{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","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":30804,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport seaborn as sns\nimport warnings","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:17.408083Z","iopub.execute_input":"2024-12-19T08:06:17.408469Z","iopub.status.idle":"2024-12-19T08:06:17.414999Z","shell.execute_reply.started":"2024-12-19T08:06:17.408431Z","shell.execute_reply":"2024-12-19T08:06:17.413599Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"warnings.filterwarnings('ignore', category=FutureWarning)\n\nsns.set(style=\"whitegrid\")\n%matplotlib inline","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:17.417781Z","iopub.execute_input":"2024-12-19T08:06:17.418222Z","iopub.status.idle":"2024-12-19T08:06:17.442166Z","shell.execute_reply.started":"2024-12-19T08:06:17.418174Z","shell.execute_reply":"2024-12-19T08:06:17.440936Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Data preview","metadata":{}},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\ntest = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\ndata_dictionary = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/data_dictionary.csv')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:17.443706Z","iopub.execute_input":"2024-12-19T08:06:17.444095Z","iopub.status.idle":"2024-12-19T08:06:17.524638Z","shell.execute_reply.started":"2024-12-19T08:06:17.444050Z","shell.execute_reply":"2024-12-19T08:06:17.523284Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Mục tiêu \n- Mục đích của cuộc thi này là dự đoán Chỉ số suy giảm nghiêm trọng (sii), thước đo mức độ sử dụng internet có vấn đề ở trẻ em và thanh thiếu niên, dựa trên dữ liệu hoạt động thể chất và các tính năng khác.\n- Sii được tính từ PCIAT-PCIAT_Total (Parent-Child Internet Addiction Test). PCIAT gồm 20 câu hỏi, mỗi câu 0-5 điểm\n- sii được định nghĩa như sau:\n  - 0: None (PCIAT-PCIAT_Total từ 0 -> 30)\n  - 1: Mild (PCIAT-PCIAT_Total từ 31 -> 49)\n  - 2: Moderate (PCIAT-PCIAT_Total từ 50 -> 79)\n  - 3: Servere (PCIAT-PCIAT_Total >= 80)\n- Task: Xây dựng model dự đoán SII từ các features\n- Chúng ta cũng có thể sử dụng PCIAT-PCIAT_Total làm continuous target variable, và triển khai regression trên PCIAT-PCIAT_Total, sau đó ánh xạ các dự đoán thành sii danh mục.","metadata":{}},{"cell_type":"markdown","source":"# 1. Train data","metadata":{}},{"cell_type":"code","source":"display(train.head())\nprint(f\"Train shape: {train.shape}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:17.526210Z","iopub.execute_input":"2024-12-19T08:06:17.526574Z","iopub.status.idle":"2024-12-19T08:06:17.556100Z","shell.execute_reply.started":"2024-12-19T08:06:17.526535Z","shell.execute_reply":"2024-12-19T08:06:17.554776Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## NX: 3960 records, 82 attributes","metadata":{}},{"cell_type":"markdown","source":"# 2. Test data","metadata":{}},{"cell_type":"code","source":"display(test.head())\nprint(f\"Test shape: {test.shape}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:17.560289Z","iopub.execute_input":"2024-12-19T08:06:17.560854Z","iopub.status.idle":"2024-12-19T08:06:17.593423Z","shell.execute_reply.started":"2024-12-19T08:06:17.560797Z","shell.execute_reply":"2024-12-19T08:06:17.592057Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## NX: 20 records, 59 attributes","metadata":{}},{"cell_type":"markdown","source":"# 3. Data dictionary ","metadata":{}},{"cell_type":"code","source":"display(data_dictionary.head())\nprint(f\"Dictionary shape: {data_dictionary.shape}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:17.595114Z","iopub.execute_input":"2024-12-19T08:06:17.595557Z","iopub.status.idle":"2024-12-19T08:06:17.616652Z","shell.execute_reply.started":"2024-12-19T08:06:17.595505Z","shell.execute_reply":"2024-12-19T08:06:17.615081Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Hàm thống kê mô tả colums","metadata":{}},{"cell_type":"code","source":"def calculate_stats(data, columns):\n    if isinstance(columns, str):\n        columns = [columns]\n\n    stats = []\n    for col in columns:\n        if data[col].dtype in ['object', 'category']:\n            counts = data[col].value_counts(dropna=False, sort=False)\n            percents = data[col].value_counts(normalize=True, dropna=False, sort=False) * 100\n            formatted = counts.astype(str) + ' (' + percents.round(2).astype(str) + '%)'\n            stats_col = pd.DataFrame({'count (%)': formatted})\n            stats.append(stats_col)\n        else:\n            stats_col = data[col].describe().to_frame().transpose()\n            stats_col['missing'] = data[col].isnull().sum()\n            stats_col.index.name = col\n            stats.append(stats_col)\n\n    return pd.concat(stats, axis=0)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:17.618272Z","iopub.execute_input":"2024-12-19T08:06:17.618774Z","iopub.status.idle":"2024-12-19T08:06:17.632976Z","shell.execute_reply.started":"2024-12-19T08:06:17.618708Z","shell.execute_reply":"2024-12-19T08:06:17.631712Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"background-color:#f7dfc6; color:black; font-family:Verdana; font-size:100%; text-align:left; border: 3px solid #d17411; border-radius:15px; padding:20px 20px;\">Target Variables and Internet use</p>","metadata":{}},{"cell_type":"markdown","source":"## Giờ ta sẽ xác định xem features nào liên quan đến target variable (sii) mà không xuất hiện trong tập test ","metadata":{}},{"cell_type":"code","source":"train_cols = set(train.columns)\ntest_cols = set(test.columns)\ncolumns_not_in_test = sorted(list(train_cols - test_cols)) # Lấy 1 mảng list gồm các cột có trong tập train không có trong tập test \ndata_dictionary[data_dictionary['Field'].isin(columns_not_in_test)] # Tìm kiếm list trên trong data_dictionary ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:17.634480Z","iopub.execute_input":"2024-12-19T08:06:17.634967Z","iopub.status.idle":"2024-12-19T08:06:17.658654Z","shell.execute_reply.started":"2024-12-19T08:06:17.634917Z","shell.execute_reply":"2024-12-19T08:06:17.657010Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### NHẬN XÉT: Ta có thể thây các trường không có trong tập test gồm: \n- PCIAT-Season: Values = Spring, Summer, Fall, Winter\t\n- PCIAT-PCIAT_01 -> PCIAT-PCIAT_20: Values = 0,1,2,3,4,5\n- PCIAT-PCIAT_Total\n- Sii (hiển nhiên)\n- Parent-Child Internet Addiction Test (PCIAT):\n  - gồm 20 items (PCIAT-PCIAT_01 -> PCIAT-PCIAT_20), mỗi câu hỏi đánh giá một khía cạnh khác nhau về hành vi của trẻ liên quan đến việc sử dụng internet. Các câu hỏi được trả lời theo thang điểm (từ 0 đến 5) và tổng điểm sẽ cho biết mức độ nghiêm trọng của chứng nghiện Internet.\n- Giờ ta sẽ xác minh rằng `PCIAT-PCIAT_Total` phù hợp với các danh mục sii tương ứng bằng cách tính điểm tối thiểu và tối đa cho mỗi danh mục sii:","metadata":{}},{"cell_type":"code","source":"pciat_min_max = train.groupby('sii')['PCIAT-PCIAT_Total'].agg(['min', 'max'])\npciat_min_max = pciat_min_max.rename(\n    columns={'min': 'Minimum PCIAT total Score', 'max': 'Maximum total PCIAT Score'}\n)\npciat_min_max","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:17.660354Z","iopub.execute_input":"2024-12-19T08:06:17.660808Z","iopub.status.idle":"2024-12-19T08:06:17.680583Z","shell.execute_reply.started":"2024-12-19T08:06:17.660757Z","shell.execute_reply":"2024-12-19T08:06:17.679032Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Nhận xét: \n- Minimum PCIAT total Score: \n    - Đây là giá trị nhỏ nhất của cột 'PCIAT-PCIAT_Total' trong từng nhóm sii\n        - Nhóm sii = 0.0: giá trị nhỏ nhất của 'PCIAT-PCIAT_Total' là 0.0.\n        - Nhóm sii = 1.0: giá trị nhỏ nhất là 31.0.\n        - Nhóm sii = 2.0: giá trị nhỏ nhất là 50.0.\n        - Nhóm sii = 3.0: giá trị nhỏ nhất là 80.0.\n\n- Maximum total PCIAT Score:\n    - Đây là giá trị lớn nhất của cột 'PCIAT-PCIAT_Total' trong từng nhóm sii.\n        - Nhóm sii = 0.0: giá trị lớn nhất của 'PCIAT-PCIAT_Total' là 30.0.\n        - Nhóm sii = 1.0: giá trị lớn nhất là 49.0.\n        - Nhóm sii = 2.0: giá trị lớn nhất là 79.0.\n        - Nhóm sii = 3.0: giá trị lớn nhất là 93.0. ","metadata":{}},{"cell_type":"code","source":"data_dictionary[data_dictionary['Field'] == 'PCIAT-PCIAT_Total']['Value Labels'].iloc[0]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:17.682176Z","iopub.execute_input":"2024-12-19T08:06:17.682549Z","iopub.status.idle":"2024-12-19T08:06:17.701140Z","shell.execute_reply.started":"2024-12-19T08:06:17.682507Z","shell.execute_reply":"2024-12-19T08:06:17.699724Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Check missing answers","metadata":{}},{"cell_type":"markdown","source":"Nhờ có một thông tin thú vị từ Broccoli Beef (tại đây), chúng ta cũng biết rằng một số câu hỏi trong Bài kiểm tra nghiện Internet dành cho cha mẹ và con cái có thể bị người trả lời bỏ qua (thiếu giá trị trong các cột PCIAT-PCIAT_01 đến PCIAT-PCIAT_20). Nhưng SII vẫn được tính toán từ tổng các giá trị non-NA, dẫn đến các giá trị SII có khả năng không hợp lệ (trừ khi, tất nhiên, một số câu trả lời đã bị cắt bỏ sau khi dữ liệu đã được thu thập, chỉ để cung cấp cho chúng ta thêm một chút thử thách","metadata":{}},{"cell_type":"code","source":"train_with_sii = train[train['sii'].notna()][columns_not_in_test] # Tìm kiếm các row trong tập train trong đó cột sii có giá trị != NA\n# Trả về 5 records \ntrain_with_sii[train_with_sii.isna().any(axis=1)].head().style.applymap(\n    lambda x: 'background-color: #FFC0CB' if pd.isna(x) else ''\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:17.705983Z","iopub.execute_input":"2024-12-19T08:06:17.706497Z","iopub.status.idle":"2024-12-19T08:06:17.738915Z","shell.execute_reply.started":"2024-12-19T08:06:17.706441Z","shell.execute_reply":"2024-12-19T08:06:17.737694Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### NX: Ví dụ, ở hàng 1 (ID: 24) và hàng 3 (ID: 104), bạn có thể thấy điểm cho một câu trả lời (PCIAT) bị thiếu. Và vì mỗi câu hỏi được chấm từ 1 đến 5 nên tổng điểm có thể cao hơn tới 5 điểm và khiến cho SII nhảy lên bậc tiếp theo (SII có thể là 0 hoặc 1 cho hàng đầu tiên và 1 hoặc 2 cho hàng thứ ba)","metadata":{}},{"cell_type":"markdown","source":"#### Còn đối với hàng thứ 2 thì PCIAT-PCIAT_TOTAL và sii có vẻ đã được fill nhầm vì mọi câu hỏi được kiểm tra đều có giá trị NA, tuy nhiên PCIAT_TOTAL và sii vẫn được fill là 0.0 và 0.0 trong khi đáng lẽ phải là Nan","metadata":{}},{"cell_type":"markdown","source":"### Chính vì vậy, giờ ta sẽ kiểm tra xem  PCIAT-PCIAT_Total có thực sự được tính là tổng các giá trị không phải NA trong các cột `PCIAT-PCIAT_01` đến `PCIAT-PCIAT_20` hay không:","metadata":{}},{"cell_type":"code","source":"PCIAT_cols = [f'PCIAT-PCIAT_{i+1:02d}' for i in range(20)] # Các cột từ PCIAT-01 -> 20\nrecalc_total_score = train_with_sii[PCIAT_cols].sum(\n    axis=1, skipna=True\n)\n(recalc_total_score == train_with_sii['PCIAT-PCIAT_Total']).all()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:17.740363Z","iopub.execute_input":"2024-12-19T08:06:17.740814Z","iopub.status.idle":"2024-12-19T08:06:17.755524Z","shell.execute_reply.started":"2024-12-19T08:06:17.740764Z","shell.execute_reply":"2024-12-19T08:06:17.754194Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Hiện tại, chúng ta có thể kết luận rằng điểm SII đôi khi không chính xác. Dưới đây tôi tính toán lại SII dựa trên PCIAT_Total và điểm tối đa có thể nếu các giá trị bị thiếu được trả lời (5 điểm)","metadata":{}},{"cell_type":"code","source":"def recalculate_sii(row):\n    if pd.isna(row['PCIAT-PCIAT_Total']):\n        return np.nan\n    max_possible = row['PCIAT-PCIAT_Total'] + row[PCIAT_cols].isna().sum() * 5\n    if row['PCIAT-PCIAT_Total'] <= 30 and max_possible <= 30:\n        return 0\n    elif 31 <= row['PCIAT-PCIAT_Total'] <= 49 and max_possible <= 49:\n        return 1\n    elif 50 <= row['PCIAT-PCIAT_Total'] <= 79 and max_possible <= 79:\n        return 2\n    elif row['PCIAT-PCIAT_Total'] >= 80 and max_possible >= 80:\n        return 3\n    return np.nan\n\ntrain['recalc_sii'] = train.apply(recalculate_sii, axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:17.757210Z","iopub.execute_input":"2024-12-19T08:06:17.757557Z","iopub.status.idle":"2024-12-19T08:06:19.343786Z","shell.execute_reply.started":"2024-12-19T08:06:17.757524Z","shell.execute_reply":"2024-12-19T08:06:19.342695Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Ta sẽ xác định các hàng có sự không khớp giữa giá trị SII ban đầu và SII được tính lại (recalc_sii)","metadata":{}},{"cell_type":"code","source":"mismatch_rows = train[\n    (train['recalc_sii'] != train['sii']) & train['sii'].notna()\n]\n\nmismatch_rows[PCIAT_cols + [\n    'PCIAT-PCIAT_Total', 'sii', 'recalc_sii'\n]].style.applymap(\n    lambda x: 'background-color: #FFC0CB' if pd.isna(x) else ''\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:19.345382Z","iopub.execute_input":"2024-12-19T08:06:19.345846Z","iopub.status.idle":"2024-12-19T08:06:19.377120Z","shell.execute_reply.started":"2024-12-19T08:06:19.345794Z","shell.execute_reply":"2024-12-19T08:06:19.376066Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"💡 Note: Ta có 17 hàng, Sii đã được tính toán không chính xác (bỏ qua các phản hồi bị thiếu).","metadata":{}},{"cell_type":"markdown","source":"<div style=\"border: 2px solid #c9c9c9; padding: 15px; border-radius: 5px; background-color: #f7f7f7;\">\n    <h3>Giải thích:</h3>\n    Nếu các câu hỏi chưa trả lời được tự động tính là 0, điều này có thể dẫn đến lỗi\n!<br><br>\n    Hãy nhìn vào hàng cuối của bảng trên. Người trả lời này đã trả lời 18 trong số 20 câu hỏi với tổng điểm (PCIAT-PCIAT_Total) là 42. SII ban đầu là 1 vì điểm từ 31-49 tương đương với SII 1 = 'Mild'. \n    <br><br>NHƯNG chúng ta không biết người này sẽ trả lời những câu hỏi còn thiếu như thế nào - họ có thể đạt 0, 5 điểm hoặc một số điểm nào đó ở giữa. <br><br>Để tính đến điều này, tôi thêm điểm tối đa có thể (5) cho mỗi câu hỏi chưa trả lời, đưa ra điểm max_possible là 52, nằm trong phạm vi SII 'Moderate' (SII = 2 nếu PCIAT-PCIAT_Total nằm trong khoảng từ 50 đến 79). <br><br>SII ban đầu = 1, có thể sai hoặc đúng - chúng ta không biết! Vì vậy, tính toán lại SII bằng hàm `recalculate_sii` của tôi sẽ cho kết quả là SII = NaN, không phải SII = 2 hoặc một kết quả nào khác.<br><br>\n    Phương pháp này đảm bảo rằng tất cả các điểm SII ambiguous (mơ hồ) (những điểm có khả năng bị ảnh hưởng bởi các câu hỏi chưa được trả lời) đều được đánh dấu là NaN.<br><br>\n</div>","metadata":{}},{"cell_type":"markdown","source":"Trong các phân tích tiếp theo, tôi sẽ chỉ sử dụng SII đã hiệu chỉnh (recal_sii). Tôi sẽ chỉ sử dụng tổng điểm nếu tất cả PCIAT_cols đều có giá trị không phải NA (tất cả các câu hỏi của Bài kiểm tra nghiện Internet dành cho cha mẹ và con cái đã được trả lời).","metadata":{}},{"cell_type":"code","source":"train['sii'] = train['recalc_sii'] # Ghi đè giá trị cũ trong cột sii bằng giá trị recalc_sii\ntrain['complete_resp_total'] = train['PCIAT-PCIAT_Total'].where(\n    train[PCIAT_cols].notna().all(axis=1), np.nan\n) # Tạo một cột mới complete_resp_total để lưu tổng điểm (PCIAT-PCIAT_Total) chỉ khi tất cả các câu hỏi trong PCIAT_cols được trả lời đầy đủ.\n\nsii_map = {0: '0 (None)', 1: '1 (Mild)', 2: '2 (Moderate)', 3: '3 (Severe)'} # Chuyển đổi các giá trị số trong cột sii (0, 1, 2, 3) thành các nhãn có ý nghĩa hơn.