.187266Z"}},"outputs":[],"execution_count":1},{"cell_type":"markdown","source":"# 1. Data Exploration","metadata":{}},{"cell_type":"markdown","source":"In this approach, we didn't use the actigraphy files due to their complexity and high missing data ratio (approximately 75%). Instead, we focused on the train and test CSV files, using the pandas library to read them as dataframes. ","metadata":{}},{"cell_type":"markdown","source":"## Read the csv file","metadata":{}},{"cell_type":"code","source":"train_data = pd.read_csv(\"/kaggle/input/child-mind-institute-problematic-internet-use/train.csv\", index_col=\"id\")\ntest_data = pd.read_csv(\"/kaggle/input/child-mind-institute-problematic-internet-use/test.csv\", index_col=\"id\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:30.189329Z","iopub.execute_input":"2024-12-23T06:05:30.189858Z","iopub.status.idle":"2024-12-23T06:05:30.265327Z","shell.execute_reply.started":"2024-12-23T06:05:30.189828Z","shell.execute_reply":"2024-12-23T06:05:30.264411Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"train_df = train_data.copy()\ntest_df = test_data.copy()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:30.265992Z","iopub.execute_input":"2024-12-23T06:05:30.266301Z","iopub.status.idle":"2024-12-23T06:05:30.279781Z","shell.execute_reply.started":"2024-12-23T06:05:30.266273Z","shell.execute_reply":"2024-12-23T06:05:30.278755Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"train_df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:30.282636Z","iopub.execute_input":"2024-12-23T06:05:30.282934Z","iopub.status.idle":"2024-12-23T06:05:30.34582Z","shell.execute_reply.started":"2024-12-23T06:05:30.282908Z","shell.execute_reply":"2024-12-23T06:05:30.345126Z"}},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"         Basic_Demos-Enroll_Season  Basic_Demos-Age  Basic_Demos-Sex  \\\nid                                                                     \n00008ff9                      Fall                5                0   \n000fd460                    Summer                9                0   \n00105258                    Summer               10                1   \n00115b9f                    Winter                9                0   \n0016bb22                    Spring               18                1   \n\n         CGAS-Season  CGAS-CGAS_Score Physical-Season  Physical-BMI  \\\nid                                                                    \n00008ff9      Winter             51.0            Fall     16.877316   \n000fd460         NaN              NaN            Fall     14.035590   \n00105258        Fall             71.0            Fall     16.648696   \n00115b9f        Fall             71.0          Summer     18.292347   \n0016bb22      Summer              NaN             NaN           NaN   \n\n          Physical-Height  Physical-Weight  Physical-Waist_Circumference  ...  \\\nid                                                                        ...   \n00008ff9             46.0             50.8                           NaN  ...   \n000fd460             48.0             46.0                          22.0  ...   \n00105258             56.5             75.6                           NaN  ...   \n00115b9f             56.0             81.6                           NaN  ...   \n0016bb22              NaN              NaN                           NaN  ...   \n\n          PCIAT-PCIAT_18  PCIAT-PCIAT_19  PCIAT-PCIAT_20 PCIAT-PCIAT_Total  \\\nid                                                                           \n00008ff9             4.0             2.0             4.0              55.0   \n000fd460             0.0             0.0             0.0               0.0   \n00105258             2.0             1.0             1.0              28.0   \n00115b9f             3.0             4.0             1.0              44.0   \n0016bb22             NaN             NaN             NaN               NaN   \n\n          SDS-Season  SDS-SDS_Total_Raw  SDS-SDS_Total_T PreInt_EduHx-Season  \\\nid                                                                             \n00008ff9         NaN                NaN              NaN                Fall   \n000fd460        Fall               46.0             64.0              Summer   \n00105258        Fall               38.0             54.0              Summer   \n00115b9f      Summer               31.0             45.0              Winter   \n0016bb22         NaN                NaN              NaN                 NaN   \n\n          PreInt_EduHx-computerinternet_hoursday  sii  \nid                                                     \n00008ff9                                     3.0  2.0  \n000fd460                                     0.0  0.0  \n00105258                                     2.0  0.0  \n00115b9f                                     0.0  1.0  \n0016bb22                                     NaN  NaN  \n\n[5 rows x 81 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>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>...</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    <tr>\n      <th>id</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      <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      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>00008ff9</th>\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>...</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>000fd460</th>\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>...</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>00105258</th>\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>...</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>00115b9f</th>\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>...</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>0016bb22</th>\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>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <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>5 rows × 81 columns</p>\n</div>"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"test_df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:30.349238Z","iopub.execute_input":"2024-12-23T06:05:30.349521Z","iopub.status.idle":"2024-12-23T06:05:30.386208Z","shell.execute_reply.started":"2024-12-23T06:05:30.349496Z","shell.execute_reply":"2024-12-23T06:05:30.385212Z"}},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"         Basic_Demos-Enroll_Season  Basic_Demos-Age  Basic_Demos-Sex  \\\nid                                                                     \n00008ff9                      Fall                5                0   \n000fd460                    Summer                9                0   \n00105258                    Summer               10                1   \n00115b9f                    Winter                9                0   \n0016bb22                    Spring               18                1   \n\n         CGAS-Season  CGAS-CGAS_Score Physical-Season  Physical-BMI  \\\nid                                                                    \n00008ff9      Winter             51.0            Fall     16.877316   \n000fd460         NaN              NaN            Fall     14.035590   \n00105258        Fall             71.0            Fall     16.648696   \n00115b9f        Fall             71.0          Summer     18.292347   \n0016bb22      Summer              NaN             NaN           NaN   \n\n          Physical-Height  Physical-Weight  Physical-Waist_Circumference  ...  \\\nid                                                                        ...   \n00008ff9             46.0             50.8                           NaN  ...   \n000fd460             48.0             46.0                          22.0  ...   \n00105258             56.5             75.6                           NaN  ...   \n00115b9f             56.0             81.6                           NaN  ...   \n0016bb22              NaN              NaN                           NaN  ...   \n\n          BIA-BIA_TBW  PAQ_A-Season  PAQ_A-PAQ_A_Total PAQ_C-Season  \\\nid                                                                    \n00008ff9      32.6909           NaN                NaN          NaN   \n000fd460      27.0552           NaN                NaN         Fall   \n00105258          NaN           NaN                NaN       Summer   \n00115b9f      45.9966           NaN                NaN       Winter   \n0016bb22          NaN        Summer               1.04          NaN   \n\n          PAQ_C-PAQ_C_Total  SDS-Season  SDS-SDS_Total_Raw SDS-SDS_Total_T  \\\nid                                                                           \n00008ff9                NaN         NaN                NaN             NaN   \n000fd460              2.340        Fall               46.0            64.0   \n00105258              2.170        Fall               38.0            54.0   \n00115b9f              2.451      Summer               31.0            45.0   \n0016bb22                NaN         NaN                NaN             NaN   \n\n          PreInt_EduHx-Season  PreInt_EduHx-computerinternet_hoursday  \nid                                                                     \n00008ff9                 Fall                                     3.0  \n000fd460               Summer                                     0.0  \n00105258               Summer                                     2.0  \n00115b9f               Winter                                     0.0  \n0016bb22                  NaN                                     NaN  \n\n[5 rows x 58 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>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>...</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    <tr>\n      <th>id</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      <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      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>00008ff9</th>\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>...</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>000fd460</th>\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>...</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>00105258</th>\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>...</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>00115b9f</th>\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>...</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>0016bb22</th>\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>...</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<p>5 rows × 58 columns</p>\n</div>"},"metadata":{}}],"execution_count":5},{"cell_type":"code","source":"print(train_df.shape)\nprint(test_df.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:30.387979Z","iopub.execute_input":"2024-12-23T06:05:30.388349Z","iopub.status.idle":"2024-12-23T06:05:30.394284Z","shell.execute_reply.started":"2024-12-23T06:05:30.388309Z","shell.execute_reply":"2024-12-23T06:05:30.393202Z"}},"outputs":[{"name":"stdout","text":"(3960, 81)\n(20, 58)\n","output_type":"stream"}],"execution_count":6},{"cell_type":"markdown","source":"#### --> The test df has less features than train df","metadata":{}},{"cell_type":"markdown","source":"# 2. Data Cleaning","metadata":{}},{"cell_type":"markdown","source":"### Visualize how many NaN values are there in the training dataset","metadata":{}},{"cell_type":"code","source":"# Count the number of missing values in each feature\nmissing_values = train_df.isnull().sum()\n\n# Plotting the chart\nplt.figure(figsize=(10, 30))\nax = missing_values.plot(kind='barh', color='skyblue')\n\nfor index, value in enumerate(missing_values):\n    ax.text(value, index, str(value), va='center', ha='left', color='black', fontweight='bold')\n\nplt.title('Number of Missing Values in Each Feature')\nplt.ylabel('Features')\nplt.xlabel('Number of Missing Values')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:30.395362Z","iopub.execute_input":"2024-12-23T06:05:30.395686Z","iopub.status.idle":"2024-12-23T06:05:31.602954Z","shell.execute_reply.started":"2024-12-23T06:05:30.395658Z","shell.execute_reply":"2024-12-23T06:05:31.602007Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x3000 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":7},{"cell_type":"markdown","source":"We notice that there is so much NaN values in the dataset so we use matplotlib to visualize how much values are missing in each feature. Through the chart, we discovered that 1,224 samples are missing the output label (target variable). Since the target label is crucial for supervised learning tasks, we decided to drop these samples to avoid noise data points and more effeciently computation. ","metadata":{}},{"cell_type":"markdown","source":"## Handle missing output samples","metadata":{}},{"cell_type":"markdown","source":"#### The presence of missing output samples is not suitable for training the model, so we will remove them.","metadata":{}},{"cell_type":"code","source":"#Remove samples without output label\ntrain_df = train_df.dropna(subset=['sii'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:31.603967Z","iopub.execute_input":"2024-12-23T06:05:31.604286Z","iopub.status.idle":"2024-12-23T06:05:31.612422Z","shell.execute_reply.started":"2024-12-23T06:05:31.604261Z","shell.execute_reply":"2024-12-23T06:05:31.611269Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"train_df.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:31.613613Z","iopub.execute_input":"2024-12-23T06:05:31.613995Z","iopub.status.idle":"2024-12-23T06:05:31.645574Z","shell.execute_reply.started":"2024-12-23T06:05:31.613958Z","shell.execute_reply":"2024-12-23T06:05:31.644408Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 2736 entries, 00008ff9 to ffed1dd5\nData columns (total 81 columns):\n #   Column                                  Non-Null Count  Dtype  \n---  ------                                  --------------  -----  \n 0   Basic_Demos-Enroll_Season               2736 non-null   object \n 1   Basic_Demos-Age                         2736 non-null   int64  \n 2   Basic_Demos-Sex                         2736 non-null   int64  \n 3   CGAS-Season                             2342 non-null   object \n 4   CGAS-CGAS_Score                         2342 non-null   float64\n 5   Physical-Season                         2595 non-null   object \n 6   Physical-BMI                            2527 non-null   float64\n 7   Physical-Height                         2530 non-null   float64\n 8   Physical-Weight                         2572 non-null   float64\n 9   Physical-Waist_Circumference            483 non-null    float64\n 10  Physical-Diastolic_BP                   2478 non-null   float64\n 11  Physical-HeartRate                      2486 non-null   float64\n 12  Physical-Systolic_BP                    2478 non-null   float64\n 13  Fitness_Endurance-Season                1260 non-null   object \n 14  Fitness_Endurance-Max_Stage             731 non-null    float64\n 15  Fitness_Endurance-Time_Mins             728 non-null    float64\n 16  Fitness_Endurance-Time_Sec              728 non-null    float64\n 17  FGC-Season                              2647 non-null   object \n 18  FGC-FGC_CU                              1919 non-null   float64\n 19  FGC-FGC_CU_Zone                         1884 non-null   float64\n 20  FGC-FGC_GSND                            872 non-null    float64\n 21  FGC-FGC_GSND_Zone                       864 non-null    float64\n 22  FGC-FGC_GSD                             871 non-null    float64\n 23  FGC-FGC_GSD_Zone                        864 non-null    float64\n 24  FGC-FGC_PU                              1909 non-null   float64\n 25  FGC-FGC_PU_Zone                         1875 non-null   float64\n 26  FGC-FGC_SRL                             1911 non-null   float64\n 27  FGC-FGC_SRL_Zone                        1877 non-null   float64\n 28  FGC-FGC_SRR                             1913 non-null   float64\n 29  FGC-FGC_SRR_Zone                        1879 non-null   float64\n 30  FGC-FGC_TL                              1919 non-null   float64\n 31  FGC-FGC_TL_Zone                         1885 non-null   float64\n 32  BIA-Season                              1844 non-null   object \n 33  BIA-BIA_Activity_Level_num              1813 non-null   float64\n 34  BIA-BIA_BMC                             1813 non-null   float64\n 35  BIA-BIA_BMI                             1813 non-null   float64\n 36  BIA-BIA_BMR                             1813 non-null   float64\n 37  BIA-BIA_DEE                             1813 non-null   float64\n 38  BIA-BIA_ECW                             1813 non-null   float64\n 39  BIA-BIA_FFM                             1813 non-null   float64\n 40  BIA-BIA_FFMI                            1813 non-null   float64\n 41  BIA-BIA_FMI                             1813 non-null   float64\n 42  BIA-BIA_Fat                             1813 