{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c426cbcc-ce31-46e2-9b46-2a427f2cec03",
   "metadata": {},
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b90c2d5-a090-4b8a-b7cf-76915d66ba47",
   "metadata": {},
   "source": [
    "<img src=\"https://thumbs.dreamstime.com/b/online-money-transfer-vector-illustration-cartoon-flat-tiny-friend-man-character-transferring-send-transaction-coin-out-178490363.jpg\" width=700>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fca8e87-391a-41f4-9635-d3c142b80efa",
   "metadata": {},
   "source": [
    "### Dataset Download  \n",
    "You can download the CSV file here:  \n",
    "[https://www.kaggle.com/competitions/santander-customer-transaction-prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337be95d-65d3-4a19-aa30-514b81eaf5a5",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "The goal of this project is to predict whether a customer will make a specific transaction in the future based on anonymized numerical features. The dataset comes from the Santander Customer Transaction Prediction challenge and represents a binary classification problem.\n",
    "\n",
    "The data contains 200 numerical features and a binary target variable. No missing values are present, which allows the focus to be placed on model selection and evaluation rather than data cleaning. Due to class imbalance, accuracy is not a reliable metric. Therefore, the ROC-AUC score is used as the main evaluation metric.\n",
    "\n",
    "Several machine learning models were trained and compared, including Logistic Regression, LightGBM, and XGBoost, to identify the model with the best predictive performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad0af12-1cc8-4153-8b02-655cab3444b2",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "64d769c7-fa0a-4031-8525-1708890c9b56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import lightgbm as lgb\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "import joblib\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_columns',100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed8d7b4-b1b1-4280-a98b-60b7f651e31c",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "977c8bf1-9e3b-4c93-aa86-1451a80bc7f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv('train.csv')\n",
    "test=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dae0603-af21-4a37-aaeb-b95edfd65528",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c19d2703-5c65-4f7f-8840-98646a98c992",
   "metadata": {},
   "outputs": [
    {
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       "      <td>11.4607</td>\n",
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       "      <td>-0.6975</td>\n",
       "      <td>9.5981</td>\n",
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       "      <td>-4.7645</td>\n",
       "      <td>-8.4254</td>\n",
       "      <td>20.8773</td>\n",
       "      <td>3.1531</td>\n",
       "      <td>18.5618</td>\n",
       "      <td>7.7423</td>\n",
       "      <td>-10.1245</td>\n",
       "      <td>13.7241</td>\n",
       "      <td>-3.5189</td>\n",
       "      <td>1.7202</td>\n",
       "      <td>-8.4051</td>\n",
       "      <td>9.0164</td>\n",
       "      <td>3.0657</td>\n",
       "      <td>14.3691</td>\n",
       "      <td>25.8398</td>\n",
       "      <td>5.8764</td>\n",
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       "      <td>-19.7159</td>\n",
       "      <td>17.5743</td>\n",
       "      <td>0.5857</td>\n",
       "      <td>4.4354</td>\n",
       "      <td>3.9642</td>\n",
       "      <td>3.1364</td>\n",
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       "      <td>-2.3978</td>\n",
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       "      <td>8.5635</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_1</td>\n",
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       "      <td>11.5006</td>\n",
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       "      <td>13.8588</td>\n",
       "      <td>5.3890</td>\n",
       "      <td>12.3622</td>\n",
       "      <td>7.0433</td>\n",
       "      <td>5.6208</td>\n",
       "      <td>16.5338</td>\n",
       "      <td>3.1468</td>\n",
       "      <td>8.0851</td>\n",
       "      <td>-0.4032</td>\n",
       "      <td>8.0585</td>\n",
       "      <td>14.0239</td>\n",
       "      <td>8.4135</td>\n",
       "      <td>5.4345</td>\n",
       "      <td>13.7003</td>\n",
       "      <td>13.8275</td>\n",
       "      <td>-15.5849</td>\n",
       "      <td>7.8000</td>\n",
       "      <td>28.5708</td>\n",
       "      <td>3.4287</td>\n",
       "      <td>2.7407</td>\n",
       "      <td>8.5524</td>\n",
       "      <td>3.3716</td>\n",
       "      <td>6.9779</td>\n",
       "      <td>13.8910</td>\n",
       "      <td>-11.7684</td>\n",
       "      <td>-2.5586</td>\n",
       "      <td>5.0464</td>\n",
       "      <td>0.5481</td>\n",
       "      <td>-9.2987</td>\n",
       "      <td>7.8755</td>\n",
       "      <td>1.2859</td>\n",
       "      <td>19.3710</td>\n",
       "      <td>11.3702</td>\n",
       "      <td>0.7399</td>\n",
       "      <td>2.7995</td>\n",
       "      <td>5.8434</td>\n",
       "      <td>10.8160</td>\n",
       "      <td>3.6783</td>\n",
       "      <td>-11.1147</td>\n",
       "      <td>1.8730</td>\n",
       "      <td>9.8775</td>\n",
       "      <td>11.7842</td>\n",
       "      <td>1.2444</td>\n",
       "      <td>-47.3797</td>\n",
       "      <td>7.3718</td>\n",
       "      <td>0.1948</td>\n",
       "      <td>...</td>\n",
       "      <td>20.6294</td>\n",
       "      <td>14.8743</td>\n",
       "      <td>9.4317</td>\n",
       "      <td>16.7242</td>\n",
       "      <td>-0.5687</td>\n",
       "      <td>0.1898</td>\n",
       "      <td>12.2419</td>\n",
       "      <td>-9.6953</td>\n",
       "      <td>22.3949</td>\n",
       "      <td>10.6261</td>\n",
       "      <td>29.4846</td>\n",
       "      <td>5.8683</td>\n",
       "      <td>3.8208</td>\n",
       "      <td>15.8348</td>\n",
       "      <td>-5.0121</td>\n",
       "      <td>15.1345</td>\n",
       "      <td>3.2003</td>\n",
       "      <td>9.3192</td>\n",
       "      <td>3.8821</td>\n",
       "      <td>5.7999</td>\n",
       "      <td>5.5378</td>\n",
       "      <td>5.0988</td>\n",
       "      <td>22.0330</td>\n",
       "      <td>5.5134</td>\n",
       "      <td>30.2645</td>\n",
       "      <td>10.4968</td>\n",
       "      <td>-7.2352</td>\n",
       "      <td>16.5721</td>\n",
       "      <td>-7.3477</td>\n",
       "      <td>11.0752</td>\n",
       "      <td>-5.5937</td>\n",
       "      <td>9.4878</td>\n",
       "      <td>-14.9100</td>\n",
       "      <td>9.4245</td>\n",
       "      <td>22.5441</td>\n",
       "      <td>-4.8622</td>\n",
       "      <td>7.6543</td>\n",
       "      <td>-15.9319</td>\n",
       "      <td>13.3175</td>\n",
       "      <td>-0.3566</td>\n",
       "      <td>7.6421</td>\n",
       "      <td>7.7214</td>\n",
       "      <td>2.5837</td>\n",
       "      <td>10.9516</td>\n",
       "      <td>15.4305</td>\n",
       "      <td>2.0339</td>\n",
       "      <td>8.1267</td>\n",
       "      <td>8.7889</td>\n",
       "      <td>18.3560</td>\n",
       "      <td>1.9518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>-2.7457</td>\n",
       "      <td>12.0805</td>\n",
       "      <td>7.8928</td>\n",
       "      <td>10.5825</td>\n",
       "      <td>-9.0837</td>\n",
       "      <td>6.9427</td>\n",
       "      <td>14.6155</td>\n",
       "      <td>-4.9193</td>\n",
       "      <td>5.9525</td>\n",
       "      <td>-0.3249</td>\n",
       "      <td>-11.2648</td>\n",
       "      <td>14.1929</td>\n",
       "      <td>7.3124</td>\n",
       "      <td>7.5244</td>\n",
       "      <td>14.6472</td>\n",
       "      <td>7.6782</td>\n",
       "      <td>-1.7395</td>\n",
       "      <td>4.7011</td>\n",
       "      <td>20.4775</td>\n",
       "      <td>17.7559</td>\n",
       "      <td>18.1377</td>\n",
       "      <td>1.2145</td>\n",
       "      <td>3.5137</td>\n",
       "      <td>5.6777</td>\n",
       "      <td>13.2177</td>\n",
       "      <td>-7.9940</td>\n",
       "      <td>-2.9029</td>\n",
       "      <td>5.8463</td>\n",
       "      <td>6.1439</td>\n",
       "      <td>-11.1025</td>\n",
       "      <td>12.4858</td>\n",
       "      <td>-2.2871</td>\n",
       "      <td>19.0422</td>\n",
       "      <td>11.0449</td>\n",
       "      <td>4.1087</td>\n",
       "      <td>4.6974</td>\n",
       "      <td>6.9346</td>\n",
       "      <td>10.8917</td>\n",
       "      <td>0.9003</td>\n",
       "      <td>-13.5174</td>\n",
       "      <td>2.2439</td>\n",
       "      <td>11.5283</td>\n",
       "      <td>12.0406</td>\n",
       "      <td>4.1006</td>\n",
       "      <td>-7.9078</td>\n",
       "      <td>11.1405</td>\n",
       "      <td>-5.7864</td>\n",
       "      <td>...</td>\n",
       "      <td>14.3330</td>\n",
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       "      <td>4.4015</td>\n",
       "      <td>14.1479</td>\n",
       "      <td>-5.1747</td>\n",
       "      <td>0.5778</td>\n",
       "      <td>14.5362</td>\n",
       "      <td>-1.7624</td>\n",
       "      <td>33.8820</td>\n",
       "      <td>11.6041</td>\n",
       "      <td>13.2070</td>\n",
       "      <td>5.8442</td>\n",
       "      <td>4.7086</td>\n",
       "      <td>5.7141</td>\n",
       "      <td>-1.0410</td>\n",
       "      <td>20.5092</td>\n",
       "      <td>3.2790</td>\n",
       "      <td>-5.5952</td>\n",
       "      <td>7.3176</td>\n",
       "      <td>5.7690</td>\n",
       "      <td>-7.0927</td>\n",
       "      <td>-3.9116</td>\n",
       "      <td>7.2569</td>\n",
       "      <td>-5.8234</td>\n",
       "      <td>25.6820</td>\n",
       "      <td>10.9202</td>\n",
       "      <td>-0.3104</td>\n",
       "      <td>8.8438</td>\n",
       "      <td>-9.7009</td>\n",
       "      <td>2.4013</td>\n",
       "      <td>-4.2935</td>\n",
       "      <td>9.3908</td>\n",
       "      <td>-13.2648</td>\n",
       "      <td>3.1545</td>\n",
       "      <td>23.0866</td>\n",
       "      <td>-5.3000</td>\n",
       "      <td>5.3745</td>\n",
       "      <td>-6.2660</td>\n",
       "      <td>10.1934</td>\n",
       "      <td>-0.8417</td>\n",
       "      <td>2.9057</td>\n",
       "      <td>9.7905</td>\n",
       "      <td>1.6704</td>\n",
       "      <td>1.6858</td>\n",
       "      <td>21.6042</td>\n",
       "      <td>3.1417</td>\n",
       "      <td>-6.5213</td>\n",
       "      <td>8.2675</td>\n",
       "      <td>14.7222</td>\n",
       "      <td>0.3965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_3</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0604</td>\n",
       "      <td>-2.1518</td>\n",
       "      <td>8.9522</td>\n",
       "      <td>7.1957</td>\n",
       "      <td>12.5846</td>\n",
       "      <td>-1.8361</td>\n",
       "      <td>5.8428</td>\n",
       "      <td>14.9250</td>\n",
       "      <td>-5.8609</td>\n",
       "      <td>8.2450</td>\n",
       "      <td>2.3061</td>\n",
       "      <td>2.8102</td>\n",
       "      <td>13.8463</td>\n",
       "      <td>11.9704</td>\n",
       "      <td>6.4569</td>\n",
       "      <td>14.8372</td>\n",
       "      <td>10.7430</td>\n",
       "      <td>-0.4299</td>\n",
       "      <td>15.9426</td>\n",
       "      <td>13.7257</td>\n",
       "      <td>20.3010</td>\n",
       "      <td>12.5579</td>\n",
       "      <td>6.8202</td>\n",
       "      <td>2.7229</td>\n",
       "      <td>12.1354</td>\n",
       "      <td>13.7367</td>\n",
       "      <td>0.8135</td>\n",
       "      <td>-0.9059</td>\n",
       "      <td>5.9070</td>\n",
       "      <td>2.8407</td>\n",
       "      <td>-15.2398</td>\n",
       "      <td>10.4407</td>\n",