\ntrain['sii'] = train['sii'].map(sii_map).fillna('Missing')\n\nsii_order = ['Missing', '0 (None)', '1 (Mild)', '2 (Moderate)', '3 (Severe)']\ntrain['sii'] = pd.Categorical(train['sii'], categories=sii_order, ordered=True)\n\ntrain.drop(columns='recalc_sii', inplace=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:19.378397Z","iopub.execute_input":"2024-12-19T08:06:19.378757Z","iopub.status.idle":"2024-12-19T08:06:19.404371Z","shell.execute_reply.started":"2024-12-19T08:06:19.378713Z","shell.execute_reply":"2024-12-19T08:06:19.402953Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Vẽ distribution của target variable (sii)","metadata":{}},{"cell_type":"code","source":"sii_counts = train['sii'].value_counts().reset_index()\ntotal = sii_counts['count'].sum()\nsii_counts['percentage'] = (sii_counts['count'] / total) * 100\n\nfig, axes = plt.subplots(1, 2, figsize=(14, 5))\n\n# SII\nsns.barplot(x='sii', y='count', data=sii_counts, palette='Blues_d', ax=axes[0])\naxes[0].set_title('Distribution of Severity Impairment Index (SII)', fontsize=14)\nfor p in axes[0].patches:\n    height = p.get_height()\n    percentage = sii_counts.loc[sii_counts['count'] == height, 'percentage'].values[0]\n    axes[0].text(\n        p.get_x() + p.get_width() / 2,\n        height + 5, f'{int(height)} ({percentage:.1f}%)',\n        ha=\"center\", fontsize=12\n    )\n\n# PCIAT_Total for complete responses\nsns.histplot(train['complete_resp_total'].dropna(), bins=20, ax=axes[1])\naxes[1].set_title('Distribution of PCIAT_Total', fontsize=14)\naxes[1].set_xlabel('PCIAT_Total for Complete PCIAT Responses')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:19.406210Z","iopub.execute_input":"2024-12-19T08:06:19.407340Z","iopub.status.idle":"2024-12-19T08:06:20.065136Z","shell.execute_reply.started":"2024-12-19T08:06:19.407284Z","shell.execute_reply":"2024-12-19T08:06:20.064062Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Nhận xét về distribution:\n1. SII\n2. PCIAT_TOTAL:\n    - (a) Nhóm có điểm thấp (0–30):\n        Đây là nhóm lớn nhất (chiếm đa số).\n        Những người thuộc nhóm này không gặp vấn đề nghiêm trọng liên quan đến việc sử dụng internet.\n        Điều này cho thấy phần lớn đối tượng khảo sát sử dụng internet ở mức độ bình thường và không bị ảnh hưởng lớn đến cuộc sống hàng ngày.\n   - (b) Nhóm điểm trung bình (30–50):\n        Có một số đối tượng nằm trong khoảng này.\n        Đây có thể là những người có dấu hiệu sử dụng internet gây rắc rối ở mức độ nhẹ đến trung bình:\n        Có thể ảnh hưởng đến học tập hoặc công việc nhưng chưa đến mức nghiêm trọng.\n        Cần được theo dõi để hỗ trợ kịp thời.\n    - (c) Nhóm có điểm cao (trên 50):\n        Số lượng rất nhỏ người đạt điểm cao (trên 70–80).\n        Những người này có thể thuộc nhóm nguy cơ cao nhất, với các hành vi sử dụng internet có thể dẫn đến nghiện hoặc ảnh hưởng tiêu cực nghiêm trọng:\n        Ảnh hưởng đến sức khỏe tinh thần, thể chất, hoặc các mối quan hệ cá nhân.\n        Nhóm này cần được ưu tiên hỗ trợ và can thiệp.\n","metadata":{}},{"cell_type":"code","source":"len(train[train['complete_resp_total'] == 0]) # số người đạt 0 điểm ở tất cả câu hỏi PCIAT","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:20.066362Z","iopub.execute_input":"2024-12-19T08:06:20.066712Z","iopub.status.idle":"2024-12-19T08:06:20.076525Z","shell.execute_reply.started":"2024-12-19T08:06:20.066677Z","shell.execute_reply":"2024-12-19T08:06:20.075371Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Note:  Rõ ràng là 40% người tham gia không bị ảnh hưởng bởi việc sử dụng Internet, 31% không được đánh giá và chỉ có một số ít (~10%) bị suy giảm ở mức trung bình đến nghiêm trọng. Có 307 người tham gia đạt 0 điểm ở tất cả các câu hỏi PCIAT.","metadata":{}},{"cell_type":"markdown","source":"## SII theo tuổi và giới tính","metadata":{}},{"cell_type":"code","source":"assert train['Basic_Demos-Age'].isna().sum() == 0\nassert train['Basic_Demos-Sex'].isna().sum() == 0","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:20.078493Z","iopub.execute_input":"2024-12-19T08:06:20.078981Z","iopub.status.idle":"2024-12-19T08:06:20.089802Z","shell.execute_reply.started":"2024-12-19T08:06:20.078931Z","shell.execute_reply":"2024-12-19T08:06:20.088786Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train['Age Group'] = pd.cut(\n    train['Basic_Demos-Age'],\n    bins=[4, 12, 18, 22],\n    labels=['Children (5-12)', 'Adolescents (13-18)', 'Adults (19-22)']\n)\ncalculate_stats(train, 'Age Group')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:20.091098Z","iopub.execute_input":"2024-12-19T08:06:20.091421Z","iopub.status.idle":"2024-12-19T08:06:20.117336Z","shell.execute_reply.started":"2024-12-19T08:06:20.091389Z","shell.execute_reply":"2024-12-19T08:06:20.116091Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sex_map = {0: 'Male', 1: 'Female'}\ntrain['Basic_Demos-Sex'] = train['Basic_Demos-Sex'].map(sex_map)\ncalculate_stats(train, 'Basic_Demos-Sex')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:20.118986Z","iopub.execute_input":"2024-12-19T08:06:20.119954Z","iopub.status.idle":"2024-12-19T08:06:20.141287Z","shell.execute_reply.started":"2024-12-19T08:06:20.119915Z","shell.execute_reply":"2024-12-19T08:06:20.140075Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n\n# SII by Age\nsns.boxplot(y=train['Basic_Demos-Age'], x=train['sii'], ax=axes[0], palette=\"Set3\")\naxes[0].set_title('SII by Age')\naxes[0].set_ylabel('Age')\naxes[0].set_xlabel('SII')\n\n# Complete PCIAT Responses by Age Group\nsns.boxplot(\n    x='Age Group', y='complete_resp_total',\n    data=train, palette=\"Set3\", ax=axes[1]\n)\naxes[1].set_title('Complete PCIAT Responses by Age Group')\naxes[1].set_ylabel('PCIAT_Total for Complete Responses')\naxes[1].set_xlabel('Age Group')\n\n# PCIAT_Total by Sex\nsns.histplot(\n    data=train, x='complete_resp_total',\n    hue='Basic_Demos-Sex', multiple='stack',\n    palette=\"Set3\", bins=20, ax=axes[2]\n)\naxes[2].set_title('PCIAT_Total Distribution by Sex')\naxes[2].set_xlabel('PCIAT_Total for Complete Responses')\naxes[2].set_ylabel('Frequency')\n\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-12-19T08:06:20.142956Z","iopub.execute_input":"2024-12-19T08:06:20.143466Z","iopub.status.idle":"2024-12-19T08:06:21.640847Z","shell.execute_reply.started":"2024-12-19T08:06:20.143410Z","shell.execute_reply":"2024-12-19T08:06:21.639530Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Nhận xét: \n1. SII theo tuổi (age)\n    - Biểu đồ này giúp quan sát mối quan hệ giữa độ tuổi và mức độ nghiện internet (SII).\n        - Trục x: Là giá trị SII, thể hiện các mức độ nghiện internet, từ 0 (None) đến 3 (Severe).\n        - Trục y: Là độ tuổi từ cột Basic_Demos-Age.\n   \n3. SII theo nhóm tuổi\n   - Biểu đồ này giúp kiểm tra xem có sự khác biệt về tổng điểm PCIAT giữa các nhóm tuổi hay không.\n        - Trục x: Là các nhóm tuổi đã phân loại trước đó (**Children (5-12), Adolescents (13-18), Adults (19-22)`).\n        - Trục y: Là complete_resp_total, tức là tổng điểm của các phản hồi đầy đủ trong bảng PCIAT. \n4. SII theo giới tính (sex)\n   - Biểu đồ này cung cấp cái nhìn về sự phân bố điểm PCIAT giữa các giới tính.\n        - Trục x: Là complete_resp_total, tức là tổng điểm PCIAT cho các phản hồi đầy đủ.\n        - Trục y: Là tần suất (frequency), tức là số lượng người tham gia khảo sát rơi vào từng nhóm điểm PCIAT.","metadata":{}},{"cell_type":"markdown","source":"### Phân phối sii cho từng lứa tuổi (age group) ","metadata":{}},{"cell_type":"code","source":"stats = train.groupby(['Age Group', 'sii']).size().unstack(fill_value=0)\nfig, axes = plt.subplots(1, len(stats), figsize=(18, 5))\n\nfor i, age_group in enumerate(stats.index):\n    group_counts = stats.loc[age_group] / stats.loc[age_group].sum()\n    axes[i].pie(\n        group_counts, labels=group_counts.index, autopct='%1.1f%%',\n        startangle=90, colors=sns.color_palette(\"Set3\"),\n        labeldistance=1.05, pctdistance=0.80\n    )\n    axes[i].set_title(f'SII Distribution for {age_group}')\n    axes[i].axis('equal')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:21.642945Z","iopub.execute_input":"2024-12-19T08:06:21.643387Z","iopub.status.idle":"2024-12-19T08:06:22.077697Z","shell.execute_reply.started":"2024-12-19T08:06:21.643339Z","shell.execute_reply":"2024-12-19T08:06:22.076354Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"stats = train.groupby(['Age Group', 'sii']).size().unstack(fill_value=0)\nstats_prop = stats.div(stats.sum(axis=1), axis=0) * 100\n\nstats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)'\nstats","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:22.079078Z","iopub.execute_input":"2024-12-19T08:06:22.079390Z","iopub.status.idle":"2024-12-19T08:06:22.104282Z","shell.execute_reply.started":"2024-12-19T08:06:22.079358Z","shell.execute_reply":"2024-12-19T08:06:22.103137Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Ghi chú:\n\n* Các biểu đồ hộp (box plot) là những biểu diễn khác nhau của biến mục tiêu dưới dạng phân loại (SII) và dạng số học (PCIAT_Total). Chúng cho thấy rằng các điểm SII cao hơn thường gắn liền với nhóm tuổi lớn hơn, nhưng có sự chồng lấp đáng kể trong các khoảng tuổi trong mỗi nhóm, và giá trị trung vị của PCIAT_Total cao hơn ở nhóm thanh thiếu niên, gợi ý một mối quan hệ hình chữ U giữa độ tuổi và suy giảm liên quan đến sử dụng Internet (đỉnh điểm của các vấn đề liên quan đến Internet có thể xảy ra trong độ tuổi thanh thiếu niên).\n\n* Theo đó, trong các biểu đồ hình tròn, phân bố của SII cho trẻ em và người lớn có xu hướng nghiêng về các giá trị thấp (không có và nhẹ), trong khi đó, đối với thanh thiếu niên, phân bố này có sự phân bổ cân bằng hơn giữa các nhóm không có, nhẹ và trung bình.\n\n* Vậy còn các con số (xem các bảng)? Số lượng thanh thiếu niên ít hơn nhiều so với trẻ em, và số lượng người tham gia là người lớn rất thấp (chỉ có 88 người, và chỉ 35 người có SII)!\n\n* Như chúng ta đã thấy từ các biểu đồ trong phần trước, phân bố tổng thể của SII nghiêng về các giá trị thấp và các trường hợp nghiêm trọng là rất hiếm. Vì vậy, có thể tồn tại những mối quan hệ mà chúng ta không thể thấy được do kích thước mẫu không đều và sự thiếu đại diện của các trường hợp nghiêm trọng.\n* Sự khác biệt giữa nam và nữ là tương đối ít.","metadata":{}},{"cell_type":"markdown","source":"# Internet Use","metadata":{}},{"cell_type":"markdown","source":"Dữ liệu sử dụng Internet rất quan trọng đối với nhiệm vụ này vìProblematic internet use (PIU), còn được gọi là nghiện Internet hoặc sử dụng Internet cưỡng chế, đề cập đến việc sử dụng Internet quá mức và không lành mạnh, ảnh hưởng đến cuộc sống hàng ngày của một người,trách nhiệm và các mối quan hệ xã hội. Dữ liệu sử dụng internet cung cấp thước đo trực tiếp về thời gian mỗi người tham gia dành trực tuyến","metadata":{}},{"cell_type":"code","source":"data = train[train['PreInt_EduHx-computerinternet_hoursday'].notna()]\nage_range = data['Basic_Demos-Age']\nprint(\n    f\"Độ tuổi của người tham gia được đo lường PreInt_EduHx-computerinternet_hoursday:\"\n    f\" {age_range.min()} - {age_range.max()} years\"\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:22.109475Z","iopub.execute_input":"2024-12-19T08:06:22.109852Z","iopub.status.idle":"2024-12-19T08:06:22.125835Z","shell.execute_reply.started":"2024-12-19T08:06:22.109815Z","shell.execute_reply":"2024-12-19T08:06:22.124669Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train['PreInt_EduHx-computerinternet_hoursday'].unique()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:22.127088Z","iopub.execute_input":"2024-12-19T08:06:22.127403Z","iopub.status.idle":"2024-12-19T08:06:22.136667Z","shell.execute_reply.started":"2024-12-19T08:06:22.127370Z","shell.execute_reply":"2024-12-19T08:06:22.135562Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"param_map = {0: '< 1h/day', 1: '~ 1h/day', 2: '~ 2hs/day', 3: '> 3hs/day'}\ntrain['internet_use_encoded'] = train[\n    'PreInt_EduHx-computerinternet_hoursday'\n].map(param_map).fillna('Missing')\n\nparam_ord = ['Missing', '< 1h/day', '~ 1h/day', '~ 2hs/day', '> 3hs/day']\ntrain['internet_use_encoded'] = pd.Categorical(\n    train['internet_use_encoded'], categories=param_ord,\n    ordered=True\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:22.138115Z","iopub.execute_input":"2024-12-19T08:06:22.138497Z","iopub.status.idle":"2024-12-19T08:06:22.152310Z","shell.execute_reply.started":"2024-12-19T08:06:22.138463Z","shell.execute_reply":"2024-12-19T08:06:22.150976Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, 'PreInt_EduHx-Season')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:22.153860Z","iopub.execute_input":"2024-12-19T08:06:22.154217Z","iopub.status.idle":"2024-12-19T08:06:22.172953Z","shell.execute_reply.started":"2024-12-19T08:06:22.154180Z","shell.execute_reply":"2024-12-19T08:06:22.171713Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n\n# Hours of Internet Use\nax1 = sns.countplot(x='internet_use_encoded', data=train, palette=\"Set3\", ax=axes[0])\naxes[0].set_title('Distribution of Hours of Internet Use')\naxes[0].set_xlabel('Hours per Day Group')\naxes[0].set_ylabel('Count')\n\ntotal = len(train['internet_use_encoded'])\nfor p in ax1.patches:\n    count = int(p.get_height())\n    percentage = '{:.1f}%'.format(100 * count / total)\n    ax1.annotate(f'{count} ({percentage})', (p.get_x() + p.get_width() / 2., p.get_height()), \n                 ha='center', va='baseline', fontsize=10, color='black', xytext=(0, 5), \n                 textcoords='offset points')\n\n# Hours of Internet Use by Age\nsns.boxplot(y=train['Basic_Demos-Age'], x=train['internet_use_encoded'], ax=axes[1], palette=\"Set3\")\naxes[1].set_title('Hours of Internet Use by Age')\naxes[1].set_ylabel('Age')\naxes[1].set_xlabel('Hours per Day Group')\n\n# Hours of Internet Use (numeric) by Age Group\nsns.boxplot(y='PreInt_EduHx-computerinternet_hoursday', x='Age Group', data=train, ax=axes[2], palette=\"Set3\")\naxes[2].set_title('Internet Hours by Age Group')\naxes[2].set_ylabel('Hours per Day (Numeric)')\naxes[2].set_xlabel('Age Group')\n\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-12-19T08:06:22.174508Z","iopub.execute_input":"2024-12-19T08:06:22.174877Z","iopub.status.idle":"2024-12-19T08:06:23.021094Z","shell.execute_reply.started":"2024-12-19T08:06:22.174832Z","shell.execute_reply":"2024-12-19T08:06:23.019970Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"stats = train.groupby(\n    ['Age Group', 'internet_use_encoded']\n).size().unstack(fill_value=0)\nfig, axes = plt.subplots(1, len(stats), figsize=(18, 5))\n\nfor i, age_group in enumerate(stats.index):\n    group_counts = stats.loc[age_group] / stats.loc[age_group].sum()\n    axes[i].pie(group_counts, labels=group_counts.index, autopct='%1.1f%%',\n                startangle=90, colors=sns.color_palette(\"Set3\"), labeldistance=1.1)\n    axes[i].set_title(f'Distribution of Hours of Internet Use\\n{age_group}')\n    axes[i].axis('equal')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:23.022580Z","iopub.execute_input":"2024-12-19T08:06:23.023014Z","iopub.status.idle":"2024-12-19T08:06:23.450295Z","shell.execute_reply.started":"2024-12-19T08:06:23.022970Z","shell.execute_reply":"2024-12-19T08:06:23.449365Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_non_na = train.dropna(subset=['PreInt_EduHx-computerinternet_hoursday'])\nrows = (train_non_na['PreInt_EduHx-computerinternet_hoursday'] == 3).sum()\nprint(f\"Non-NA Rows - Internet use 3h or more: {(rows / len(train_non_na)) * 100:.2f}%\")\n\nrows = (train_non_na['PreInt_EduHx-computerinternet_hoursday'] == 0).sum()\nprint(f\"Non-NA Rows - Internet use 1h or less: {(rows / len(train_non_na)) * 100:.2f}%\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:23.451506Z","iopub.execute_input":"2024-12-19T08:06:23.451953Z","iopub.status.idle":"2024-12-19T08:06:23.469866Z","shell.execute_reply.started":"2024-12-19T08:06:23.451904Z","shell.execute_reply":"2024-12-19T08:06:23.468677Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"stats = train.groupby(['Basic_Demos-Sex', 'internet_use_encoded']\n).size().unstack(fill_value=0)\nstats_prop = stats.div(stats.sum(axis=1), axis=0) * 100\n\nstats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)'\nstats","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:23.471465Z","iopub.execute_input":"2024-12-19T08:06:23.472401Z","iopub.status.idle":"2024-12-19T08:06:23.495176Z","shell.execute_reply.started":"2024-12-19T08:06:23.472348Z","shell.execute_reply":"2024-12-19T08:06:23.493791Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"💡 Note:\n*  Internet Missing data là 16,6% người tham gia, trong đó 38,5% Internet < 1h mỗi ngày.\n* Tương tự như dữ liệu SII, biểu đồ hộp cho thấy việc sử dụng internet hàng ngày cao hơn có liên quan đến tuổi tác cao hơn, với sự chồng chéo đáng kể về độ tuổi trong mỗi danh mục sử dụng internet. Nhưng ở đây, cả biểu diễn theo danh mục và số lượng về thời gian dành cho trực tuyến đều chỉ ra mối quan hệ tuyến tính nhất quán.