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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Report[[:space:]]Project.pdf filter=lfs diff=lfs merge=lfs -text

Report Project.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0e25158be5abecf74fa4bedf1d8557fa486d7dadddfead04d6720cc75ca2bea
+size 833995

video_report_link

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+https://drive.google.com/file/d/19DcvTFzvZM6kIBqHA-f7PWvzziYKgjNu/view?usp=sharing

votingfinal.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "082fc435",
+   "metadata": {
+    "papermill": {
+     "duration": 0.005415,
+     "end_time": "2024-12-22T02:22:33.724559",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:33.719144",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Extract:\n",
+    "Nhóm mình sử dụng Voting Regressor để voting các model chính: LightGBM, XGBoost và CatBoost.\n",
+    "\n",
+    "LightGBM, XGBoost và CatBoost là các mô hình dạng Gradient Boosting. Nói đại khái là sử dụng nhiều mô hình nhỏ học lần lượt. Mô hình sau sẽ cải tiến điểm yếu của mô hình trước. Và cuối cùng vẫn cho Voting các Model yếu để hoàn thiện mô hình một cách tối ưu."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2616f6de",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004335,
+     "end_time": "2024-12-22T02:22:33.733527",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:33.729192",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Thêm các thư viện cần thiết"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d22f42fc",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:22:33.743698Z",
+     "iopub.status.busy": "2024-12-22T02:22:33.743312Z",
+     "iopub.status.idle": "2024-12-22T02:22:49.437492Z",
+     "shell.execute_reply": "2024-12-22T02:22:49.436542Z"
+    },
+    "papermill": {
+     "duration": 15.701408,
+     "end_time": "2024-12-22T02:22:49.439287",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:33.737879",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import re\n",
+    "from sklearn.base import clone\n",
+    "from sklearn.metrics import cohen_kappa_score\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from scipy.optimize import minimize\n",
+    "from concurrent.futures import ThreadPoolExecutor\n",
+    "from tqdm import tqdm\n",
+    "import polars as pl\n",
+    "import polars.selectors as cs\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import MaxNLocator, FormatStrFormatter, PercentFormatter\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import matplotlib.pyplot as plt\n",
+    "from keras.models import Model\n",
+    "from keras.layers import Input, Dense\n",
+    "from keras.optimizers import Adam\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "\n",
+    "from colorama import Fore, Style\n",
+    "from IPython.display import clear_output\n",
+    "import warnings\n",
+    "from lightgbm import LGBMRegressor\n",
+    "from xgboost import XGBRegressor\n",
+    "from catboost import CatBoostRegressor\n",
+    "from sklearn.ensemble import VotingRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
+    "from sklearn.impute import SimpleImputer, KNNImputer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "warnings.filterwarnings('ignore')\n",
+    "pd.options.display.max_columns = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "721e9104",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004393,
+     "end_time": "2024-12-22T02:22:49.451298",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.446905",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Xử lý dữ liệu\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c27d5918",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:22:49.461518Z",
+     "iopub.status.busy": "2024-12-22T02:22:49.460838Z",
+     "iopub.status.idle": "2024-12-22T02:22:49.466795Z",
+     "shell.execute_reply": "2024-12-22T02:22:49.466144Z"
+    },
+    "papermill": {
+     "duration": 0.012195,
+     "end_time": "2024-12-22T02:22:49.468011",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.455816",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Tiền xử lý dữ liệu\n",
+    "def data_preprocessing(data):\n",
+    "    \n",
+    "    # Loại bỏ các cột chứa Season\n",
+    "    season_cols = [col for col in data.columns if 'Season' in col]\n",
+    "    data = data.drop(season_cols, axis=1)\n",
+    "     \n",
+    "    # Tạo một số feature mới hữu dụng\n",
+    "    data['BMI_Age'] = data['Physical-BMI'] * data['Basic_Demos-Age']\n",
+    "    data['Internet_Hours_Age'] = data['PreInt_EduHx-computerinternet_hoursday'] * data['Basic_Demos-Age']\n",
+    "    data['BMI_Internet_Hours'] = data['Physical-BMI'] * data['PreInt_EduHx-computerinternet_hoursday']\n",
+    "    data['BFP_BMI'] = data['BIA-BIA_Fat'] / data['BIA-BIA_BMI']\n",
+    "    data['FFMI_BFP'] = data['BIA-BIA_FFMI'] / data['BIA-BIA_Fat']\n",
+    "    data['FMI_BFP'] = data['BIA-BIA_FMI'] / data['BIA-BIA_Fat']\n",
+    "    data['LST_TBW'] = data['BIA-BIA_LST'] / data['BIA-BIA_TBW']\n",
+    "    data['BFP_BMR'] = data['BIA-BIA_Fat'] * data['BIA-BIA_BMR']\n",
+    "    data['BFP_DEE'] = data['BIA-BIA_Fat'] * data['BIA-BIA_DEE']\n",
+    "    data['BMR_Weight'] = data['BIA-BIA_BMR'] / data['Physical-Weight']\n",
+    "    data['DEE_Weight'] = data['BIA-BIA_DEE'] / data['Physical-Weight']\n",
+    "    data['SMM_Height'] = data['BIA-BIA_SMM'] / data['Physical-Height']\n",
+    "    data['Muscle_to_Fat'] = data['BIA-BIA_SMM'] / data['BIA-BIA_FMI']\n",
+    "    data['Hydration_Status'] = data['BIA-BIA_TBW'] / data['Physical-Weight']\n",
+    "    data['ICW_TBW'] = data['BIA-BIA_ICW'] / data['BIA-BIA_TBW']\n",
+    "    \n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a3e9e099",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:22:49.477745Z",
+     "iopub.status.busy": "2024-12-22T02:22:49.477500Z",
+     "iopub.status.idle": "2024-12-22T02:22:49.482339Z",
+     "shell.execute_reply": "2024-12-22T02:22:49.481716Z"