       "      <td>-2.5731</td>\n",
       "      <td>6.1796</td>\n",
       "      <td>10.6093</td>\n",
       "      <td>-5.9158</td>\n",
       "      <td>8.1723</td>\n",
       "      <td>2.8521</td>\n",
       "      <td>9.1738</td>\n",
       "      <td>0.6665</td>\n",
       "      <td>-3.8294</td>\n",
       "      <td>-1.0370</td>\n",
       "      <td>11.7770</td>\n",
       "      <td>11.2834</td>\n",
       "      <td>8.0485</td>\n",
       "      <td>-24.6840</td>\n",
       "      <td>12.7404</td>\n",
       "      <td>-35.1659</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0984</td>\n",
       "      <td>17.1765</td>\n",
       "      <td>7.6508</td>\n",
       "      <td>18.2452</td>\n",
       "      <td>17.0336</td>\n",
       "      <td>-10.9370</td>\n",
       "      <td>12.0500</td>\n",
       "      <td>-1.2155</td>\n",
       "      <td>19.9750</td>\n",
       "      <td>12.3892</td>\n",
       "      <td>31.8833</td>\n",
       "      <td>5.9684</td>\n",
       "      <td>7.2084</td>\n",
       "      <td>3.8899</td>\n",
       "      <td>-11.0882</td>\n",
       "      <td>17.2502</td>\n",
       "      <td>2.5881</td>\n",
       "      <td>-2.7018</td>\n",
       "      <td>0.5641</td>\n",
       "      <td>5.3430</td>\n",
       "      <td>-7.1541</td>\n",
       "      <td>-6.1920</td>\n",
       "      <td>18.2366</td>\n",
       "      <td>11.7134</td>\n",
       "      <td>14.7483</td>\n",
       "      <td>8.1013</td>\n",
       "      <td>11.8771</td>\n",
       "      <td>13.9552</td>\n",
       "      <td>-10.4701</td>\n",
       "      <td>5.6961</td>\n",
       "      <td>-3.7546</td>\n",
       "      <td>8.4117</td>\n",
       "      <td>1.8986</td>\n",
       "      <td>7.2601</td>\n",
       "      <td>-0.4639</td>\n",
       "      <td>-0.0498</td>\n",
       "      <td>7.9336</td>\n",
       "      <td>-12.8279</td>\n",
       "      <td>12.4124</td>\n",
       "      <td>1.8489</td>\n",
       "      <td>4.4666</td>\n",
       "      <td>4.7433</td>\n",
       "      <td>0.7178</td>\n",
       "      <td>1.4214</td>\n",
       "      <td>23.0347</td>\n",
       "      <td>-1.2706</td>\n",
       "      <td>-2.9275</td>\n",
       "      <td>10.2922</td>\n",
       "      <td>17.9697</td>\n",
       "      <td>-8.9996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_4</td>\n",
       "      <td>0</td>\n",
       "      <td>9.8369</td>\n",
       "      <td>-1.4834</td>\n",
       "      <td>12.8746</td>\n",
       "      <td>6.6375</td>\n",
       "      <td>12.2772</td>\n",
       "      <td>2.4486</td>\n",
       "      <td>5.9405</td>\n",
       "      <td>19.2514</td>\n",
       "      <td>6.2654</td>\n",
       "      <td>7.6784</td>\n",
       "      <td>-9.4458</td>\n",
       "      <td>-12.1419</td>\n",
       "      <td>13.8481</td>\n",
       "      <td>7.8895</td>\n",
       "      <td>7.7894</td>\n",
       "      <td>15.0553</td>\n",
       "      <td>8.4871</td>\n",
       "      <td>-3.0680</td>\n",
       "      <td>6.5263</td>\n",
       "      <td>11.3152</td>\n",
       "      <td>21.4246</td>\n",
       "      <td>18.9608</td>\n",
       "      <td>10.1102</td>\n",
       "      <td>2.7142</td>\n",
       "      <td>14.2080</td>\n",
       "      <td>13.5433</td>\n",
       "      <td>3.1736</td>\n",
       "      <td>-3.3423</td>\n",
       "      <td>5.9015</td>\n",
       "      <td>7.9352</td>\n",
       "      <td>-3.1582</td>\n",
       "      <td>9.4668</td>\n",
       "      <td>-0.0083</td>\n",
       "      <td>19.3239</td>\n",
       "      <td>12.4057</td>\n",
       "      <td>0.6329</td>\n",
       "      <td>2.7922</td>\n",
       "      <td>5.8184</td>\n",
       "      <td>19.3038</td>\n",
       "      <td>1.4450</td>\n",
       "      <td>-5.5963</td>\n",
       "      <td>14.0685</td>\n",
       "      <td>11.9171</td>\n",
       "      <td>11.5111</td>\n",
       "      <td>6.9087</td>\n",
       "      <td>-65.4863</td>\n",
       "      <td>13.8657</td>\n",
       "      <td>0.0444</td>\n",
       "      <td>...</td>\n",
       "      <td>20.1461</td>\n",
       "      <td>1.2995</td>\n",
       "      <td>5.8493</td>\n",
       "      <td>19.8234</td>\n",
       "      <td>4.7022</td>\n",
       "      <td>10.6101</td>\n",
       "      <td>13.0021</td>\n",
       "      <td>-12.6068</td>\n",
       "      <td>27.0846</td>\n",
       "      <td>8.0913</td>\n",
       "      <td>33.5107</td>\n",
       "      <td>5.6953</td>\n",
       "      <td>5.4663</td>\n",
       "      <td>18.2201</td>\n",
       "      <td>6.5769</td>\n",
       "      <td>21.2607</td>\n",
       "      <td>3.2304</td>\n",
       "      <td>-1.7759</td>\n",
       "      <td>3.1283</td>\n",
       "      <td>5.5518</td>\n",
       "      <td>1.4493</td>\n",
       "      <td>-2.6627</td>\n",
       "      <td>19.8056</td>\n",
       "      <td>2.3705</td>\n",
       "      <td>18.4685</td>\n",
       "      <td>16.3309</td>\n",
       "      <td>-3.3456</td>\n",
       "      <td>13.5261</td>\n",
       "      <td>1.7189</td>\n",
       "      <td>5.1743</td>\n",
       "      <td>-7.6938</td>\n",
       "      <td>9.7685</td>\n",
       "      <td>4.8910</td>\n",
       "      <td>12.2198</td>\n",
       "      <td>11.8503</td>\n",
       "      <td>-7.8931</td>\n",
       "      <td>6.4209</td>\n",
       "      <td>5.9270</td>\n",
       "      <td>16.0201</td>\n",
       "      <td>-0.2829</td>\n",
       "      <td>-1.4905</td>\n",
       "      <td>9.5214</td>\n",
       "      <td>-0.1508</td>\n",
       "      <td>9.1942</td>\n",
       "      <td>13.2876</td>\n",
       "      <td>-1.5121</td>\n",
       "      <td>3.9267</td>\n",
       "      <td>9.5031</td>\n",
       "      <td>17.9974</td>\n",
       "      <td>-8.8104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 202 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID_code  target    var_0   var_1    var_2   var_3    var_4   var_5   var_6  \\\n",
       "0  train_0       0   8.9255 -6.7863  11.9081  5.0930  11.4607 -9.2834  5.1187   \n",
       "1  train_1       0  11.5006 -4.1473  13.8588  5.3890  12.3622  7.0433  5.6208   \n",
       "2  train_2       0   8.6093 -2.7457  12.0805  7.8928  10.5825 -9.0837  6.9427   \n",
       "3  train_3       0  11.0604 -2.1518   8.9522  7.1957  12.5846 -1.8361  5.8428   \n",
       "4  train_4       0   9.8369 -1.4834  12.8746  6.6375  12.2772  2.4486  5.9405   \n",
       "\n",
       "     var_7   var_8   var_9  var_10   var_11   var_12   var_13  var_14  \\\n",
       "0  18.6266 -4.9200  5.7470  2.9252   3.1821  14.0137   0.5745  8.7989   \n",
       "1  16.5338  3.1468  8.0851 -0.4032   8.0585  14.0239   8.4135  5.4345   \n",
       "2  14.6155 -4.9193  5.9525 -0.3249 -11.2648  14.1929   7.3124  7.5244   \n",
       "3  14.9250 -5.8609  8.2450  2.3061   2.8102  13.8463  11.9704  6.4569   \n",
       "4  19.2514  6.2654  7.6784 -9.4458 -12.1419  13.8481   7.8895  7.7894   \n",
       "\n",
       "    var_15   var_16   var_17   var_18   var_19   var_20   var_21   var_22  \\\n",
       "0  14.5691   5.7487  -7.2393   4.2840  30.7133  10.5350  16.2191   2.5791   \n",
       "1  13.7003  13.8275 -15.5849   7.8000  28.5708   3.4287   2.7407   8.5524   \n",
       "2  14.6472   7.6782  -1.7395   4.7011  20.4775  17.7559  18.1377   1.2145   \n",
       "3  14.8372  10.7430  -0.4299  15.9426  13.7257  20.3010  12.5579   6.8202   \n",
       "4  15.0553   8.4871  -3.0680   6.5263  11.3152  21.4246  18.9608  10.1102   \n",
       "\n",
       "   var_23   var_24   var_25   var_26  var_27  var_28  var_29   var_30  \\\n",
       "0  2.4716  14.3831  13.4325  -5.1488 -0.4073  4.9306  5.9965  -0.3085   \n",
       "1  3.3716   6.9779  13.8910 -11.7684 -2.5586  5.0464  0.5481  -9.2987   \n",
       "2  3.5137   5.6777  13.2177  -7.9940 -2.9029  5.8463  6.1439 -11.1025   \n",
       "3  2.7229  12.1354  13.7367   0.8135 -0.9059  5.9070  2.8407 -15.2398   \n",
       "4  2.7142  14.2080  13.5433   3.1736 -3.3423  5.9015  7.9352  -3.1582   \n",
       "\n",
       "    var_31  var_32   var_33   var_34   var_35  var_36  var_37   var_38  \\\n",
       "0  12.9041 -3.8766  16.8911  11.1920  10.5785  0.6764  7.8871   4.6667   \n",
       "1   7.8755  1.2859  19.3710  11.3702   0.7399  2.7995  5.8434  10.8160   \n",
       "2  12.4858 -2.2871  19.0422  11.0449   4.1087  4.6974  6.9346  10.8917   \n",
       "3  10.4407 -2.5731   6.1796  10.6093  -5.9158  8.1723  2.8521   9.1738   \n",
       "4   9.4668 -0.0083  19.3239  12.4057   0.6329  2.7922  5.8184  19.3038   \n",
       "\n",
       "   var_39   var_40   var_41   var_42   var_43   var_44   var_45   var_46  \\\n",
       "0  3.8743  -5.2387   7.3746  11.5767  12.0446  11.6418  -7.0170   5.9226   \n",
       "1  3.6783 -11.1147   1.8730   9.8775  11.7842   1.2444 -47.3797   7.3718   \n",
       "2  0.9003 -13.5174   2.2439  11.5283  12.0406   4.1006  -7.9078  11.1405   \n",
       "3  0.6665  -3.8294  -1.0370  11.7770  11.2834   8.0485 -24.6840  12.7404   \n",
       "4  1.4450  -5.5963  14.0685  11.9171  11.5111   6.9087 -65.4863  13.8657   \n",
       "\n",
       "    var_47  ...  var_150  var_151  var_152  var_153  var_154  var_155  \\\n",
       "0 -14.2136  ...  18.5177  10.7800   9.0056  16.6964  10.4838   1.6573   \n",
       "1   0.1948  ...  20.6294  14.8743   9.4317  16.7242  -0.5687   0.1898   \n",
       "2  -5.7864  ...  14.3330   8.0088   4.4015  14.1479  -5.1747   0.5778   \n",
       "3 -35.1659  ...  18.0984  17.1765   7.6508  18.2452  17.0336 -10.9370   \n",
       "4   0.0444  ...  20.1461   1.2995   5.8493  19.8234   4.7022  10.6101   \n",
       "\n",
       "   var_156  var_157  var_158  var_159  var_160  var_161  var_162  var_163  \\\n",
       "0  12.1749 -13.1324  17.6054  11.5423  15.4576   5.3133   3.6159   5.0384   \n",
       "1  12.2419  -9.6953  22.3949  10.6261  29.4846   5.8683   3.8208  15.8348   \n",
       "2  14.5362  -1.7624  33.8820  11.6041  13.2070   5.8442   4.7086   5.7141   \n",
       "3  12.0500  -1.2155  19.9750  12.3892  31.8833   5.9684   7.2084   3.8899   \n",
       "4  13.0021 -12.6068  27.0846   8.0913  33.5107   5.6953   5.4663  18.2201   \n",
       "\n",
       "   var_164  var_165  var_166  var_167  var_168  var_169  var_170  var_171  \\\n",
       "0   6.6760  12.6644   2.7004  -0.6975   9.5981   5.4879  -4.7645  -8.4254   \n",
       "1  -5.0121  15.1345   3.2003   9.3192   3.8821   5.7999   5.5378   5.0988   \n",
       "2  -1.0410  20.5092   3.2790  -5.5952   7.3176   5.7690  -7.0927  -3.9116   \n",
       "3 -11.0882  17.2502   2.5881  -2.7018   0.5641   5.3430  -7.1541  -6.1920   \n",
       "4   6.5769  21.2607   3.2304  -1.7759   3.1283   5.5518   1.4493  -2.6627   \n",
       "\n",
       "   var_172  var_173  var_174  var_175  var_176  var_177  var_178  var_179  \\\n",
       "0  20.8773   3.1531  18.5618   7.7423 -10.1245  13.7241  -3.5189   1.7202   \n",
       "1  22.0330   5.5134  30.2645  10.4968  -7.2352  16.5721  -7.3477  11.0752   \n",
       "2   7.2569  -5.8234  25.6820  10.9202  -0.3104   8.8438  -9.7009   2.4013   \n",
       "3  18.2366  11.7134  14.7483   8.1013  11.8771  13.9552 -10.4701   5.6961   \n",
       "4  19.8056   2.3705  18.4685  16.3309  -3.3456  13.5261   1.7189   5.1743   \n",
       "\n",
       "   var_180  var_181  var_182  var_183  var_184  var_185  var_186  var_187  \\\n",
       "0  -8.4051   9.0164   3.0657  14.3691  25.8398   5.8764  11.8411 -19.7159   \n",
       "1  -5.5937   9.4878 -14.9100   9.4245  22.5441  -4.8622   7.6543 -15.9319   \n",
       "2  -4.2935   9.3908 -13.2648   3.1545  23.0866  -5.3000   5.3745  -6.2660   \n",
       "3  -3.7546   8.4117   1.8986   7.2601  -0.4639  -0.0498   7.9336 -12.8279   \n",
       "4  -7.6938   9.7685   4.8910  12.2198  11.8503  -7.8931   6.4209   5.9270   \n",
       "\n",
       "   var_188  var_189  var_190  var_191  var_192  var_193  var_194  var_195  \\\n",