\n* Biểu đồ hình tròn cho các nhóm tuổi được căn chỉnh tốt và cho thấy điều tương tự. Việc tạo ra một tính năng tương tác giữa việc sử dụng internet và độ tuổi có thể hữu ích cho việc lập mô hình. Việc sử dụng internet khá giống nhau đối với cả hai giới.","metadata":{}},{"cell_type":"markdown","source":"# Bổ sung: 1 vài mối quan hệ mới ","metadata":{}},{"cell_type":"markdown","source":"## 1. Biểu đồ phân phối thời gian sử dụng Internet theo giới tính","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(8, 5))\nsns.countplot(x='internet_use_encoded', hue='Basic_Demos-Sex', data=train, palette=\"pastel\")\nplt.title('Internet Use Distribution by Gender')\nplt.xlabel('Hours per Day Group')\nplt.ylabel('Count')\nplt.legend(title='Gender', labels=['Male', 'Female'])\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:23.497245Z","iopub.execute_input":"2024-12-19T08:06:23.497756Z","iopub.status.idle":"2024-12-19T08:06:23.813351Z","shell.execute_reply.started":"2024-12-19T08:06:23.497704Z","shell.execute_reply":"2024-12-19T08:06:23.812172Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 2. Biểu đồ mối quan hệ giữa BMI và thời gian sử dụng Internet","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(8, 5))\nsns.scatterplot(x='Physical-BMI', y='PreInt_EduHx-computerinternet_hoursday', hue='internet_use_encoded', data=train, palette=\"viridis\")\nplt.title('Internet Hours vs BMI')\nplt.xlabel('BMI')\nplt.ylabel('Hours of Internet Use (Numeric)')\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:23.814897Z","iopub.execute_input":"2024-12-19T08:06:23.815256Z","iopub.status.idle":"2024-12-19T08:06:24.548492Z","shell.execute_reply.started":"2024-12-19T08:06:23.815222Z","shell.execute_reply":"2024-12-19T08:06:24.547204Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 3.Biểu đồ phân nhóm tuổi và thời gian sử dụng Internet (line plot)","metadata":{}},{"cell_type":"code","source":"train['Age Group'] = pd.cut(train['Basic_Demos-Age'], bins=[10, 15, 20, 25, 30], labels=['10-15', '16-20', '21-25', '26-30'])\nage_group_avg = train.groupby('Age Group')['PreInt_EduHx-computerinternet_hoursday'].mean()\nplt.figure(figsize=(8, 5))\nage_group_avg.plot(kind='line', marker='o', color='blue')\nplt.title('Average Internet Hours by Age Group')\nplt.xlabel('Age Group')\nplt.ylabel('Average Hours of Internet Use')\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:24.550189Z","iopub.execute_input":"2024-12-19T08:06:24.550638Z","iopub.status.idle":"2024-12-19T08:06:24.897012Z","shell.execute_reply.started":"2024-12-19T08:06:24.550570Z","shell.execute_reply":"2024-12-19T08:06:24.895641Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 4. Biểu đồ phân phối theo mức độ hoạt động (Activity Level)","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(8, 5))\nsns.boxplot(x='BIA-BIA_Activity_Level_num', y='PreInt_EduHx-computerinternet_hoursday', data=train, palette=\"coolwarm\")\nplt.title('Internet Hours by Activity Level')\nplt.xlabel('Activity Level')\nplt.ylabel('Hours of Internet Use (Numeric)')\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:24.899025Z","iopub.execute_input":"2024-12-19T08:06:24.899467Z","iopub.status.idle":"2024-12-19T08:06:25.293326Z","shell.execute_reply.started":"2024-12-19T08:06:24.899417Z","shell.execute_reply":"2024-12-19T08:06:25.292136Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Internet usage và SII (target)","metadata":{}},{"cell_type":"markdown","source":"Mô tả cuộc thi nêu rõ mục tiêu là: phát hiện sớm các dấu hiệu sử dụng Internet và công nghệ có vấn đề (PIU), trong khi định nghĩa về PUI bao gồm việc sử dụng Internet quá mức:\n> PUI is an umbrella term that encompasses a set of potentially harmful online behaviors that are repetitive and uncontrolled, to the point that they are prioritized over other life interests and persist despite negative consequences.\n\nVì vậy, hãy cùng xem những người tham gia có điểm số khiếm khuyết (SII) khác nhau dành bao nhiêu thời gian trực tuyến trong tập dữ liệu này.","metadata":{}},{"cell_type":"code","source":"sii_reported = train[train['sii'] != \"Missing\"] # Lọc ra các dòng trong bảng train mà cột sii không phải là \"Missing\".\nsii_reported.loc[:, 'sii'] = sii_reported['sii'].cat.remove_unused_categories() #  Loại bỏ các giá trị SII không còn được sử dụng:","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:25.295093Z","iopub.execute_input":"2024-12-19T08:06:25.295525Z","iopub.status.idle":"2024-12-19T08:06:25.313546Z","shell.execute_reply.started":"2024-12-19T08:06:25.295477Z","shell.execute_reply":"2024-12-19T08:06:25.312576Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"stats = sii_reported.groupby(\n    ['internet_use_encoded', 'sii']\n).size().unstack(fill_value=0)\nstats_prop = stats.div(stats.sum(axis=1), axis=0) * 100\n\nstats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)' #  số lượng và tỷ lệ phần trăm của các người tham gia trong từng nhóm internet_use_encoded và sii.\nstats","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:25.314902Z","iopub.execute_input":"2024-12-19T08:06:25.315339Z","iopub.status.idle":"2024-12-19T08:06:25.339842Z","shell.execute_reply.started":"2024-12-19T08:06:25.315292Z","shell.execute_reply":"2024-12-19T08:06:25.338717Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig = plt.figure(figsize=(12, 10))\ngs = fig.add_gridspec(2, 2, height_ratios=[1, 1.5])\n\n# SII vs Hours of Internet Use\nax1 = fig.add_subplot(gs[0, 0])\nsns.boxplot(\n    x='sii', y='PreInt_EduHx-computerinternet_hoursday',\n    data=sii_reported,\n    ax=ax1, palette=\"Set3\"\n)\nax1.set_title('SII vs Hours of Internet Use')\nax1.set_ylabel('Hours per Day')\nax1.set_xlabel('SII')\n\n# PCIAT_Total for Complete PCIAT Responses by Hours of Internet Use\nax2 = fig.add_subplot(gs[0, 1])\nsns.boxplot(\n    x='internet_use_encoded', y='complete_resp_total',\n    data=sii_reported,\n    palette=\"Set3\", ax=ax2\n)\nax2.set_title('PCIAT_Total by Hours of Internet Use')\nax2.set_ylabel('PCIAT_Total for Complete PCIAT Responses')\nax2.set_xlabel('Hours per Day Group')\n\n# SII vs Hours of Internet Use by Age Group (Full width)\nax3 = fig.add_subplot(gs[1, :])\nsns.boxplot(\n    x='internet_use_encoded', y='complete_resp_total',\n    data=sii_reported,\n    hue='Age Group', ax=ax3, palette=\"Set3\"\n)\nax3.set_title('PCIAT_Total vs Hours of Internet Use by Age Group')\nax3.set_ylabel('PCIAT_Total for Complete PCIAT Responses')\nax3.set_xlabel('Hours per Day Group')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:25.341376Z","iopub.execute_input":"2024-12-19T08:06:25.341926Z","iopub.status.idle":"2024-12-19T08:06:26.628213Z","shell.execute_reply.started":"2024-12-19T08:06:25.341861Z","shell.execute_reply":"2024-12-19T08:06:26.627084Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"stats = sii_reported.groupby(\n    ['sii', 'internet_use_encoded']\n).size().unstack(fill_value=0)\nfig, axes = plt.subplots(1, len(stats), figsize=(18, 5))\n\nfor i, sii_group in enumerate(stats.index):\n    group_counts = stats.loc[sii_group] / stats.loc[sii_group].sum()\n    axes[i].pie(\n        group_counts, labels=group_counts.index, autopct='%1.1f%%',\n        startangle=90, colors=sns.color_palette(\"Set3\"), labeldistance=1.1\n    )\n    axes[i].set_title(f'Hours of using computer/internet\\n for SII = {sii_group}')\n    axes[i].axis('equal')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:26.629771Z","iopub.execute_input":"2024-12-19T08:06:26.630203Z","iopub.status.idle":"2024-12-19T08:06:27.208683Z","shell.execute_reply.started":"2024-12-19T08:06:26.630153Z","shell.execute_reply":"2024-12-19T08:06:27.207464Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"stats = sii_reported.groupby(\n    ['sii', 'internet_use_encoded']\n).size().unstack(fill_value=0)\nstats_prop = stats.div(stats.sum(axis=1), axis=0) * 100\n\nstats = stats.astype(str) +' (' + stats_prop.round(1).astype(str) + '%)'\nstats","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:27.210469Z","iopub.execute_input":"2024-12-19T08:06:27.210939Z","iopub.status.idle":"2024-12-19T08:06:27.236356Z","shell.execute_reply.started":"2024-12-19T08:06:27.210888Z","shell.execute_reply":"2024-12-19T08:06:27.235278Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train[\n    (train['internet_use_encoded'] == '< 1h/day') & \n    (train['sii'].isin(['2 (Moderate)', '3 (Severe)']))\n]['Basic_Demos-Age'].describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:27.237807Z","iopub.execute_input":"2024-12-19T08:06:27.238125Z","iopub.status.idle":"2024-12-19T08:06:27.250952Z","shell.execute_reply.started":"2024-12-19T08:06:27.238094Z","shell.execute_reply":"2024-12-19T08:06:27.249761Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"* Trong các biểu đồ boxplot, mặc dù có sự chồng lấp đáng kể giữa các nhóm SII và các nhóm sử dụng internet, chúng ta vẫn thấy một xu hướng tích cực giữa PIU (tổn thương liên quan đến sử dụng internet) và thời gian sử dụng internet, với những người có điểm số SII cao hơn thường dành nhiều thời gian hơn trên mạng (điều này sẽ thật kỳ lạ nếu ngược lại, vì việc sử dụng internet quá mức là một giả định trong định nghĩa PIU). \n* Tuy nhiên, khi mối quan hệ giữa PCIAT_Total và thời gian sử dụng internet được phân tích chi tiết hơn theo nhóm tuổi (biểu đồ boxplot phía dưới), mối quan hệ phi tuyến giữa tuổi tác, việc sử dụng internet và PIU xuất hiện, với nhóm thanh thiếu niên là nhóm bị ảnh hưởng nhiều nhất ở tất cả các mức độ sử dụng internet.\n* Các biểu đồ hình tròn cũng cho thấy có một tỷ lệ đáng kể người tham gia (83 người tổng cộng), thuộc mọi độ tuổi, dành rất ít thời gian trực tuyến (dưới 1 giờ mỗi ngày) nhưng lại có điểm SII cao (20,7% với SII 2 - tổn thương vừa phải và 14,7% với SII 3 - tổn thương nặng).","metadata":{}},{"cell_type":"markdown","source":"Tóm tắt kết quả\n\n* Điểm SII có xu hướng tăng theo độ tuổi nhưng lại có mối quan hệ hình chữ U, với nhóm thanh thiếu niên có điểm PCIAT trung vị cao nhất.\n* Càng lớn tuổi, người tham gia càng dành nhiều thời gian hơn trên internet (xu hướng tuyến tính rõ ràng).\n* Những người có điểm SII cao hơn thường dành nhiều thời gian trực tuyến hơn, nhưng thanh thiếu niên nổi bật là nhóm bị ảnh hưởng nhiều nhất ở tất cả các mức độ sử dụng internet.\nCó người tham gia ở hầu hết các độ tuổi (5 đến 21) dành dưới một giờ mỗi ngày trên internet nhưng lại có điểm SII cao.\n* Lưu ý: Những kết quả này cần được giải thích cẩn thận, vì có sự chồng lấp đáng kể giữa các nhóm SII và các nhóm sử dụng internet, và các trường hợp nặng cũng như người lớn tuổi chưa được đại diện đầy đủ trong dữ liệu.\n\n\n\n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"Mối quan hệ giữa SII và thời gian sử dụng internet không đơn giản như ta có thể mong đợi, dựa trên định nghĩa của PIU (hay nghiện internet). Điều này là vì các yếu tố ngoài số giờ dành cho internet còn ảnh hưởng đến SII, và phân tích trên cho thấy tuổi tác đặc biệt quan trọng: thanh thiếu niên có vẻ như có SII cao nhất ở tất cả các mức độ sử dụng internet... Nhưng làm thế nào để giải thích điều này: họ có dễ bị PIU hơn, hay liệu bảng câu hỏi này chỉ nhạy cảm hơn với PIU ở nhóm tuổi này? Hãy thử hiểu rõ hơn về những gì mà biến mục tiêu của chúng ta phản ánh.\n\nCác câu hỏi trong bảng câu hỏi PCIAT (được sử dụng để tính toán SII, xem data_dict.csv) dường như được thiết kế để đo lường những tác động cảm xúc và xã hội liên quan đến việc sử dụng internet (sự phụ thuộc cảm xúc vào internet, sự cô lập xã hội, bỏ bê trách nhiệm, và tác động của việc sử dụng internet đến các mối quan hệ và tâm trạng). Nói cách khác, mục đích là đo lường mức độ vấn đề trong hành vi liên quan đến việc sử dụng internet. Tuy nhiên, nhận thức của cha mẹ một cách tự nhiên sẽ có sự thiên lệch và bị ảnh hưởng bởi các yếu tố khác nhau - như thói quen sử dụng internet của họ, thái độ văn hóa, hoặc những mong muốn/quan điểm của họ về cách con cái nên cư xử.\n\nHơn nữa, bạn có thể tưởng tượng việc dành ít hơn một giờ mỗi ngày trên internet có thể dẫn đến những vấn đề như căng thẳng cảm xúc, bỏ bê nghĩa vụ hoặc rút lui khỏi gia đình? Tôi không nghĩ một giờ mỗi ngày với bất kỳ nội dung nào trên internet có thể dẫn đến những điều đó... Sự xuất hiện của điều này trong dữ liệu chỉ chứng minh rằng người tham gia không trung thực khi trả lời các câu hỏi PCIAT và sử dụng internet, hoặc điểm SII đang bị ảnh hưởng bởi các yếu tố khác không liên quan đến PIU.\n\nĐiều này chỉ ra rằng đặc điểm duy nhất kết nối tất cả các dữ liệu chúng ta có (hoạt động thể chất, dữ liệu gia tốc, giấc ngủ, v.v.) với việc sử dụng internet (và chúng ta cần sự kết nối này để dự đoán tác động của PIU) có thể không đáng tin cậy và có sự thiên lệch, cũng giống như biến mục tiêu.\n\nĐiều này ngụ ý rằng người tham gia có thể có các hành vi xã hội hoặc tâm trạng khác nhau đã có từ trước mà không liên quan đến việc sử dụng internet (và PIU) thực tế. Bảng câu hỏi là một công cụ chủ quan, ngay cả khi được hoàn thành bởi cha mẹ, trong khi tuổi thiếu niên là một giai đoạn nổi bật trong cuộc đời - thời kỳ hình thành bản sắc, phát triển các mối quan hệ bạn bè, và tìm kiếm sự độc lập - tất cả những điều này có thể làm tăng các hành vi như thay đổi tâm trạng, sự chống đối, và sự bốc đồng. Do đó, SII có thể đang đo lường mức độ vấn đề của những hành vi phát triển rộng hơn này chứ không phải sử dụng internet một cách cụ thể.\n\nNgoài ra, tính ứng dụng của bảng câu hỏi đối với tất cả các độ tuổi là điều cần phải xem xét. Tôi nghĩ rằng tất cả các câu hỏi trong PCIAT đều phù hợp hơn với thanh thiếu niên. Ví dụ:\n\nMột đứa trẻ 5-7 tuổi có thể không có công việc nhà, vì điều này phụ thuộc vào các chuẩn mực văn hóa.\nCâu hỏi về tác động học thuật có thể không áp dụng cho những đứa trẻ nhỏ chưa đến trường hoặc những người trưởng thành đã tốt nghiệp.\nViệc sử dụng email và nhận cuộc gọi điện thoại từ \"bạn bè trực tuyến\" có vẻ không phù hợp với trẻ nhỏ.\nCác câu hỏi về phản ứng đối với thời gian được phép dành cho internet (ít nhất có ba câu hỏi) thường không áp dụng cho người lớn.\nNhững câu hỏi không áp dụng cho độ tuổi của người tham gia có thể dẫn đến những câu trả lời lệch lạc hoặc không liên quan. Tất cả những điều này thách thức tính hợp lệ của khái niệm SII và đặt câu hỏi liệu nó có đo lường chính xác PIU hay bị ảnh hưởng bởi các yếu tố hành vi khác.","metadata":{}},{"cell_type":"markdown","source":"# Features EDA by Groups","metadata":{}},{"cell_type":"markdown","source":"Sau đây là cách chúng ta có thể phân loại các loại tính năng trong tập dữ liệu này: \n- Dữ liệu phân loại (Categorical): Các biến có các giá trị rời rạc theo từng nhóm nhưng không có thứ tự rõ ràng (được biểu diễn bằng chuỗi ký tự, ví dụ như mùa tuyển sinh)\n- Dữ liệu phân loại đã được mã hóa (Encoded categorical features): Các biến phân loại đã được mã hóa thành số nguyên (ví dụ: giới tính).