+    },
+    "papermill": {
+     "duration": 0.011158,
+     "end_time": "2024-12-22T02:22:49.483650",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.472492",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Đọc và xử lý dữ liệu parquet\n",
+    "def process_parquet_file(file_name, file_path):\n",
+    "    df = pd.read_parquet(os.path.join(file_path, file_name, 'part-0.parquet'))\n",
+    "    df.drop('step', axis=1, inplace=True)\n",
+    "    return df.describe().values.reshape(-1), file_name.split('=')[1]\n",
+    "\n",
+    "def load_parquet_file(file_path) -> pd.DataFrame:\n",
+    "    # Liệt kê các tệp\n",
+    "    file_list = os.listdir(file_path)\n",
+    "    \n",
+    "    # ThreadPool hỗ trợ xử lý đa luồng\n",
+    "    with ThreadPoolExecutor() as executor:\n",
+    "        results = list(tqdm(executor.map(lambda fname: process_parquet_file(fname, file_path), file_list), total=len(file_list)))\n",
+    "    \n",
+    "    # Trả về thống kê và các chỉ số\n",
+    "    stats, indexes = zip(*results)\n",
+    "    \n",
+    "    df = pd.DataFrame(stats, columns=[f\"stat_{i}\" for i in range(len(stats[0]))])\n",
+    "    df['id'] = indexes\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f9609c25",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:22:49.494124Z",
+     "iopub.status.busy": "2024-12-22T02:22:49.493853Z",
+     "iopub.status.idle": "2024-12-22T02:22:49.502960Z",
+     "shell.execute_reply": "2024-12-22T02:22:49.502215Z"
+    },
+    "papermill": {
+     "duration": 0.016439,
+     "end_time": "2024-12-22T02:22:49.504500",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.488061",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Mã hóa dữ liệu sử dụng AutoEncoder\n",
+    "class AutoEncoder(nn.Module):\n",
+    "    def __init__(self, input_dimen, encode_dimen):\n",
+    "        super(AutoEncoder, self).__init__()\n",
+    "        self.encoder = nn.Sequential(\n",
+    "            nn.Linear(input_dimen, encode_dimen*3),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(encode_dimen*3, encode_dimen*2),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(encode_dimen*2, encode_dimen),\n",
+    "            nn.ReLU()\n",
+    "        )\n",
+    "        self.decoder = nn.Sequential(\n",
+    "            nn.Linear(encode_dimen, input_dimen*2),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(input_dimen*2, input_dimen*3),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(input_dimen*3, input_dimen),\n",
+    "            nn.Sigmoid()\n",
+    "        )\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        encoded = self.encoder(x)\n",
+    "        decoded = self.decoder(encoded)\n",
+    "        return decoded\n",
+    "\n",
+    "# Mã hóa dữ liệu về 50 chiều\n",
+    "def perform_autoencoder(df, encoding_dim=50, epochs=50, batch_size=32):\n",
+    "    # Chuẩn hóa dữ liệu: đưa về z (trung bình = 0, phương sai = 1)\n",
+    "    scaler = StandardScaler()\n",
+    "    df_scaled = scaler.fit_transform(df)\n",
+    "    \n",
+    "    # Chuyển dữ liệu đã chuẩn hóa sang dạng tensor để sử dụng trong mô hình NN\n",
+    "    data_tensor = torch.FloatTensor(df_scaled)\n",
+    "    \n",
+    "    # Khởi tạo AutoEncoder\n",
+    "    input_dim = data_tensor.shape[1]\n",
+    "    autoencoder = AutoEncoder(input_dim, encoding_dim)\n",
+    "    \n",
+    "    # Cài đặt hàm mất mát và tối ưu\n",
+    "    criterion = nn.MSELoss()\n",
+    "    optimizer = optim.Adam(autoencoder.parameters())\n",
+    "    \n",
+    "    # Huấn luyện mô hình Encoder\n",
+    "    for epoch in range(epochs):\n",
+    "        for i in range(0, len(data_tensor), batch_size):\n",
+    "            batch = data_tensor[i : i + batch_size]\n",
+    "            optimizer.zero_grad()\n",
+    "            reconstructed = autoencoder(batch)\n",
+    "            loss = criterion(reconstructed, batch)\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "            \n",
+    "        # Sau mỗi 10 epoch, in ra Loss để theo dõi\n",
+    "        if (epoch + 1) % 10 == 0:\n",
+    "            print(f'Epoch thứ [{epoch + 1}/{epochs}], Loss = {loss.item():.4f}]')\n",
+    "    # Lấy dữ liệu đã được mã hóa & chuyển thành dataframe             \n",
+    "    with torch.no_grad():\n",
+    "        encoded_data = autoencoder.encoder(data_tensor).numpy()\n",
+    "        \n",
+    "    df_encoded = pd.DataFrame(encoded_data, columns=[f'Enc_{i + 1}' for i in range(encoded_data.shape[1])])\n",
+    "    \n",
+    "    return df_encoded"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f9e4c6c",
+   "metadata": {
+    "papermill": {
+     "duration": 0.00949,
+     "end_time": "2024-12-22T02:22:49.520476",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.510986",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# HÀM MÔ HÌNH HUẤN LUYỆN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fca86cc4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:22:49.535637Z",
+     "iopub.status.busy": "2024-12-22T02:22:49.535246Z",
+     "iopub.status.idle": "2024-12-22T02:22:49.540231Z",
+     "shell.execute_reply": "2024-12-22T02:22:49.538983Z"
+    },
+    "papermill": {
+     "duration": 0.015813,
+     "end_time": "2024-12-22T02:22:49.542828",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.527015",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "SEED = 42\n",
+    "n_splits = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9c1b3948",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:22:49.559292Z",
+     "iopub.status.busy": "2024-12-22T02:22:49.559028Z",
+     "iopub.status.idle": "2024-12-22T02:22:49.564024Z",
+     "shell.execute_reply": "2024-12-22T02:22:49.563175Z"
+    },
+    "papermill": {
+     "duration": 0.013845,
+     "end_time": "2024-12-22T02:22:49.565786",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.551941",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Khởi tạo hàm và tính điểm kappa\n",