       "0  17.5743   0.5857   4.4354   3.9642   3.1364   1.6910  18.5227  -2.3978   \n",
       "1  13.3175  -0.3566   7.6421   7.7214   2.5837  10.9516  15.4305   2.0339   \n",
       "2  10.1934  -0.8417   2.9057   9.7905   1.6704   1.6858  21.6042   3.1417   \n",
       "3  12.4124   1.8489   4.4666   4.7433   0.7178   1.4214  23.0347  -1.2706   \n",
       "4  16.0201  -0.2829  -1.4905   9.5214  -0.1508   9.1942  13.2876  -1.5121   \n",
       "\n",
       "   var_196  var_197  var_198  var_199  \n",
       "0   7.8784   8.5635  12.7803  -1.0914  \n",
       "1   8.1267   8.7889  18.3560   1.9518  \n",
       "2  -6.5213   8.2675  14.7222   0.3965  \n",
       "3  -2.9275  10.2922  17.9697  -8.9996  \n",
       "4   3.9267   9.5031  17.9974  -8.8104  \n",
       "\n",
       "[5 rows x 202 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "300ee221-b125-4e55-a4b2-83cddc63045a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_code</th>\n",
       "      <th>target</th>\n",
       "      <th>var_0</th>\n",
       "      <th>var_1</th>\n",
       "      <th>var_2</th>\n",
       "      <th>var_3</th>\n",
       "      <th>var_4</th>\n",
       "      <th>var_5</th>\n",
       "      <th>var_6</th>\n",
       "      <th>var_7</th>\n",
       "      <th>var_8</th>\n",
       "      <th>var_9</th>\n",
       "      <th>var_10</th>\n",
       "      <th>var_11</th>\n",
       "      <th>var_12</th>\n",
       "      <th>var_13</th>\n",
       "      <th>var_14</th>\n",
       "      <th>var_15</th>\n",
       "      <th>var_16</th>\n",
       "      <th>var_17</th>\n",
       "      <th>var_18</th>\n",
       "      <th>var_19</th>\n",
       "      <th>var_20</th>\n",
       "      <th>var_21</th>\n",
       "      <th>var_22</th>\n",
       "      <th>var_23</th>\n",
       "      <th>var_24</th>\n",
       "      <th>var_25</th>\n",
       "      <th>var_26</th>\n",
       "      <th>var_27</th>\n",
       "      <th>var_28</th>\n",
       "      <th>var_29</th>\n",
       "      <th>var_30</th>\n",
       "      <th>var_31</th>\n",
       "      <th>var_32</th>\n",
       "      <th>var_33</th>\n",
       "      <th>var_34</th>\n",
       "      <th>var_35</th>\n",
       "      <th>var_36</th>\n",
       "      <th>var_37</th>\n",
       "      <th>var_38</th>\n",
       "      <th>var_39</th>\n",
       "      <th>var_40</th>\n",
       "      <th>var_41</th>\n",
       "      <th>var_42</th>\n",
       "      <th>var_43</th>\n",
       "      <th>var_44</th>\n",
       "      <th>var_45</th>\n",
       "      <th>var_46</th>\n",
       "      <th>var_47</th>\n",
       "      <th>...</th>\n",
       "      <th>var_150</th>\n",
       "      <th>var_151</th>\n",
       "      <th>var_152</th>\n",
       "      <th>var_153</th>\n",
       "      <th>var_154</th>\n",
       "      <th>var_155</th>\n",
       "      <th>var_156</th>\n",
       "      <th>var_157</th>\n",
       "      <th>var_158</th>\n",
       "      <th>var_159</th>\n",
       "      <th>var_160</th>\n",
       "      <th>var_161</th>\n",
       "      <th>var_162</th>\n",
       "      <th>var_163</th>\n",
       "      <th>var_164</th>\n",
       "      <th>var_165</th>\n",
       "      <th>var_166</th>\n",
       "      <th>var_167</th>\n",
       "      <th>var_168</th>\n",
       "      <th>var_169</th>\n",
       "      <th>var_170</th>\n",
       "      <th>var_171</th>\n",
       "      <th>var_172</th>\n",
       "      <th>var_173</th>\n",
       "      <th>var_174</th>\n",
       "      <th>var_175</th>\n",
       "      <th>var_176</th>\n",
       "      <th>var_177</th>\n",
       "      <th>var_178</th>\n",
       "      <th>var_179</th>\n",
       "      <th>var_180</th>\n",
       "      <th>var_181</th>\n",
       "      <th>var_182</th>\n",
       "      <th>var_183</th>\n",
       "      <th>var_184</th>\n",
       "      <th>var_185</th>\n",
       "      <th>var_186</th>\n",
       "      <th>var_187</th>\n",
       "      <th>var_188</th>\n",
       "      <th>var_189</th>\n",
       "      <th>var_190</th>\n",
       "      <th>var_191</th>\n",
       "      <th>var_192</th>\n",
       "      <th>var_193</th>\n",
       "      <th>var_194</th>\n",
       "      <th>var_195</th>\n",
       "      <th>var_196</th>\n",
       "      <th>var_197</th>\n",
       "      <th>var_198</th>\n",
       "      <th>var_199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199995</th>\n",
       "      <td>train_199995</td>\n",
       "      <td>0</td>\n",
       "      <td>11.4880</td>\n",
       "      <td>-0.4956</td>\n",
       "      <td>8.2622</td>\n",
       "      <td>3.5142</td>\n",
       "      <td>10.3404</td>\n",
       "      <td>11.6081</td>\n",
       "      <td>5.6709</td>\n",
       "      <td>15.1516</td>\n",
       "      <td>-0.6209</td>\n",
       "      <td>5.6669</td>\n",
       "      <td>3.7574</td>\n",
       "      <td>-9.5348</td>\n",
       "      <td>13.9860</td>\n",
       "      <td>5.2982</td>\n",
       "      <td>8.2705</td>\n",
       "      <td>14.1527</td>\n",
       "      <td>7.4540</td>\n",
       "      <td>-5.0105</td>\n",
       "      <td>12.0465</td>\n",
       "      <td>8.6349</td>\n",
       "      <td>9.9137</td>\n",
       "      <td>25.1376</td>\n",
       "      <td>1.0914</td>\n",
       "      <td>3.2326</td>\n",
       "      <td>7.7802</td>\n",
       "      <td>13.9939</td>\n",
       "      <td>2.9085</td>\n",
       "      <td>0.1005</td>\n",
       "      <td>4.2369</td>\n",
       "      <td>7.5665</td>\n",
       "      <td>-9.2149</td>\n",
       "      <td>9.5746</td>\n",
       "      <td>1.4012</td>\n",
       "      <td>7.4211</td>\n",
       "      <td>11.0075</td>\n",
       "      <td>7.8080</td>\n",
       "      <td>4.5567</td>\n",
       "      <td>4.9861</td>\n",
       "      <td>9.7471</td>\n",
       "      <td>0.0722</td>\n",
       "      <td>5.9053</td>\n",
       "      <td>8.1743</td>\n",
       "      <td>10.8800</td>\n",
       "      <td>11.1665</td>\n",
       "      <td>4.2600</td>\n",
       "      <td>-2.1296</td>\n",
       "      <td>8.7833</td>\n",
       "      <td>-15.5727</td>\n",
       "      <td>...</td>\n",
       "      <td>14.3025</td>\n",
       "      <td>8.1596</td>\n",
       "      <td>7.9609</td>\n",
       "      <td>18.3343</td>\n",
       "      <td>4.3086</td>\n",
       "      <td>1.3546</td>\n",
       "      <td>12.4158</td>\n",
       "      <td>-5.3985</td>\n",
       "      <td>16.3683</td>\n",
       "      <td>10.4522</td>\n",
       "      <td>35.4923</td>\n",
       "      <td>5.5477</td>\n",
       "      <td>7.4244</td>\n",
       "      <td>12.5459</td>\n",
       "      <td>-6.7840</td>\n",
       "      <td>31.1895</td>\n",
       "      <td>2.6529</td>\n",
       "      <td>-11.1867</td>\n",
       "      <td>9.8865</td>\n",
       "      <td>5.4730</td>\n",
       "      <td>-5.3880</td>\n",
       "      <td>-0.4698</td>\n",
       "      <td>24.4025</td>\n",
       "      <td>-5.4493</td>\n",
       "      <td>11.3529</td>\n",
       "      <td>7.7075</td>\n",
       "      <td>-5.0491</td>\n",
       "      <td>13.0756</td>\n",
       "      <td>15.8271</td>\n",
       "      <td>3.3580</td>\n",
       "      <td>-14.3371</td>\n",
       "      <td>10.4421</td>\n",
       "      <td>7.6530</td>\n",
       "      <td>9.4585</td>\n",
       "      <td>22.7783</td>\n",
       "      <td>-4.0305</td>\n",
       "      <td>4.2233</td>\n",
       "      <td>-6.3906</td>\n",
       "      <td>13.5058</td>\n",
       "      <td>-0.4594</td>\n",
       "      <td>6.1415</td>\n",
       "      <td>13.2305</td>\n",
       "      <td>3.9901</td>\n",
       "      <td>0.9388</td>\n",
       "      <td>18.0249</td>\n",
       "      <td>-1.7939</td>\n",
       "      <td>2.1661</td>\n",
       "      <td>8.5326</td>\n",
       "      <td>16.6660</td>\n",
       "      <td>-17.8661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199996</th>\n",
       "      <td>train_199996</td>\n",
       "      <td>0</td>\n",
       "      <td>4.9149</td>\n",
       "      <td>-2.4484</td>\n",
       "      <td>16.7052</td>\n",
       "      <td>6.6345</td>\n",
       "      <td>8.3096</td>\n",
       "      <td>-10.5628</td>\n",
       "      <td>5.8802</td>\n",
       "      <td>21.5940</td>\n",
       "      <td>-3.6797</td>\n",
       "      <td>6.0019</td>\n",
       "      <td>6.5576</td>\n",
       "      <td>-11.8776</td>\n",
       "      <td>14.4131</td>\n",
       "      <td>3.3087</td>\n",
       "      <td>3.5800</td>\n",
       "      <td>14.1597</td>\n",
       "      <td>7.5191</td>\n",
       "      <td>-8.8715</td>\n",
       "      <td>17.9467</td>\n",
       "      <td>17.0237</td>\n",
       "      <td>6.6459</td>\n",
       "      <td>18.2345</td>\n",
       "      <td>0.8982</td>\n",
       "      <td>2.2532</td>\n",
       "      <td>15.4977</td>\n",
       "      <td>13.3282</td>\n",
       "      <td>5.2281</td>\n",
       "      <td>-3.7424</td>\n",
       "      <td>5.5144</td>\n",
       "      <td>5.7148</td>\n",
       "      <td>-13.7470</td>\n",
       "      <td>7.4369</td>\n",
       "      <td>1.3041</td>\n",
       "      <td>12.7552</td>\n",
       "      <td>12.5362</td>\n",
       "      <td>-1.1002</td>\n",
       "      <td>2.4370</td>\n",
       "      <td>6.2631</td>\n",
       "      <td>14.8565</td>\n",
       "      <td>-2.9862</td>\n",
       "      <td>-7.8820</td>\n",
       "      <td>7.1320</td>\n",
       "      <td>11.8869</td>\n",
       "      <td>11.4218</td>\n",
       "      <td>8.9282</td>\n",
       "      <td>-27.2007</td>\n",
       "      <td>14.5962</td>\n",
       "      <td>-19.8502</td>\n",
       "      <td>...</td>\n",
       "      <td>18.7807</td>\n",
       "      <td>9.4546</td>\n",
       "      <td>4.4657</td>\n",
       "      <td>17.8085</td>\n",
       "      <td>13.3077</td>\n",
       "      <td>-1.3209</td>\n",
       "      <td>12.7288</td>\n",
       "      <td>-12.3625</td>\n",
       "      <td>15.3500</td>\n",
       "      <td>11.1798</td>\n",
       "      <td>35.1445</td>\n",
       "      <td>5.5375</td>\n",
       "      <td>5.6397</td>\n",
       "      <td>17.0598</td>\n",
       "      <td>-9.7142</td>\n",
       "      <td>15.5117</td>\n",
       "      <td>3.3696</td>\n",
       "      <td>-17.1855</td>\n",
       "      <td>2.8292</td>\n",
       "      <td>5.2606</td>\n",
       "      <td>2.6836</td>\n",
       "      <td>5.8767</td>\n",
       "      <td>25.1262</td>\n",
       "      <td>7.3478</td>\n",
       "      <td>27.1264</td>\n",
       "      <td>11.8542</td>\n",
       "      <td>9.7999</td>\n",
       "      <td>11.1395</td>\n",
       "      <td>-3.2870</td>\n",
       "      <td>0.4285</td>\n",
       "      <td>2.5058</td>\n",
       "      <td>10.0339</td>\n",
       "      <td>9.1610</td>\n",
       "      <td>9.4318</td>\n",
       "      <td>13.4913</td>\n",
       "      <td>4.6247</td>\n",
       "      <td>6.2906</td>\n",
       "      <td>-17.8522</td>\n",
       "      <td>18.6751</td>\n",
       "      <td>-0.1162</td>\n",
       "      <td>4.9611</td>\n",
       "      <td>4.6549</td>\n",
       "      <td>0.6998</td>\n",
       "      <td>1.8341</td>\n",
       "      <td>22.2717</td>\n",
       "      <td>1.7337</td>\n",
       "      <td>-2.1651</td>\n",
       "      <td>6.7419</td>\n",
       "      <td>15.9054</td>\n",
       "      <td>0.3388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199997</th>\n",
       "      <td>train_199997</td>\n",
       "      <td>0</td>\n",
       "      <td>11.2232</td>\n",
       "      <td>-5.0518</td>\n",
       "      <td>10.5127</td>\n",
       "      <td>5.6456</td>\n",
       "      <td>9.3410</td>\n",
       "      <td>-5.4086</td>\n",
       "      <td>4.5555</td>\n",
       "      <td>21.5571</td>\n",
       "      <td>0.1202</td>\n",