\n- Dữ liệu liên tục (Continuous): Các biến có thể nhận bất kỳ giá trị nào trong một khoảng xác định (ví dụ: tuổi, giá trị \"enmo\", nhịp tim)\n- Dữ liệu thứ tự (Ordinal): Các biến có thứ tự xác định nhưng không nhất thiết các khoảng cách giữa các giá trị là bằng nhau (ví dụ: các câu trả lời từ bảng câu hỏi).","metadata":{}},{"cell_type":"markdown","source":"Và dưới đây là các nhóm features khác nhau:","metadata":{}},{"cell_type":"code","source":"# Nhóm các trường dữ liệu (Field) trong DataFrame data_dictionary theo Instrument\ngroups = data_dictionary.groupby('Instrument')['Field'].apply(list).to_dict()\nfor instrument, features in groups.items():\n    print(f\"{instrument}: {features}\\n\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:27.252403Z","iopub.execute_input":"2024-12-19T08:06:27.252790Z","iopub.status.idle":"2024-12-19T08:06:27.269309Z","shell.execute_reply.started":"2024-12-19T08:06:27.252753Z","shell.execute_reply":"2024-12-19T08:06:27.268206Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Các cột liên quan đến season","metadata":{}},{"cell_type":"markdown","source":"Sự hiện diện của các cột liên quan đến các mùa khác nhau có khả năng phản ánh thời điểm thu thập dữ liệu hoặc thời gian tham gia vào nghiên cứu. Những thay đổi theo mùa có thể đóng vai trò quan trọng trong các biến số được đo lường (ví dụ: thể chất, hoạt động thể chất, thói quen ngủ, và tất nhiên là việc sử dụng internet).","metadata":{}},{"cell_type":"code","source":"season_columns = [col for col in train.columns if 'Season' in col] # Lọc các cột chứa từ Season\nseason_df = train[season_columns]\nseason_df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:27.270644Z","iopub.execute_input":"2024-12-19T08:06:27.270987Z","iopub.status.idle":"2024-12-19T08:06:27.292538Z","shell.execute_reply.started":"2024-12-19T08:06:27.270946Z","shell.execute_reply":"2024-12-19T08:06:27.291276Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train[season_columns] = train[season_columns].fillna(\"Missing\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:27.294325Z","iopub.execute_input":"2024-12-19T08:06:27.295315Z","iopub.status.idle":"2024-12-19T08:06:27.312125Z","shell.execute_reply.started":"2024-12-19T08:06:27.295264Z","shell.execute_reply":"2024-12-19T08:06:27.310319Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Nhóm các features theo type và measurement method\nSau khi xem xét nội dung của data_dictionary một cách chi tiết, tôi tin rằng các đặc điểm cũng có thể được nhóm lại dựa trên loại và phương pháp đo lường của chúng","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:a0d75f9a-d6ee-4f4b-bac4-647e4982ff01.png)","metadata":{},"attachments":{"b840f936-d3f9-4ea3-8bd2-7501ed8f6b08.png":{"image/png":"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"},"82a27ba5-0e00-42c9-9550-6480d9e408ef.png":{"image/png":"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Kết nối tiềm năng với việc sử dụng Internet có vấn đề (PIU)\n1. Hành vi (báo cáo chủ quan):\n\n    - Một người không thể có PIU nếu họ không sử dụng Internet, vì vậy tôi kỳ vọng `PreInt_EduHx-computerinternet_hoursday` sẽ là đặc điểm quan trọng nhất. Tuy nhiên, như đã thấy ở trên, mối quan hệ giữa đặc điểm này với biến mục tiêu có thể không tuyến tính.\n    - Các xu hướng hành vi liên quan đến PIU có thể được phản ánh trong điểm số hoạt động thể chất được rút ra từ các bảng câu hỏi (`PAQ_A-PAQ_A_Total` và `PAQ_C-PAQ_C_Total`).\n    - Tuy nhiên, cả hai đặc điểm này đều dựa trên tự báo cáo và có khả năng bị sai lệch hoặc không chính xác, vì vậy tôi kỳ vọng sẽ có nhiều nhiễu trong dữ liệu này.\n\n2. Sức khỏe thể chất và thể lực (đo lường khách quan):\n\n    - Thang đánh giá toàn cầu dành cho trẻ em (Children's Global Assessment Scale - `CGAS-CGAS_Score`) là một điểm số do bác sĩ đánh giá, phản ánh chức năng tổng thể. Đối với những người có PIU, điểm số này có thể chỉ ra mức độ PIU ảnh hưởng đến chức năng tổng thể.\n    - Các chỉ số sức khỏe thể chất bao gồm thành phần cơ thể và các dấu hiệu sinh học (các cột bắt đầu bằng `Physical-`), có thể phản ánh mức độ PIU ảnh hưởng đến sức khỏe tổng quát (lưu ý rằng chiều cao riêng lẻ có thể không quá liên quan, nhưng kết hợp với cân nặng sẽ cho ra BMI - một chỉ số đo lường mỡ cơ thể).\n    - Các biện pháp khách quan về hoạt động thể chất bao gồm kết quả FitnessGram (độ bền, gập, nắm, chống đẩy, vươn người, nâng thân - các cột bắt đầu với `Fitness_` và `FGC-FGC_`). Những đặc điểm này có thể cho thấy mức độ PIU ảnh hưởng đến sức mạnh và độ săn chắc của cơ bắp.\n    - Đánh giá các vấn đề liên quan đến giấc ngủ (các cột đặc trưng `SDS-SDS_Total_Raw`, `SDS-SDS_Total_T`) có thể phản ánh mức độ PIU làm gián đoạn các mô hình giấc ngủ.\n3. Đặc điểm nhân khẩu học:\n\n    - Tuổi và giới tính có thể cực kỳ quan trọng, vì có thể có các mẫu hình liên quan đến Internet và PIU đặc thù theo giới tính và đặc biệt là theo tuổi (như đã thấy ở trên).","metadata":{}},{"cell_type":"code","source":"data_dictionary = data_dictionary[data_dictionary['Instrument'] != 'Parent-Child Internet Addiction Test']\ncontinuous_cols = data_dictionary[data_dictionary['Type'].str.contains(\n    'float|int', case=False\n)]['Field'].tolist()\n\n# target = train[['sii']]\n# train = train.drop(columns = columns_not_in_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:27.313718Z","iopub.execute_input":"2024-12-19T08:06:27.314478Z","iopub.status.idle":"2024-12-19T08:06:27.322914Z","shell.execute_reply.started":"2024-12-19T08:06:27.314438Z","shell.execute_reply":"2024-12-19T08:06:27.321275Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"background-color:#FFFFE0; color:black; font-family:Verdana; font-size:100%; text-align:left; border: 3px solid #FFD700; border-radius:15px; padding:20px 20px;\">- Demographics (Nhân khẩu học)</p>","metadata":{}},{"cell_type":"code","source":"groups.get('Demographics', [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:27.324357Z","iopub.execute_input":"2024-12-19T08:06:27.324771Z","iopub.status.idle":"2024-12-19T08:06:27.342605Z","shell.execute_reply.started":"2024-12-19T08:06:27.324721Z","shell.execute_reply":"2024-12-19T08:06:27.341297Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n\n# Season of Enrollment (phân phối số lượng ghi danh qua các mùa.)\nseason_counts = train['Basic_Demos-Enroll_Season'].value_counts(dropna=False)\nprint(season_counts)\naxes[0].pie(\n    season_counts, labels=season_counts.index,\n    autopct='%1.1f%%', startangle=90,\n    colors=sns.color_palette(\"Set3\")\n)\naxes[0].set_title('Season of Enrollment')\naxes[0].axis('equal')\n\n# Age Distribution by Sex (kiểm tra sự khác biệt về phân phối độ tuổi giữa các giới tính.)\nsns.histplot(\n    data=train, x='Basic_Demos-Age',\n    hue='Basic_Demos-Sex', multiple='dodge',\n    palette=\"Set2\", bins=20, ax=axes[1]\n)\naxes[1].set_title('Age Distribution by Sex')\naxes[1].set_xlabel('Age')\naxes[1].set_ylabel('Count')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:27.344487Z","iopub.execute_input":"2024-12-19T08:06:27.345782Z","iopub.status.idle":"2024-12-19T08:06:28.245045Z","shell.execute_reply.started":"2024-12-19T08:06:27.345725Z","shell.execute_reply":"2024-12-19T08:06:28.243809Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, 'Basic_Demos-Age')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:28.246315Z","iopub.execute_input":"2024-12-19T08:06:28.246643Z","iopub.status.idle":"2024-12-19T08:06:28.265433Z","shell.execute_reply.started":"2024-12-19T08:06:28.246588Z","shell.execute_reply":"2024-12-19T08:06:28.264204Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"0=Male, 1=Female","metadata":{}},{"cell_type":"markdown","source":"💡 Note:\n\n    - Phân phối ghi danh theo mùa tương đối cân bằng, với số lượng ghi danh cao nhất vào mùa Xuân (28.5%) và thấp nhất vào mùa Thu (21.9%).\n    - Số lượng nam giới (0) cao hơn ở hầu hết các nhóm tuổi, trong khi số lượng nữ giới (1) ít hơn, đặc biệt là ở các nhóm tuổi trẻ hơn.\n    - Mối quan hệ với biến mục tiêu được thể hiện trong phần 'SII theo độ tuổi và giới tính' (không có sự khác biệt về SII giữa nam và nữ, mối quan hệ hình chữ U giữa tuổi tác và suy giảm do sử dụng internet quá mức - PIU).","metadata":{}},{"cell_type":"markdown","source":"# <p style=\"background-color:#dff5e9; color:black; font-family:Verdana; font-size:100%; text-align:left; border: 3px solid #57c98a; border-radius:15px; padding:20px 20px;\">Physical Health and Fitness (Phép đo khách quan)</p>","metadata":{}},{"cell_type":"markdown","source":"## 1. Children's Global Assessment Scale (Thang đánh giá toàn cầu trẻ em)","metadata":{}},{"cell_type":"code","source":"groups.get(\"Children's Global Assessment Scale\", [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:28.266994Z","iopub.execute_input":"2024-12-19T08:06:28.267440Z","iopub.status.idle":"2024-12-19T08:06:28.279607Z","shell.execute_reply.started":"2024-12-19T08:06:28.267389Z","shell.execute_reply":"2024-12-19T08:06:28.278478Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data = train[train['CGAS-CGAS_Score'].notnull()]\nage_range = data['Basic_Demos-Age']\nprint(\n    f\"Age range for participants with CGAS-CGAS_Score data:\"\n    f\" {age_range.min()} - {age_range.max()} years\"\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:28.281068Z","iopub.execute_input":"2024-12-19T08:06:28.281446Z","iopub.status.idle":"2024-12-19T08:06:28.294852Z","shell.execute_reply.started":"2024-12-19T08:06:28.281403Z","shell.execute_reply":"2024-12-19T08:06:28.293514Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, 'CGAS-CGAS_Score')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:28.296676Z","iopub.execute_input":"2024-12-19T08:06:28.297122Z","iopub.status.idle":"2024-12-19T08:06:28.322242Z","shell.execute_reply.started":"2024-12-19T08:06:28.297087Z","shell.execute_reply":"2024-12-19T08:06:28.321222Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train[train['CGAS-CGAS_Score'] > 100]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:28.323533Z","iopub.execute_input":"2024-12-19T08:06:28.323870Z","iopub.status.idle":"2024-12-19T08:06:28.352062Z","shell.execute_reply.started":"2024-12-19T08:06:28.323837Z","shell.execute_reply":"2024-12-19T08:06:28.350913Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"💡 Note: \n    - Có một giá trị ngoại lệ cực đại (CGAS-CGAS_Score = 999), rõ ràng đây là lỗi.","metadata":{}},{"cell_type":"code","source":"train.loc[train['CGAS-CGAS_Score'] == 999, 'CGAS-CGAS_Score'] = np.nan","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:28.353813Z","iopub.execute_input":"2024-12-19T08:06:28.354291Z","iopub.status.idle":"2024-12-19T08:06:28.362006Z","shell.execute_reply.started":"2024-12-19T08:06:28.354238Z","shell.execute_reply":"2024-12-19T08:06:28.360870Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Phân phối điểm CGAS theo giới tính và mùa ghi danh (BỔ SUNG)\nplt.figure(figsize=(10,5))\n\nsns.boxplot(\n    data=train,\n    x='Basic_Demos-Enroll_Season',\n    y='CGAS-CGAS_Score',\n    hue='Basic_Demos-Sex',\n    palette='Set2'\n)\n\nplt.title('Distribution of CGAS Scores by Enrollment Season and Sex', fontsize=14)\nplt.xlabel('Enrollment Season', fontsize=12)\nplt.ylabel('CGAS Score', fontsize=12)\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:28.363418Z","iopub.execute_input":"2024-12-19T08:06:28.363853Z","iopub.status.idle":"2024-12-19T08:06:28.856762Z","shell.execute_reply.started":"2024-12-19T08:06:28.363794Z","shell.execute_reply":"2024-12-19T08:06:28.855656Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"💡 Note:\n\n    - Điểm CGAS phân phối khá đồng đều qua các mùa. Mùa Đông và Xuân có phân phối điểm cao hơn một chút so với mùa Hè và Thu, điều này có thể phản ánh sự khác biệt về tâm lý hoặc điều kiện môi trường theo mùa.\n    \n    - Không có sự khác biệt đáng kể về điểm CGAS giữa nam và nữ ở bất kỳ mùa nào.","metadata":{}},{"cell_type":"code","source":"# điểm CGAS trung bình theo nhóm tuổi và giới tính. (BỔ SUNG)\n# Tạo một cột mới để phân loại độ tuổi thành các nhóm\ntrain['Age_Group'] = pd.cut(\n    train['Basic_Demos-Age'],\n    bins=[0, 5, 10, 15, 20, 25],\n    labels=['0-5', '6-10', '11-15', '16-20', '21-25']\n)\n\n# Tính điểm CGAS trung bình theo nhóm tuổi và giới tính\nage_cgas_avg = train.groupby(['Age_Group', 'Basic_Demos-Sex'])['CGAS-CGAS_Score'].mean().reset_index()\n\n# Vẽ biểu đồ bar\nplt.figure(figsize=(10, 6))\n\nsns.barplot(\n    data=age_cgas_avg,\n    x='Age_Group',\n    y='CGAS-CGAS_Score',\n    hue='Basic_Demos-Sex',\n    palette='Set2',\n    width=0.5\n)\n\nplt.title('Average CGAS Score by Age Group and Sex', fontsize=14)\nplt.xlabel('Age Group', fontsize=12)\nplt.ylabel('Average CGAS Score', fontsize=12)\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:28.858409Z","iopub.execute_input":"2024-12-19T08:06:28.858869Z","iopub.status.idle":"2024-12-19T08:06:29.163298Z","shell.execute_reply.started":"2024-12-19T08:06:28.858817Z","shell.execute_reply":"2024-12-19T08:06:29.162151Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"💡 Note:\n\n    - Điểm CGAS phân phối khá đồng đều qua các mùa. Mùa Đông và Xuân có phân phối điểm cao hơn một chút so với mùa Hè và Thu, điều này có thể phản ánh sự khác biệt về tâm lý hoặc điều kiện môi trường theo mùa. Không có sự khác biệt đáng kể về điểm CGAS giữa nam và nữ ở bất kỳ mùa nào.\n    - Trong từng nhóm tuổi, điểm CGAS giữa nam và nữ khá tương đồng. Một số nhóm tuổi (như 0–5 và 6–10) có sự chênh lệch nhẹ, nhưng không đáng kể.\n\n=>Tổng hợp từ 2 biểu đồ: Điều này gợi ý rằng tuổi tác có tác động mạnh hơn đến điểm CGAS so với mùa ghi danh hay giới tính. Các yếu tố môi trường theo mùa có thể ảnh hưởng ít hơn đến sức khỏe tâm lý của trẻ em trong nghiên cứu này.","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(12, 5))\n\n# CGAS-Season\nplt.subplot(1, 2, 1)\ncgas_season_counts = train['CGAS-Season'].value_counts(normalize=True)\nplt.pie(\n    cgas_season_counts, \n    labels=cgas_season_counts.index, \n    autopct='%1.1f%%', \n    startangle=90, \n    colors=sns.color_palette(\"Set3\")\n)\nplt.title('CGAS-Season')\nplt.axis('equal')\n\n# CGAS-CGAS_Score without outliers (score == 999)\nplt.subplot(1, 2, 2)\nsns.histplot(\n    train['CGAS-CGAS_Score'].dropna(),\n    bins=20, kde=True\n)\nplt.title('CGAS-CGAS_Score (Without Outlier)')\nplt.xlabel('CGAS Score')\nplt.ylabel('Count')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:29.164599Z","iopub.execute_input":"2024-12-19T08:06:29.164939Z","iopub.status.idle":"2024-12-19T08:06:29.742329Z","shell.execute_reply.started":"2024-12-19T08:06:29.164907Z","shell.execute_reply":"2024-12-19T08:06:29.741147Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Số liệu thống kê không có giá trị ngoại lệ:","metadata":{}},{"cell_type":"code","source":"calculate_stats(train, 'CGAS-CGAS_Score')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:29.743838Z","iopub.execute_input":"2024-12-19T08:06:29.744268Z","iopub.status.idle":"2024-12-19T08:06:29.764887Z","shell.execute_reply.started":"2024-12-19T08:06:29.744219Z","shell.execute_reply":"2024-12-19T08:06:29.763494Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### CGAS Interpretation\nCGAS là thang đo đánh giá chức năng chung cho trẻ em và thanh thiếu niên từ 4-16 tuổi. CGAS yêu cầu bác sĩ đánh giá trẻ từ 1 đến 100 dựa trên mức độ chức năng thấp nhất của trẻ, không tính đến điều trị hoặc tiên lượng, trong một khoảng thời gian xác định.\n\nVì CGAS là một thước đo về chức năng chung, và SII phản ánh mức độ nghiêm trọng của tác động từ việc sử dụng internet lên chức năng đó, tôi mong rằng tính năng này, cùng với việc sử dụng internet, sẽ là yếu tố quan trọng nhất trong việc dự đoán SII.\n\nHãy phân loại cột CGAS-CGAS_Score dựa trên các hạng mục điểm đã được xác định và vẽ đồ thị tần suất.","metadata":{}},{"cell_type":"code","source":"bins = np.arange(0, 101, 10)\nlabels = [\n    \"1-10: Needs constant supervision (24 hour care)\",\n    \"11-20: Needs considerable supervision\",\n    \"21-30: Unable to function in almost all areas\",\n    \"31-40: Major impairment in functioning in several areas\",\n    \"41-50: Moderate degree of interference in functioning\",\n    \"51-60: Variable functioning with sporadic difficulties\",\n    \"61-70: Some difficulty in a single area\",\n    \"71-80: No more than slight impairment in functioning\",\n    \"81-90: Good functioning in all areas\",\n    \"91-100: Superior functioning\"\n]\n\ntrain['CGAS_Score_Bin'] = pd.cut(\n    train['CGAS-CGAS_Score'], bins=bins, labels=labels\n)\n\ncounts = train['CGAS_Score_Bin'].value_counts().reindex(labels)\nprop = (counts / counts.sum() * 100).round(1)\ncount_prop_labels = counts.astype(str) + \" (\" + prop.astype(str) + \"%)\"\n\nplt.figure(figsize=(18, 6))\nbars = plt.barh(labels, counts)\nplt.xlabel('Count')\nplt.title('CGAS Score Distribution')\n\nfor bar, label in zip(bars, count_prop_labels):\n    plt.text(\n        bar.get_width(), bar.get_y() + bar.get_height() / 2, label, va='center'\n    )\n\nplt.gca().invert_yaxis()\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:29.766254Z","iopub.execute_input":"2024-12-19T08:06:29.766599Z","iopub.status.idle":"2024-12-19T08:06:30.245090Z","shell.execute_reply.started":"2024-12-19T08:06:29.766563Z","shell.execute_reply":"2024-12-19T08:06:30.243864Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> \n    💡 Note: \n    <ul style=\"list-style:circle\">\n<li>\nĐại đa số các cá nhân có điểm CGAS từ 51-80 (79.7%), tức là gặp phải khó khăn không liên tục hoặc chỉ có suy giảm nhẹ.\n\n<li>Có hai người tham gia gặp phải khó khăn nghiêm trọng trong chức năng.