+    "def quadratic_weighted_kappa(y_true, y_pred):\n",
+    "    return cohen_kappa_score(y_true, y_pred, weights='quadratic')\n",
+    "def evaluate_predictions(thresholds, y_true, oof_non_rounded):\n",
+    "    rounded_p = threshold_Rounder(oof_non_rounded, thresholds)\n",
+    "    return -quadratic_weighted_kappa(y_true, rounded_p)\n",
+    "\n",
+    "# Làm tròn giá trị dự đoán\n",
+    "def threshold_Rounder(oof_non_rounded, thresholds):\n",
+    "    return np.where(oof_non_rounded < thresholds[0], 0,\n",
+    "                    np.where(oof_non_rounded < thresholds[1], 1,\n",
+    "                             np.where(oof_non_rounded < thresholds[2], 2, 3)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1850886e",
+   "metadata": {
+    "papermill": {
+     "duration": 0.009357,
+     "end_time": "2024-12-22T02:22:49.582352",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.572995",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Thực hiện huấn luyện và đánh giá mô hình. Trọng tâm hàm là tính toán điểm số Quadratic Weighted Kappa (QWK) và tối ưu hóa bằng Neler-Mead"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "74880719",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:22:49.595047Z",
+     "iopub.status.busy": "2024-12-22T02:22:49.594782Z",
+     "iopub.status.idle": "2024-12-22T02:22:49.602816Z",
+     "shell.execute_reply": "2024-12-22T02:22:49.602128Z"
+    },
+    "papermill": {
+     "duration": 0.014679,
+     "end_time": "2024-12-22T02:22:49.604069",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.589390",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def TrainingModel(model_class, test_data):\n",
+    "    X = train.drop(['sii'], axis=1)\n",
+    "    y = train['sii']\n",
+    "\n",
+    "    SKF = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED)\n",
+    "    \n",
+    "    train_S = []\n",
+    "    test_S = []\n",
+    "    \n",
+    "    oof_non_rounded = np.zeros(len(y), dtype=float) \n",
+    "    oof_rounded = np.zeros(len(y), dtype=int) \n",
+    "    test_preds = np.zeros((len(test_data), n_splits))\n",
+    "\n",
+    "    for fold, (train_idx, test_idx) in enumerate(tqdm(SKF.split(X, y), desc=\"Training Folds\", total=n_splits)):\n",
+    "        X_train, X_val = X.iloc[train_idx], X.iloc[test_idx]\n",
+    "        y_train, y_val = y.iloc[train_idx], y.iloc[test_idx]\n",
+    "\n",
+    "        model = clone(model_class)\n",
+    "        model.fit(X_train, y_train)\n",
+    "\n",
+    "        y_train_pred = model.predict(X_train)\n",
+    "        y_val_pred = model.predict(X_val)\n",
+    "\n",
+    "        oof_non_rounded[test_idx] = y_val_pred\n",
+    "        y_val_pred_rounded = y_val_pred.round(0).astype(int)\n",
+    "        oof_rounded[test_idx] = y_val_pred_rounded\n",
+    "\n",
+    "        train_kappa = quadratic_weighted_kappa(y_train, y_train_pred.round(0).astype(int))\n",
+    "        val_kappa = quadratic_weighted_kappa(y_val, y_val_pred_rounded)\n",
+    "\n",
+    "        train_S.append(train_kappa)\n",
+    "        test_S.append(val_kappa)\n",
+    "        \n",
+    "        test_preds[:, fold] = model.predict(test_data)\n",
+    "        \n",
+    "        print(f\"Fold {fold+1} - Train QWK: {train_kappa:.4f}, Test QWK: {val_kappa:.4f}\")\n",
+    "        clear_output(wait=True)\n",
+    "\n",
+    "    print(f\"QWK TB train --> {np.mean(train_S):.4f}\")\n",
+    "    print(f\"QWK TB test ---> {np.mean(test_S):.4f}\")\n",
+    "\n",
+    "    KappaOPtimizer = minimize(evaluate_predictions,\n",
+    "                              x0=[0.5, 1.5, 2.5], args=(y, oof_non_rounded), \n",
+    "                              method='Nelder-Mead')\n",
+    "    assert KappaOPtimizer.success, \"Tối ưu không hội tụ.\"\n",
+    "    \n",
+    "    oof_tuned = threshold_Rounder(oof_non_rounded, KappaOPtimizer.x)\n",
+    "    tKappa = quadratic_weighted_kappa(y, oof_tuned)\n",
+    "\n",
+    "    print(f\"----> || Điểm QWK đã tối ưu :: {Fore.CYAN}{Style.BRIGHT} {tKappa:.3f}{Style.RESET_ALL}\")\n",
+    "\n",
+    "    tpm = test_preds.mean(axis=1)\n",
+    "    tpTuned = threshold_Rounder(tpm, KappaOPtimizer.x)\n",
+    "    \n",
+    "    submission = pd.DataFrame({\n",
+    "        'id': sample['id'],\n",
+    "        'sii': tpTuned\n",
+    "    })\n",
+    "\n",
+    "    return submission"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72251ff2",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004411,
+     "end_time": "2024-12-22T02:22:49.613290",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.608879",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Hiệu chỉnh tham số cho các mô hình sử dụng"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "79977149",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:22:49.624145Z",
+     "iopub.status.busy": "2024-12-22T02:22:49.623908Z",
+     "iopub.status.idle": "2024-12-22T02:22:49.628867Z",
+     "shell.execute_reply": "2024-12-22T02:22:49.628127Z"
+    },
+    "papermill": {
+     "duration": 0.011969,
+     "end_time": "2024-12-22T02:22:49.630218",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.618249",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# LightGBM\n",
+    "Params = {\n",
+    "    'learning_rate': 0.046,\n",
+    "    'max_depth': 12,\n",
+    "    'num_leaves': 478,\n",
+    "    'min_data_in_leaf': 13,\n",
+    "    'feature_fraction': 0.893,\n",
+    "    'bagging_fraction': 0.784,\n",
+    "    'bagging_freq': 4,\n",
+    "    'lambda_l1': 10,  \n",
+    "    'lambda_l2': 0.01,  \n",
+    "    'random_state': SEED,\n",
+    "    'verbose': -1,\n",
+    "    'n_estimator': 300,\n",
+    "    'device': 'gpu'\n",
+    "\n",
+    "}\n",
+    "\n",
+    "\n",
+    "# XGBoost \n",
+    "XGB_Params = {\n",
+    "    'learning_rate': 0.05,\n",
+    "    'max_depth': 6,\n",
+    "    'n_estimators': 200,\n",
+    "    'subsample': 0.8,\n",
+    "    'colsample_bytree': 0.8,\n",