       "      <td>6.1629</td>\n",
       "      <td>4.4004</td>\n",
       "      <td>-0.4651</td>\n",
       "      <td>13.8775</td>\n",
       "      <td>9.7414</td>\n",
       "      <td>10.9044</td>\n",
       "      <td>14.5597</td>\n",
       "      <td>9.6214</td>\n",
       "      <td>-1.6429</td>\n",
       "      <td>23.1127</td>\n",
       "      <td>12.1517</td>\n",
       "      <td>16.2577</td>\n",
       "      <td>3.1453</td>\n",
       "      <td>3.1008</td>\n",
       "      <td>2.1497</td>\n",
       "      <td>10.2715</td>\n",
       "      <td>13.5637</td>\n",
       "      <td>4.9473</td>\n",
       "      <td>-0.9905</td>\n",
       "      <td>6.2801</td>\n",
       "      <td>9.4902</td>\n",
       "      <td>-12.8549</td>\n",
       "      <td>11.0403</td>\n",
       "      <td>1.4306</td>\n",
       "      <td>13.8533</td>\n",
       "      <td>11.7484</td>\n",
       "      <td>6.8969</td>\n",
       "      <td>6.4162</td>\n",
       "      <td>3.4246</td>\n",
       "      <td>12.1170</td>\n",
       "      <td>3.4096</td>\n",
       "      <td>-8.8763</td>\n",
       "      <td>9.5230</td>\n",
       "      <td>11.2566</td>\n",
       "      <td>11.4025</td>\n",
       "      <td>11.8492</td>\n",
       "      <td>-49.5007</td>\n",
       "      <td>7.4376</td>\n",
       "      <td>-21.2946</td>\n",
       "      <td>...</td>\n",
       "      <td>20.6307</td>\n",
       "      <td>10.2890</td>\n",
       "      <td>5.6890</td>\n",
       "      <td>13.4601</td>\n",
       "      <td>-0.9774</td>\n",
       "      <td>2.3728</td>\n",
       "      <td>11.7245</td>\n",
       "      <td>-9.6385</td>\n",
       "      <td>17.3101</td>\n",
       "      <td>14.0422</td>\n",
       "      <td>19.9293</td>\n",
       "      <td>5.3427</td>\n",
       "      <td>5.4776</td>\n",
       "      <td>13.1202</td>\n",
       "      <td>5.3500</td>\n",
       "      <td>31.7346</td>\n",
       "      <td>3.1693</td>\n",
       "      <td>-19.4779</td>\n",
       "      <td>6.8053</td>\n",
       "      <td>5.6281</td>\n",
       "      <td>-0.8774</td>\n",
       "      <td>-8.9508</td>\n",
       "      <td>17.4931</td>\n",
       "      <td>-1.6530</td>\n",
       "      <td>32.0032</td>\n",
       "      <td>12.5749</td>\n",
       "      <td>5.8756</td>\n",
       "      <td>8.8059</td>\n",
       "      <td>-10.6367</td>\n",
       "      <td>5.4401</td>\n",
       "      <td>-12.7967</td>\n",
       "      <td>8.7990</td>\n",
       "      <td>0.7021</td>\n",
       "      <td>14.9744</td>\n",
       "      <td>18.9211</td>\n",
       "      <td>0.3016</td>\n",
       "      <td>11.2869</td>\n",
       "      <td>-6.3741</td>\n",
       "      <td>12.9726</td>\n",
       "      <td>2.3425</td>\n",
       "      <td>4.0651</td>\n",
       "      <td>5.4414</td>\n",
       "      <td>3.1032</td>\n",
       "      <td>4.8793</td>\n",
       "      <td>23.5311</td>\n",
       "      <td>-1.5736</td>\n",
       "      <td>1.2832</td>\n",
       "      <td>8.7155</td>\n",
       "      <td>13.8329</td>\n",
       "      <td>4.1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199998</th>\n",
       "      <td>train_199998</td>\n",
       "      <td>0</td>\n",
       "      <td>9.7148</td>\n",
       "      <td>-8.6098</td>\n",
       "      <td>13.6104</td>\n",
       "      <td>5.7930</td>\n",
       "      <td>12.5173</td>\n",
       "      <td>0.5339</td>\n",
       "      <td>6.0479</td>\n",
       "      <td>17.0152</td>\n",
       "      <td>-2.1926</td>\n",
       "      <td>8.7542</td>\n",
       "      <td>1.4245</td>\n",
       "      <td>0.7086</td>\n",
       "      <td>14.2110</td>\n",
       "      <td>6.5641</td>\n",
       "      <td>7.6177</td>\n",
       "      <td>13.8771</td>\n",
       "      <td>9.0479</td>\n",
       "      <td>-11.8164</td>\n",
       "      <td>14.0831</td>\n",
       "      <td>-2.0345</td>\n",
       "      <td>18.3863</td>\n",
       "      <td>3.0911</td>\n",
       "      <td>5.5803</td>\n",
       "      <td>3.7091</td>\n",
       "      <td>12.8219</td>\n",
       "      <td>13.8866</td>\n",
       "      <td>-3.3859</td>\n",
       "      <td>-0.4440</td>\n",
       "      <td>5.4817</td>\n",
       "      <td>4.0902</td>\n",
       "      <td>-7.7085</td>\n",
       "      <td>10.3952</td>\n",
       "      <td>2.5739</td>\n",
       "      <td>17.8529</td>\n",
       "      <td>11.3433</td>\n",
       "      <td>5.0534</td>\n",
       "      <td>-3.0055</td>\n",
       "      <td>3.9433</td>\n",
       "      <td>11.0759</td>\n",
       "      <td>1.2173</td>\n",
       "      <td>-11.7669</td>\n",
       "      <td>11.8626</td>\n",
       "      <td>10.7766</td>\n",
       "      <td>11.6900</td>\n",
       "      <td>12.9929</td>\n",
       "      <td>-42.9704</td>\n",
       "      <td>12.7881</td>\n",
       "      <td>4.4044</td>\n",
       "      <td>...</td>\n",
       "      <td>18.8580</td>\n",
       "      <td>8.2192</td>\n",
       "      <td>-0.4073</td>\n",
       "      <td>16.7224</td>\n",
       "      <td>8.8882</td>\n",
       "      <td>-3.2567</td>\n",
       "      <td>12.9142</td>\n",
       "      <td>-8.5421</td>\n",
       "      <td>15.9319</td>\n",
       "      <td>5.8348</td>\n",
       "      <td>40.3378</td>\n",
       "      <td>5.5357</td>\n",
       "      <td>4.6151</td>\n",
       "      <td>8.5910</td>\n",
       "      <td>-12.6998</td>\n",
       "      <td>25.8578</td>\n",
       "      <td>2.2346</td>\n",
       "      <td>-6.4988</td>\n",
       "      <td>2.6702</td>\n",
       "      <td>5.3868</td>\n",
       "      <td>-7.1875</td>\n",
       "      <td>8.1477</td>\n",
       "      <td>22.4362</td>\n",
       "      <td>-2.5914</td>\n",
       "      <td>8.8704</td>\n",
       "      <td>11.6621</td>\n",
       "      <td>7.4904</td>\n",
       "      <td>8.1808</td>\n",
       "      <td>-11.4177</td>\n",
       "      <td>2.8379</td>\n",
       "      <td>3.8748</td>\n",
       "      <td>8.7410</td>\n",
       "      <td>8.9998</td>\n",
       "      <td>16.4058</td>\n",
       "      <td>11.3244</td>\n",
       "      <td>-2.1751</td>\n",
       "      <td>12.4735</td>\n",
       "      <td>-18.3932</td>\n",
       "      <td>12.6337</td>\n",
       "      <td>0.3243</td>\n",
       "      <td>2.6840</td>\n",
       "      <td>8.6587</td>\n",
       "      <td>2.7337</td>\n",
       "      <td>11.1178</td>\n",
       "      <td>20.4158</td>\n",
       "      <td>-0.0786</td>\n",
       "      <td>6.7980</td>\n",
       "      <td>10.0342</td>\n",
       "      <td>15.5289</td>\n",
       "      <td>-13.9001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199999</th>\n",
       "      <td>train_199999</td>\n",
       "      <td>0</td>\n",
       "      <td>10.8762</td>\n",
       "      <td>-5.7105</td>\n",
       "      <td>12.1183</td>\n",
       "      <td>8.0328</td>\n",
       "      <td>11.5577</td>\n",
       "      <td>0.3488</td>\n",
       "      <td>5.2839</td>\n",
       "      <td>15.2058</td>\n",
       "      <td>-0.4541</td>\n",
       "      <td>9.3688</td>\n",
       "      <td>-7.3826</td>\n",
       "      <td>-8.7049</td>\n",
       "      <td>14.2486</td>\n",
       "      <td>15.0849</td>\n",
       "      <td>5.2313</td>\n",
       "      <td>14.3572</td>\n",
       "      <td>12.5523</td>\n",
       "      <td>-6.5066</td>\n",
       "      <td>11.3592</td>\n",
       "      <td>11.4779</td>\n",
       "      <td>15.4997</td>\n",
       "      <td>3.8474</td>\n",
       "      <td>2.4381</td>\n",
       "      <td>2.8295</td>\n",
       "      <td>10.6681</td>\n",
       "      <td>13.7167</td>\n",
       "      <td>-7.7771</td>\n",
       "      <td>-2.7798</td>\n",
       "      <td>6.2885</td>\n",
       "      <td>6.0089</td>\n",
       "      <td>2.1547</td>\n",
       "      <td>10.8181</td>\n",
       "      <td>-0.2712</td>\n",
       "      <td>12.5254</td>\n",
       "      <td>11.6304</td>\n",
       "      <td>-1.4949</td>\n",
       "      <td>7.9509</td>\n",
       "      <td>2.2480</td>\n",
       "      <td>8.1459</td>\n",
       "      <td>0.7928</td>\n",
       "      <td>-7.9028</td>\n",
       "      <td>7.4223</td>\n",
       "      <td>11.4249</td>\n",
       "      <td>11.9103</td>\n",
       "      <td>8.7002</td>\n",
       "      <td>-6.6883</td>\n",
       "      <td>10.5219</td>\n",
       "      <td>-25.9933</td>\n",
       "      <td>...</td>\n",
       "      <td>11.6612</td>\n",
       "      <td>13.1571</td>\n",
       "      <td>8.5043</td>\n",
       "      <td>17.0369</td>\n",
       "      <td>7.1124</td>\n",
       "      <td>-13.1967</td>\n",
       "      <td>13.9404</td>\n",
       "      <td>-8.3303</td>\n",
       "      <td>29.0140</td>\n",
       "      <td>9.6174</td>\n",
       "      <td>15.9041</td>\n",
       "      <td>5.3187</td>\n",
       "      <td>6.2987</td>\n",
       "      <td>13.0729</td>\n",
       "      <td>-4.2045</td>\n",
       "      <td>19.2141</td>\n",
       "      <td>3.2902</td>\n",
       "      <td>-1.2175</td>\n",
       "      <td>4.1583</td>\n",
       "      <td>5.7675</td>\n",
       "      <td>5.7719</td>\n",
       "      <td>-1.2139</td>\n",
       "      <td>21.8496</td>\n",
       "      <td>-3.5368</td>\n",
       "      <td>25.9094</td>\n",
       "      <td>11.7673</td>\n",
       "      <td>1.9765</td>\n",
       "      <td>15.9218</td>\n",
       "      <td>3.9350</td>\n",
       "      <td>4.3993</td>\n",
       "      <td>-10.3268</td>\n",
       "      <td>10.5200</td>\n",
       "      <td>9.9587</td>\n",
       "      <td>11.9242</td>\n",
       "      <td>7.0626</td>\n",
       "      <td>-6.5429</td>\n",
       "      <td>10.5947</td>\n",
       "      <td>-3.8827</td>\n",
       "      <td>16.3552</td>\n",
       "      <td>1.7535</td>\n",
       "      <td>8.9842</td>\n",
       "      <td>1.6893</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>0.3766</td>\n",
       "      <td>15.2101</td>\n",
       "      <td>-2.4907</td>\n",
       "      <td>-2.2342</td>\n",
       "      <td>8.1857</td>\n",
       "      <td>12.1284</td>\n",
       "      <td>0.1385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 202 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID_code  target    var_0   var_1    var_2   var_3    var_4  \\\n",
       "199995  train_199995       0  11.4880 -0.4956   8.2622  3.5142  10.3404   \n",
       "199996  train_199996       0   4.9149 -2.4484  16.7052  6.6345   8.3096   \n",
       "199997  train_199997       0  11.2232 -5.0518  10.5127  5.6456   9.3410   \n",
       "199998  train_199998       0   9.7148 -8.6098  13.6104  5.7930  12.5173   \n",
       "199999  train_199999       0  10.8762 -5.7105  12.1183  8.0328  11.5577   \n",
       "\n",
       "          var_5   var_6    var_7   var_8   var_9  var_10   var_11   var_12  \\\n",
       "199995  11.6081  5.6709  15.1516 -0.6209  5.6669  3.7574  -9.5348  13.9860   \n",
       "199996 -10.5628  5.8802  21.5940 -3.6797  6.0019  6.5576 -11.8776  14.4131   \n",
       "199997  -5.4086  4.5555  21.5571  0.1202  6.1629  4.4004  -0.4651  13.8775   \n",
       "199998   0.5339  6.0479  17.0152 -2.1926  8.7542  1.4245   0.7086  14.2110   \n",
       "199999   0.3488  5.2839  15.2058 -0.4541  9.3688 -7.3826  -8.7049  14.2486   \n",
       "\n",
       "         var_13   var_14   var_15   var_16   var_17   var_18   var_19  \\\n",
       "199995   5.2982   8.2705  14.1527   7.4540  -5.0105  12.0465   8.6349   \n",
       "199996   3.3087   3.5800  14.1597   7.5191  -8.8715  17.9467  17.0237   \n",
       "199997   9.7414  10.9044  14.5597   9.6214  -1.6429  23.1127  12.1517   \n",
       "199998   6.5641   7.6177  13.8771   9.0479 -11.8164  14.0831  -2.0345   \n",
       "199999  15.0849   5.2313  14.3572  12.5523  -6.5066  11.3592  11.4779   \n",
       "\n",