non-null   float64\n 43  BIA-BIA_Frame_num                       1813 non-null   float64\n 44  BIA-BIA_ICW                             1813 non-null   float64\n 45  BIA-BIA_LDM                             1813 non-null   float64\n 46  BIA-BIA_LST                             1813 non-null   float64\n 47  BIA-BIA_SMM                             1813 non-null   float64\n 48  BIA-BIA_TBW                             1813 non-null   float64\n 49  PAQ_A-Season                            363 non-null    object \n 50  PAQ_A-PAQ_A_Total                       363 non-null    float64\n 51  PAQ_C-Season                            1440 non-null   object \n 52  PAQ_C-PAQ_C_Total                       1440 non-null   float64\n 53  PCIAT-Season                            2736 non-null   object \n 54  PCIAT-PCIAT_01                          2733 non-null   float64\n 55  PCIAT-PCIAT_02                          2734 non-null   float64\n 56  PCIAT-PCIAT_03                          2731 non-null   float64\n 57  PCIAT-PCIAT_04                          2731 non-null   float64\n 58  PCIAT-PCIAT_05                          2729 non-null   float64\n 59  PCIAT-PCIAT_06                          2732 non-null   float64\n 60  PCIAT-PCIAT_07                          2729 non-null   float64\n 61  PCIAT-PCIAT_08                          2730 non-null   float64\n 62  PCIAT-PCIAT_09                          2730 non-null   float64\n 63  PCIAT-PCIAT_10                          2733 non-null   float64\n 64  PCIAT-PCIAT_11                          2734 non-null   float64\n 65  PCIAT-PCIAT_12                          2731 non-null   float64\n 66  PCIAT-PCIAT_13                          2729 non-null   float64\n 67  PCIAT-PCIAT_14                          2732 non-null   float64\n 68  PCIAT-PCIAT_15                          2730 non-null   float64\n 69  PCIAT-PCIAT_16                          2728 non-null   float64\n 70  PCIAT-PCIAT_17                          2725 non-null   float64\n 71  PCIAT-PCIAT_18                          2728 non-null   float64\n 72  PCIAT-PCIAT_19                          2730 non-null   float64\n 73  PCIAT-PCIAT_20                          2733 non-null   float64\n 74  PCIAT-PCIAT_Total                       2736 non-null   float64\n 75  SDS-Season                              2527 non-null   object \n 76  SDS-SDS_Total_Raw                       2527 non-null   float64\n 77  SDS-SDS_Total_T                         2525 non-null   float64\n 78  PreInt_EduHx-Season                     2719 non-null   object \n 79  PreInt_EduHx-computerinternet_hoursday  2654 non-null   float64\n 80  sii                                     2736 non-null   float64\ndtypes: float64(68), int64(2), object(11)\nmemory usage: 1.7+ MB\n","output_type":"stream"}],"execution_count":9},{"cell_type":"markdown","source":"After doing data cleaning, we check how many samples left in train data and we got 2736 samples","metadata":{}},{"cell_type":"markdown","source":"# 3. Feature Engineering","metadata":{}},{"cell_type":"markdown","source":"##### First take a look how train and test dataset look like after data cleaning","metadata":{}},{"cell_type":"code","source":"train_df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:31.646809Z","iopub.execute_input":"2024-12-23T06:05:31.647219Z","iopub.status.idle":"2024-12-23T06:05:31.672211Z","shell.execute_reply.started":"2024-12-23T06:05:31.64716Z","shell.execute_reply":"2024-12-23T06:05:31.67123Z"}},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"         Basic_Demos-Enroll_Season  Basic_Demos-Age  Basic_Demos-Sex  \\\nid                                                                     \n00008ff9                      Fall                5                0   \n000fd460                    Summer                9                0   \n00105258                    Summer               10                1   \n00115b9f                    Winter                9                0   \n001f3379                    Spring               13                1   \n\n         CGAS-Season  CGAS-CGAS_Score Physical-Season  Physical-BMI  \\\nid                                                                    \n00008ff9      Winter             51.0            Fall     16.877316   \n000fd460         NaN              NaN            Fall     14.035590   \n00105258        Fall             71.0            Fall     16.648696   \n00115b9f        Fall             71.0          Summer     18.292347   \n001f3379      Winter             50.0          Summer     22.279952   \n\n          Physical-Height  Physical-Weight  Physical-Waist_Circumference  ...  \\\nid                                                                        ...   \n00008ff9             46.0             50.8                           NaN  ...   \n000fd460             48.0             46.0                          22.0  ...   \n00105258             56.5             75.6                           NaN  ...   \n00115b9f             56.0             81.6                           NaN  ...   \n001f3379             59.5            112.2                           NaN  ...   \n\n          PCIAT-PCIAT_18  PCIAT-PCIAT_19  PCIAT-PCIAT_20 PCIAT-PCIAT_Total  \\\nid                                                                           \n00008ff9             4.0             2.0             4.0              55.0   \n000fd460             0.0             0.0             0.0               0.0   \n00105258             2.0             1.0             1.0              28.0   \n00115b9f             3.0             4.0             1.0              44.0   \n001f3379             1.0             2.0             1.0              34.0   \n\n          SDS-Season  SDS-SDS_Total_Raw  SDS-SDS_Total_T PreInt_EduHx-Season  \\\nid                                                                             \n00008ff9         NaN                NaN              NaN                Fall   \n000fd460        Fall               46.0             64.0              Summer   \n00105258        Fall               38.0             54.0              Summer   \n00115b9f      Summer               31.0             45.0              Winter   \n001f3379      Summer               40.0             56.0              Spring   \n\n          PreInt_EduHx-computerinternet_hoursday  sii  \nid                                                     \n00008ff9                                     3.0  2.0  \n000fd460                                     0.0  0.0  \n00105258                                     2.0  0.0  \n00115b9f                                     0.0  1.0  \n001f3379                                     0.0  1.0  \n\n[5 rows x 81 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>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>...</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    <tr>\n      <th>id</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      <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      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>00008ff9</th>\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>...</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>000fd460</th>\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>...</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>00105258</th>\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>...</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>00115b9f</th>\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>...</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>001f3379</th>\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>...</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    </tr>\n  </tbody>\n</table>\n<p>5 rows × 81 columns</p>\n</div>"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"test_df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:31.672915Z","iopub.execute_input":"2024-12-23T06:05:31.673234Z","iopub.status.idle":"2024-12-23T06:05:31.706914Z","shell.execute_reply.started":"2024-12-23T06:05:31.673193Z","shell.execute_reply":"2024-12-23T06:05:31.705822Z"}},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"         Basic_Demos-Enroll_Season  Basic_Demos-Age  Basic_Demos-Sex  \\\nid                                                                     \n00008ff9                      Fall                5                0   \n000fd460                    Summer                9                0   \n00105258                    Summer               10                1   \n00115b9f                    Winter                9                0   \n0016bb22                    Spring               18                1   \n\n         CGAS-Season  CGAS-CGAS_Score Physical-Season  Physical-BMI  \\\nid                                                                    \n00008ff9      Winter             51.0            Fall     16.877316   \n000fd460         NaN              NaN            Fall     14.035590   \n00105258        Fall             71.0            Fall     16.648696   \n00115b9f        Fall             71.0          Summer     18.292347   \n0016bb22      Summer              NaN             NaN           NaN   \n\n          Physical-Height  Physical-Weight  Physical-Waist_Circumference  ...  \\\nid                                                                        ...   \n00008ff9             46.0             50.8                           NaN  ...   \n000fd460             48.0             46.0                          22.0  ...   \n00105258             56.5             75.6                           NaN  ...   \n00115b9f             56.0             81.6                           NaN  ...   \n0016bb22              NaN              NaN                           NaN  ...   \n\n          BIA-BIA_TBW  PAQ_A-Season  PAQ_A-PAQ_A_Total PAQ_C-Season  \\\nid                                                                    \n00008ff9      32.6909           NaN                NaN          NaN   \n000fd460      27.0552           NaN                NaN         Fall   \n00105258          NaN           NaN                NaN       Summer   \n00115b9f      45.9966           NaN                NaN       Winter   \n0016bb22          NaN        Summer               1.04          NaN   \n\n          PAQ_C-PAQ_C_Total  SDS-Season  SDS-SDS_Total_Raw SDS-SDS_Total_T  \\\nid                                                                           \n00008ff9                NaN         NaN                NaN             NaN   \n000fd460              2.340        Fall               46.0            64.0   \n00105258              2.170        Fall               38.0            54.0   \n00115b9f              2.451      Summer               31.0            45.0   \n0016bb22                NaN         NaN                NaN             NaN   \n\n          PreInt_EduHx-Season  PreInt_EduHx-computerinternet_hoursday  \nid                                                                     \n00008ff9                 Fall                                     3.0  \n000fd460               Summer                                     0.0  \n00105258               Summer                                     2.0  \n00115b9f               Winter                                     0.0  \n0016bb22                  NaN                                     NaN  \n\n[5 rows x 58 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>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>...</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    <tr>\n      <th>id</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      <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      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>00008ff9</th>\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>...</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>000fd460</th>\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>...</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>00105258</th>\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>...</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>00115b9f</th>\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>...</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>0016bb22</th>\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>...</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<p>5 rows × 58 columns</p>\n</div>"},"metadata":{}}],"execution_count":11},{"cell_type":"markdown","source":"This dataset has so much features with various type, so we need to have some methods to handle.","metadata":{}},{"cell_type":"markdown","source":"## 3.1. Feature Extraction","metadata":{}},{"cell_type":"markdown","source":"Firstly, we performed feature extraction. We dropped features that only appeared in the training set, which were the 'PCIAT' features. ","metadata":{}},{"cell_type":"markdown","source":"### Drop features that do not appear in the test set","metadata":{}},{"cell_type":"code","source":"train_df.drop(columns=[col for col in train_df if 'PCIAT' in col], inplace=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:31.707946Z","iopub.execute_input":"2024-12-23T06:05:31.708334Z","iopub.status.idle":"2024-12-23T06:05:31.723375Z","shell.execute_reply.started":"2024-12-23T06:05:31.708296Z","shell.execute_reply":"2024-12-23T06:05:31.722264Z"}},"outputs":[],"execution_count":12},{"cell_type":"markdown","source":"For the remaining features, we used matplotlib to visualize their missing value ratios. We discovered that many features had high missing ratios. ","metadata":{}},{"cell_type":"markdown","source":"### Check missing values per feature","metadata":{}},{"cell_type":"code","source":"missing_ratio = train_df.isna().mean()\n\nmissing_ratio = missing_ratio[missing_ratio > 0]\n\nmissing_df = missing_ratio.reset_index()\nmissing_df.columns = ['Feature', 'Missing Ratio']\n\nplt.figure(figsize=(15, 6))\nsns.barplot(data=missing_df, x='Feature', y='Missing Ratio', palette='coolwarm')\nplt.title('Missing Ratio per Feature', fontsize=12)\nplt.ylabel('Missing Ratio (%)', fontsize=12)\nplt.xlabel('Features', fontsize=4)\nplt.xticks(rotation=45, ha='right')\nplt.grid(axis='y', linestyle='--', alpha=0.7)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:31.724644Z","iopub.execute_input":"2024-12-23T06:05:31.724997Z","iopub.status.idle":"2024-12-23T06:05:32.43473Z","shell.execute_reply.started":"2024-12-23T06:05:31.724955Z","shell.execute_reply":"2024-12-23T06:05:32.433652Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x600 with 1 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\n"},"metadata":{}}],"execution_count":13},{"cell_type":"markdown","source":"To reduce unefficient features, we dropped any features that were missing more than 50% of their values.","metadata":{}},{"cell_type":"markdown","source":"### Remove features which miss more than a half","metadata":{}},{"cell_type":"code","source":"threshold = 0.5\nmissing_ratio = train_df.isnull().mean()\ndropped_columns = missing_ratio[missing_ratio > threshold].index.tolist()\n\ntrain_df.drop(columns=dropped_columns, inplace=True)\ntest_df.drop(columns=dropped_columns, inplace=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.435953Z","iopub.execute_input":"2024-12-23T06:05:32.436335Z","iopub.status.idle":"2024-12-23T06:05:32.446052Z","shell.execute_reply.started":"2024-12-23T06:05:32.436305Z","shell.execute_reply":"2024-12-23T06:05:32.444995Z"}},"outputs":[],"execution_count":14},{"cell_type":"markdown","source":"## 3.2. Feature Categorization","metadata":{}},{"cell_type":"markdown","source":"We performed feature categorization because the various type of features.  After discover the data dictionary file, we find out that there’re 4 types of features\n- Categorical features in object type: feature has ‘Season’ in name\n - Multiclass categorical features in number type: `BIA-BIA_Activity_Level_num`, `FGC-FGC_GSD_Zone`,`FGC-FGC_GSND_Zone`,`BIA-BIA_Frame_num`, `PreInt_EduHx-computerinternet_hoursday`\n  - Binary features: also categorical features but only have value 0 or 1\n - Numerical features: remaining features","metadata":{}},{"cell_type":"code","source":"int_cols = ['BIA-BIA_Activity_Level_num', 'FGC-FGC_GSD_Zone','FGC-FGC_GSND_Zone','BIA-BIA_Frame_num', 'PreInt_EduHx-computerinternet_hoursday']\ncategorical_int_cols = [col for col in int_cols if col not in dropped_columns]\n\ncategorical_str_cols = [col for col in train_df.columns if 'Season' in col and col not in dropped_columns]\n\n#Combine 2 types of categorical features\ncategorical_cols = categorical_str_cols + categorical_int_cols","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.447023Z","iopub.execute_input":"2024-12-23T06:05:32.447318Z","iopub.status.idle":"2024-12-23T06:05:32.467819Z","shell.execute_reply.started":"2024-12-23T06:05:32.447292Z","shell.execute_reply":"2024-12-23T06:05:32.46666Z"}},"outputs":[],"execution_count":15},{"cell_type":"code","source":"binary_cols = [col for col in train_df.columns if train_df[col].nunique() == 