+    "    'reg_alpha': 1,  \n",
+    "    'reg_lambda': 5, \n",
+    "    'random_state': SEED,\n",
+    "    'tree_method': 'gpu_hist',\n",
+    "\n",
+    "}\n",
+    "\n",
+    "# CatBoost\n",
+    "CatBoost_Params = {\n",
+    "    'learning_rate': 0.05,\n",
+    "    'depth': 6,\n",
+    "    'iterations': 200,\n",
+    "    'random_seed': SEED,\n",
+    "    'verbose': 0,\n",
+    "    'l2_leaf_reg': 10, \n",
+    "    'task_type': 'GPU'\n",
+    "\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "dd66629e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:22:49.640960Z",
+     "iopub.status.busy": "2024-12-22T02:22:49.640751Z",
+     "iopub.status.idle": "2024-12-22T02:22:49.647155Z",
+     "shell.execute_reply": "2024-12-22T02:22:49.646494Z"
+    },
+    "papermill": {
+     "duration": 0.01302,
+     "end_time": "2024-12-22T02:22:49.648386",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.635366",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "Light = LGBMRegressor(**Params)\n",
+    "XGB_Model = XGBRegressor(**XGB_Params)\n",
+    "CatBoost_Model = CatBoostRegressor(**CatBoost_Params)\n",
+    "\n",
+    "voting_model = VotingRegressor(estimators=[\n",
+    "    ('lightgbm', Light),\n",
+    "    ('xgboost', XGB_Model),\n",
+    "    ('catboost', CatBoost_Model)\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7c14723",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004132,
+     "end_time": "2024-12-22T02:22:49.656866",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.652734",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Submission 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "db5f5da4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:22:49.666728Z",
+     "iopub.status.busy": "2024-12-22T02:22:49.666480Z",
+     "iopub.status.idle": "2024-12-22T02:24:18.483837Z",
+     "shell.execute_reply": "2024-12-22T02:24:18.483067Z"
+    },
+    "papermill": {
+     "duration": 88.824157,
+     "end_time": "2024-12-22T02:24:18.485372",
+     "exception": false,
+     "start_time": "2024-12-22T02:22:49.661215",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 996/996 [01:09<00:00, 14.38it/s]\n",
+      "100%|██████████| 2/2 [00:00<00:00,  9.94it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch thứ [10/100], Loss = 1.6273]\n",
+      "Epoch thứ [20/100], Loss = 1.5442]\n",
+      "Epoch thứ [30/100], Loss = 1.5088]\n",
+      "Epoch thứ [40/100], Loss = 1.5025]\n",
+      "Epoch thứ [50/100], Loss = 1.5003]\n",
+      "Epoch thứ [60/100], Loss = 1.4989]\n",
+      "Epoch thứ [70/100], Loss = 1.3855]\n",
+      "Epoch thứ [80/100], Loss = 1.3827]\n",
+      "Epoch thứ [90/100], Loss = 1.3842]\n",
+      "Epoch thứ [100/100], Loss = 1.3826]\n",
+      "Epoch thứ [10/100], Loss = 1.0255]\n",
+      "Epoch thứ [20/100], Loss = 0.6005]\n",
+      "Epoch thứ [30/100], Loss = 0.4271]\n",
+      "Epoch thứ [40/100], Loss = 0.4271]\n",
+      "Epoch thứ [50/100], Loss = 0.4271]\n",
+      "Epoch thứ [60/100], Loss = 0.4271]\n",
+      "Epoch thứ [70/100], Loss = 0.4271]\n",
+      "Epoch thứ [80/100], Loss = 0.4271]\n",
+      "Epoch thứ [90/100], Loss = 0.4271]\n",
+      "Epoch thứ [100/100], Loss = 0.4271]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Đọc các bảng dữ liệu\n",
+    "train = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\n",
+    "test = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\n",
+    "sample = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/sample_submission.csv')\n",
+    "\n",
+    "train_pq = load_parquet_file(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet\")\n",
+    "test_pq = load_parquet_file(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet\")\n",
+    "\n",
+    "# Xóa cột id\n",
+    "df_train = train_pq.drop('id', axis=1)\n",
+    "df_test = test_pq.drop('id', axis=1)\n",
+    "\n",
+    "# Encode tập parquet\n",
+    "train_pq_encoded = perform_autoencoder(df_train, encoding_dim=60, epochs=100, batch_size=32)\n",
+    "test_pq_encoded = perform_autoencoder(df_test, encoding_dim=60, epochs=100, batch_size=32)\n",
+    "\n",
+    "# Danh sách các cột parquet đã mã hóa\n",
+    "parquet_cols = train_pq_encoded.columns.tolist()\n",
+    "\n",
+    "# Gán id vào dữ liệu đã encode\n",
+    "train_pq_encoded[\"id\"]=train_pq[\"id\"]\n",
+    "test_pq_encoded['id']=test_pq[\"id\"]\n",
+    "\n",
+    "# Kết hợp dữ liệu đã mã hóa vào tập huấn luyện\n",
+    "train = pd.merge(train, train_pq_encoded, how=\"left\", on='id')\n",
+    "test = pd.merge(test, test_pq_encoded, how=\"left\", on='id')\n",
+    "# Dùng K-Nearest Neighbors điền các giá trị thiếu\n",
+    "imputer = KNNImputer(n_neighbors=5)\n",
+    "numeric_cols = train.select_dtypes(include=['float64', 'int64']).columns\n",
+    "imputed_data = imputer.fit_transform(train[numeric_cols])\n",
+    "train_imputed = pd.DataFrame(imputed_data, columns=numeric_cols)\n",
+    "train_imputed['sii'] = train_imputed['sii'].round().astype(int)\n",
+    "for col in train.columns:\n",
+    "    if col not in numeric_cols:\n",
+    "        train_imputed[col] = train[col]\n",
+    "        \n",
+    "train = train_imputed\n",
+    "\n",
+    "# Tiến hành tiền xử lý dữ liệu cho tập train và test\n",
+    "train = data_preprocessing(train)\n",
+    "test = data_preprocessing(test)\n",
+    "\n",
+    "# Hàng nào ít hơn 10 giá trị hợp lệ thì bỏ \n",
+    "train = train.dropna(thresh=10, axis=0)\n",
+    "\n",
+    "# Xóa cột id\n",
+    "train = train.drop('id', axis=1)\n",
+    "test  = test .drop('id', axis=1)   \n",
+    "\n",
+    "# Xác định các cột đặc trưng cho tập train và tập test\n",
+    "trainingCols = ['Basic_Demos-Age', 'Basic_Demos-Sex',\n",
+    "                'CGAS-CGAS_Score', 'Physical-BMI',\n",
+    "                'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n",
+    "                'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n",
+    "                'Fitness_Endurance-Max_Stage',\n",