       "         var_20   var_21  var_22  var_23   var_24   var_25  var_26  var_27  \\\n",
       "199995   9.9137  25.1376  1.0914  3.2326   7.7802  13.9939  2.9085  0.1005   \n",
       "199996   6.6459  18.2345  0.8982  2.2532  15.4977  13.3282  5.2281 -3.7424   \n",
       "199997  16.2577   3.1453  3.1008  2.1497  10.2715  13.5637  4.9473 -0.9905   \n",
       "199998  18.3863   3.0911  5.5803  3.7091  12.8219  13.8866 -3.3859 -0.4440   \n",
       "199999  15.4997   3.8474  2.4381  2.8295  10.6681  13.7167 -7.7771 -2.7798   \n",
       "\n",
       "        var_28  var_29   var_30   var_31  var_32   var_33   var_34  var_35  \\\n",
       "199995  4.2369  7.5665  -9.2149   9.5746  1.4012   7.4211  11.0075  7.8080   \n",
       "199996  5.5144  5.7148 -13.7470   7.4369  1.3041  12.7552  12.5362 -1.1002   \n",
       "199997  6.2801  9.4902 -12.8549  11.0403  1.4306  13.8533  11.7484  6.8969   \n",
       "199998  5.4817  4.0902  -7.7085  10.3952  2.5739  17.8529  11.3433  5.0534   \n",
       "199999  6.2885  6.0089   2.1547  10.8181 -0.2712  12.5254  11.6304 -1.4949   \n",
       "\n",
       "        var_36  var_37   var_38  var_39   var_40   var_41   var_42   var_43  \\\n",
       "199995  4.5567  4.9861   9.7471  0.0722   5.9053   8.1743  10.8800  11.1665   \n",
       "199996  2.4370  6.2631  14.8565 -2.9862  -7.8820   7.1320  11.8869  11.4218   \n",
       "199997  6.4162  3.4246  12.1170  3.4096  -8.8763   9.5230  11.2566  11.4025   \n",
       "199998 -3.0055  3.9433  11.0759  1.2173 -11.7669  11.8626  10.7766  11.6900   \n",
       "199999  7.9509  2.2480   8.1459  0.7928  -7.9028   7.4223  11.4249  11.9103   \n",
       "\n",
       "         var_44   var_45   var_46   var_47  ...  var_150  var_151  var_152  \\\n",
       "199995   4.2600  -2.1296   8.7833 -15.5727  ...  14.3025   8.1596   7.9609   \n",
       "199996   8.9282 -27.2007  14.5962 -19.8502  ...  18.7807   9.4546   4.4657   \n",
       "199997  11.8492 -49.5007   7.4376 -21.2946  ...  20.6307  10.2890   5.6890   \n",
       "199998  12.9929 -42.9704  12.7881   4.4044  ...  18.8580   8.2192  -0.4073   \n",
       "199999   8.7002  -6.6883  10.5219 -25.9933  ...  11.6612  13.1571   8.5043   \n",
       "\n",
       "        var_153  var_154  var_155  var_156  var_157  var_158  var_159  \\\n",
       "199995  18.3343   4.3086   1.3546  12.4158  -5.3985  16.3683  10.4522   \n",
       "199996  17.8085  13.3077  -1.3209  12.7288 -12.3625  15.3500  11.1798   \n",
       "199997  13.4601  -0.9774   2.3728  11.7245  -9.6385  17.3101  14.0422   \n",
       "199998  16.7224   8.8882  -3.2567  12.9142  -8.5421  15.9319   5.8348   \n",
       "199999  17.0369   7.1124 -13.1967  13.9404  -8.3303  29.0140   9.6174   \n",
       "\n",
       "        var_160  var_161  var_162  var_163  var_164  var_165  var_166  \\\n",
       "199995  35.4923   5.5477   7.4244  12.5459  -6.7840  31.1895   2.6529   \n",
       "199996  35.1445   5.5375   5.6397  17.0598  -9.7142  15.5117   3.3696   \n",
       "199997  19.9293   5.3427   5.4776  13.1202   5.3500  31.7346   3.1693   \n",
       "199998  40.3378   5.5357   4.6151   8.5910 -12.6998  25.8578   2.2346   \n",
       "199999  15.9041   5.3187   6.2987  13.0729  -4.2045  19.2141   3.2902   \n",
       "\n",
       "        var_167  var_168  var_169  var_170  var_171  var_172  var_173  \\\n",
       "199995 -11.1867   9.8865   5.4730  -5.3880  -0.4698  24.4025  -5.4493   \n",
       "199996 -17.1855   2.8292   5.2606   2.6836   5.8767  25.1262   7.3478   \n",
       "199997 -19.4779   6.8053   5.6281  -0.8774  -8.9508  17.4931  -1.6530   \n",
       "199998  -6.4988   2.6702   5.3868  -7.1875   8.1477  22.4362  -2.5914   \n",
       "199999  -1.2175   4.1583   5.7675   5.7719  -1.2139  21.8496  -3.5368   \n",
       "\n",
       "        var_174  var_175  var_176  var_177  var_178  var_179  var_180  \\\n",
       "199995  11.3529   7.7075  -5.0491  13.0756  15.8271   3.3580 -14.3371   \n",
       "199996  27.1264  11.8542   9.7999  11.1395  -3.2870   0.4285   2.5058   \n",
       "199997  32.0032  12.5749   5.8756   8.8059 -10.6367   5.4401 -12.7967   \n",
       "199998   8.8704  11.6621   7.4904   8.1808 -11.4177   2.8379   3.8748   \n",
       "199999  25.9094  11.7673   1.9765  15.9218   3.9350   4.3993 -10.3268   \n",
       "\n",
       "        var_181  var_182  var_183  var_184  var_185  var_186  var_187  \\\n",
       "199995  10.4421   7.6530   9.4585  22.7783  -4.0305   4.2233  -6.3906   \n",
       "199996  10.0339   9.1610   9.4318  13.4913   4.6247   6.2906 -17.8522   \n",
       "199997   8.7990   0.7021  14.9744  18.9211   0.3016  11.2869  -6.3741   \n",
       "199998   8.7410   8.9998  16.4058  11.3244  -2.1751  12.4735 -18.3932   \n",
       "199999  10.5200   9.9587  11.9242   7.0626  -6.5429  10.5947  -3.8827   \n",
       "\n",
       "        var_188  var_189  var_190  var_191  var_192  var_193  var_194  \\\n",
       "199995  13.5058  -0.4594   6.1415  13.2305   3.9901   0.9388  18.0249   \n",
       "199996  18.6751  -0.1162   4.9611   4.6549   0.6998   1.8341  22.2717   \n",
       "199997  12.9726   2.3425   4.0651   5.4414   3.1032   4.8793  23.5311   \n",
       "199998  12.6337   0.3243   2.6840   8.6587   2.7337  11.1178  20.4158   \n",
       "199999  16.3552   1.7535   8.9842   1.6893   0.1276   0.3766  15.2101   \n",
       "\n",
       "        var_195  var_196  var_197  var_198  var_199  \n",
       "199995  -1.7939   2.1661   8.5326  16.6660 -17.8661  \n",
       "199996   1.7337  -2.1651   6.7419  15.9054   0.3388  \n",
       "199997  -1.5736   1.2832   8.7155  13.8329   4.1995  \n",
       "199998  -0.0786   6.7980  10.0342  15.5289 -13.9001  \n",
       "199999  -2.4907  -2.2342   8.1857  12.1284   0.1385  \n",
       "\n",
       "[5 rows x 202 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "795fc0f0-6b42-4e28-a261-b12ec057822c",
   "metadata": {},
   "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_code</th>\n",
       "      <th>var_0</th>\n",
       "      <th>var_1</th>\n",
       "      <th>var_2</th>\n",
       "      <th>var_3</th>\n",
       "      <th>var_4</th>\n",
       "      <th>var_5</th>\n",
       "      <th>var_6</th>\n",
       "      <th>var_7</th>\n",
       "      <th>var_8</th>\n",
       "      <th>var_9</th>\n",
       "      <th>var_10</th>\n",
       "      <th>var_11</th>\n",
       "      <th>var_12</th>\n",
       "      <th>var_13</th>\n",
       "      <th>var_14</th>\n",
       "      <th>var_15</th>\n",
       "      <th>var_16</th>\n",
       "      <th>var_17</th>\n",
       "      <th>var_18</th>\n",
       "      <th>var_19</th>\n",
       "      <th>var_20</th>\n",
       "      <th>var_21</th>\n",
       "      <th>var_22</th>\n",
       "      <th>var_23</th>\n",
       "      <th>var_24</th>\n",
       "      <th>var_25</th>\n",
       "      <th>var_26</th>\n",
       "      <th>var_27</th>\n",
       "      <th>var_28</th>\n",
       "      <th>var_29</th>\n",
       "      <th>var_30</th>\n",
       "      <th>var_31</th>\n",
       "      <th>var_32</th>\n",
       "      <th>var_33</th>\n",
       "      <th>var_34</th>\n",
       "      <th>var_35</th>\n",
       "      <th>var_36</th>\n",
       "      <th>var_37</th>\n",
       "      <th>var_38</th>\n",
       "      <th>var_39</th>\n",
       "      <th>var_40</th>\n",
       "      <th>var_41</th>\n",
       "      <th>var_42</th>\n",
       "      <th>var_43</th>\n",
       "      <th>var_44</th>\n",
       "      <th>var_45</th>\n",
       "      <th>var_46</th>\n",
       "      <th>var_47</th>\n",
       "      <th>var_48</th>\n",
       "      <th>...</th>\n",
       "      <th>var_150</th>\n",
       "      <th>var_151</th>\n",
       "      <th>var_152</th>\n",
       "      <th>var_153</th>\n",
       "      <th>var_154</th>\n",
       "      <th>var_155</th>\n",
       "      <th>var_156</th>\n",
       "      <th>var_157</th>\n",
       "      <th>var_158</th>\n",
       "      <th>var_159</th>\n",
       "      <th>var_160</th>\n",
       "      <th>var_161</th>\n",
       "      <th>var_162</th>\n",
       "      <th>var_163</th>\n",
       "      <th>var_164</th>\n",
       "      <th>var_165</th>\n",
       "      <th>var_166</th>\n",
       "      <th>var_167</th>\n",
       "      <th>var_168</th>\n",
       "      <th>var_169</th>\n",
       "      <th>var_170</th>\n",
       "      <th>var_171</th>\n",
       "      <th>var_172</th>\n",
       "      <th>var_173</th>\n",
       "      <th>var_174</th>\n",
       "      <th>var_175</th>\n",
       "      <th>var_176</th>\n",
       "      <th>var_177</th>\n",
       "      <th>var_178</th>\n",
       "      <th>var_179</th>\n",
       "      <th>var_180</th>\n",
       "      <th>var_181</th>\n",
       "      <th>var_182</th>\n",
       "      <th>var_183</th>\n",
       "      <th>var_184</th>\n",
       "      <th>var_185</th>\n",
       "      <th>var_186</th>\n",
       "      <th>var_187</th>\n",
       "      <th>var_188</th>\n",
       "      <th>var_189</th>\n",
       "      <th>var_190</th>\n",
       "      <th>var_191</th>\n",
       "      <th>var_192</th>\n",
       "      <th>var_193</th>\n",
       "      <th>var_194</th>\n",
       "      <th>var_195</th>\n",
       "      <th>var_196</th>\n",
       "      <th>var_197</th>\n",
       "      <th>var_198</th>\n",
       "      <th>var_199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199995</th>\n",
       "      <td>test_199995</td>\n",
       "      <td>13.1678</td>\n",
       "      <td>1.0136</td>\n",
       "      <td>10.4333</td>\n",
       "      <td>6.7997</td>\n",
       "      <td>8.5974</td>\n",
       "      <td>-4.1641</td>\n",
       "      <td>4.8579</td>\n",
       "      <td>14.7625</td>\n",
       "      <td>-2.7239</td>\n",
       "      <td>6.9937</td>\n",
       "      <td>2.6802</td>\n",
       "      <td>6.1565</td>\n",
       "      <td>14.3201</td>\n",
       "      <td>17.4594</td>\n",
       "      <td>5.3712</td>\n",
       "      <td>14.8984</td>\n",
       "      <td>5.8064</td>\n",
       "      <td>-10.0334</td>\n",
       "      <td>16.4220</td>\n",
       "      <td>30.7786</td>\n",
       "      <td>22.7696</td>\n",
       "      <td>9.8526</td>\n",
       "      <td>4.0207</td>\n",
       "      <td>3.8469</td>\n",
       "      <td>13.1617</td>\n",
       "      <td>13.2522</td>\n",
       "      <td>-12.4547</td>\n",
       "      <td>-1.9127</td>\n",
       "      <td>5.3696</td>\n",
       "      <td>6.9779</td>\n",
       "      <td>-17.9649</td>\n",
       "      <td>10.7856</td>\n",
       "      <td>0.2776</td>\n",
       "      <td>5.8071</td>\n",
       "      <td>10.4332</td>\n",
       "      <td>8.6681</td>\n",
       "      <td>0.9646</td>\n",
       "      <td>11.2181</td>\n",
       "      <td>11.8660</td>\n",
       "      <td>6.3732</td>\n",
       "      <td>-2.7215</td>\n",
       "      <td>13.3420</td>\n",
       "      <td>11.1627</td>\n",
       "      <td>11.6436</td>\n",
       "      <td>18.4112</td>\n",
       "      <td>-21.3080</td>\n",
       "      <td>10.0185</td>\n",
       "      <td>-32.6883</td>\n",
       "      <td>3.5985</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0401</td>\n",
       "      <td>15.7083</td>\n",
       "      <td>5.0690</td>\n",
       "      <td>20.4789</td>\n",
       "      <td>5.6559</td>\n",
       "      <td>7.0860</td>\n",
       "      <td>11.3302</td>\n",
       "      <td>-4.6820</td>\n",
       "      <td>12.0228</td>\n",
       "      <td>11.1629</td>\n",
       "      <td>21.4193</td>\n",
       "      <td>5.3269</td>\n",
       "      <td>6.8510</td>\n",
       "      <td>15.7062</td>\n",
       "      <td>-3.0267</td>\n",
       "      <td>21.8848</td>\n",
       "      <td>3.5995</td>\n",
       "      <td>-2.5442</td>\n",
       "      <td>3.7888</td>\n",
       "      <td>6.6096</td>\n",
       "      <td>-6.3101</td>\n",
       "      <td>2.2246</td>\n",