\n    </ul>\n</div>","metadata":{}},{"cell_type":"markdown","source":"Kiểm tra mối quan hệ với biến mục tiêu:","metadata":{}},{"cell_type":"code","source":"train_filt = train.dropna(subset=['CGAS_Score_Bin', 'complete_resp_total'])\ntrain_filt.loc[:, 'CGAS_Score_Bin'] = train_filt['CGAS_Score_Bin'].cat.remove_unused_categories()\ntrain_filt.loc[:, 'sii'] = train_filt['sii'].cat.remove_unused_categories()\nlen(train_filt)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:30.246707Z","iopub.execute_input":"2024-12-19T08:06:30.247093Z","iopub.status.idle":"2024-12-19T08:06:30.269020Z","shell.execute_reply.started":"2024-12-19T08:06:30.247059Z","shell.execute_reply":"2024-12-19T08:06:30.267898Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 2, figsize=(16, 5))\n\n# CGAS-CGAS_Score vs sii\nsns.boxplot(\n    data=train_filt,\n    x='sii', y='CGAS-CGAS_Score',\n    palette='Set3', ax=axes[0]\n)\naxes[0].set_xlabel('SII Score')\naxes[0].set_ylabel('CGAS Score')\naxes[0].set_title('Distribution of CGAS Scores by SII')\n\n# complete_resp_total vs CGAS_Score_Bin\nsns.boxplot(\n    data=train_filt,\n    x='CGAS_Score_Bin', y='complete_resp_total',\n    ax=axes[1], palette='Set3'\n)\n\n# Get the tick positions and match the labels\nrange_labels = [label.split(\":\")[0] for label in train_filt['CGAS_Score_Bin'].cat.categories]\naxes[1].set_xticklabels(range_labels)\n\naxes[1].set_xlabel('CGAS Score category')\naxes[1].set_ylabel('PCIAT_Total for Complete PCIAT Responses')\naxes[1].set_title('Distribution of PCIAT_Total by CGAS Score categories')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:30.271216Z","iopub.execute_input":"2024-12-19T08:06:30.271715Z","iopub.status.idle":"2024-12-19T08:06:30.946915Z","shell.execute_reply.started":"2024-12-19T08:06:30.271661Z","shell.execute_reply":"2024-12-19T08:06:30.945531Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"score_min_max = train.groupby('sii')['CGAS-CGAS_Score'].agg(['min', 'max'])\nscore_min_max = score_min_max.rename(\n    columns={'min': 'Minimum CGAS Score', 'max': 'Maximum CGAS Score'}\n)\nscore_min_max","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:30.960685Z","iopub.execute_input":"2024-12-19T08:06:30.961065Z","iopub.status.idle":"2024-12-19T08:06:30.978241Z","shell.execute_reply.started":"2024-12-19T08:06:30.961032Z","shell.execute_reply":"2024-12-19T08:06:30.976639Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Hãy kiểm tra dữ liệu SII và sử dụng Internet của những người tham gia có chức năng toàn cầu kém nhất:","metadata":{}},{"cell_type":"code","source":"train_filt[train_filt['CGAS-CGAS_Score'] < 35][\n    ['Basic_Demos-Age', 'Basic_Demos-Sex', 'sii',\n     'CGAS-CGAS_Score',\n     'PreInt_EduHx-computerinternet_hoursday']\n]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:30.980157Z","iopub.execute_input":"2024-12-19T08:06:30.980558Z","iopub.status.idle":"2024-12-19T08:06:31.001631Z","shell.execute_reply.started":"2024-12-19T08:06:30.980520Z","shell.execute_reply":"2024-12-19T08:06:31.000266Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train[train['CGAS-CGAS_Score'] > 90][\n    ['Basic_Demos-Age', 'Basic_Demos-Sex', 'sii',\n     'CGAS-CGAS_Score',\n     'PreInt_EduHx-computerinternet_hoursday']\n]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:31.003304Z","iopub.execute_input":"2024-12-19T08:06:31.003724Z","iopub.status.idle":"2024-12-19T08:06:31.023826Z","shell.execute_reply.started":"2024-12-19T08:06:31.003685Z","shell.execute_reply":"2024-12-19T08:06:31.022693Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> \n    💡 Note: \n    <ul style=\"list-style:circle\">\n<li>Tôi kỳ vọng rằng SII càng cao thì điểm CGAS trung vị càng thấp, nhưng sự giảm sút ở đây là rất nhỏ\n<li>Tuy nhiên, không có người tham gia nào có điểm SII cao nhất (3 hoặc sử dụng internet gây vấn đề nghiêm trọng) mà lại có điểm CGAS tốt (81-100: chức năng tốt/xuất sắc trong tất cả các lĩnh vực). Điều này cho thấy các phản hồi của phụ huynh trong bảng hỏi PCIAT (biến mục tiêu của chúng ta) có thể phản ánh một số ảnh hưởng của PIU đối với sức khỏe và chức năng toàn cầu.\n<li>Những người tham gia có điểm CGAS kém và tốt nhất đều có điểm SII 0 hoặc 1 (không có hoặc mức độ PIU nhẹ) và báo cáo việc sử dụng internet khác nhau (dưới 1 giờ/ngày đến 3 giờ trở lên mỗi ngày). Điều này có nghĩa là trong dữ liệu huấn luyện có những người tham gia gặp vấn đề sức khỏe nghiêm trọng không liên quan đến PIU\n<li>Sự biến động lớn khiến việc rút ra kết luận rõ ràng, nhất quán về mối quan hệ giữa CGAS và điểm SII trở nên khó khăn. \n<li>Kích thước mẫu nhỏ trong một số hạng mục CGAS khiến việc tổng quát hóa kết quả trở nên khó khăn và có thể dẫn đến những diễn giải sai lệch.\n    </ul>\n</div>","metadata":{}},{"cell_type":"markdown","source":"## 2. Physical Measures","metadata":{}},{"cell_type":"code","source":"# các field thuộc Physical Measures\ngroups.get('Physical Measures', [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:31.025074Z","iopub.execute_input":"2024-12-19T08:06:31.025364Z","iopub.status.idle":"2024-12-19T08:06:31.040516Z","shell.execute_reply.started":"2024-12-19T08:06:31.025335Z","shell.execute_reply":"2024-12-19T08:06:31.039337Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"features_physical = groups.get('Physical Measures', [])\ncols = [col for col in features_physical if col in continuous_cols]\n\nplt.figure(figsize=(24, 10))\nn_cols = 4\nn_rows = len(cols) // n_cols + 1\n\nfor i, col in enumerate(cols):\n    plt.subplot(n_rows, n_cols, i + 1)\n    train[col].hist(bins=20)\n    plt.title(col)\n\n# Histogram \nplt.subplot(n_rows, n_cols, len(cols) + 1)\nseason_counts = train['Physical-Season'].value_counts(dropna=False)\n\n# Pie \nplt.pie(\n    season_counts,\n    labels=season_counts.index,\n    autopct='%1.1f%%',\n    startangle=90,\n    colors=sns.color_palette(\"Set3\")\n)\nplt.title('Physical-Season')\n\nplt.suptitle('Histograms for Physical Measures and Physical-Season Pie Chart', y=1.05)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:31.042185Z","iopub.execute_input":"2024-12-19T08:06:31.042679Z","iopub.status.idle":"2024-12-19T08:06:33.494009Z","shell.execute_reply.started":"2024-12-19T08:06:31.042588Z","shell.execute_reply":"2024-12-19T08:06:33.492893Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, cols)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:33.495231Z","iopub.execute_input":"2024-12-19T08:06:33.495532Z","iopub.status.idle":"2024-12-19T08:06:33.534285Z","shell.execute_reply.started":"2024-12-19T08:06:33.495502Z","shell.execute_reply":"2024-12-19T08:06:33.533165Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Chiều cao cân cặng","metadata":{}},{"cell_type":"code","source":"wh_cols = [\n    'Physical-BMI', 'Physical-Height',\n    'Physical-Weight', 'Physical-Waist_Circumference'\n]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:33.535597Z","iopub.execute_input":"2024-12-19T08:06:33.535981Z","iopub.status.idle":"2024-12-19T08:06:33.541235Z","shell.execute_reply.started":"2024-12-19T08:06:33.535946Z","shell.execute_reply":"2024-12-19T08:06:33.540045Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Giá trị tối thiểu 0 cho các phép đo như BMI, cân nặng và huyết áp là không thực tế về mặt sinh học và có thể chỉ ra dữ liệu bị thiếu hoặc sai. Ta sẽ kiểm tra số lượng số không trong các cột này:","metadata":{}},{"cell_type":"code","source":"# Đếm xem có bao nhiêu giá trị = 0 của mỗi cột trong wh_cols \n(train[wh_cols] == 0).sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:33.542551Z","iopub.execute_input":"2024-12-19T08:06:33.542898Z","iopub.status.idle":"2024-12-19T08:06:33.562877Z","shell.execute_reply.started":"2024-12-19T08:06:33.542866Z","shell.execute_reply":"2024-12-19T08:06:33.561711Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Kết quả về cho thấy dữ liệu phi lí \n- Physical-Weight có 61 giá trị = 0\n- Physical-BMI có 7 giá trị = 0 \nTa sẽ thay thế giá trị 0 bằng NaN và kiểm tra lại số liệu thống kê:","metadata":{}},{"cell_type":"code","source":"train[wh_cols] = train[wh_cols].replace(0, np.nan)\ncalculate_stats(train, wh_cols)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:33.564106Z","iopub.execute_input":"2024-12-19T08:06:33.564409Z","iopub.status.idle":"2024-12-19T08:06:33.602422Z","shell.execute_reply.started":"2024-12-19T08:06:33.564379Z","shell.execute_reply":"2024-12-19T08:06:33.601309Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Chuyển đổi cân nặng sang kilôgam và chiều cao sang cm và tính lại BMI:","metadata":{}},{"cell_type":"code","source":"lbs_to_kg = 0.453592\ninches_to_cm = 2.54\n\ntrain['Physical-Weight'] = train['Physical-Weight'] * lbs_to_kg\ntrain['Physical-Height'] = train['Physical-Height'] * inches_to_cm\ntrain['Physical-Waist_Circumference'] = train['Physical-Waist_Circumference'] * inches_to_cm\n\n# Recalculate BMI: BMI = weight (kg) / (height (m)^2)\ntrain['Physical-BMI'] = np.where(\n    train['Physical-Weight'].notna() & train['Physical-Height'].notna(),\n    train['Physical-Weight'] / ((train['Physical-Height'] / 100) ** 2),\n    np.nan  # If either is NaN, set BMI to NaN\n)\n\ncalculate_stats(train, wh_cols)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:33.603704Z","iopub.execute_input":"2024-12-19T08:06:33.604045Z","iopub.status.idle":"2024-12-19T08:06:33.637871Z","shell.execute_reply.started":"2024-12-19T08:06:33.604011Z","shell.execute_reply":"2024-12-19T08:06:33.636574Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Rất nhiều giá trị có vẻ nằm ngoài phạm vi bình thường =)) Đặc biệt là giá trị tối đa của cân nặng (142kg) và vòng eo (127cm).\n","metadata":{}},{"cell_type":"code","source":"# Bổ sung\nplt.figure(figsize=(20, 15))\n\n# 1. BMI Distribution by Season\nplt.subplot(3, 3, 1)\nsns.boxplot(x='Physical-Season', y='Physical-BMI', data=train, palette='Set2')\nplt.title('BMI Distribution by Season')\nplt.xlabel('Season')\nplt.ylabel('BMI')\n\n# 2. Heart Rate by Age\nplt.subplot(3, 3, 2)\nsns.scatterplot(x='Basic_Demos-Age', y='Physical-HeartRate', data=train, alpha=0.7)\nplt.title('Heart Rate by Age')\nplt.xlabel('Age')\nplt.ylabel('Heart Rate (bpm)')\n\n# 3. Systolic vs Diastolic Blood Pressure\nplt.subplot(3, 3, 3)\nsns.scatterplot(x='Physical-Systolic_BP', y='Physical-Diastolic_BP', data=train, alpha=0.7)\nplt.title('Systolic vs Diastolic Blood Pressure')\nplt.xlabel('Systolic BP (mmHg)')\nplt.ylabel('Diastolic BP (mmHg)')\n\n# 4. Waist Circumference by Age\nplt.subplot(3, 3, 4)\nsns.scatterplot(x='Basic_Demos-Age', y='Physical-Waist_Circumference', data=train, alpha=0.7)\nplt.title('Waist Circumference by Age')\nplt.xlabel('Age')\nplt.ylabel('Waist Circumference (cm)')\n\n# 5. Weight by Season\nplt.subplot(3, 3, 5)\nsns.boxplot(x='Physical-Season', y='Physical-Weight', data=train, palette='Set3')\nplt.title('Weight Distribution by Season')\nplt.xlabel('Season')\nplt.ylabel('Weight (kg)')\n\n# 6. Height by Season\nplt.subplot(3, 3, 6)\nsns.boxplot(x='Physical-Season', y='Physical-Height', data=train, palette='coolwarm')\nplt.title('Height Distribution by Season')\nplt.xlabel('Season')\nplt.ylabel('Height (cm)')\n\n# 7. Heart Rate by Season\nplt.subplot(3, 3, 7)\nsns.boxplot(x='Physical-Season', y='Physical-HeartRate', data=train, palette='viridis')\nplt.title('Heart Rate Distribution by Season')\nplt.xlabel('Season')\nplt.ylabel('Heart Rate (bpm)')\n\n# 8. BMI vs Waist Circumference\nplt.subplot(3, 3, 8)\nsns.scatterplot(x='Physical-BMI', y='Physical-Waist_Circumference', data=train, alpha=0.7)\nplt.title('BMI vs Waist Circumference')\nplt.xlabel('BMI')\nplt.ylabel('Waist Circumference (cm)')\n\nplt.tight_layout()\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:11:31.746901Z","iopub.execute_input":"2024-12-19T08:11:31.747320Z","iopub.status.idle":"2024-12-19T08:11:34.217170Z","shell.execute_reply.started":"2024-12-19T08:11:31.747284Z","shell.execute_reply":"2024-12-19T08:11:34.216028Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Note: \n- Các yếu tố như BMI, Weight, Heart Rate và Waist Circumference không có sự biến động lớn theo mùa.\n- Mối quan hệ giữa các biến số như Systolic/Diastolic BP và BMI/Waist Circumference phản ánh các quy luật sinh học thường thấy.\n- Có thể cần phân tích thêm về các yếu tố tác động khác như Sex, BIA, SDS ","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(20, 20))\n\n# 1. BMI Distribution by Sex\nplt.subplot(4, 3, 1)\nsns.boxplot(x='Basic_Demos-Sex', y='Physical-BMI', data=train, palette='Set2')\nplt.title('BMI Distribution by Sex')\nplt.xlabel('Sex')\nplt.ylabel('BMI')\n\n# 2. Weight Distribution by Sex\nplt.subplot(4, 3, 2)\nsns.boxplot(x='Basic_Demos-Sex', y='Physical-Weight', data=train, palette='Set3')\nplt.title('Weight Distribution by Sex')\nplt.xlabel('Sex')\nplt.ylabel('Weight (kg)')\n\n# 3. Height Distribution by Sex\nplt.subplot(4, 3, 3)\nsns.boxplot(x='Basic_Demos-Sex', y='Physical-Height', data=train, palette='coolwarm')\nplt.title('Height Distribution by Sex')\nplt.xlabel('Sex')\nplt.ylabel('Height (cm)')\n\n# 4. Waist Circumference by Sex\nplt.subplot(4, 3, 4)\nsns.boxplot(x='Basic_Demos-Sex', y='Physical-Waist_Circumference', data=train, palette='mako')\nplt.title('Waist Circumference by Sex')\nplt.xlabel('Sex')\nplt.ylabel('Waist Circumference (cm)')\n\n# 5. Systolic BP Distribution by Sex\nplt.subplot(4, 3, 5)\nsns.boxplot(x='Basic_Demos-Sex', y='Physical-Systolic_BP', data=train, palette='flare')\nplt.title('Systolic Blood Pressure by Sex')\nplt.xlabel('Sex')\nplt.ylabel('Systolic BP (mmHg)')\n\n# 6. Diastolic BP Distribution by Sex\nplt.subplot(4, 3, 6)\nsns.boxplot(x='Basic_Demos-Sex', y='Physical-Diastolic_BP', data=train, palette='crest')\nplt.title('Diastolic Blood Pressure by Sex')\nplt.xlabel('Sex')\nplt.ylabel('Diastolic BP (mmHg)')\n\n# 7. Heart Rate Distribution by Sex\nplt.subplot(4, 3, 7)\nsns.boxplot(x='Basic_Demos-Sex', y='Physical-HeartRate', data=train, palette='viridis')\nplt.title('Heart Rate Distribution by Sex')\nplt.xlabel('Sex')\nplt.ylabel('Heart Rate (bpm)')\n\n# 8. BMI by Season and Sex\nplt.subplot(4, 3, 8)\nsns.barplot(x='Physical-Season', y='Physical-BMI', hue='Basic_Demos-Sex', data=train, palette='Set2', errorbar=None)\nplt.title('Average BMI by Season and Sex')\nplt.xlabel('Season')\nplt.ylabel('Average BMI')\nplt.legend(title='Sex')\n\n# 9. Weight by Season and Sex\nplt.subplot(4, 3, 9)\nsns.barplot(x='Physical-Season', y='Physical-Weight', hue='Basic_Demos-Sex', data=train, palette='Set3', errorbar=None)\nplt.title('Average Weight by Season and Sex')\nplt.xlabel('Season')\nplt.ylabel('Average Weight (kg)')\nplt.legend(title='Sex')\n\n# 10. Height by Season and Sex\nplt.subplot(4, 3, 10)\nsns.barplot(x='Physical-Season', y='Physical-Height', hue='Basic_Demos-Sex', data=train, palette='coolwarm', errorbar=None)\nplt.title('Average Height by Season and Sex')\nplt.xlabel('Season')\nplt.ylabel('Average Height (cm)')\nplt.legend(title='Sex')\n\n# 11. Waist Circumference by Season and Sex\nplt.subplot(4, 3, 11)\nsns.barplot(x='Physical-Season', y='Physical-Waist_Circumference', hue='Basic_Demos-Sex', data=train, palette='mako', errorbar=None)\nplt.title('Average Waist Circumference by Season and Sex')\nplt.xlabel('Season')\nplt.ylabel('Average Waist Circumference (cm)')\nplt.legend(title='Sex')\n\n# Adjust layout\nplt.tight_layout()\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:25:43.404865Z","iopub.execute_input":"2024-12-19T08:25:43.405274Z","iopub.status.idle":"2024-12-19T08:25:46.695590Z","shell.execute_reply.started":"2024-12-19T08:25:43.405237Z","shell.execute_reply":"2024-12-19T08:25:46.694361Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Note: \n- Không có sự khác biệt quá lớn giữa Nam và Nữ ","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(18, 5))\n\n# Physical-Weight by Age\nplt.subplot(1, 3, 1)\nsns.scatterplot(x='Basic_Demos-Age', y='Physical-Weight', data=train)\nplt.title('Physical-Weight by Age')\nplt.xlabel('Age')\nplt.ylabel('Weight (kg)')\n\n# Physical-Height by Age\nplt.subplot(1, 3, 2)\nsns.scatterplot(x='Basic_Demos-Age', y='Physical-Height', data=train)\nplt.title('Physical-Height by Age')\nplt.xlabel('Age')\nplt.ylabel('Height (cm)')\n\n# Physical-Waist_Circumference vs Physical-Weight\nplt.subplot(1, 3, 3)\nsns.scatterplot(x='Physical-Weight', y='Physical-Waist_Circumference', data=train)\nplt.title('Waist Circumference vs Weight')\nplt.xlabel('Weight (kg)')\nplt.ylabel('Waist Circumference (cm)')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:36.443472Z","iopub.execute_input":"2024-12-19T08:06:36.443909Z","iopub.status.idle":"2024-12-19T08:06:37.497816Z","shell.execute_reply.started":"2024-12-19T08:06:36.443861Z","shell.execute_reply":"2024-12-19T08:06:37.496602Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> \n    💡 Note: \n    <ul style=\"list-style:circle\">\n<li>Cân nặng và chiều cao đều tăng theo độ tuổi, và vòng eo và cân nặng có mối tương quan cao, như dự đoán.