+    "                'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n",
+    "                'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n",
+    "                'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n",
+    "                'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n",
+    "                'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone',\n",
+    "                'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n",
+    "                'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n",
+    "                'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n",
+    "                'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n",
+    "                'BIA-BIA_TBW', 'PAQ_A-PAQ_A_Total',\n",
+    "                'PAQ_C-PAQ_C_Total', 'SDS-SDS_Total_Raw',\n",
+    "                'SDS-SDS_Total_T',\n",
+    "                'PreInt_EduHx-computerinternet_hoursday', 'sii', 'BMI_Age','Internet_Hours_Age','BMI_Internet_Hours',\n",
+    "                'BFP_BMI', 'FFMI_BFP', 'FMI_BFP', 'LST_TBW', 'BFP_BMR', 'BFP_DEE', 'BMR_Weight', 'DEE_Weight',\n",
+    "                'SMM_Height', 'Muscle_to_Fat', 'Hydration_Status', 'ICW_TBW']\n",
+    "testingCols = ['Basic_Demos-Age', 'Basic_Demos-Sex',\n",
+    "                'CGAS-CGAS_Score', 'Physical-BMI',\n",
+    "                'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n",
+    "                'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n",
+    "                'Fitness_Endurance-Max_Stage',\n",
+    "                'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n",
+    "                'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n",
+    "                'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n",
+    "                'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n",
+    "                'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone',\n",
+    "                'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n",
+    "                'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n",
+    "                'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n",
+    "                'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n",
+    "                'BIA-BIA_TBW', 'PAQ_A-PAQ_A_Total',\n",
+    "                'PAQ_C-PAQ_C_Total', 'SDS-SDS_Total_Raw',\n",
+    "                'SDS-SDS_Total_T',\n",
+    "                'PreInt_EduHx-computerinternet_hoursday', 'BMI_Age','Internet_Hours_Age','BMI_Internet_Hours',\n",
+    "                'BFP_BMI', 'FFMI_BFP', 'FMI_BFP', 'LST_TBW', 'BFP_BMR', 'BFP_DEE', 'BMR_Weight', 'DEE_Weight',\n",
+    "                'SMM_Height', 'Muscle_to_Fat', 'Hydration_Status', 'ICW_TBW']\n",
+    "# Thêm các đặc trưng lấy từ parquet\n",
+    "trainingCols += parquet_cols\n",
+    "testingCols += parquet_cols\n",
+    "\n",
+    "# Cập nhật lại tập dữ liệu\n",
+    "train = train[trainingCols]\n",
+    "test = test[testingCols]\n",
+    "\n",
+    "# Xóa các cột sii bị rỗng\n",
+    "train = train.dropna(subset='sii')\n",
+    "\n",
+    "# Xử lý giá trị vô cùng\n",
+    "if np.any(np.isinf(train)):\n",
+    "    train = train.replace([np.inf, -np.inf], np.nan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67b75666",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:24:18.524362Z",
+     "iopub.status.busy": "2024-12-22T02:24:18.523758Z",
+     "iopub.status.idle": "2024-12-22T02:24:52.697201Z",
+     "shell.execute_reply": "2024-12-22T02:24:52.696347Z"
+    },
+    "papermill": {
+     "duration": 34.194455,
+     "end_time": "2024-12-22T02:24:52.698746",
+     "exception": false,
+     "start_time": "2024-12-22T02:24:18.504291",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training Folds: 100%|██████████| 5/5 [00:34<00:00,  6.81s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "QWK TB train --> 0.7698\n",
+      "QWK TB test ---> 0.4876\n",
+      "----> || Điểm QWK đã tối ưu :: \u001b[36m\u001b[1m 0.537\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>sii</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>00008ff9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>000fd460</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>00105258</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>00115b9f</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0016bb22</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>001f3379</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0038ba98</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0068a485</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0069fbed</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0083e397</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0087dd65</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>00abe655</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>00ae59c9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>00af6387</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>00bd4359</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>00c0cd71</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>00d56d4b</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>00d9913d</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>00e6167c</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>00ebc35d</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          id  sii\n",
+       "0   00008ff9    1\n",
+       "1   000fd460    0\n",
+       "2   00105258    1\n",
+       "3   00115b9f    0\n",
+       "4   0016bb22    1\n",
+       "5   001f3379    1\n",
+       "6   0038ba98    1\n",
+       "7   0068a485    0\n",
+       "8   0069fbed    1\n",
+       "9   0083e397    0\n",
+       "10  0087dd65    0\n",
+       "11  00abe655    0\n",
+       "12  00ae59c9    1\n",
+       "13  00af6387    1\n",
+       "14  00bd4359    1\n",
+       "15  00c0cd71    1\n",