       "      <td>29.0310</td>\n",
       "      <td>5.0331</td>\n",
       "      <td>26.2206</td>\n",
       "      <td>14.3811</td>\n",
       "      <td>-4.5834</td>\n",
       "      <td>9.8823</td>\n",
       "      <td>12.3426</td>\n",
       "      <td>7.3865</td>\n",
       "      <td>-11.8810</td>\n",
       "      <td>10.4412</td>\n",
       "      <td>-0.9849</td>\n",
       "      <td>10.9760</td>\n",
       "      <td>12.3183</td>\n",
       "      <td>-2.7719</td>\n",
       "      <td>5.8820</td>\n",
       "      <td>8.5245</td>\n",
       "      <td>17.5356</td>\n",
       "      <td>1.0903</td>\n",
       "      <td>2.0544</td>\n",
       "      <td>9.6849</td>\n",
       "      <td>4.6734</td>\n",
       "      <td>-1.3660</td>\n",
       "      <td>12.8721</td>\n",
       "      <td>1.2013</td>\n",
       "      <td>-4.6195</td>\n",
       "      <td>9.1568</td>\n",
       "      <td>18.2102</td>\n",
       "      <td>4.8801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199996</th>\n",
       "      <td>test_199996</td>\n",
       "      <td>9.7171</td>\n",
       "      <td>-9.1462</td>\n",
       "      <td>7.3443</td>\n",
       "      <td>9.1421</td>\n",
       "      <td>12.8936</td>\n",
       "      <td>3.0191</td>\n",
       "      <td>5.6888</td>\n",
       "      <td>18.8862</td>\n",
       "      <td>5.0915</td>\n",
       "      <td>6.3545</td>\n",
       "      <td>3.2618</td>\n",
       "      <td>-2.0445</td>\n",
       "      <td>13.8246</td>\n",
       "      <td>6.6547</td>\n",
       "      <td>5.0309</td>\n",
       "      <td>14.1999</td>\n",
       "      <td>8.5685</td>\n",
       "      <td>3.8190</td>\n",
       "      <td>11.4428</td>\n",
       "      <td>19.6761</td>\n",
       "      <td>22.4473</td>\n",
       "      <td>16.6921</td>\n",
       "      <td>5.5064</td>\n",
       "      <td>3.6160</td>\n",
       "      <td>10.4145</td>\n",
       "      <td>13.5557</td>\n",
       "      <td>0.0592</td>\n",
       "      <td>-1.8571</td>\n",
       "      <td>6.3351</td>\n",
       "      <td>6.4782</td>\n",
       "      <td>-15.0057</td>\n",
       "      <td>11.0107</td>\n",
       "      <td>1.1463</td>\n",
       "      <td>11.1037</td>\n",
       "      <td>11.7266</td>\n",
       "      <td>2.2244</td>\n",
       "      <td>1.3367</td>\n",
       "      <td>3.8213</td>\n",
       "      <td>2.7258</td>\n",
       "      <td>4.1475</td>\n",
       "      <td>-12.1092</td>\n",
       "      <td>23.2927</td>\n",
       "      <td>11.1598</td>\n",
       "      <td>11.4884</td>\n",
       "      <td>4.9641</td>\n",
       "      <td>-48.3446</td>\n",
       "      <td>12.1540</td>\n",
       "      <td>8.2999</td>\n",
       "      <td>10.1927</td>\n",
       "      <td>...</td>\n",
       "      <td>14.8000</td>\n",
       "      <td>9.1898</td>\n",
       "      <td>4.2972</td>\n",
       "      <td>20.3867</td>\n",
       "      <td>13.5508</td>\n",
       "      <td>-7.4707</td>\n",
       "      <td>13.2292</td>\n",
       "      <td>-3.5045</td>\n",
       "      <td>9.0808</td>\n",
       "      <td>8.0147</td>\n",
       "      <td>25.5856</td>\n",
       "      <td>5.6828</td>\n",
       "      <td>3.9202</td>\n",
       "      <td>10.5955</td>\n",
       "      <td>-2.9063</td>\n",
       "      <td>18.2834</td>\n",
       "      <td>3.0946</td>\n",
       "      <td>-13.0401</td>\n",
       "      <td>10.6693</td>\n",
       "      <td>5.4541</td>\n",
       "      <td>0.0289</td>\n",
       "      <td>1.5879</td>\n",
       "      <td>8.3102</td>\n",
       "      <td>6.5891</td>\n",
       "      <td>20.1379</td>\n",
       "      <td>14.4782</td>\n",
       "      <td>-7.6099</td>\n",
       "      <td>8.8498</td>\n",
       "      <td>-11.0107</td>\n",
       "      <td>-1.7399</td>\n",
       "      <td>-1.4862</td>\n",
       "      <td>10.0511</td>\n",
       "      <td>-15.1250</td>\n",
       "      <td>7.5211</td>\n",
       "      <td>26.2435</td>\n",
       "      <td>-0.8879</td>\n",
       "      <td>6.4135</td>\n",
       "      <td>-1.1382</td>\n",
       "      <td>15.4816</td>\n",
       "      <td>1.7106</td>\n",
       "      <td>5.0071</td>\n",
       "      <td>6.6548</td>\n",
       "      <td>1.8197</td>\n",
       "      <td>2.4104</td>\n",
       "      <td>18.9037</td>\n",
       "      <td>-0.9337</td>\n",
       "      <td>2.9995</td>\n",
       "      <td>9.1112</td>\n",
       "      <td>18.1740</td>\n",
       "      <td>-20.7689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199997</th>\n",
       "      <td>test_199997</td>\n",
       "      <td>11.6360</td>\n",
       "      <td>2.2769</td>\n",
       "      <td>11.2074</td>\n",
       "      <td>7.7649</td>\n",
       "      <td>12.6796</td>\n",
       "      <td>11.3224</td>\n",
       "      <td>5.3883</td>\n",
       "      <td>18.3794</td>\n",
       "      <td>1.6603</td>\n",
       "      <td>5.7341</td>\n",
       "      <td>9.8596</td>\n",
       "      <td>-0.3412</td>\n",
       "      <td>14.0675</td>\n",
       "      <td>13.9975</td>\n",
       "      <td>6.2574</td>\n",
       "      <td>15.1456</td>\n",
       "      <td>8.1250</td>\n",
       "      <td>-7.3642</td>\n",
       "      <td>15.4767</td>\n",
       "      <td>15.9472</td>\n",
       "      <td>16.6580</td>\n",
       "      <td>16.0286</td>\n",
       "      <td>0.6066</td>\n",
       "      <td>3.5773</td>\n",
       "      <td>4.4758</td>\n",
       "      <td>13.8995</td>\n",
       "      <td>-9.1787</td>\n",
       "      <td>-2.3696</td>\n",
       "      <td>4.6504</td>\n",
       "      <td>7.7074</td>\n",
       "      <td>-14.7738</td>\n",
       "      <td>9.6374</td>\n",
       "      <td>-0.4069</td>\n",
       "      <td>11.3680</td>\n",
       "      <td>11.3627</td>\n",
       "      <td>6.7611</td>\n",
       "      <td>7.6036</td>\n",
       "      <td>8.1942</td>\n",
       "      <td>6.4839</td>\n",
       "      <td>-4.3884</td>\n",
       "      <td>-5.9079</td>\n",
       "      <td>5.0280</td>\n",
       "      <td>11.5624</td>\n",
       "      <td>11.4365</td>\n",
       "      <td>12.9517</td>\n",
       "      <td>-18.5914</td>\n",
       "      <td>12.8493</td>\n",
       "      <td>3.0741</td>\n",
       "      <td>21.9976</td>\n",
       "      <td>...</td>\n",
       "      <td>11.9457</td>\n",
       "      <td>10.7800</td>\n",
       "      <td>4.8995</td>\n",
       "      <td>16.5055</td>\n",
       "      <td>0.9823</td>\n",
       "      <td>9.0450</td>\n",
       "      <td>12.2711</td>\n",
       "      <td>-16.1130</td>\n",
       "      <td>11.0062</td>\n",
       "      <td>7.5311</td>\n",
       "      <td>31.7520</td>\n",
       "      <td>5.7419</td>\n",
       "      <td>7.3339</td>\n",
       "      <td>9.1077</td>\n",
       "      <td>-1.9905</td>\n",
       "      <td>11.9434</td>\n",
       "      <td>3.2239</td>\n",
       "      <td>-9.1386</td>\n",
       "      <td>4.1733</td>\n",
       "      <td>5.9515</td>\n",
       "      <td>-5.8944</td>\n",
       "      <td>2.6633</td>\n",
       "      <td>30.7599</td>\n",
       "      <td>-13.1990</td>\n",
       "      <td>18.9432</td>\n",
       "      <td>15.5096</td>\n",
       "      <td>-6.8353</td>\n",
       "      <td>11.1724</td>\n",
       "      <td>-0.5545</td>\n",
       "      <td>-2.9957</td>\n",
       "      <td>-2.1730</td>\n",
       "      <td>11.1961</td>\n",
       "      <td>13.0570</td>\n",
       "      <td>17.0153</td>\n",
       "      <td>1.7004</td>\n",
       "      <td>-7.2363</td>\n",
       "      <td>8.1679</td>\n",
       "      <td>-15.7536</td>\n",
       "      <td>16.3071</td>\n",
       "      <td>-1.0926</td>\n",
       "      <td>5.1536</td>\n",
       "      <td>2.6498</td>\n",
       "      <td>2.4937</td>\n",
       "      <td>-0.0637</td>\n",
       "      <td>20.0609</td>\n",
       "      <td>-1.1742</td>\n",
       "      <td>-4.1524</td>\n",
       "      <td>9.1933</td>\n",
       "      <td>11.7905</td>\n",
       "      <td>-22.2762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199998</th>\n",
       "      <td>test_199998</td>\n",
       "      <td>13.5745</td>\n",
       "      <td>-0.5134</td>\n",
       "      <td>13.6584</td>\n",
       "      <td>7.4855</td>\n",
       "      <td>11.2241</td>\n",
       "      <td>-11.3037</td>\n",
       "      <td>4.1959</td>\n",
       "      <td>16.8280</td>\n",
       "      <td>5.3208</td>\n",
       "      <td>8.9032</td>\n",
       "      <td>5.5000</td>\n",
       "      <td>-13.1346</td>\n",
       "      <td>14.3051</td>\n",
       "      <td>4.2644</td>\n",
       "      <td>11.1258</td>\n",
       "      <td>14.7810</td>\n",
       "      <td>10.7184</td>\n",
       "      <td>-11.1223</td>\n",
       "      <td>14.3151</td>\n",
       "      <td>11.8348</td>\n",
       "      <td>12.2356</td>\n",
       "      <td>20.9599</td>\n",
       "      <td>1.1101</td>\n",
       "      <td>3.0801</td>\n",
       "      <td>6.2626</td>\n",
       "      <td>14.0242</td>\n",
       "      <td>3.6157</td>\n",
       "      <td>-1.3483</td>\n",
       "      <td>6.5041</td>\n",
       "      <td>6.5306</td>\n",
       "      <td>-20.2249</td>\n",
       "      <td>10.7930</td>\n",
       "      <td>-5.3353</td>\n",
       "      <td>9.9596</td>\n",
       "      <td>11.4595</td>\n",
       "      <td>8.1176</td>\n",
       "      <td>2.5997</td>\n",
       "      <td>3.1370</td>\n",
       "      <td>3.5781</td>\n",
       "      <td>-1.5041</td>\n",
       "      <td>1.9454</td>\n",
       "      <td>17.6266</td>\n",
       "      <td>10.1011</td>\n",
       "      <td>11.5196</td>\n",
       "      <td>15.5804</td>\n",
       "      <td>-40.0510</td>\n",
       "      <td>13.1581</td>\n",
       "      <td>-7.2331</td>\n",
       "      <td>9.7570</td>\n",
       "      <td>...</td>\n",
       "      <td>16.8626</td>\n",
       "      <td>9.3239</td>\n",
       "      <td>7.4385</td>\n",
       "      <td>14.7483</td>\n",
       "      <td>13.3585</td>\n",
       "      <td>-4.1428</td>\n",
       "      <td>14.1710</td>\n",
       "      <td>-6.1347</td>\n",
       "      <td>9.6632</td>\n",
       "      <td>17.8940</td>\n",
       "      <td>28.5510</td>\n",
       "      <td>5.6456</td>\n",
       "      <td>3.5223</td>\n",
       "      <td>7.3218</td>\n",
       "      <td>-0.4352</td>\n",
       "      <td>20.1410</td>\n",
       "      <td>3.2078</td>\n",
       "      <td>-3.3613</td>\n",
       "      <td>2.5097</td>\n",
       "      <td>5.3997</td>\n",
       "      <td>-5.8704</td>\n",
       "      <td>-0.7192</td>\n",
       "      <td>22.2659</td>\n",
       "      <td>2.2206</td>\n",
       "      <td>13.2572</td>\n",
       "      <td>9.2657</td>\n",
       "      <td>-12.6796</td>\n",
       "      <td>10.6895</td>\n",
       "      <td>-15.4493</td>\n",
       "      <td>2.5477</td>\n",
       "      <td>2.1028</td>\n",
       "      <td>11.3081</td>\n",
       "      <td>-0.9160</td>\n",
       "      <td>15.2015</td>\n",
       "      <td>-0.9905</td>\n",
       "      <td>-13.6271</td>\n",
       "      <td>11.5390</td>\n",
       "      <td>-24.8621</td>\n",
       "      <td>6.6792</td>\n",
       "      <td>0.8885</td>\n",
       "      <td>3.4259</td>\n",
       "      <td>8.5012</td>\n",
       "      <td>2.2713</td>\n",
       "      <td>5.7621</td>\n",
       "      <td>17.0056</td>\n",
       "      <td>1.1763</td>\n",
       "      <td>-2.3761</td>\n",
       "      <td>8.1079</td>\n",
       "      <td>8.7735</td>\n",
       "      <td>-0.2122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199999</th>\n",
       "      <td>test_199999</td>\n",
       "      <td>10.4664</td>\n",
       "      <td>1.8070</td>\n",
       "      <td>10.2277</td>\n",
       "      <td>6.0654</td>\n",
       "      <td>10.0258</td>\n",
       "      <td>1.0789</td>\n",