2]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.468943Z","iopub.execute_input":"2024-12-23T06:05:32.469292Z","iopub.status.idle":"2024-12-23T06:05:32.492524Z","shell.execute_reply.started":"2024-12-23T06:05:32.469265Z","shell.execute_reply":"2024-12-23T06:05:32.491126Z"}},"outputs":[],"execution_count":16},{"cell_type":"code","source":"numerical_cols = [col for col in train_df.columns if col != 'sii' and col not in categorical_cols and col not in binary_cols]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.493402Z","iopub.execute_input":"2024-12-23T06:05:32.493667Z","iopub.status.idle":"2024-12-23T06:05:32.498155Z","shell.execute_reply.started":"2024-12-23T06:05:32.493644Z","shell.execute_reply":"2024-12-23T06:05:32.497203Z"}},"outputs":[],"execution_count":17},{"cell_type":"markdown","source":"## 3.3. Imputation","metadata":{}},{"cell_type":"markdown","source":"- After categorize the features, we use imputation to each type of features:\n    - For the categorical features and binary features: we filll out the NaN value by the mode value (most frequently occurring value)\n    - For numerical features: we fill out the NaN value by the mean value","metadata":{}},{"cell_type":"code","source":"for col in categorical_cols:\n    mode_value = train_df[col].mode()[0]\n    train_df[col] = train_df[col].fillna(mode_value)\n    train_df[col] = train_df[col].astype(object)\n\nfor col in binary_cols:\n    mode_value = train_df[col].mode()[0]\n    train_df[col] = train_df[col].fillna(mode_value)\n    train_df[col] = train_df[col].astype(int)\n\nfor col in numerical_cols:\n    mean_value = train_df[col].mean()\n    train_df[col] = train_df[col].fillna(mean_value)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.499214Z","iopub.execute_input":"2024-12-23T06:05:32.49957Z","iopub.status.idle":"2024-12-23T06:05:32.544196Z","shell.execute_reply.started":"2024-12-23T06:05:32.499536Z","shell.execute_reply":"2024-12-23T06:05:32.543093Z"}},"outputs":[],"execution_count":18},{"cell_type":"code","source":"for col in categorical_cols:\n    mode_value = test_df[col].mode()[0]\n    test_df[col] = test_df[col].fillna(mode_value)\n    test_df[col] = test_df[col].astype(object)\n\nfor col in binary_cols:\n    mode_value = test_df[col].mode()[0]\n    test_df[col] = test_df[col].fillna(mode_value)\n    test_df[col] = test_df[col].astype(int)\n\nfor col in numerical_cols:\n    mean_value = test_df[col].mean()\n    test_df[col] = test_df[col].fillna(mean_value)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.548633Z","iopub.execute_input":"2024-12-23T06:05:32.548907Z","iopub.status.idle":"2024-12-23T06:05:32.583213Z","shell.execute_reply.started":"2024-12-23T06:05:32.548884Z","shell.execute_reply":"2024-12-23T06:05:32.582082Z"}},"outputs":[],"execution_count":19},{"cell_type":"markdown","source":"## 3.4. Feature Scaling","metadata":{}},{"cell_type":"markdown","source":"Then, we apply feature scaling to numerical features using Standard Scaler from sklearn, a preprocessing technique that standardizes features by removing the mean and scaling them to unit variance, ensuring that each feature has a mean of 0 and a standard deviation of 1.","metadata":{}},{"cell_type":"markdown","source":"#### We use standard scaler to scale the numerical features","metadata":{}},{"cell_type":"code","source":"scaler = StandardScaler()\n\n# Helper function\ndef standardize(df):\n    columns_to_standardize = [col for col in numerical_cols]\n    df[columns_to_standardize] = scaler.fit_transform(df[columns_to_standardize])\n    return df\n\n#Proceed with standardizing\ntrain_df = standardize(train_df)\ntest_df = standardize(test_df)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.585221Z","iopub.execute_input":"2024-12-23T06:05:32.585566Z","iopub.status.idle":"2024-12-23T06:05:32.606142Z","shell.execute_reply.started":"2024-12-23T06:05:32.585538Z","shell.execute_reply":"2024-12-23T06:05:32.605092Z"}},"outputs":[],"execution_count":20},{"cell_type":"markdown","source":"## 3.5. Feature Encoding","metadata":{}},{"cell_type":"markdown","source":"After that, we apply one hot encoding to encode the categorical features, after one hot there are a few features which only appear in train set, so we continue drop them to avoid noise points","metadata":{}},{"cell_type":"markdown","source":"#### We use one-hot encoding to encode the numerical features","metadata":{}},{"cell_type":"code","source":"# Helper function\ndef OneHot_Encoding(original_dataframe, feature_to_encode):\n    dummies = pd.get_dummies(original_dataframe[[feature_to_encode]], dtype=int)\n    original_dataframe = pd.concat([original_dataframe, dummies], axis=1)\n    original_dataframe = original_dataframe.drop([feature_to_encode], axis=1)\n    return original_dataframe\n\nfor col in categorical_cols:\n    train_df = OneHot_Encoding(train_df, col)\n    test_df = OneHot_Encoding(test_df, col)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.607273Z","iopub.execute_input":"2024-12-23T06:05:32.60764Z","iopub.status.idle":"2024-12-23T06:05:32.686934Z","shell.execute_reply.started":"2024-12-23T06:05:32.607604Z","shell.execute_reply":"2024-12-23T06:05:32.685909Z"}},"outputs":[],"execution_count":21},{"cell_type":"code","source":"# Remove feature which does not appear in test data after encoding, excluding 'sii'\ntrain_miss = (set(train_df.columns) - set(test_df.columns)) - {'sii'}\n\ntrain_df = train_df.drop(columns=train_miss)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.68801Z","iopub.execute_input":"2024-12-23T06:05:32.688323Z","iopub.status.idle":"2024-12-23T06:05:32.693669Z","shell.execute_reply.started":"2024-12-23T06:05:32.688298Z","shell.execute_reply":"2024-12-23T06:05:32.692872Z"}},"outputs":[],"execution_count":22},{"cell_type":"markdown","source":"#### Now, let’s review the data after applying these feature processing steps.","metadata":{}},{"cell_type":"code","source":"train_df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.694551Z","iopub.execute_input":"2024-12-23T06:05:32.694817Z","iopub.status.idle":"2024-12-23T06:05:32.727522Z","shell.execute_reply.started":"2024-12-23T06:05:32.694762Z","shell.execute_reply":"2024-12-23T06:05:32.726425Z"}},"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"          Basic_Demos-Age  Basic_Demos-Sex  CGAS-CGAS_Score  Physical-BMI  \\\nid                                                                          \n00008ff9        -1.528487                0        -1.296014     -0.476635   \n000fd460        -0.361407                0         0.000000     -1.079060   \n00105258        -0.069637                1         0.534609     -0.525100   \n00115b9f        -0.361407                0         0.534609     -0.176658   \n001f3379         0.805674                1        -1.387545      0.668686   \n\n          Physical-Height  Physical-Weight  Physical-Diastolic_BP  \\\nid                                                                  \n00008ff9        -1.392050        -0.881360               0.000000   \n000fd460        -1.110744        -0.995576               0.399730   \n00105258         0.084807        -0.291242              -0.362392   \n00115b9f         0.014480        -0.148472              -0.743453   \n001f3379         0.506766         0.579658              -0.743453   \n\n          Physical-HeartRate  Physical-Systolic_BP  FGC-FGC_CU  ...  \\\nid                                                              ...   \n00008ff9       -1.082896e-15         -8.672395e-16   -1.187686  ...   \n000fd460       -9.009683e-01          2.973259e-01   -0.888595  ...   \n00105258        9.278811e-01         -7.806868e-03    0.806251  ...   \n00115b9f        1.156487e+00         -7.806868e-03    0.606857  ...   \n001f3379       -6.723621e-01         -9.232052e-01    0.008676  ...   \n\n          PreInt_EduHx-Season_Winter  BIA-BIA_Activity_Level_num_2.0  \\\nid                                                                     \n00008ff9                           0                               1   \n000fd460                           0                               1   \n00105258                           0                               0   \n00115b9f                           1                               0   \n001f3379                           0                               1   \n\n          BIA-BIA_Activity_Level_num_3.0  BIA-BIA_Activity_Level_num_5.0  \\\nid                                                                         \n00008ff9                               0                               0   \n000fd460                               0                               0   \n00105258                               1                               0   \n00115b9f                               1                               0   \n001f3379                               0                               0   \n\n          BIA-BIA_Frame_num_1.0  BIA-BIA_Frame_num_2.0  \\\nid                                                       \n00008ff9                      1                      0   \n000fd460                      1                      0   \n00105258                      0                      1   \n00115b9f                      0                      1   \n001f3379                      0                      1   \n\n          PreInt_EduHx-computerinternet_hoursday_0.0  \\\nid                                                     \n00008ff9                                           0   \n000fd460                                           1   \n00105258                                           0   \n00115b9f                                           1   \n001f3379                                           1   \n\n          PreInt_EduHx-computerinternet_hoursday_1.0  \\\nid                                                     \n00008ff9                                           0   \n000fd460                                           0   \n00105258                                           0   \n00115b9f                                           0   \n001f3379                                           0   \n\n          PreInt_EduHx-computerinternet_hoursday_2.0  \\\nid                                                     \n00008ff9                                           0   \n000fd460                                           0   \n00105258                                           1   \n00115b9f                                           0   \n001f3379                                           0   \n\n          PreInt_EduHx-computerinternet_hoursday_3.0  \nid                                                    \n00008ff9                                           1  \n000fd460                                           0  \n00105258                                           0  \n00115b9f                                           0  \n001f3379                                           0  \n\n[5 rows x 77 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>Basic_Demos-Age</th>\n      <th>Basic_Demos-Sex</th>\n      <th>CGAS-CGAS_Score</th>\n      <th>Physical-BMI</th>\n      <th>Physical-Height</th>\n      <th>Physical-Weight</th>\n      <th>Physical-Diastolic_BP</th>\n      <th>Physical-HeartRate</th>\n      <th>Physical-Systolic_BP</th>\n      <th>FGC-FGC_CU</th>\n      <th>...</th>\n      <th>PreInt_EduHx-Season_Winter</th>\n      <th>BIA-BIA_Activity_Level_num_2.0</th>\n      <th>BIA-BIA_Activity_Level_num_3.0</th>\n      <th>BIA-BIA_Activity_Level_num_5.0</th>\n      <th>BIA-BIA_Frame_num_1.0</th>\n      <th>BIA-BIA_Frame_num_2.0</th>\n      <th>PreInt_EduHx-computerinternet_hoursday_0.0</th>\n      <th>PreInt_EduHx-computerinternet_hoursday_1.0</th>\n      <th>PreInt_EduHx-computerinternet_hoursday_2.0</th>\n      <th>PreInt_EduHx-computerinternet_hoursday_3.0</th>\n    </tr>\n    <tr>\n      <th>id</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      <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      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>00008ff9</th>\n      <td>-1.528487</td>\n      <td>0</td>\n      <td>-1.296014</td>\n      <td>-0.476635</td>\n      <td>-1.392050</td>\n      <td>-0.881360</td>\n      <td>0.000000</td>\n      <td>-1.082896e-15</td>\n      <td>-8.672395e-16</td>\n      <td>-1.187686</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>000fd460</th>\n      <td>-0.361407</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>-1.079060</td>\n      <td>-1.110744</td>\n      <td>-0.995576</td>\n      <td>0.399730</td>\n      <td>-9.009683e-01</td>\n      <td>2.973259e-01</td>\n      <td>-0.888595</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>00105258</th>\n      <td>-0.069637</td>\n      <td>1</td>\n      <td>0.534609</td>\n      <td>-0.525100</td>\n      <td>0.084807</td>\n      <td>-0.291242</td>\n      <td>-0.362392</td>\n      <td>9.278811e-01</td>\n      <td>-7.806868e-03</td>\n      <td>0.806251</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>00115b9f</th>\n      <td>-0.361407</td>\n      <td>0</td>\n      <td>0.534609</td>\n      <td>-0.176658</td>\n      <td>0.014480</td>\n      <td>-0.148472</td>\n      <td>-0.743453</td>\n      <td>1.156487e+00</td>\n      <td>-7.806868e-03</td>\n      <td>0.606857</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>001f3379</th>\n      <td>0.805674</td>\n      <td>1</td>\n      <td>-1.387545</td>\n      <td>0.668686</td>\n      <td>0.506766</td>\n      <td>0.579658</td>\n      <td>-0.743453</td>\n      <td>-6.723621e-01</td>\n      <td>-9.232052e-01</td>\n      <td>0.008676</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 77 columns</p>\n</div>"},"metadata":{}}],"execution_count":23},{"cell_type":"code","source":"test_df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.728482Z","iopub.execute_input":"2024-12-23T06:05:32.728799Z","iopub.status.idle":"2024-12-23T06:05:32.757206Z","shell.execute_reply.started":"2024-12-23T06:05:32.728772Z","shell.execute_reply":"2024-12-23T06:05:32.75625Z"}},"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"          Basic_Demos-Age  Basic_Demos-Sex  CGAS-CGAS_Score  Physical-BMI  \\\nid                                                                          \n00008ff9        -1.583385                0        -1.723923     -0.775133   \n000fd460        -0.481900                0         0.000000     -1.519641   \n00105258        -0.206529                1         1.274204     -0.835030   \n00115b9f        -0.481900                0         1.274204     -0.404408   \n0016bb22         1.996442                1         0.000000      0.000000   \n\n          Physical-Height  Physical-Weight  Physical-Diastolic_BP  \\\nid                                                                  \n00008ff9    -1.294562e+00    -1.551454e+00               0.000000   \n000fd460    -9.226433e-01    -1.813672e+00               0.334979   \n00105258     6.580092e-01    -1.966632e-01              -0.417015   \n00115b9f     5.650296e-01     1.311088e-01              -0.793012   \n0016bb22    -1.321319e-15     7.763200e-16               0.000000   \n\n          Physical-HeartRate  Physical-Systolic_BP  FGC-FGC_CU  ...  \\\nid                                                              ...   \n00008ff9            0.000000              0.000000   -1.420614  ...   \n000fd460           -1.688635              0.296288   -0.930314  ...   \n00105258            1.785129             -0.036280    1.848055  ...   \n00115b9f            2.219349             -0.036280    1.521188  ...   \n0016bb22            0.000000              0.000000    0.000000  ...   \n\n          PreInt_EduHx-Season_Winter  BIA-BIA_Activity_Level_num_2.0  \\\nid                                                                     \n00008ff9                           0                               1   \n000fd460                           0                               1   \n00105258                           0                               1   \n00115b9f                           1                               0   \n0016bb22                           0                               1   \n\n          BIA-BIA_Activity_Level_num_3.0  BIA-BIA_Activity_Level_num_5.0  \\\nid                                                                         \n00008ff9                               0                               0   \n000fd460                               0                               0   \n00105258                               0                               0   \n00115b9f                               1                               0   \n0016bb22                               0                               0   \n\n          BIA-BIA_Frame_num_1.0  BIA-BIA_Frame_num_2.0  \\\nid                                                       \n00008ff9                      1                      0   \n000fd460                      1                      0   \n00105258                      0                      1   \n00115b9f                      0                      1   \n0016bb22                      0                      1   \n\n          PreInt_EduHx-computerinternet_hoursday_0.0  \\\nid                                                     \n00008ff9                                           0   \n000fd460                                           1   \n00105258                                           0   \n00115b9f                                           1   \n0016bb22                                           0   \n\n          PreInt_EduHx-computerinternet_hoursday_1.0  \\\nid                                                     \n00008ff9                                           0   \n000fd460                                           0   \n00105258                                           0   \n00115b9f                                           0   \n0016bb22                                           0   \n\n          PreInt_EduHx-computerinternet_hoursday_2.0  \\\nid                                                     \n00008ff9                                           0   \n000fd460                                           0   \n00105258                                           1   \n00115b9f                                           0   \n0016bb22                                           1   \n\n          PreInt_EduHx-computerinternet_hoursday_3.0  \nid                                                    \n00008ff9                                           1  \n000fd460                                           0  \n00105258                                           0  \n00115b9f                                           0  \n0016bb22                                           0  \n\n[5 rows x 76 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>Basic_Demos-Age</th>\n      <th>Basic_Demos-Sex</th>\n      <th>CGAS-CGAS_Score</th>\n      <th>Physical-BMI</th>\n      <th>Physical-Height</th>\n      <th>Physical-Weight</th>\n      <th>Physical-Diastolic_BP</th>\n      <th>Physical-HeartRate</th>\n      <th>Physical-Systolic_BP</th>\n      <th>FGC-FGC_CU</th>\n      <th>...</th>\n      <th>PreInt_EduHx-Season_Winter</th>\n      <th>BIA-BIA_Activity_Level_num_2.0</th>\n      <th>BIA-BIA_Activity_Level_num_3.0</th>\n      <th>BIA-BIA_Activity_Level_num_5.0</th>\n      <th>BIA-BIA_Frame_num_1.0</th>\n      <th>BIA-BIA_Frame_num_2.0</th>\n      <th>PreInt_EduHx-computerinternet_hoursday_0.0</th>\n      <th>PreInt_EduHx-computerinternet_hoursday_1.0</th>\n      <th>PreInt_EduHx-computerinternet_hoursday_2.0</th>\n      <th>PreInt_EduHx-computerinternet_hoursday_3.0</th>\n    </tr>\n    <tr>\n      <th>id</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      <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      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>00008ff9</th>\n      <td>-1.583385</td>\n      <td>0</td>\n      <td>-1.723923</td>\n      <td>-0.775133</td>\n      <td>-1.294562e+00</td>\n      <td>-1.551454e+00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-1.420614</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>000fd460</th>\n      <td>-0.481900</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>-1.519641</td>\n      <td>-9.226433e-01</td>\n      <td>-1.813672e+00</td>\n      <td>0.334979</td>\n      <td>-1.688635</td>\n      <td>0.296288</td>\n      <td>-0.930314</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>00105258</th>\n      <td>-0.206529</td>\n      <td>1</td>\n      <td>1.274204</td>\n      <td>-0.835030</td>\n      <td>6.580092e-01</td>\n      <td>-1.966632e-01</td>\n      <td>-0.417015</td>\n      <td>1.785129</td>\n      <td>-0.036280</td>\n      <td>1.848055</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>00115b9f</th>\n      <td>-0.481900</td>\n      <td>0</td>\n      <td>1.274204</td>\n      <td>-0.404408</td>\n      <td>5.650296e-01</td>\n      <td>1.311088e-01</td>\n      <td>-0.793012</td>\n      <td>2.219349</td>\n      <td>-0.036280</td>\n      <td>1.521188</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>0016bb22</th>\n      <td>1.996442</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-1.321319e-15</td>\n      <td>7.763200e-16</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 76 columns</p>\n</div>"},"metadata":{}}],"execution_count":24},{"cell_type":"markdown","source":"# 4. Define and train model","metadata":{}},{"cell_type":"markdown","source":"## 4.1. Extract data frame","metadata":{}},{"cell_type":"code","source":"features = [col for col in train_df.columns if col != 'sii']\nX = train_df[features]\ny = train_df.sii","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.758249Z","iopub.execute_input":"2024-12-23T06:05:32.758634Z","iopub.status.idle":"2024-12-23T06:05:32.774421Z","shell.execute_reply.started":"2024-12-23T06:05:32.758594Z","shell.execute_reply":"2024-12-23T06:05:32.773135Z"}},"outputs":[],"execution_count":25},{"cell_type":"markdown","source":"## 4.2. Split training data","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.775472Z","iopub.execute_input":"2024-12-23T06:05:32.775841Z","iopub.status.idle":"2024-12-23T06:05:32.795382Z","shell.execute_reply.started":"2024-12-23T06:05:32.775805Z","shell.execute_reply":"2024-12-23T06:05:32.794152Z"}},"outputs":[],"execution_count":26},{"cell_type":"markdown","source":"## 4.3. Model selection","metadata":{}},{"cell_type":"code","source":"value_counts = y.value_counts()\nvalue_counts.plot(kind='bar', color='skyblue', edgecolor='black')\nplt.title('Frequency of Categorical Values in y')\nplt.xlabel('Values')\nplt.ylabel('Frequency')\nplt.grid(axis='y', linestyle='--', alpha=0.7)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:32.796597Z","iopub.execute_input":"2024-12-23T06:05:32.796909Z","iopub.status.idle":"2024-12-23T06:05:33.050476Z","shell.execute_reply.started":"2024-12-23T06:05:32.796885Z","shell.execute_reply":"2024-12-23T06:05:33.049392Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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output distribution is imbalanced, so we have chosen the `Random Forest Classifier` to train the model. This classifier is highly effective for handling imbalanced data by setting the `class_weight` parameter to `balanced`.**","metadata":{}},{"cell_type":"markdown","source":"Random Forest is an ensemble learning method based on decision trees. It builds multiple decision trees during training and combines their predictions to produce a more robust and accurate result. For classification tasks, Random Forest aggregates the predictions of individual trees through majority voting, ensuring stability and reducing overfitting.\n\nWhen dealing with imbalanced data, Random Forest can address the issue by assigning higher weights to minority classes, ensuring they have a stronger influence during tree construction.\n\nBy leveraging these characteristics, Random Forest Classifier provides a robust solution for imbalanced datasets, making it an ideal choice for our problem.","metadata":{}},{"cell_type":"markdown","source":"## 4.4. Train model","metadata":{}},{"cell_type":"code","source":"# Validation model,train on X_val test, to evaluate the model\nval_model = RandomForestClassifier(\n    class_weight='balanced',\n    random_state=42\n)\n\n# Train the validation model\nval_model.fit(X_train, y_train)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:33.051602Z","iopub.execute_input":"2024-12-23T06:05:33.051978Z","iopub.status.idle":"2024-12-23T06:05:33.750145Z","shell.execute_reply.started":"2024-12-23T06:05:33.051941Z","shell.execute_reply":"2024-12-23T06:05:33.749247Z"}},"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier(class_weight='balanced', random_state=42)","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, random_state=42)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, random_state=42)</pre></div></div></div></div></div>"},"metadata":{}}],"execution_count":28},{"cell_type":"markdown","source":"## 4.5. Evaluate on validation df","metadata":{}},{"cell_type":"markdown","source":"The eval metric is quadratic weight kappa as mentioned in competition description.","metadata":{}},{"cell_type":"code","source":"def quadratic_weighted_kappa(y_true, y_pred):\n    return cohen_kappa_score(y_true, y_pred, weights='quadratic')\n\nqwk_scorer = make_scorer(quadratic_weighted_kappa)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:33.751331Z","iopub.execute_input":"2024-12-23T06:05:33.751701Z","iopub.status.idle":"2024-12-23T06:05:33.756268Z","shell.execute_reply.started":"2024-12-23T06:05:33.751666Z","shell.execute_reply":"2024-12-23T06:05:33.755245Z"}},"outputs":[],"execution_count":29},{"cell_type":"code","source":"val_preds = val_model.predict(X_val)\nval_preds = np.array(val_preds).astype(int)\n\ny_val = np.array(y_val).astype(int)\n\nprint(quadratic_weighted_kappa(y_val, val_preds))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:33.757277Z","iopub.execute_input":"2024-12-23T06:05:33.757634Z","iopub.status.idle":"2024-12-23T06:05:33.798876Z","shell.execute_reply.started":"2024-12-23T06:05:33.7576Z","shell.execute_reply":"2024-12-23T06:05:33.798006Z"}},"outputs":[{"name":"stdout","text":"0.25274135202351344\n","output_type":"stream"}],"execution_count":30},{"cell_type":"markdown","source":"**---> Not a good score, so we decide to optimize the model by using GridSearch CV to find out the best hyperparameters**","metadata":{}},{"cell_type":"markdown","source":"# 5. Model Optimization","metadata":{}},{"cell_type":"markdown","source":"We decide to optimize the model by using GridSearch CV - a model optimization method from sklearn to find out the best hyperparameters.  