+       "16  00d56d4b    0\n",
+       "17  00d9913d    1\n",
+       "18  00e6167c    0\n",
+       "19  00ebc35d    1"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Submission1 = TrainingModel(voting_model, test)\n",
+    "\n",
+    "Submission1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20076c4d",
+   "metadata": {
+    "papermill": {
+     "duration": 0.018445,
+     "end_time": "2024-12-22T02:24:52.736989",
+     "exception": false,
+     "start_time": "2024-12-22T02:24:52.718544",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Submission 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6e8a8410",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:24:52.774560Z",
+     "iopub.status.busy": "2024-12-22T02:24:52.774283Z",
+     "iopub.status.idle": "2024-12-22T02:26:00.818617Z",
+     "shell.execute_reply": "2024-12-22T02:26:00.817729Z"
+    },
+    "papermill": {
+     "duration": 68.064588,
+     "end_time": "2024-12-22T02:26:00.819911",
+     "exception": false,
+     "start_time": "2024-12-22T02:24:52.755323",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 996/996 [01:07<00:00, 14.70it/s]\n",
+      "100%|██████████| 2/2 [00:00<00:00, 12.87it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "train = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\n",
+    "test = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\n",
+    "sample = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/sample_submission.csv')\n",
+    "train_pq = load_parquet_file(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet\")\n",
+    "test_pq = load_parquet_file(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet\")\n",
+    "\n",
+    "pq_cols = train_pq.columns.tolist()\n",
+    "pq_cols.remove(\"id\")\n",
+    "\n",
+    "train = pd.merge(train, train_pq, how=\"left\", on='id')\n",
+    "test = pd.merge(test, test_pq, how=\"left\", on='id')\n",
+    "\n",
+    "train = train.drop('id', axis=1)\n",
+    "test = test.drop('id', axis=1)   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5d148efe",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:26:00.887973Z",
+     "iopub.status.busy": "2024-12-22T02:26:00.887683Z",
+     "iopub.status.idle": "2024-12-22T02:26:00.896872Z",
+     "shell.execute_reply": "2024-12-22T02:26:00.896194Z"
+    },
+    "papermill": {
+     "duration": 0.044616,
+     "end_time": "2024-12-22T02:26:00.898182",
+     "exception": false,
+     "start_time": "2024-12-22T02:26:00.853566",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "trainingCols = ['Basic_Demos-Enroll_Season', 'Basic_Demos-Age', 'Basic_Demos-Sex',\n",
+    "                'CGAS-Season', 'CGAS-CGAS_Score', 'Physical-Season', 'Physical-BMI',\n",
+    "                'Physical-Height', 'Physical-Weight', 'Physical-Waist_Circumference',\n",
+    "                'Physical-Diastolic_BP', 'Physical-HeartRate', 'Physical-Systolic_BP',\n",
+    "                'Fitness_Endurance-Season', 'Fitness_Endurance-Max_Stage',\n",
+    "                'Fitness_Endurance-Time_Mins', 'Fitness_Endurance-Time_Sec',\n",
+    "                'FGC-Season', 'FGC-FGC_CU', 'FGC-FGC_CU_Zone', 'FGC-FGC_GSND',\n",
+    "                'FGC-FGC_GSND_Zone', 'FGC-FGC_GSD', 'FGC-FGC_GSD_Zone', 'FGC-FGC_PU',\n",
+    "                'FGC-FGC_PU_Zone', 'FGC-FGC_SRL', 'FGC-FGC_SRL_Zone', 'FGC-FGC_SRR',\n",
+    "                'FGC-FGC_SRR_Zone', 'FGC-FGC_TL', 'FGC-FGC_TL_Zone', 'BIA-Season',\n",
+    "                'BIA-BIA_Activity_Level_num', 'BIA-BIA_BMC', 'BIA-BIA_BMI',\n",
+    "                'BIA-BIA_BMR', 'BIA-BIA_DEE', 'BIA-BIA_ECW', 'BIA-BIA_FFM',\n",
+    "                'BIA-BIA_FFMI', 'BIA-BIA_FMI', 'BIA-BIA_Fat', 'BIA-BIA_Frame_num',\n",
+    "                'BIA-BIA_ICW', 'BIA-BIA_LDM', 'BIA-BIA_LST', 'BIA-BIA_SMM',\n",
+    "                'BIA-BIA_TBW', 'PAQ_A-Season', 'PAQ_A-PAQ_A_Total', 'PAQ_C-Season',\n",
+    "                'PAQ_C-PAQ_C_Total', 'SDS-Season', 'SDS-SDS_Total_Raw',\n",
+    "                'SDS-SDS_Total_T', 'PreInt_EduHx-Season',\n",
+    "                'PreInt_EduHx-computerinternet_hoursday', 'sii']\n",
+    "\n",
+    "trainingCols += pq_cols\n",
+    "train = train[trainingCols]\n",
+    "train = train.dropna(subset='sii')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4f4cffd",
+   "metadata": {
+    "papermill": {
+     "duration": 0.032505,
+     "end_time": "2024-12-22T02:26:00.964002",
+     "exception": false,
+     "start_time": "2024-12-22T02:26:00.931497",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Xử lý các cột phân loại"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3f4acdf8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:26:01.030711Z",
+     "iopub.status.busy": "2024-12-22T02:26:01.030343Z",
+     "iopub.status.idle": "2024-12-22T02:26:01.084012Z",
+     "shell.execute_reply": "2024-12-22T02:26:01.083264Z"
+    },
+    "papermill": {
+     "duration": 0.088597,
+     "end_time": "2024-12-22T02:26:01.085367",
+     "exception": false,
+     "start_time": "2024-12-22T02:26:00.996770",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "categoryFeatures = ['Basic_Demos-Enroll_Season', 'CGAS-Season', 'Physical-Season', \n",
+    "          'Fitness_Endurance-Season', 'FGC-Season', 'BIA-Season', \n",
+    "          'PAQ_A-Season', 'PAQ_C-Season', 'SDS-Season', 'PreInt_EduHx-Season']\n",
+    "\n",
+    "def update(df):\n",
+    "    global categoryFeatures\n",
+    "    for c in categoryFeatures: \n",
+    "        df[c] = df[c].fillna('Missing')\n",
+    "        df[c] = df[c].astype('category')\n",
+    "    return df\n",
+    "        \n",
+    "train = update(train)\n",
+    "test = update(test)\n",
+    "\n",
+    "# Hàm ánh xạ sang dạng enum\n",
+    "def create_mapping(column, dataset):\n",
+    "    unique_values = dataset[column].unique()\n",
+    "    return {value: idx for idx, value in enumerate(unique_values)}\n",
+    "\n",
+    "for col in categoryFeatures:\n",