       "      <td>4.8879</td>\n",
       "      <td>14.4892</td>\n",
       "      <td>-0.5902</td>\n",
       "      <td>7.8362</td>\n",
       "      <td>8.4796</td>\n",
       "      <td>-5.8960</td>\n",
       "      <td>13.8333</td>\n",
       "      <td>2.4590</td>\n",
       "      <td>7.8881</td>\n",
       "      <td>14.8566</td>\n",
       "      <td>10.4665</td>\n",
       "      <td>-11.8235</td>\n",
       "      <td>25.2430</td>\n",
       "      <td>20.9039</td>\n",
       "      <td>19.0743</td>\n",
       "      <td>15.5896</td>\n",
       "      <td>-2.1275</td>\n",
       "      <td>2.9672</td>\n",
       "      <td>2.0289</td>\n",
       "      <td>13.9641</td>\n",
       "      <td>-2.7192</td>\n",
       "      <td>0.9102</td>\n",
       "      <td>4.1522</td>\n",
       "      <td>3.9101</td>\n",
       "      <td>-8.6119</td>\n",
       "      <td>11.5158</td>\n",
       "      <td>2.8218</td>\n",
       "      <td>11.5082</td>\n",
       "      <td>11.2149</td>\n",
       "      <td>5.4528</td>\n",
       "      <td>-0.5887</td>\n",
       "      <td>2.8780</td>\n",
       "      <td>7.0659</td>\n",
       "      <td>5.8230</td>\n",
       "      <td>-2.1438</td>\n",
       "      <td>1.5189</td>\n",
       "      <td>11.0499</td>\n",
       "      <td>11.6297</td>\n",
       "      <td>9.2466</td>\n",
       "      <td>-21.8501</td>\n",
       "      <td>11.9929</td>\n",
       "      <td>-24.7056</td>\n",
       "      <td>13.6930</td>\n",
       "      <td>...</td>\n",
       "      <td>17.4201</td>\n",
       "      <td>12.3167</td>\n",
       "      <td>8.9068</td>\n",
       "      <td>20.6013</td>\n",
       "      <td>13.3664</td>\n",
       "      <td>-10.3179</td>\n",
       "      <td>12.5572</td>\n",
       "      <td>-9.7303</td>\n",
       "      <td>9.5033</td>\n",
       "      <td>12.4379</td>\n",
       "      <td>26.7010</td>\n",
       "      <td>5.8830</td>\n",
       "      <td>3.8301</td>\n",
       "      <td>15.0473</td>\n",
       "      <td>4.8121</td>\n",
       "      <td>17.1152</td>\n",
       "      <td>2.4527</td>\n",
       "      <td>-6.0539</td>\n",
       "      <td>5.3711</td>\n",
       "      <td>6.1772</td>\n",
       "      <td>0.9131</td>\n",
       "      <td>0.3922</td>\n",
       "      <td>15.0415</td>\n",
       "      <td>-1.4703</td>\n",
       "      <td>30.1926</td>\n",
       "      <td>10.3408</td>\n",
       "      <td>-5.4143</td>\n",
       "      <td>13.9104</td>\n",
       "      <td>1.6210</td>\n",
       "      <td>2.0273</td>\n",
       "      <td>-2.9853</td>\n",
       "      <td>10.4163</td>\n",
       "      <td>-12.1494</td>\n",
       "      <td>10.0160</td>\n",
       "      <td>13.3454</td>\n",
       "      <td>-8.1344</td>\n",
       "      <td>4.6123</td>\n",
       "      <td>3.4033</td>\n",
       "      <td>16.8310</td>\n",
       "      <td>0.6155</td>\n",
       "      <td>0.1398</td>\n",
       "      <td>9.2828</td>\n",
       "      <td>1.3601</td>\n",
       "      <td>4.8985</td>\n",
       "      <td>20.0926</td>\n",
       "      <td>-1.3048</td>\n",
       "      <td>-2.5981</td>\n",
       "      <td>10.3378</td>\n",
       "      <td>14.3340</td>\n",
       "      <td>-7.7094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 201 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID_code    var_0   var_1    var_2   var_3    var_4    var_5  \\\n",
       "199995  test_199995  13.1678  1.0136  10.4333  6.7997   8.5974  -4.1641   \n",
       "199996  test_199996   9.7171 -9.1462   7.3443  9.1421  12.8936   3.0191   \n",
       "199997  test_199997  11.6360  2.2769  11.2074  7.7649  12.6796  11.3224   \n",
       "199998  test_199998  13.5745 -0.5134  13.6584  7.4855  11.2241 -11.3037   \n",
       "199999  test_199999  10.4664  1.8070  10.2277  6.0654  10.0258   1.0789   \n",
       "\n",
       "         var_6    var_7   var_8   var_9  var_10   var_11   var_12   var_13  \\\n",
       "199995  4.8579  14.7625 -2.7239  6.9937  2.6802   6.1565  14.3201  17.4594   \n",
       "199996  5.6888  18.8862  5.0915  6.3545  3.2618  -2.0445  13.8246   6.6547   \n",
       "199997  5.3883  18.3794  1.6603  5.7341  9.8596  -0.3412  14.0675  13.9975   \n",
       "199998  4.1959  16.8280  5.3208  8.9032  5.5000 -13.1346  14.3051   4.2644   \n",
       "199999  4.8879  14.4892 -0.5902  7.8362  8.4796  -5.8960  13.8333   2.4590   \n",
       "\n",
       "         var_14   var_15   var_16   var_17   var_18   var_19   var_20  \\\n",
       "199995   5.3712  14.8984   5.8064 -10.0334  16.4220  30.7786  22.7696   \n",
       "199996   5.0309  14.1999   8.5685   3.8190  11.4428  19.6761  22.4473   \n",
       "199997   6.2574  15.1456   8.1250  -7.3642  15.4767  15.9472  16.6580   \n",
       "199998  11.1258  14.7810  10.7184 -11.1223  14.3151  11.8348  12.2356   \n",
       "199999   7.8881  14.8566  10.4665 -11.8235  25.2430  20.9039  19.0743   \n",
       "\n",
       "         var_21  var_22  var_23   var_24   var_25   var_26  var_27  var_28  \\\n",
       "199995   9.8526  4.0207  3.8469  13.1617  13.2522 -12.4547 -1.9127  5.3696   \n",
       "199996  16.6921  5.5064  3.6160  10.4145  13.5557   0.0592 -1.8571  6.3351   \n",
       "199997  16.0286  0.6066  3.5773   4.4758  13.8995  -9.1787 -2.3696  4.6504   \n",
       "199998  20.9599  1.1101  3.0801   6.2626  14.0242   3.6157 -1.3483  6.5041   \n",
       "199999  15.5896 -2.1275  2.9672   2.0289  13.9641  -2.7192  0.9102  4.1522   \n",
       "\n",
       "        var_29   var_30   var_31  var_32   var_33   var_34  var_35  var_36  \\\n",
       "199995  6.9779 -17.9649  10.7856  0.2776   5.8071  10.4332  8.6681  0.9646   \n",
       "199996  6.4782 -15.0057  11.0107  1.1463  11.1037  11.7266  2.2244  1.3367   \n",
       "199997  7.7074 -14.7738   9.6374 -0.4069  11.3680  11.3627  6.7611  7.6036   \n",
       "199998  6.5306 -20.2249  10.7930 -5.3353   9.9596  11.4595  8.1176  2.5997   \n",
       "199999  3.9101  -8.6119  11.5158  2.8218  11.5082  11.2149  5.4528 -0.5887   \n",
       "\n",
       "         var_37   var_38  var_39   var_40   var_41   var_42   var_43   var_44  \\\n",
       "199995  11.2181  11.8660  6.3732  -2.7215  13.3420  11.1627  11.6436  18.4112   \n",
       "199996   3.8213   2.7258  4.1475 -12.1092  23.2927  11.1598  11.4884   4.9641   \n",
       "199997   8.1942   6.4839 -4.3884  -5.9079   5.0280  11.5624  11.4365  12.9517   \n",
       "199998   3.1370   3.5781 -1.5041   1.9454  17.6266  10.1011  11.5196  15.5804   \n",
       "199999   2.8780   7.0659  5.8230  -2.1438   1.5189  11.0499  11.6297   9.2466   \n",
       "\n",
       "         var_45   var_46   var_47   var_48  ...  var_150  var_151  var_152  \\\n",
       "199995 -21.3080  10.0185 -32.6883   3.5985  ...  20.0401  15.7083   5.0690   \n",
       "199996 -48.3446  12.1540   8.2999  10.1927  ...  14.8000   9.1898   4.2972   \n",
       "199997 -18.5914  12.8493   3.0741  21.9976  ...  11.9457  10.7800   4.8995   \n",
       "199998 -40.0510  13.1581  -7.2331   9.7570  ...  16.8626   9.3239   7.4385   \n",
       "199999 -21.8501  11.9929 -24.7056  13.6930  ...  17.4201  12.3167   8.9068   \n",
       "\n",
       "        var_153  var_154  var_155  var_156  var_157  var_158  var_159  \\\n",
       "199995  20.4789   5.6559   7.0860  11.3302  -4.6820  12.0228  11.1629   \n",
       "199996  20.3867  13.5508  -7.4707  13.2292  -3.5045   9.0808   8.0147   \n",
       "199997  16.5055   0.9823   9.0450  12.2711 -16.1130  11.0062   7.5311   \n",
       "199998  14.7483  13.3585  -4.1428  14.1710  -6.1347   9.6632  17.8940   \n",
       "199999  20.6013  13.3664 -10.3179  12.5572  -9.7303   9.5033  12.4379   \n",
       "\n",
       "        var_160  var_161  var_162  var_163  var_164  var_165  var_166  \\\n",
       "199995  21.4193   5.3269   6.8510  15.7062  -3.0267  21.8848   3.5995   \n",
       "199996  25.5856   5.6828   3.9202  10.5955  -2.9063  18.2834   3.0946   \n",
       "199997  31.7520   5.7419   7.3339   9.1077  -1.9905  11.9434   3.2239   \n",
       "199998  28.5510   5.6456   3.5223   7.3218  -0.4352  20.1410   3.2078   \n",
       "199999  26.7010   5.8830   3.8301  15.0473   4.8121  17.1152   2.4527   \n",
       "\n",
       "        var_167  var_168  var_169  var_170  var_171  var_172  var_173  \\\n",
       "199995  -2.5442   3.7888   6.6096  -6.3101   2.2246  29.0310   5.0331   \n",
       "199996 -13.0401  10.6693   5.4541   0.0289   1.5879   8.3102   6.5891   \n",
       "199997  -9.1386   4.1733   5.9515  -5.8944   2.6633  30.7599 -13.1990   \n",
       "199998  -3.3613   2.5097   5.3997  -5.8704  -0.7192  22.2659   2.2206   \n",
       "199999  -6.0539   5.3711   6.1772   0.9131   0.3922  15.0415  -1.4703   \n",
       "\n",
       "        var_174  var_175  var_176  var_177  var_178  var_179  var_180  \\\n",
       "199995  26.2206  14.3811  -4.5834   9.8823  12.3426   7.3865 -11.8810   \n",
       "199996  20.1379  14.4782  -7.6099   8.8498 -11.0107  -1.7399  -1.4862   \n",
       "199997  18.9432  15.5096  -6.8353  11.1724  -0.5545  -2.9957  -2.1730   \n",
       "199998  13.2572   9.2657 -12.6796  10.6895 -15.4493   2.5477   2.1028   \n",
       "199999  30.1926  10.3408  -5.4143  13.9104   1.6210   2.0273  -2.9853   \n",
       "\n",
       "        var_181  var_182  var_183  var_184  var_185  var_186  var_187  \\\n",
       "199995  10.4412  -0.9849  10.9760  12.3183  -2.7719   5.8820   8.5245   \n",
       "199996  10.0511 -15.1250   7.5211  26.2435  -0.8879   6.4135  -1.1382   \n",
       "199997  11.1961  13.0570  17.0153   1.7004  -7.2363   8.1679 -15.7536   \n",
       "199998  11.3081  -0.9160  15.2015  -0.9905 -13.6271  11.5390 -24.8621   \n",
       "199999  10.4163 -12.1494  10.0160  13.3454  -8.1344   4.6123   3.4033   \n",
       "\n",
       "        var_188  var_189  var_190  var_191  var_192  var_193  var_194  \\\n",
       "199995  17.5356   1.0903   2.0544   9.6849   4.6734  -1.3660  12.8721   \n",
       "199996  15.4816   1.7106   5.0071   6.6548   1.8197   2.4104  18.9037   \n",
       "199997  16.3071  -1.0926   5.1536   2.6498   2.4937  -0.0637  20.0609   \n",
       "199998   6.6792   0.8885   3.4259   8.5012   2.2713   5.7621  17.0056   \n",
       "199999  16.8310   0.6155   0.1398   9.2828   1.3601   4.8985  20.0926   \n",
       "\n",
       "        var_195  var_196  var_197  var_198  var_199  \n",
       "199995   1.2013  -4.6195   9.1568  18.2102   4.8801  \n",
       "199996  -0.9337   2.9995   9.1112  18.1740 -20.7689  \n",
       "199997  -1.1742  -4.1524   9.1933  11.7905 -22.2762  \n",
       "199998   1.1763  -2.3761   8.1079   8.7735  -0.2122  \n",
       "199999  -1.3048  -2.5981  10.3378  14.3340  -7.7094  \n",
       "\n",
       "[5 rows x 201 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "296951d5-c0c3-49c4-95aa-811e19bcf6b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200000 entries, 0 to 199999\n",
      "Columns: 202 entries, ID_code to var_199\n",
      "dtypes: float64(200), int64(1), object(1)\n",
      "memory usage: 308.2+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b920656e-7db9-457d-8690-5343cba43c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()[train.isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9cd1494-8c7c-4537-a97d-ba3796fd1f3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()[train.isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5ae11f-152b-4657-8e60-03bc8713440c",
   "metadata": {},
   "source": [
    "The dataset does not contain any missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cafc912b-507d-41f2-8d33-b4bede900785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 202)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a7de5693-d4b3-47b7-accf-215db5d4bbfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 201)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "22856630-5903-4b2a-a629-1bdf67a39813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    179902\n",