The hyperparameters sets in gridsearch cv were selected based on insights gained from our previous submissions.","metadata":{}},{"cell_type":"markdown","source":"## Run GridSearch CV","metadata":{}},{"cell_type":"code","source":"%%time\n\nparam_grid = {\n    'n_estimators': [150, 200, 250, 300],\n    'max_depth': [5, 8, 10],\n    'min_samples_split': [2, 3, 4, 5],\n    'min_samples_leaf': [1, 2, 3, 4],\n    'criterion':['entropy', 'gini'],\n}\n\n# Model initialization\nrf_model = RandomForestClassifier(\n    random_state=42,\n    class_weight='balanced',\n    max_features='sqrt',\n)\n\n# GridSearchCV with 3-fold cv\ngrid_search = GridSearchCV(\n    estimator=rf_model,\n    param_grid=param_grid,\n    scoring=qwk_scorer,\n    cv=3,\n    verbose=1,\n    n_jobs=-1\n)\n\n# Searching\ngrid_search.fit(X, y)\n\nprint(\"Best parameters found:\", grid_search.best_params_)\nprint(\"Best cross-validation accuracy:\", grid_search.best_score_)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:05:33.799719Z","iopub.execute_input":"2024-12-23T06:05:33.800004Z","iopub.status.idle":"2024-12-23T06:13:38.572998Z","shell.execute_reply.started":"2024-12-23T06:05:33.799979Z","shell.execute_reply":"2024-12-23T06:13:38.571854Z"}},"outputs":[{"name":"stdout","text":"Fitting 3 folds for each of 384 candidates, totalling 1152 fits\nBest parameters found: {'criterion': 'gini', 'max_depth': 8, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 300}\nBest cross-validation accuracy: 0.42513800529318296\nCPU times: user 8.98 s, sys: 1.79 s, total: 10.8 s\nWall time: 8min 4s\n","output_type":"stream"}],"execution_count":31},{"cell_type":"markdown","source":"After fitting all possible combinations, the grid search provides us with the best parameters corresponding to the highest score. Then, we will assign these hyperparameters back to the model and train it","metadata":{}},{"cell_type":"code","source":"final_model = RandomForestClassifier(\n    n_estimators=300,\n    max_depth=8,\n    max_features='sqrt',\n    min_samples_split=5,\n    min_samples_leaf=1,\n    class_weight='balanced',\n    random_state=42,\n)\n\nfinal_model.fit(X, y)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:13:38.574202Z","iopub.execute_input":"2024-12-23T06:13:38.574614Z","iopub.status.idle":"2024-12-23T06:13:40.247189Z","shell.execute_reply.started":"2024-12-23T06:13:38.574552Z","shell.execute_reply":"2024-12-23T06:13:40.246302Z"}},"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier(class_weight='balanced', max_depth=8,\n                       min_samples_split=5, n_estimators=300, random_state=42)","text/html":"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=8,\n                       min_samples_split=5, n_estimators=300, random_state=42)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=8,\n                       min_samples_split=5, n_estimators=300, random_state=42)</pre></div></div></div></div></div>"},"metadata":{}}],"execution_count":32},{"cell_type":"markdown","source":"# 6. Model Evaluation","metadata":{}},{"cell_type":"markdown","source":"### QWK Score","metadata":{}},{"cell_type":"code","source":"scores = cross_val_score(final_model, X, y, cv=3, scoring=qwk_scorer)\nprint(\"QWK Scores:\", scores)\nprint(\"----> Mean QWK Score:\", np.mean(scores))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:13:40.248069Z","iopub.execute_input":"2024-12-23T06:13:40.248365Z","iopub.status.idle":"2024-12-23T06:13:44.167865Z","shell.execute_reply.started":"2024-12-23T06:13:40.248342Z","shell.execute_reply":"2024-12-23T06:13:44.166892Z"}},"outputs":[{"name":"stdout","text":"QWK Scores: [0.41271546 0.43165367 0.43104488]\n----> Mean QWK Score: 0.42513800529318296\n","output_type":"stream"}],"execution_count":33},{"cell_type":"markdown","source":"After training we evaluate it and we  got the score of 0.425. Now we will use this model to predict the test data and submit to competition","metadata":{}},{"cell_type":"markdown","source":"### Some additional metrics","metadata":{}},{"cell_type":"code","source":"# Accuracy\naccuracy_scores = cross_val_score(final_model, X, y, cv=3, scoring='accuracy')\nprint(\"Mean Accuracy Score:\", np.mean(accuracy_scores))\n\n#Precision\nprecision_scores = cross_val_score(final_model, X, y, cv=3, scoring='precision_macro')\nprint(\"Mean Precision Score:\", np.mean(precision_scores))\n\n# F1\nf1_scores = cross_val_score(final_model, X, y, cv=3, scoring='f1_macro')\nprint(\"Mean F1 Score:\", np.mean(f1_scores))\n\n# Recall\nrecall_scores = cross_val_score(final_model, X, y, cv=3, scoring='recall_macro')\nprint(\"Mean Recall Score:\", np.mean(recall_scores))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:13:44.16886Z","iopub.execute_input":"2024-12-23T06:13:44.169127Z","iopub.status.idle":"2024-12-23T06:13:59.877096Z","shell.execute_reply.started":"2024-12-23T06:13:44.169104Z","shell.execute_reply":"2024-12-23T06:13:59.876055Z"}},"outputs":[{"name":"stdout","text":"Mean Accuracy Score: 0.5672514619883041\nMean Precision Score: 0.3652830065038981\nMean F1 Score: 0.3701287213885644\nMean Recall Score: 0.3781062639306915\n","output_type":"stream"}],"execution_count":34},{"cell_type":"markdown","source":"# 7. Submit to competition","metadata":{}},{"cell_type":"markdown","source":"## Predict the test df","metadata":{}},{"cell_type":"code","source":"preds = final_model.predict(test_df)\npreds = np.array(preds).astype(int)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:13:59.878058Z","iopub.execute_input":"2024-12-23T06:13:59.878461Z","iopub.status.idle":"2024-12-23T06:13:59.904709Z","shell.execute_reply.started":"2024-12-23T06:13:59.87842Z","shell.execute_reply":"2024-12-23T06:13:59.903514Z"}},"outputs":[],"execution_count":35},{"cell_type":"markdown","source":"## Save output to submission file","metadata":{}},{"cell_type":"code","source":"output = pd.DataFrame({'id': test_df.index,\n                       'sii': preds})\noutput.to_csv('submission.csv',index=False)\noutput","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-23T06:13:59.90567Z","iopub.execute_input":"2024-12-23T06:13:59.906002Z","iopub.status.idle":"2024-12-23T06:13:59.920078Z","shell.execute_reply.started":"2024-12-23T06:13:59.905976Z","shell.execute_reply":"2024-12-23T06:13:59.919043Z"}},"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"          id  sii\n0   00008ff9    0\n1   000fd460    0\n2   00105258    1\n3   00115b9f    0\n4   0016bb22    1\n5   001f3379    1\n6   0038ba98    1\n7   0068a485    0\n8   0069fbed    2\n9   0083e397    1\n10  0087dd65    1\n11  00abe655    2\n12  00ae59c9    0\n13  00af6387    1\n14  00bd4359    1\n15  00c0cd71    1\n16  00d56d4b    0\n17  00d9913d    1\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>0</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>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>001f3379</td>\n      <td>1</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>1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0087dd65</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>00abe655</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>00ae59c9</td>\n      <td>0</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>1</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>1</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":36},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}