\n<li>Tuy nhiên, có những cá nhân cao bất thường so với nhóm tuổi của họ hoặc thừa cân nghiêm trọng.\n<li>Cũng có một vài điểm ngoài vùng giá trị trong các phép đo vòng eo, có thể là do sai sót (ví dụ: vòng eo 100 cm đối với cân nặng 40 kg).\n<li>Vấn đề với việc làm sạch dữ liệu ở đây là chúng ta không thể đoán được đâu là dữ liệu chính xác. Ví dụ, chúng ta có thể thấy một sự kết hợp phi lý giữa vòng eo 100cm và cân nặng 40kg của một người tham gia, nhưng làm sao để xác định lỗi ở vòng eo hay cân nặng? Hoặc chiều cao khoảng 175cm đối với một đứa trẻ 7 tuổi... liệu chiều cao hay tuổi có bị nhập sai không? Hay đây là dữ liệu chính xác và đứa trẻ này mắc chứng khổng lồ hoặc một rối loạn khác liên quan đến hormone tăng trưởng?\n    </ul>\n</div>","metadata":{}},{"cell_type":"markdown","source":"### Blood Pressure & Heart Rate (Huyết áp và nhịp tim)","metadata":{}},{"cell_type":"markdown","source":"Có 1000% dữ liệu không chính xác trong các cột BP/HR vì các giá trị tối thiểu gây tử vong cho con người. Chúng ta có thể dọn dẹp những lỗi như thế này.","metadata":{}},{"cell_type":"code","source":"bp_hr_cols = [\n    'Physical-Diastolic_BP', 'Physical-Systolic_BP',\n    'Physical-HeartRate'\n]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:37.499211Z","iopub.execute_input":"2024-12-19T08:06:37.499537Z","iopub.status.idle":"2024-12-19T08:06:37.505104Z","shell.execute_reply.started":"2024-12-19T08:06:37.499504Z","shell.execute_reply":"2024-12-19T08:06:37.503962Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"(train[bp_hr_cols] < 50).sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:37.506437Z","iopub.execute_input":"2024-12-19T08:06:37.506865Z","iopub.status.idle":"2024-12-19T08:06:37.523693Z","shell.execute_reply.started":"2024-12-19T08:06:37.506825Z","shell.execute_reply":"2024-12-19T08:06:37.522243Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Chúng ta cũng biết rằng huyết áp tâm thu không thể thấp hơn huyết áp tâm trương:","metadata":{}},{"cell_type":"code","source":"train[train['Physical-Systolic_BP'] <= train['Physical-Diastolic_BP']][bp_hr_cols]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:37.525306Z","iopub.execute_input":"2024-12-19T08:06:37.525696Z","iopub.status.idle":"2024-12-19T08:06:37.543512Z","shell.execute_reply.started":"2024-12-19T08:06:37.525656Z","shell.execute_reply":"2024-12-19T08:06:37.542431Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Đây chắc chắn là những phép đo sai. Nhưng một lần nữa, chúng ta không thể chắc chắn đâu là thông tin chính xác, vì vậy chúng ta có thể đánh dấu các dòng này để kiểm tra thủ công từng cái một, hoặc thay thế tất cả các giá trị nghi ngờ bằng NaN. Đối với phân tích này, tôi chỉ loại bỏ giá trị 0 và cả hai chỉ số huyết áp nếu huyết áp tâm thu nhỏ hơn hoặc bằng huyết áp tâm trương.","metadata":{}},{"cell_type":"code","source":"train[cols] = train[cols].replace(0, np.nan)\ntrain.loc[train['Physical-Systolic_BP'] <= train['Physical-Diastolic_BP'], bp_hr_cols] = np.nan","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:37.544919Z","iopub.execute_input":"2024-12-19T08:06:37.545284Z","iopub.status.idle":"2024-12-19T08:06:37.571302Z","shell.execute_reply.started":"2024-12-19T08:06:37.545248Z","shell.execute_reply":"2024-12-19T08:06:37.570077Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(12, 5))\n\n# Diastolic BP vs Heart Rate\nplt.subplot(1, 2, 1)\nsns.scatterplot(x='Physical-Diastolic_BP', y='Physical-HeartRate', data=train)\nplt.title('Diastolic BP vs Heart Rate')\nplt.xlabel('Diastolic Blood Pressure (mmHg)')\nplt.ylabel('Heart rate (beats/min)')\n\n# Systolic BP vs Heart Rate\nplt.subplot(1, 2, 2)\nsns.scatterplot(x='Physical-Systolic_BP', y='Physical-HeartRate', data=train)\nplt.title('Systolic BP vs Heart Rate')\nplt.xlabel('Systolic Blood Pressure (mmHg)')\nplt.ylabel('Heart rate (beats/min)')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:37.572712Z","iopub.execute_input":"2024-12-19T08:06:37.573077Z","iopub.status.idle":"2024-12-19T08:06:38.268524Z","shell.execute_reply.started":"2024-12-19T08:06:37.573020Z","shell.execute_reply":"2024-12-19T08:06:38.267189Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Note: \n* Việc không có mối tương quan trực tiếp rõ ràng giữa nhịp tim và huyết áp trong các biểu đồ cho thấy các phép đo có thể được thực hiện khi đang nghỉ ngơi hoặc trong điều kiện không căng thẳng.","metadata":{}},{"cell_type":"markdown","source":"Thông thường, huyết áp tâm thu (SBP) và huyết áp tâm trương (DBP) có mối tương quan dương, vì cả hai đều phản ánh chức năng của hệ tim mạch. Tuy nhiên, vẫn có thể xảy ra những sai lệch:\n    - Tăng huyết áp tâm thu đơn độc: SBP cao trong khi DBP bình thường\n    - Tăng huyết áp tâm trương đơn độc: SBP bình thường trong khi DBP cao\n    - Tăng huyết áp toàn diện: Cả SBP và DBP đều tăng\nChỉ số BMI thường được sử dụng như một chỉ báo về lượng mỡ cơ thể tổng thể và có thể liên quan đến huyết áp (ví dụ: giá trị BMI cao hơn cho thấy thừa cân hoặc béo phì, thường liên quan đến huyết áp cao). Hãy kiểm tra xem điều này có đúng với những người tham gia nghiên cứu hay không.\n","metadata":{}},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n\n# BMI vs Systolic Blood Pressure\nsns.scatterplot(x='Physical-BMI', y='Physical-Systolic_BP', data=train, ax=axes[0], color='b')\naxes[0].set_title('BMI vs Systolic Blood Pressure')\naxes[0].set_xlabel('Body Mass Index (BMI) (kg/m^2)')\naxes[0].set_ylabel('Systolic Blood Pressure (mmHg)')\n\n# Systolic Blood Pressure vs Diastolic Blood Pressure\nsns.scatterplot(\n    x='Physical-Systolic_BP', y='Physical-Diastolic_BP',\n    data=train, ax=axes[1], color='g'\n)\naxes[1].set_title('Systolic Blood Pressure vs Diastolic Blood Pressure')\naxes[1].set_xlabel('Systolic Blood Pressure (mmHg)')\naxes[1].set_ylabel('Diastolic Blood Pressure (mmHg)')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:38.269913Z","iopub.execute_input":"2024-12-19T08:06:38.270317Z","iopub.status.idle":"2024-12-19T08:06:39.011640Z","shell.execute_reply.started":"2024-12-19T08:06:38.270207Z","shell.execute_reply":"2024-12-19T08:06:39.010461Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Note:\n- Không có vẻ tồn tại mối tương quan mạnh mẽ và rõ ràng giữa chỉ số khối cơ thể (BMI) và huyết áp tâm thu (BP).\n- Như dự đoán, có mối tương quan dương mạnh giữa huyết áp tâm thu và huyết áp tâm trương, nhưng vẫn có những trường hợp đáng chú ý về tăng huyết áp tâm thu hoặc tâm trương riêng lẻ (hoặc có thể là lỗi trong dữ liệu, ai mà biết được?).","metadata":{}},{"cell_type":"markdown","source":"### Só sánh với normal rages ","metadata":{}},{"cell_type":"markdown","source":"Bây giờ chúng ta sẽ xác định các khoảng giá trị bình thường xấp xỉ cho từng cột và đếm số hàng nằm ngoài các khoảng này. Vì các giá trị bình thường có thể thay đổi đáng kể trong độ tuổi từ 5 đến 22, tôi sẽ sử dụng các giá trị ước lượng chung; để có kết quả chính xác hơn, bạn có thể tham khảo các biểu đồ tăng trưởng BMI theo tuổi trên các trang web của CDC hoặc WHO, chẳng hạn.","metadata":{}},{"cell_type":"code","source":"normal_ranges = {\n    'Physical-BMI': (18.5, 24.9),\n    'Physical-Height': (100, 193),\n    'Physical-Weight': (20, 120),\n    'Physical-Waist_Circumference': (50, 90),\n    'Physical-Diastolic_BP': (60, 80),\n    'Physical-HeartRate': (60, 100),\n    'Physical-Systolic_BP': (90, 120)\n}\n\ndef count_out_of_range(data, column, low, high):\n    return ((data[column] < low) | (data[column] > high)).sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:39.013077Z","iopub.execute_input":"2024-12-19T08:06:39.013464Z","iopub.status.idle":"2024-12-19T08:06:39.021781Z","shell.execute_reply.started":"2024-12-19T08:06:39.013421Z","shell.execute_reply":"2024-12-19T08:06:39.020457Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"out_of_range_counts = {\n    col: count_out_of_range(train, col, *normal_ranges[col])\n    for col in normal_ranges\n}\nprint(\"Number of rows with values outside normal ranges:\")\n\nfor col, count in out_of_range_counts.items():\n    total_valid = train[col].notna().sum()\n    percentage = (count / total_valid) * 100\n    print(f\"{col}: {count} ({percentage:.2f}%)\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:39.023221Z","iopub.execute_input":"2024-12-19T08:06:39.023570Z","iopub.status.idle":"2024-12-19T08:06:39.042005Z","shell.execute_reply.started":"2024-12-19T08:06:39.023534Z","shell.execute_reply":"2024-12-19T08:06:39.040780Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"BMI nhóm theo mức độ béo phì theo WHO BMI theo độ tuổi (5-19 tuổi)","metadata":{}},{"cell_type":"code","source":"bmi_categories = [\n    ('Underweight', train['Physical-BMI'] < 18.5),\n    ('Normal weight', (train['Physical-BMI'] >= 18.5) & (train['Physical-BMI'] <= 24.9)),\n    ('Overweight', (train['Physical-BMI'] >= 25) & (train['Physical-BMI'] <= 29.9)),\n    ('Obesity', train['Physical-BMI'] >= 30)\n]\nbmi_category_counts = {label: condition.sum() for label, condition in bmi_categories}\n\nplt.figure(figsize=(5, 6))\nplt.pie(bmi_category_counts.values(),\n        labels=bmi_category_counts.keys(),\n        autopct='%1.1f%%', startangle=90,\n        colors=plt.cm.Set3.colors)\nplt.title('BMI Distribution by Category')\nplt.axis('equal')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:39.043479Z","iopub.execute_input":"2024-12-19T08:06:39.043949Z","iopub.status.idle":"2024-12-19T08:06:39.228112Z","shell.execute_reply.started":"2024-12-19T08:06:39.043897Z","shell.execute_reply":"2024-12-19T08:06:39.226886Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Kiểm tra các trường hợp độ lệch cực đại","metadata":{}},{"cell_type":"code","source":"train[train['Physical-BMI'] < 12][cols + ['Basic_Demos-Age']].sort_values(by = 'Physical-BMI')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:39.229483Z","iopub.execute_input":"2024-12-19T08:06:39.229997Z","iopub.status.idle":"2024-12-19T08:06:39.261250Z","shell.execute_reply.started":"2024-12-19T08:06:39.229947Z","shell.execute_reply":"2024-12-19T08:06:39.260128Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train[train['Physical-Systolic_BP'] > 160][cols + ['Basic_Demos-Age']].sort_values(by = 'Physical-Systolic_BP')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:39.262548Z","iopub.execute_input":"2024-12-19T08:06:39.263026Z","iopub.status.idle":"2024-12-19T08:06:39.289217Z","shell.execute_reply.started":"2024-12-19T08:06:39.262976Z","shell.execute_reply":"2024-12-19T08:06:39.288054Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> \n    💡 Note: \n    <ul style=\"list-style:circle\">\n        <li>Một số lượng đáng kể người tham gia, đặc biệt là về BMI và huyết áp, nằm ngoài các khoảng giá trị bình thường mong đợi.\n        <li>Chiều cao và cân nặng của hầu hết người tham gia nằm trong các khoảng hợp lý, nhưng nhiều người có BMI nằm ngoài khoảng bình thường xấp xỉ, điều này gợi ý rằng nhiều người tham gia có thể có tỷ lệ cơ thể không cân đối (hoặc các phép đo không chính xác?). Để hiểu chính xác hơn, cần sử dụng các giá trị tham chiếu theo độ tuổi.\n    </ul>\n</div>","metadata":{}},{"cell_type":"markdown","source":"### Mối quan hệ với biến mục tiêu - SII (PCIAT_Total cho các phản hồi PCIAT đầy đủ)","metadata":{}},{"cell_type":"code","source":"data_subset = train[cols + ['complete_resp_total']]\n\ncorr_matrix = data_subset.corr()\n\nplt.figure(figsize=(10, 8))\nsns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', vmin=-1, vmax=1)\nplt.title('Correlation Heatmap')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:39.290923Z","iopub.execute_input":"2024-12-19T08:06:39.291304Z","iopub.status.idle":"2024-12-19T08:06:39.864270Z","shell.execute_reply.started":"2024-12-19T08:06:39.291268Z","shell.execute_reply":"2024-12-19T08:06:39.863056Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> \n    💡 Note: \n    <ul style=\"list-style:circle\">\n<li>Mối tương quan dương với biến mục tiêu được ghi nhận ở chiều cao, cân nặng và vòng eo, nghĩa là những người cao hơn và béo hơn có xu hướng có SII cao hơn. Tuy nhiên, vì các thông số thể chất này tăng theo độ tuổi và chúng ta đã biết rằng SII có xu hướng cao nhất ở độ tuổi vị thành niên, điều này có thể cho thấy rằng chúng đóng vai trò như một đại diện thay thế cho độ tuổi (có khả năng phản ánh các xu hướng liên quan đến tuổi tác).\n<li>Các chỉ số tim mạch (huyết áp tâm thu, huyết áp tâm trương và nhịp tim) cũng thay đổi theo độ tuổi, nhưng không thay đổi mạnh mẽ như các chỉ số thể chất giữa thời thơ ấu và vị thành niên, và có thể không nhạy cảm với các hành vi như sử dụng internet. Các chỉ số này cũng có mức độ biến động cao hơn, như chúng ta đã thấy trong các biểu đồ trước, vì vậy mối tương quan yếu có thể chỉ ra rằng sức khỏe tim mạch không liên quan chặt chẽ đến PIU, hoặc dữ liệu này bị phân tán và nhiễu nhiều hơn, làm mối quan hệ với PIU bị pha loãng.\n    </ul>\n</div>","metadata":{}},{"cell_type":"markdown","source":"## 3.  Phân tích Bio-electric Impedance ","metadata":{}},{"cell_type":"code","source":"data_dictionary[data_dictionary['Instrument'] == 'Bio-electric Impedance Analysis']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:39.866088Z","iopub.execute_input":"2024-12-19T08:06:39.866429Z","iopub.status.idle":"2024-12-19T08:06:39.881291Z","shell.execute_reply.started":"2024-12-19T08:06:39.866394Z","shell.execute_reply":"2024-12-19T08:06:39.880131Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Không có thông tin trong mô tả cuộc thi về thiết bị đã được sử dụng, liệu đây là dữ liệu thô hay họ đã sử dụng một số mô hình phương trình BIA để ước tính các thông số. Nhưng có khả năng dữ liệu BIA đã được xử lý bằng một mô hình phương trình BIA. Điều rất quan trọng cần lưu ý là BIA không phải là một phương pháp chính xác, ví dụ, nó có xu hướng đánh giá quá cao khối lượng cơ bắp, vì vậy các phương trình đã được phát triển để ước tính khối lượng cơ dựa trên các yếu tố như tuổi, giới tính, chiều cao, cân nặng và điện trở kháng và/hoặc phản kháng được BIA ước tính... một số lượng lớn các mô hình phương trình dự đoán đã được tạo ra thông qua các nghiên cứu xác nhận khác nhau (liên kết). Điều cần thiết là tất cả các bản ghi phải được xử lý bằng cùng một phương trình, nhưng chúng ta không thể chắc chắn về điều này.","metadata":{}},{"cell_type":"code","source":"bia_data_dict = data_dictionary[data_dictionary['Instrument'] == 'Bio-electric Impedance Analysis']\ncategorical_columns = bia_data_dict[bia_data_dict['Type'] == 'categorical int']['Field'].tolist()\ncontinuous_columns = bia_data_dict[bia_data_dict['Type'] == 'float']['Field'].tolist()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:39.882461Z","iopub.execute_input":"2024-12-19T08:06:39.882800Z","iopub.status.idle":"2024-12-19T08:06:39.906215Z","shell.execute_reply.started":"2024-12-19T08:06:39.882766Z","shell.execute_reply":"2024-12-19T08:06:39.905115Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n\n# Season\nseason_counts = train['BIA-Season'].value_counts(normalize=True)\naxes[0].pie(\n    season_counts, \n    labels=season_counts.index, \n    autopct='%1.1f%%', \n    startangle=90, \n    colors=sns.color_palette(\"Set3\")\n)\naxes[0].set_title(\n    f\"{bia_data_dict[bia_data_dict['Field'] == 'BIA-Season']['Description'].values[0]}\"\n)\naxes[0].axis('equal')\n\n# Other categorical columns\nfor idx, col in enumerate(categorical_columns):\n    sns.countplot(x=col, data=train, palette=\"Set3\", ax=axes[idx+1])\n    axes[idx+1].set_title(data_dictionary[data_dictionary['Field'] == col]['Description'].values[0])\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:39.907634Z","iopub.execute_input":"2024-12-19T08:06:39.907991Z","iopub.status.idle":"2024-12-19T08:06:40.615952Z","shell.execute_reply.started":"2024-12-19T08:06:39.907957Z","shell.execute_reply":"2024-12-19T08:06:40.614735Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(24, 20))\n\nfor idx, col in enumerate(continuous_columns):\n    plt.subplot(4, 4, idx + 1)\n    sns.histplot(train[col].dropna(), bins=20, kde=True)\n    plt.title(data_dictionary[data_dictionary['Field'] == col]['Description'].values[0])\n    plt.xlabel('Value')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:40.617575Z","iopub.execute_input":"2024-12-19T08:06:40.618084Z","iopub.status.idle":"2024-12-19T08:06:46.497579Z","shell.execute_reply.started":"2024-12-19T08:06:40.618032Z","shell.execute_reply":"2024-12-19T08:06:46.496288Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, continuous_columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:46.499428Z","iopub.execute_input":"2024-12-19T08:06:46.499941Z","iopub.status.idle":"2024-12-19T08:06:46.565090Z","shell.execute_reply.started":"2024-12-19T08:06:46.499887Z","shell.execute_reply":"2024-12-19T08:06:46.563969Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> \n    💡 Note: \n    <ul style=\"list-style:circle\">\n<li>Phân bố của các phép đo phân tích trở kháng điện sinh học khác nhau trong tập dữ liệu cho thấy hầu hết chúng không hữu ích: phân bố lệch mạnh, với phần lớn người tham gia có giá trị cận biên và một số ngoại lệ (có thể là lỗi đo lường).