+    "    mapping = create_mapping(col, train)\n",
+    "    mappingTe = create_mapping(col, test)\n",
+    "    \n",
+    "    train[col] = train[col].replace(mapping).astype(int)\n",
+    "    test[col] = test[col].replace(mappingTe).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "10b08a36",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:26:01.151607Z",
+     "iopub.status.busy": "2024-12-22T02:26:01.151297Z",
+     "iopub.status.idle": "2024-12-22T02:26:14.923765Z",
+     "shell.execute_reply": "2024-12-22T02:26:14.922695Z"
+    },
+    "papermill": {
+     "duration": 13.807665,
+     "end_time": "2024-12-22T02:26:14.925912",
+     "exception": false,
+     "start_time": "2024-12-22T02:26:01.118247",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training Folds: 100%|██████████| 5/5 [00:13<00:00,  2.70s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "QWK TB train --> 0.7259\n",
+      "QWK TB test ---> 0.3804\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----> || Điểm QWK đã tối ưu :: \u001b[36m\u001b[1m 0.464\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>sii</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>00008ff9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>000fd460</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>00105258</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>00115b9f</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0016bb22</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>001f3379</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0038ba98</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0068a485</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0069fbed</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0083e397</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0087dd65</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>00abe655</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>00ae59c9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>00af6387</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>00bd4359</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>00c0cd71</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>00d56d4b</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>00d9913d</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>00e6167c</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>00ebc35d</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          id  sii\n",
+       "0   00008ff9    1\n",
+       "1   000fd460    0\n",
+       "2   00105258    0\n",
+       "3   00115b9f    0\n",
+       "4   0016bb22    0\n",
+       "5   001f3379    1\n",
+       "6   0038ba98    0\n",
+       "7   0068a485    0\n",
+       "8   0069fbed    1\n",
+       "9   0083e397    0\n",
+       "10  0087dd65    0\n",
+       "11  00abe655    0\n",
+       "12  00ae59c9    1\n",
+       "13  00af6387    1\n",
+       "14  00bd4359    1\n",
+       "15  00c0cd71    1\n",
+       "16  00d56d4b    0\n",
+       "17  00d9913d    0\n",
+       "18  00e6167c    0\n",
+       "19  00ebc35d    0"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Submission2 = TrainingModel(voting_model, test)\n",
+    "\n",
+    "Submission2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f34b237c",
+   "metadata": {
+    "papermill": {
+     "duration": 0.033669,
+     "end_time": "2024-12-22T02:26:15.009998",
+     "exception": false,
+     "start_time": "2024-12-22T02:26:14.976329",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Submission 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a57cc9b5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:26:15.078216Z",
+     "iopub.status.busy": "2024-12-22T02:26:15.077888Z",
+     "iopub.status.idle": "2024-12-22T02:28:14.547425Z",
+     "shell.execute_reply": "2024-12-22T02:28:14.546584Z"
+    },
+    "papermill": {
+     "duration": 119.505453,
+     "end_time": "2024-12-22T02:28:14.548971",
+     "exception": false,
+     "start_time": "2024-12-22T02:26:15.043518",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training Folds: 100%|██████████| 5/5 [01:59<00:00, 23.85s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "QWK TB train --> 0.9175\n",
+      "QWK TB test ---> 0.3803\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----> || Điểm QWK đã tối ưu :: \u001b[36m\u001b[1m 0.450\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>sii</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>00008ff9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>000fd460</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>00105258</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>00115b9f</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0016bb22</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>001f3379</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0038ba98</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0068a485</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0069fbed</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0083e397</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0087dd65</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>00abe655</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>00ae59c9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>00af6387</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>00bd4359</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>00c0cd71</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>00d56d4b</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>00d9913d</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>00e6167c</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>00ebc35d</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          id  sii\n",
+       "0   00008ff9    2\n",
+       "1   000fd460    0\n",
+       "2   00105258    0\n",
+       "3   00115b9f    0\n",
+       "4   0016bb22    1\n",
+       "5   001f3379    1\n",
+       "6   0038ba98    0\n",
+       "7   0068a485    0\n",
+       "8   0069fbed    2\n",
+       "9   0083e397    0\n",
+       "10  0087dd65    1\n",
+       "11  00abe655    0\n",
+       "12  00ae59c9    2\n",
+       "13  00af6387    1\n",
+       "14  00bd4359    2\n",
+       "15  00c0cd71    2\n",
+       "16  00d56d4b    0\n",
+       "17  00d9913d    0\n",
+       "18  00e6167c    0\n",
+       "19  00ebc35d    1"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "imputer = SimpleImputer(strategy='median')\n",
+    "\n",
+    "ensemble = VotingRegressor(estimators=[\n",
+    "    ('lgb', Pipeline(steps=[('imputer', imputer), ('regressor', LGBMRegressor(random_state=SEED))])),\n",
+    "    ('xgb', Pipeline(steps=[('imputer', imputer), ('regressor', XGBRegressor(random_state=SEED))])),\n",
+    "    ('cat', Pipeline(steps=[('imputer', imputer), ('regressor', CatBoostRegressor(random_state=SEED, silent=True))])),\n",
+    "    ('rf', Pipeline(steps=[('imputer', imputer), ('regressor', RandomForestRegressor(random_state=SEED))])),\n",
+    "    ('gb', Pipeline(steps=[('imputer', imputer), ('regressor', GradientBoostingRegressor(random_state=SEED))]))\n",
+    "])\n",
+    "\n",
+    "Submission3 = TrainingModel(ensemble, test)\n",
+    "\n",
+    "Submission3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "03321cf9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:28:14.620213Z",
+     "iopub.status.busy": "2024-12-22T02:28:14.619956Z",
+     "iopub.status.idle": "2024-12-22T02:28:14.636832Z",
+     "shell.execute_reply": "2024-12-22T02:28:14.635901Z"
+    },
+    "papermill": {
+     "duration": 0.052844,
+     "end_time": "2024-12-22T02:28:14.638109",
+     "exception": false,
+     "start_time": "2024-12-22T02:28:14.585265",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Majority voting completed and saved to 'Final_Submission.csv'\n"
+     ]
+    }
+   ],
+   "source": [
+    "sub1 = Submission1\n",
+    "sub2 = Submission2\n",
+    "sub3 = Submission3\n",
+    "\n",
+    "sub1 = sub1.sort_values(by='id').reset_index(drop=True)\n",
+    "sub2 = sub2.sort_values(by='id').reset_index(drop=True)\n",
+    "sub3 = sub3.sort_values(by='id').reset_index(drop=True)\n",
+    "\n",
+    "combined = pd.DataFrame({\n",
+    "    'id': sub1['id'],\n",
+    "    'sii_1': sub1['sii'],\n",
+    "    'sii_2': sub2['sii'],\n",
+    "    'sii_3': sub3['sii']\n",
+    "})\n",
+    "\n",
+    "def majority_vote(row):\n",
+    "    return row.mode()[0]\n",
+    "\n",
+    "combined['final_sii'] = combined[['sii_1', 'sii_2', 'sii_3']].apply(majority_vote, axis=1)\n",
+    "\n",
+    "final_submission = combined[['id', 'final_sii']].rename(columns={'final_sii': 'sii'})\n",
+    "\n",
+    "final_submission.to_csv('submission.csv', index=False)\n",
+    "\n",
+    "print(\"Majority voting completed and saved to 'Final_Submission.csv'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d403b134",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-22T02:28:14.707888Z",
+     "iopub.status.busy": "2024-12-22T02:28:14.707573Z",
+     "iopub.status.idle": "2024-12-22T02:28:14.715425Z",
+     "shell.execute_reply": "2024-12-22T02:28:14.714480Z"
+    },
+    "papermill": {
+     "duration": 0.043866,
+     "end_time": "2024-12-22T02:28:14.716800",
+     "exception": false,
+     "start_time": "2024-12-22T02:28:14.672934",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>sii</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>00008ff9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>000fd460</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>00105258</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>00115b9f</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0016bb22</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>001f3379</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0038ba98</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0068a485</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0069fbed</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0083e397</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0087dd65</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>00abe655</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>00ae59c9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>00af6387</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>00bd4359</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>00c0cd71</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>00d56d4b</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>00d9913d</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>00e6167c</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>00ebc35d</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          id  sii\n",
+       "0   00008ff9    1\n",
+       "1   000fd460    0\n",
+       "2   00105258    0\n",
+       "3   00115b9f    0\n",
+       "4   0016bb22    1\n",
+       "5   001f3379    1\n",
+       "6   0038ba98    0\n",
+       "7   0068a485    0\n",
+       "8   0069fbed    1\n",
+       "9   0083e397    0\n",
+       "10  0087dd65    0\n",
+       "11  00abe655    0\n",
+       "12  00ae59c9    1\n",
+       "13  00af6387    1\n",
+       "14  00bd4359    1\n",
+       "15  00c0cd71    1\n",
+       "16  00d56d4b    0\n",
+       "17  00d9913d    0\n",
+       "18  00e6167c    0\n",
+       "19  00ebc35d    1"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_submission"
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "gpu",
+   "dataSources": [
+    {
+     "databundleVersionId": 9643020,
+     "sourceId": 81933,
+     "sourceType": "competition"
+    }
+   ],
+   "dockerImageVersionId": 30823,
+   "isGpuEnabled": true,
+   "isInternetEnabled": false,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 346.01461,
+   "end_time": "2024-12-22T02:28:17.567852",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2024-12-22T02:22:31.553242",
+   "version": "2.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}