\n<li>Một số biến, chẳng hạn như Chỉ số Khối Lượng Mỡ (Fat Mass Index) và Tỷ Lệ Mỡ Cơ Thể (Body Fat Percentage), có các giá trị âm không hợp lý và hầu hết đều có giá trị cực kỳ cao, điều này cho thấy các vấn đề tiềm ẩn về chất lượng dữ liệu.\n    </ul>\n</div>","metadata":{}},{"cell_type":"markdown","source":"### So sánh hai chỉ số BMI được đo","metadata":{}},{"cell_type":"code","source":"bmi_data = train[['BIA-BIA_BMI', 'Physical-BMI']].dropna()\n\nplt.figure(figsize=(8, 6))\nsns.scatterplot(\n    x='BIA-BIA_BMI', y='Physical-BMI',\n    data=bmi_data,\n    color='b'\n)\nplt.title('Comparison of BIA-BMI vs Physical-BMI')\nplt.xlabel('BIA-BMI')\nplt.ylabel('Physical-BMI')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:46.566582Z","iopub.execute_input":"2024-12-19T08:06:46.566971Z","iopub.status.idle":"2024-12-19T08:06:46.982523Z","shell.execute_reply.started":"2024-12-19T08:06:46.566937Z","shell.execute_reply":"2024-12-19T08:06:46.981380Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"bmi_measures = train[['BIA-Season', 'Physical-Season']].dropna()\nbmi_measures.groupby(['BIA-Season', 'Physical-Season']).size().reset_index(name='Count')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:46.983808Z","iopub.execute_input":"2024-12-19T08:06:46.984121Z","iopub.status.idle":"2024-12-19T08:06:47.003203Z","shell.execute_reply.started":"2024-12-19T08:06:46.984089Z","shell.execute_reply":"2024-12-19T08:06:47.001860Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> \n    💡 Note: Điều này có thể không hoàn toàn chính xác, vì ở trên tôi đã phát hiện có các giá trị bằng 0 trong các phép đo thể chất và đã tính lại chỉ số BMI... Như chúng ta có thể thấy, chỉ số BMI được đo trong quá trình phân tích trở kháng điện sinh học cũng chứa các giá trị bằng 0 mà tôi không thể giải thích và dường như là lỗi.\n</div>","metadata":{}},{"cell_type":"markdown","source":"## 4. FitnessGram","metadata":{}},{"cell_type":"markdown","source":"## FitnessGram Vitals và Treadmill","metadata":{}},{"cell_type":"code","source":"groups.get('FitnessGram Vitals and Treadmill', [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:47.004707Z","iopub.execute_input":"2024-12-19T08:06:47.005159Z","iopub.status.idle":"2024-12-19T08:06:47.012816Z","shell.execute_reply.started":"2024-12-19T08:06:47.005108Z","shell.execute_reply":"2024-12-19T08:06:47.011665Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data = train[train['Fitness_Endurance-Max_Stage'].notnull()]\nage_range = data['Basic_Demos-Age']\nprint(\n    f\"Age range for participants with Fitness_Endurance-Max_Stage data:\"\n    f\" {age_range.min()} - {age_range.max()} years\"\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:47.014115Z","iopub.execute_input":"2024-12-19T08:06:47.014474Z","iopub.status.idle":"2024-12-19T08:06:47.025870Z","shell.execute_reply.started":"2024-12-19T08:06:47.014427Z","shell.execute_reply":"2024-12-19T08:06:47.024796Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 4, figsize=(24, 5))\n\n# Fitness Endurance Season\ntrain['Fitness_Endurance-Season'].value_counts(normalize=True).plot.pie(\n    autopct='%1.1f%%', colors=plt.cm.Set3.colors, ax=axes[0]\n)\naxes[0].set_title('Fitness Endurance Season')\naxes[0].axis('equal')  # Equal aspect ratio ensures the pie is drawn as a circle.\n\n# Box plot for Max Stage by Season\nsns.violinplot(\n    x='Fitness_Endurance-Season',\n    y='Fitness_Endurance-Max_Stage',\n    data=train, palette=\"Set3\",\n    ax=axes[1]\n)\naxes[1].set_title('Max Stage by Season')\naxes[1].set_xlabel('Season')\naxes[1].set_ylabel('Max Stage')\n\n# Fitness Endurance Time (Minutes)\nsns.histplot(train['Fitness_Endurance-Time_Mins'], bins=20, kde=True, ax=axes[2])\naxes[2].set_title('Fitness Endurance Time (Minutes)')\naxes[2].set_xlabel('Time (Minutes)')\n\n# Fitness Endurance Time (Seconds)\nsns.histplot(train['Fitness_Endurance-Time_Sec'], bins=20, kde=True, ax=axes[3])\naxes[3].set_title('Fitness Endurance Time (Seconds)')\naxes[3].set_xlabel('Time (Seconds)')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:47.027292Z","iopub.execute_input":"2024-12-19T08:06:47.027640Z","iopub.status.idle":"2024-12-19T08:06:48.406471Z","shell.execute_reply.started":"2024-12-19T08:06:47.027587Z","shell.execute_reply":"2024-12-19T08:06:48.405302Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Sức bền theo độ tuổi (Edurance) ","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(12, 5))\n\nsns.violinplot(x='Basic_Demos-Age', y='Fitness_Endurance-Max_Stage', data=train, palette=\"Set3\")\nplt.title('Fitness Endurance Max Stage by Age')\nplt.xlabel('Age')\nplt.ylabel('Max Stage')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:48.408095Z","iopub.execute_input":"2024-12-19T08:06:48.408533Z","iopub.status.idle":"2024-12-19T08:06:49.062303Z","shell.execute_reply.started":"2024-12-19T08:06:48.408483Z","shell.execute_reply":"2024-12-19T08:06:49.061212Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"cols = [\n    'Fitness_Endurance-Max_Stage',\n    'Fitness_Endurance-Time_Mins',\n    'Fitness_Endurance-Time_Sec'\n]\ncalculate_stats(train, cols)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:49.063720Z","iopub.execute_input":"2024-12-19T08:06:49.064067Z","iopub.status.idle":"2024-12-19T08:06:49.092477Z","shell.execute_reply.started":"2024-12-19T08:06:49.064026Z","shell.execute_reply":"2024-12-19T08:06:49.091058Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"- Fitness_Endurance-Max_Stage:  Có thể đại diện cho mức độ tối đa mà người tham gia đạt được trong một bài kiểm tra sức bền. Trong các bài kiểm tra sức bền như bài kiểm tra trên máy chạy bộ hoặc bài kiểm tra đa cấp (beep test), người tham gia tiến tới các mức độ khó tăng dần (tốc độ hoặc độ nghiêng), và cột này ghi lại mức độ hoặc giai đoạn cao nhất mà người tham gia hoàn thành trước khi dừng lại.\n- Fitness_Endurance-Time_Mins: Có thể là khoảng thời gian mà người tham gia có thể duy trì bài kiểm tra trước khi kiệt sức, được đo bằng phút.\n- Fitness_Endurance-Time_Sec: Tôi đoán rằng việc kết hợp cả hai cột (phút và giây) sẽ cho tổng thời gian chính xác của bài kiểm tra sức bền mà người tham gia đã hoàn thành.","metadata":{}},{"cell_type":"markdown","source":"### Kiểm tra các kết hợp của các giá trị bị thiếu","metadata":{}},{"cell_type":"code","source":"train[\n    (train['Fitness_Endurance-Max_Stage'].notna()) & \n    (train['Fitness_Endurance-Time_Mins'].isna() | \n     train['Fitness_Endurance-Time_Sec'].isna())\n][cols]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:49.094088Z","iopub.execute_input":"2024-12-19T08:06:49.094591Z","iopub.status.idle":"2024-12-19T08:06:49.111076Z","shell.execute_reply.started":"2024-12-19T08:06:49.094535Z","shell.execute_reply":"2024-12-19T08:06:49.109893Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Có thể trong quá trình nhập liệu, phút hoặc giây bị bỏ trống (được nhập là NaN) khi lẽ ra chúng phải được ghi là 0 phút/giây. Mặc dù việc thiếu giây không quan trọng lắm, nhưng việc thiếu phút có thể thực sự là dữ liệu bị thiếu, và việc xem chúng là 0 sẽ dẫn đến kết quả kiểm tra không chính xác. Tôi nghĩ tốt hơn là chỉ loại bỏ những trường hợp nghi ngờ này.","metadata":{}},{"cell_type":"code","source":"train.loc[\n    (train['Fitness_Endurance-Max_Stage'].notna()) & \n    (train['Fitness_Endurance-Time_Mins'].isna() | \n     train['Fitness_Endurance-Time_Sec'].isna()), cols\n] = np.nan","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:49.112788Z","iopub.execute_input":"2024-12-19T08:06:49.113175Z","iopub.status.idle":"2024-12-19T08:06:49.127107Z","shell.execute_reply.started":"2024-12-19T08:06:49.113140Z","shell.execute_reply":"2024-12-19T08:06:49.125901Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Lấy một cột thời gian (phút + giây)","metadata":{}},{"cell_type":"code","source":"train['Fitness_Endurance-Total_Time_Sec'] = train[\n    'Fitness_Endurance-Time_Mins'\n] * 60 + train['Fitness_Endurance-Time_Sec']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:49.128648Z","iopub.execute_input":"2024-12-19T08:06:49.129099Z","iopub.status.idle":"2024-12-19T08:06:49.140591Z","shell.execute_reply.started":"2024-12-19T08:06:49.129049Z","shell.execute_reply":"2024-12-19T08:06:49.139346Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Tính toán lại số liệu thống kê:","metadata":{}},{"cell_type":"code","source":"calculate_stats(train, ['Fitness_Endurance-Max_Stage', 'Fitness_Endurance-Total_Time_Sec'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:49.142238Z","iopub.execute_input":"2024-12-19T08:06:49.142708Z","iopub.status.idle":"2024-12-19T08:06:49.170295Z","shell.execute_reply.started":"2024-12-19T08:06:49.142655Z","shell.execute_reply":"2024-12-19T08:06:49.169241Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> \n    💡 Note: \n    <ul style=\"list-style:circle\">\n        <li>Trung bình, người tham gia đạt đến giai đoạn 5 trong bài kiểm tra sức bền.</li>\n        <li>Một số người tham gia không hoàn thành được giai đoạn đầu tiên (min = 0), hoặc đây lại là lỗi dữ liệu.</li>\n        <li>Có một số ít người tham gia có sức bền đặc biệt cao ở độ tuổi 7-8.</li>\n        <li>Có một lượng lớn dữ liệu bị thiếu (hơn 80% dữ liệu thiếu thông tin này).</li>\n    </ul>\n</div>\n","metadata":{}},{"cell_type":"markdown","source":"## FitnessGram Child","metadata":{}},{"cell_type":"code","source":"data_dictionary[data_dictionary['Instrument'] == 'FitnessGram Child']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:49.172190Z","iopub.execute_input":"2024-12-19T08:06:49.172634Z","iopub.status.idle":"2024-12-19T08:06:49.189369Z","shell.execute_reply.started":"2024-12-19T08:06:49.172567Z","shell.execute_reply":"2024-12-19T08:06:49.188223Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fgc_data_dict = data_dictionary[data_dictionary['Instrument'] == 'FitnessGram Child']\n\nfgc_columns = []\n\nfor index, row in fgc_data_dict.iterrows():\n    if '_Zone' not in row['Field']:\n        measure_field = row['Field']\n        measure_desc = row['Description']\n        \n        zone_field = measure_field + '_Zone'\n        zone_row = fgc_data_dict[fgc_data_dict['Field'] == zone_field]\n        \n        if not zone_row.empty:\n            zone_desc = zone_row['Description'].values[0]\n            fgc_columns.append((measure_field, zone_field, measure_desc, zone_desc))\n            \nfig, axes = plt.subplots(2, 4, figsize=(24, 10))\n\nfor idx, (measure, zone, measure_desc, zone_desc) in enumerate(fgc_columns):\n    row = idx // 4\n    col = idx % 4\n    \n    sns.histplot(\n        data=train, x=measure,\n        hue=zone, bins=20, palette='Set2',\n        ax=axes[row, col], kde=True\n    )\n    axes[row, col].set_title(f'{measure_desc}')\n\nseason_counts = train['FGC-Season'].value_counts(normalize=True)\naxes[1, 3].pie(\n    season_counts, labels=season_counts.index,\n    autopct='%1.1f%%', startangle=90,\n    colors=sns.color_palette(\"Set3\")\n)\naxes[1, 3].set_title('Season of participation')\naxes[1, 3].axis('equal') \n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:49.190906Z","iopub.execute_input":"2024-12-19T08:06:49.191344Z","iopub.status.idle":"2024-12-19T08:06:53.307016Z","shell.execute_reply.started":"2024-12-19T08:06:49.191294Z","shell.execute_reply":"2024-12-19T08:06:53.305813Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> \n    💡 Note: \n    <ul style=\"list-style:circle\">\n        <li>Hầu hết các phân phối dữ liệu đều nghiêng về phía tổng điểm hiệu suất thấp hơn.</li>\n        <li>Điều kỳ lạ là có một tỷ lệ lớn người tham gia đạt được vùng thể lực khỏe mạnh cho bài kiểm tra nâng thân.</li>\n        <li>Tôi mong đợi các phạm vi khác nhau cho mỗi vùng, nhưng các giá trị của các vùng khác nhau lại có sự chồng chéo đáng kể. Điều này có thể là vì phạm vi các vùng khác nhau tùy theo độ tuổi.</li>\n    </ul>\n</div>\n","metadata":{}},{"cell_type":"code","source":"measurement_columns = [measure for measure, _, _, _ in fgc_columns]\ncalculate_stats(train, measurement_columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:53.308478Z","iopub.execute_input":"2024-12-19T08:06:53.308921Z","iopub.status.idle":"2024-12-19T08:06:53.347093Z","shell.execute_reply.started":"2024-12-19T08:06:53.308874Z","shell.execute_reply":"2024-12-19T08:06:53.345917Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Overlap giữa fitness zones","metadata":{}},{"cell_type":"code","source":"def compute_min_max_by_sex(train, sex, fgc_columns):\n    results = []\n    \n    for measure, zone, _, _ in fgc_columns:\n        sorted_zones = sorted(train[zone].dropna().unique())\n        \n        for zone_value in sorted_zones:\n            data = train[(train[zone] == zone_value) & \n                         (train['Basic_Demos-Sex'] == sex)][measure]\n            \n            if not data.empty:\n                min_val, max_val = data.min(), data.max()\n                results.append({\n                    'Zone': int(zone_value),\n                    'Measure': measure,\n                    'Min-Max': f'{min_val} - {max_val}'\n                })\n    \n    df = pd.DataFrame(results).pivot_table(\n        index='Zone', columns='Measure', values='Min-Max', aggfunc='first'\n    )\n    \n    return df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:53.348671Z","iopub.execute_input":"2024-12-19T08:06:53.349115Z","iopub.status.idle":"2024-12-19T08:06:53.358869Z","shell.execute_reply.started":"2024-12-19T08:06:53.349062Z","shell.execute_reply":"2024-12-19T08:06:53.357550Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Phạm vi output cho từng biện pháp và vùng dành cho nam giới:","metadata":{}},{"cell_type":"code","source":"compute_min_max_by_sex(train, 'Male', fgc_columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:53.360295Z","iopub.execute_input":"2024-12-19T08:06:53.360697Z","iopub.status.idle":"2024-12-19T08:06:53.425662Z","shell.execute_reply.started":"2024-12-19T08:06:53.360652Z","shell.execute_reply":"2024-12-19T08:06:53.424595Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Tương tự cho nữ giới: ","metadata":{}},{"cell_type":"code","source":"compute_min_max_by_sex(train, 'Female', fgc_columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:53.427039Z","iopub.execute_input":"2024-12-19T08:06:53.427434Z","iopub.status.idle":"2024-12-19T08:06:53.480490Z","shell.execute_reply.started":"2024-12-19T08:06:53.427385Z","shell.execute_reply":"2024-12-19T08:06:53.479337Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Phạm vi cho từng biện pháp và vùng theo độ tuổi (chỉ dành cho nam giới, chỉ để kiểm tra xem sự chồng chéo có còn tồn tại hay không):","metadata":{}},{"cell_type":"code","source":"results_male = []\n\nfor measure, zone, _, _ in fgc_columns:\n    sorted_zones = sorted(train[zone].dropna().unique())\n    for zone_value in sorted_zones:\n        age_sex_data_by_zone = train[train[zone] == zone_value][\n            ['Basic_Demos-Age', 'Basic_Demos-Sex', measure]\n        ]\n        unique_ages = age_sex_data_by_zone['Basic_Demos-Age'].dropna().unique()\n\n        for age in sorted(unique_ages):\n            age_sex_data = age_sex_data_by_zone[\n                (age_sex_data_by_zone['Basic_Demos-Age'] == age) &\n                (age_sex_data_by_zone['Basic_Demos-Sex'] == 'Male')\n            ][measure]\n            \n            if not age_sex_data.empty:\n                min_val, max_val = age_sex_data.min(), age_sex_data.max()\n                results_male.append({\n                    'Age': age,\n                    'Sex': 'Male',\n                    'Zone': zone_value,\n                    'Measure': measure,\n                    'Min-Max': f'{min_val} - {max_val}'\n                })\n\ndf_male = pd.DataFrame(results_male).pivot_table(\n    index=['Age', 'Sex', 'Zone'], columns='Measure', values='Min-Max', aggfunc='first'\n)\n\ndf_male","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:53.482005Z","iopub.execute_input":"2024-12-19T08:06:53.482319Z","iopub.status.idle":"2024-12-19T08:06:53.719944Z","shell.execute_reply.started":"2024-12-19T08:06:53.482288Z","shell.execute_reply":"2024-12-19T08:06:53.718605Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> \n    💡 Note: \n    <ul style=\"list-style:circle\">\n        <li>Bảng hiển thị phạm vi giá trị tối thiểu và tối đa cho các chỉ số thể chất khác nhau (như số lần nâng người, sức mạnh tay, chống đẩy, đo tầm với và nâng thân) ở các độ tuổi và khu vực khác nhau.</li>\n        <li>Có sự chồng chéo đáng kể trong phạm vi min-max của các khu vực khác nhau trong cùng một nhóm tuổi đối với một số chỉ số. Ví dụ, ở độ tuổi 9: 6 đến 10 lần nâng người có thể thuộc vào Khu vực 0 (Cần cải thiện) hoặc Khu vực 1 (Vùng thể lực khỏe mạnh).</li>\n        <li>Sự chồng chéo này chỉ ra rằng tiêu chí cho mỗi khu vực không được xác định rõ ràng bằng các phạm vi cụ thể, ngay cả đối với cùng một độ tuổi, và các cột khu vực này có vẻ như chỉ là các tính năng thêm nhiễu, tôi sẽ không sử dụng chúng trong mô hình hóa.</li>\n    </ul>\n</div>\n","metadata":{}},{"cell_type":"markdown","source":"### Phạm vi độ tuổi cho mỗi cột đo lường","metadata":{}},{"cell_type":"code","source":"age_ranges = []\n\nfor measure in measurement_columns:\n    valid_rows = train[~train[measure].isna()]\n    \n    min_age = valid_rows['Basic_Demos-Age'].min()\n    max_age = valid_rows['Basic_Demos-Age'].max()\n    \n    age_ranges.append({\n        'Measurement': measure,\n        'Min Age': min_age,\n        'Max Age': max_age\n    })\n\nage_ranges_df = pd.DataFrame(age_ranges)\nage_ranges_df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:53.721385Z","iopub.execute_input":"2024-12-19T08:06:53.721877Z","iopub.status.idle":"2024-12-19T08:06:53.752564Z","shell.execute_reply.started":"2024-12-19T08:06:53.721822Z","shell.execute_reply":"2024-12-19T08:06:53.751417Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Ngoài ra, việc gọi đây là FitnessGram dành cho trẻ em cũng không hợp lý vì những người tham gia ở hầu hết mọi lứa tuổi (5-21) đều đã được thử nghiệm.","metadata":{}},{"cell_type":"markdown","source":"\n## Mối quan hệ với biến mục tiêu (PCIAT_Total cho các câu trả lời hoàn chỉnh của PCIAT)","metadata":{}},{"cell_type":"code","source":"cols = [col for col in train.columns if col.startswith('FGC-') \n        and 'Zone' not in col and 'Season' not in col]\ncols.extend(['Fitness_Endurance-Max_Stage', 'Fitness_Endurance-Total_Time_Sec'])\n\ndata_subset = train[cols + ['complete_resp_total']]\n\ncorr_matrix = data_subset.corr()\n\nplt.figure(figsize=(10, 8))\nsns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', vmin=-1, vmax=1)\nplt.title('Correlation Heatmap')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:53.754199Z","iopub.execute_input":"2024-12-19T08:06:53.754517Z","iopub.status.idle":"2024-12-19T08:06:54.480452Z","shell.execute_reply.started":"2024-12-19T08:06:53.754487Z","shell.execute_reply":"2024-12-19T08:06:54.479215Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> 💡 Lưu ý: <ul style=\"list-style:circle\"> <li>Có sự tương quan rõ ràng giữa các chỉ số thể chất (FGC-FGC_GSD (sức mạnh nắm tay tay thuận) và FGC-FGC_GSND (sức mạnh nắm tay không thuận), FGC-FGC_SRL (ngồi và với tay trái) và FGC-FGC_SRR (ngồi và với tay phải)), và chúng dự đoán sẽ tương tự nhau.</li> <li>Mối quan hệ với biến mục tiêu có vẻ nghịch lý: số lần chống đẩy và số lần gập bụng cho thấy mối quan hệ tích cực vừa phải với mức độ nghiêm trọng của PIU, trong khi độ nâng thân và sức mạnh nắm tay cho thấy sự tương quan yếu, điều này gợi ý rằng khả năng thể chất cải thiện khi mức độ PIU tăng lên...</li> <li>Thành tích tốt hơn trong các bài kiểm tra thể lực không nhất thiết phản ánh mức độ hoạt động thể chất hàng ngày cao hơn. Bên cạnh đó, các chỉ số thể lực có thể phản ánh quá khứ - chúng ta không biết thời điểm đo đạc các chỉ số này.</li> <li>Tuy nhiên, điều quan trọng nhất cần nhớ ở đây là thành tích thể chất cũng cải thiện theo độ tuổi, vì vậy mối tương quan tích cực giữa thành tích thể chất và mức độ nghiêm trọng của PIU có thể chỉ do độ tuổi chi phối.</li> <li>Và đây là một câu hỏi chưa có lời giải đáp: các bài kiểm tra thể lực có được tiến hành theo cách chuẩn hóa ở tất cả các tham gia viên không?</li> </ul> </div>","metadata":{}},{"cell_type":"markdown","source":"Chúng ta hãy xem bức tranh thay đổi như thế nào khi chúng ta vẽ cùng một đồ thị theo nhóm tuổi và thêm tuổi để xem liệu các biện pháp có còn tương quan với tuổi hay không.","metadata":{}},{"cell_type":"code","source":"age_groups = train['Age Group'].unique()\n\nfig, axes = plt.subplots(1, 3, figsize=(18, 6), sharey=True)\n\nfor i, age_group in enumerate(age_groups):\n    group_data = train[train['Age Group'] == age_group]\n    corr_matrix = group_data[cols + ['complete_resp_total', 'Basic_Demos-Age']].corr()\n    sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.1f',\n                vmin=-1, vmax=1, ax=axes[i], cbar=i == 0)\n    axes[i].set_title(f'{age_group}')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:54.482100Z","iopub.execute_input":"2024-12-19T08:06:54.482549Z","iopub.status.idle":"2024-12-19T08:06:56.492501Z","shell.execute_reply.started":"2024-12-19T08:06:54.482497Z","shell.execute_reply":"2024-12-19T08:06:56.490880Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train[\n    (train['Age Group'] == 'Adults (19-22)') &\n    (train['complete_resp_total'].notna()) &\n    (train[cols].notna().any(axis=1))\n][cols + ['complete_resp_total', 'Basic_Demos-Age']]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.493691Z","iopub.status.idle":"2024-12-19T08:06:56.494234Z","shell.execute_reply.started":"2024-12-19T08:06:56.493975Z","shell.execute_reply":"2024-12-19T08:06:56.494001Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> 💡 Lưu ý: <ul style=\"list-style:circle\"> <li>Trong mỗi nhóm tuổi, chúng ta thấy rằng độ tuổi có sự tương quan tốt với hầu hết các chỉ số thể chất (đặc biệt là đối với trẻ em và người lớn).</li> <li>Sự tương quan giữa độ tuổi và mức độ nghiêm trọng của PIU vẫn tồn tại ở trẻ em từ 5-12 tuổi, gây nhầm lẫn trong mối quan hệ giữa thể lực và PIU.</li> <li>Đối với thanh thiếu niên, sự tương quan của biến mục tiêu với tất cả các chỉ số thể lực đều yếu hoặc không có, và đối với người lớn đã vượt qua bài kiểm tra thể lực, chỉ có 1 người có dữ liệu về mức độ nghiêm trọng của PIU.</li> <li>Tổng thể, các chỉ số thể lực không cho thấy sự tương quan rõ rệt với mức độ nghiêm trọng của PIU, và có vẻ như độ tuổi có thể là yếu tố thúc đẩy cả việc tăng cường hiệu suất thể lực và mức độ nghiêm trọng của PIU.</li> </ul> </div>","metadata":{}},{"cell_type":"markdown","source":"## 5. Sleep Disturbance Scale","metadata":{}},{"cell_type":"code","source":"groups.get('Sleep Disturbance Scale', [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.496029Z","iopub.status.idle":"2024-12-19T08:06:56.496564Z","shell.execute_reply.started":"2024-12-19T08:06:56.496311Z","shell.execute_reply":"2024-12-19T08:06:56.496338Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data = train[train['SDS-SDS_Total_Raw'].notnull()]\nage_range = data['Basic_Demos-Age']\nprint(\n    f\"Age range for participants with SDS-SDS_Total_Raw data:\"\n    f\" {age_range.min()} - {age_range.max()} years\"\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.498776Z","iopub.status.idle":"2024-12-19T08:06:56.499315Z","shell.execute_reply.started":"2024-12-19T08:06:56.499065Z","shell.execute_reply":"2024-12-19T08:06:56.499090Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(18, 5))\n\n# SDS-Season (Pie Chart)\nplt.subplot(1, 3, 1)\nsds_season_counts = train['SDS-Season'].value_counts(normalize=True)\nplt.pie(\n    sds_season_counts, \n    labels=sds_season_counts.index, \n    autopct='%1.1f%%', \n    startangle=90, \n    colors=sns.color_palette(\"Set3\")\n)\nplt.title('SDS-Season')\n\n# SDS-SDS_Total_Raw\nplt.subplot(1, 3, 2)\nsns.histplot(train['SDS-SDS_Total_Raw'].dropna(), bins=20, kde=True)\nplt.title('SDS-SDS_Total_Raw')\nplt.xlabel('Value')\n\n# SDS-SDS_Total_T\nplt.subplot(1, 3, 3)\nsns.histplot(train['SDS-SDS_Total_T'].dropna(), bins=20, kde=True)\nplt.title('SDS-SDS_Total_T')\nplt.xlabel('Value')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.501310Z","iopub.status.idle":"2024-12-19T08:06:56.501908Z","shell.execute_reply.started":"2024-12-19T08:06:56.501599Z","shell.execute_reply":"2024-12-19T08:06:56.501650Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, ['SDS-SDS_Total_Raw', 'SDS-SDS_Total_T'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.503311Z","iopub.status.idle":"2024-12-19T08:06:56.503749Z","shell.execute_reply.started":"2024-12-19T08:06:56.503521Z","shell.execute_reply":"2024-12-19T08:06:56.503541Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> \n    💡 Note: \n    <ul style=\"list-style:circle\">\n<li>Cả điểm thô và điểm T cho rối loạn giấc ngủ đều có sự biến động vừa phải, với một số giá trị cực đoan cho thấy rối loạn giấc ngủ nghiêm trọng ở một nhóm nhỏ người tham gia.    </ul>\n</div>","metadata":{}},{"cell_type":"markdown","source":"# <p style=\"background-color:#f3ebff; color:black; font-family:Verdana; font-size:100%; text-align:left; border: 3px solid #a281fc; border-radius:15px; padding:20px 20px;\">Behavioral (Báo cáo chủ quan)</p>","metadata":{}},{"cell_type":"markdown","source":"# Phiếu câu hỏi về hoạt động thể chất\n","metadata":{}},{"cell_type":"code","source":"groups.get('Physical Activity Questionnaire (Adolescents)', [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.505340Z","iopub.status.idle":"2024-12-19T08:06:56.505791Z","shell.execute_reply.started":"2024-12-19T08:06:56.505545Z","shell.execute_reply":"2024-12-19T08:06:56.505565Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data = train[train['PAQ_A-PAQ_A_Total'].notnull()]\nage_range = data['Basic_Demos-Age']\nprint(\n    f\"Age range for Adolescents (with PAQ_A_Total data):\"\n    f\" {age_range.min()} - {age_range.max()} years\"\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.507010Z","iopub.status.idle":"2024-12-19T08:06:56.507369Z","shell.execute_reply.started":"2024-12-19T08:06:56.507195Z","shell.execute_reply":"2024-12-19T08:06:56.507212Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(18, 5))\n\n# PAQ_A-Season\nplt.subplot(1, 3, 1)\ntrain['PAQ_A-Season'].value_counts(normalize=True).plot.pie(\n    autopct='%1.1f%%', colors=plt.cm.Set3.colors\n)\nplt.title('PAQ_A-Season (Adolescents)')\n\n# PAQ_A-PAQ_A_Total\nplt.subplot(1, 3, 2)\nsns.histplot(train['PAQ_A-PAQ_A_Total'], bins=20, kde=True)\nplt.title('PAQ_A-PAQ_A_Total (Adolescents)')\n\n# PAQ_A_Total by Season\nplt.subplot(1, 3, 3)\nsns.violinplot(x='PAQ_A-Season', y='PAQ_A-PAQ_A_Total', data=train, palette=\"Set3\")\nplt.title('PAQ_A_Total by Season (Adolescents)')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.508361Z","iopub.status.idle":"2024-12-19T08:06:56.508847Z","shell.execute_reply.started":"2024-12-19T08:06:56.508546Z","shell.execute_reply":"2024-12-19T08:06:56.508565Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, ['PAQ_A-PAQ_A_Total'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.510639Z","iopub.status.idle":"2024-12-19T08:06:56.511036Z","shell.execute_reply.started":"2024-12-19T08:06:56.510850Z","shell.execute_reply":"2024-12-19T08:06:56.510870Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Trẻ con","metadata":{}},{"cell_type":"code","source":"groups.get('Physical Activity Questionnaire (Children)', [])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.512017Z","iopub.status.idle":"2024-12-19T08:06:56.512375Z","shell.execute_reply.started":"2024-12-19T08:06:56.512200Z","shell.execute_reply":"2024-12-19T08:06:56.512218Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data = train[train['PAQ_C-PAQ_C_Total'].notnull()]\nage_range = data['Basic_Demos-Age']\nprint(\n    f\"Age range for Children (with PAQ_C_Total data):\"\n    f\" {age_range.min()} - {age_range.max()} years\"\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.513422Z","iopub.status.idle":"2024-12-19T08:06:56.513794Z","shell.execute_reply.started":"2024-12-19T08:06:56.513586Z","shell.execute_reply":"2024-12-19T08:06:56.513603Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(18, 5))\n\n# PAQ_C-Season\nplt.subplot(1, 3, 1)\ntrain['PAQ_C-Season'].value_counts(normalize=True).plot.pie(\n    autopct='%1.1f%%', colors=plt.cm.Set3.colors\n)\nplt.title('PAQ_C-Season (Children)')\n\n# PAQ_C-PAQ_C_Total\nplt.subplot(1, 3, 2)\nsns.histplot(train['PAQ_C-PAQ_C_Total'], bins=20, kde=True)\nplt.title('PAQ_C-PAQ_C_Total (Children)')\n\n# PAQ_C_Total by Season\nplt.subplot(1, 3, 3)\nsns.violinplot(x='PAQ_C-Season', y='PAQ_C-PAQ_C_Total', data=train, palette=\"Set3\")\nplt.title('PAQ_C_Total by Season (Children)')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.515857Z","iopub.status.idle":"2024-12-19T08:06:56.516222Z","shell.execute_reply.started":"2024-12-19T08:06:56.516052Z","shell.execute_reply":"2024-12-19T08:06:56.516071Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"calculate_stats(train, ['PAQ_C-PAQ_C_Total'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.518079Z","iopub.status.idle":"2024-12-19T08:06:56.518472Z","shell.execute_reply.started":"2024-12-19T08:06:56.518291Z","shell.execute_reply":"2024-12-19T08:06:56.518310Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<div style=\"line-height:24px; font-size:16px;border-left: 5px solid silver; padding-left: 26px;\"> \n    💡 Note: \n    <ul style=\"list-style:circle\">\n<li>Việc phân chia thành thanh thiếu niên và trẻ em có vẻ không chính xác (các người tham gia có dữ liệu trong các cột trẻ em (PAQ_C_Total) có độ tuổi từ 7 - 17 tuổi, trùng lặp với những người tham gia có dữ liệu không thiếu trong các cột thanh thiếu niên - từ 13 - 18 tuổi).\n<li>Mức độ hoạt động thể chất khá ổn định qua các mùa, chỉ có sự thay đổi nhỏ, mặc dù hơi thấp hơn vào mùa thu và mùa đông đối với thanh thiếu niên và trẻ em, tương ứng.\n<li>Có nhiều giá trị thiếu cho các tính năng này.\n    </ul>\n</div>\n","metadata":{}},{"cell_type":"markdown","source":"# Kiểm tra xem có người tham gia nào có dữ liệu cho cả cột PAQ của trẻ em (PAQ_C) và PAQ của thanh thiếu niên (PAQ_A) không\n","metadata":{}},{"cell_type":"code","source":"paq_columns = [col for col in train.columns if 'PAQ' in col]\ntrain[(train['PAQ_A-PAQ_A_Total'].notnull()) &\n      (train['PAQ_C-PAQ_C_Total'].notnull())][\n    paq_columns + ['Basic_Demos-Age']\n]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T08:06:56.519608Z","iopub.status.idle":"2024-12-19T08:06:56.520000Z","shell.execute_reply.started":"2024-12-19T08:06:56.519822Z","shell.execute_reply":"2024-12-19T08:06:56.519841Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Có thể hợp lý hơn khi kết hợp PAQ_A-PAQ_A_Total và PAQ_C-PAQ_C_Total thành một cột duy nhất và lấy giá trị trung bình khi có cả hai giá trị.","metadata":{}}]}