       "1     20098\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d4eab6c2-ff5a-4ad3-87ef-216efd55027f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['target'].value_counts().plot(kind='bar')\n",
    "plt.title('Target Distribution')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f696b64-87ed-4e49-9643-385c202729dd",
   "metadata": {},
   "source": [
    "The dataset shows a clear class imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "31357616-932d-4f53-9fa8-68233b5bf823",
   "metadata": {},
   "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>target</th>\n",
       "      <th>var_0</th>\n",
       "      <th>var_1</th>\n",
       "      <th>var_2</th>\n",
       "      <th>var_3</th>\n",
       "      <th>var_4</th>\n",
       "      <th>var_5</th>\n",
       "      <th>var_6</th>\n",
       "      <th>var_7</th>\n",
       "      <th>var_8</th>\n",
       "      <th>var_9</th>\n",
       "      <th>var_10</th>\n",
       "      <th>var_11</th>\n",
       "      <th>var_12</th>\n",
       "      <th>var_13</th>\n",
       "      <th>var_14</th>\n",
       "      <th>var_15</th>\n",
       "      <th>var_16</th>\n",
       "      <th>var_17</th>\n",
       "      <th>var_18</th>\n",
       "      <th>var_19</th>\n",
       "      <th>var_20</th>\n",
       "      <th>var_21</th>\n",
       "      <th>var_22</th>\n",
       "      <th>var_23</th>\n",
       "      <th>var_24</th>\n",
       "      <th>var_25</th>\n",
       "      <th>var_26</th>\n",
       "      <th>var_27</th>\n",
       "      <th>var_28</th>\n",
       "      <th>var_29</th>\n",
       "      <th>var_30</th>\n",
       "      <th>var_31</th>\n",
       "      <th>var_32</th>\n",
       "      <th>var_33</th>\n",
       "      <th>var_34</th>\n",
       "      <th>var_35</th>\n",
       "      <th>var_36</th>\n",
       "      <th>var_37</th>\n",
       "      <th>var_38</th>\n",
       "      <th>var_39</th>\n",
       "      <th>var_40</th>\n",
       "      <th>var_41</th>\n",
       "      <th>var_42</th>\n",
       "      <th>var_43</th>\n",
       "      <th>var_44</th>\n",
       "      <th>var_45</th>\n",
       "      <th>var_46</th>\n",
       "      <th>var_47</th>\n",
       "      <th>var_48</th>\n",
       "      <th>...</th>\n",
       "      <th>var_150</th>\n",
       "      <th>var_151</th>\n",
       "      <th>var_152</th>\n",
       "      <th>var_153</th>\n",
       "      <th>var_154</th>\n",
       "      <th>var_155</th>\n",
       "      <th>var_156</th>\n",
       "      <th>var_157</th>\n",
       "      <th>var_158</th>\n",
       "      <th>var_159</th>\n",
       "      <th>var_160</th>\n",
       "      <th>var_161</th>\n",
       "      <th>var_162</th>\n",
       "      <th>var_163</th>\n",
       "      <th>var_164</th>\n",
       "      <th>var_165</th>\n",
       "      <th>var_166</th>\n",
       "      <th>var_167</th>\n",
       "      <th>var_168</th>\n",
       "      <th>var_169</th>\n",
       "      <th>var_170</th>\n",
       "      <th>var_171</th>\n",
       "      <th>var_172</th>\n",
       "      <th>var_173</th>\n",
       "      <th>var_174</th>\n",
       "      <th>var_175</th>\n",
       "      <th>var_176</th>\n",
       "      <th>var_177</th>\n",
       "      <th>var_178</th>\n",
       "      <th>var_179</th>\n",
       "      <th>var_180</th>\n",
       "      <th>var_181</th>\n",
       "      <th>var_182</th>\n",
       "      <th>var_183</th>\n",
       "      <th>var_184</th>\n",
       "      <th>var_185</th>\n",
       "      <th>var_186</th>\n",
       "      <th>var_187</th>\n",
       "      <th>var_188</th>\n",
       "      <th>var_189</th>\n",
       "      <th>var_190</th>\n",
       "      <th>var_191</th>\n",
       "      <th>var_192</th>\n",
       "      <th>var_193</th>\n",
       "      <th>var_194</th>\n",
       "      <th>var_195</th>\n",
       "      <th>var_196</th>\n",
       "      <th>var_197</th>\n",
       "      <th>var_198</th>\n",
       "      <th>var_199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.100490</td>\n",
       "      <td>10.679914</td>\n",
       "      <td>-1.627622</td>\n",
       "      <td>10.715192</td>\n",
       "      <td>6.796529</td>\n",
       "      <td>11.078333</td>\n",
       "      <td>-5.065317</td>\n",
       "      <td>5.408949</td>\n",
       "      <td>16.545850</td>\n",
       "      <td>0.284162</td>\n",
       "      <td>7.567236</td>\n",
       "      <td>0.394340</td>\n",
       "      <td>-3.245596</td>\n",
       "      <td>14.023978</td>\n",
       "      <td>8.530232</td>\n",
       "      <td>7.537606</td>\n",
       "      <td>14.573126</td>\n",
       "      <td>9.333264</td>\n",
       "      <td>-5.696731</td>\n",
       "      <td>15.244013</td>\n",
       "      <td>12.438567</td>\n",
       "      <td>13.290894</td>\n",
       "      <td>17.257883</td>\n",
       "      <td>4.305430</td>\n",
       "      <td>3.019540</td>\n",
       "      <td>10.584400</td>\n",
       "      <td>13.667496</td>\n",
       "      <td>-4.055133</td>\n",
       "      <td>-1.137908</td>\n",
       "      <td>5.532980</td>\n",
       "      <td>5.053874</td>\n",
       "      <td>-7.687740</td>\n",
       "      <td>10.393046</td>\n",
       "      <td>-0.512886</td>\n",
       "      <td>14.774147</td>\n",
       "      <td>11.434250</td>\n",
       "      <td>3.842499</td>\n",
       "      <td>2.187230</td>\n",
       "      <td>5.868899</td>\n",
       "      <td>10.642131</td>\n",
       "      <td>0.662956</td>\n",
       "      <td>-6.725505</td>\n",
       "      <td>9.299858</td>\n",
       "      <td>11.222356</td>\n",
       "      <td>11.569954</td>\n",
       "      <td>8.948289</td>\n",
       "      <td>-12.699667</td>\n",
       "      <td>11.326488</td>\n",
       "      <td>-12.471737</td>\n",
       "      <td>14.704713</td>\n",
       "      <td>...</td>\n",
       "      <td>16.817671</td>\n",
       "      <td>10.141542</td>\n",
       "      <td>7.633199</td>\n",
       "      <td>16.727902</td>\n",
       "      <td>6.974955</td>\n",
       "      <td>-2.074128</td>\n",
       "      <td>13.209272</td>\n",
       "      <td>-4.813552</td>\n",
       "      <td>17.914591</td>\n",
       "      <td>10.223282</td>\n",
       "      <td>24.259300</td>\n",
       "      <td>5.633293</td>\n",
       "      <td>5.362896</td>\n",
       "      <td>11.002170</td>\n",
       "      <td>-2.871906</td>\n",
       "      <td>19.315753</td>\n",
       "      <td>2.963335</td>\n",
       "      <td>-4.151155</td>\n",
       "      <td>4.937124</td>\n",
       "      <td>5.636008</td>\n",
       "      <td>-0.004962</td>\n",
       "      <td>-0.831777</td>\n",
       "      <td>19.817094</td>\n",
       "      <td>-0.677967</td>\n",
       "      <td>20.210677</td>\n",
       "      <td>11.640613</td>\n",
       "      <td>-2.799585</td>\n",
       "      <td>11.882933</td>\n",
       "      <td>-1.014064</td>\n",
       "      <td>2.591444</td>\n",
       "      <td>-2.741666</td>\n",
       "      <td>10.085518</td>\n",
       "      <td>0.719109</td>\n",
       "      <td>8.769088</td>\n",
       "      <td>12.756676</td>\n",
       "      <td>-3.983261</td>\n",
       "      <td>8.970274</td>\n",
       "      <td>-10.335043</td>\n",
       "      <td>15.377174</td>\n",
       "      <td>0.746072</td>\n",
       "      <td>3.234440</td>\n",
       "      <td>7.438408</td>\n",
       "      <td>1.927839</td>\n",
       "      <td>3.331774</td>\n",
       "      <td>17.993784</td>\n",
       "      <td>-0.142088</td>\n",
       "      <td>2.303335</td>\n",
       "      <td>8.908158</td>\n",
       "      <td>15.870720</td>\n",
       "      <td>-3.326537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.300653</td>\n",
       "      <td>3.040051</td>\n",
       "      <td>4.050044</td>\n",
       "      <td>2.640894</td>\n",
       "      <td>2.043319</td>\n",
       "      <td>1.623150</td>\n",
       "      <td>7.863267</td>\n",
       "      <td>0.866607</td>\n",
       "      <td>3.418076</td>\n",
       "      <td>3.332634</td>\n",
       "      <td>1.235070</td>\n",
       "      <td>5.500793</td>\n",
       "      <td>5.970253</td>\n",
       "      <td>0.190059</td>\n",
       "      <td>4.639536</td>\n",
       "      <td>2.247908</td>\n",
       "      <td>0.411711</td>\n",
       "      <td>2.557421</td>\n",
       "      <td>6.712612</td>\n",
       "      <td>7.851370</td>\n",
       "      <td>7.996694</td>\n",
       "      <td>5.876254</td>\n",
       "      <td>8.196564</td>\n",
       "      <td>2.847958</td>\n",
       "      <td>0.526893</td>\n",
       "      <td>3.777245</td>\n",
       "      <td>0.285535</td>\n",
       "      <td>5.922210</td>\n",
       "      <td>1.523714</td>\n",
       "      <td>0.783367</td>\n",
       "      <td>2.615942</td>\n",
       "      <td>7.965198</td>\n",
       "      <td>2.159891</td>\n",
       "      <td>2.587830</td>\n",
       "      <td>4.322325</td>\n",
       "      <td>0.541614</td>\n",
       "      <td>5.179559</td>\n",
       "      <td>3.119978</td>\n",
       "      <td>2.249730</td>\n",
       "      <td>4.278903</td>\n",
       "      <td>4.068845</td>\n",
       "      <td>8.279259</td>\n",
       "      <td>5.938088</td>\n",
       "      <td>0.695991</td>\n",
       "      <td>0.309599</td>\n",
       "      <td>5.903073</td>\n",
       "      <td>21.404912</td>\n",
       "      <td>2.860511</td>\n",
       "      <td>10.579862</td>\n",
       "      <td>11.384332</td>\n",
       "      <td>...</td>\n",
       "      <td>2.464157</td>\n",
       "      <td>3.962426</td>\n",
       "      <td>3.005373</td>\n",
       "      <td>2.014200</td>\n",
       "      <td>4.961678</td>\n",
       "      <td>5.771261</td>\n",
       "      <td>0.955140</td>\n",
       "      <td>5.570272</td>\n",
       "      <td>7.885579</td>\n",
       "      <td>4.122912</td>\n",
       "      <td>10.880263</td>\n",
       "      <td>0.217938</td>\n",
       "      <td>1.419612</td>\n",
       "      <td>5.262056</td>\n",
       "      <td>5.457784</td>\n",
       "      <td>5.024182</td>\n",
       "      <td>0.369684</td>\n",
       "      <td>7.798020</td>\n",
       "      <td>3.105986</td>\n",
       "      <td>0.369437</td>\n",
       "      <td>4.424621</td>\n",
       "      <td>5.378008</td>\n",
       "      <td>8.674171</td>\n",
       "      <td>5.966674</td>\n",
       "      <td>7.136427</td>\n",
       "      <td>2.892167</td>\n",
       "      <td>7.513939</td>\n",
       "      <td>2.628895</td>\n",
       "      <td>8.579810</td>\n",
       "      <td>2.798956</td>\n",
       "      <td>5.261243</td>\n",
       "      <td>1.371862</td>\n",
       "      <td>8.963434</td>\n",
       "      <td>4.474924</td>\n",
       "      <td>9.318280</td>\n",
       "      <td>4.725167</td>\n",
       "      <td>3.189759</td>\n",
       "      <td>11.574708</td>\n",
       "      <td>3.944604</td>\n",
       "      <td>0.976348</td>\n",
       "      <td>4.559922</td>\n",
       "      <td>3.023272</td>\n",
       "      <td>1.478423</td>\n",
       "      <td>3.992030</td>\n",
       "      <td>3.135162</td>\n",
       "      <td>1.429372</td>\n",
       "      <td>5.454369</td>\n",
       "      <td>0.921625</td>\n",
       "      <td>3.010945</td>\n",
       "      <td>10.438015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.408400</td>\n",
       "      <td>-15.043400</td>\n",
       "      <td>2.117100</td>\n",
       "      <td>-0.040200</td>\n",
       "      <td>5.074800</td>\n",
       "      <td>-32.562600</td>\n",
       "      <td>2.347300</td>\n",
       "      <td>5.349700</td>\n",
       "      <td>-10.505500</td>\n",
       "      <td>3.970500</td>\n",
       "      <td>-20.731300</td>\n",
       "      <td>-26.095000</td>\n",
       "      <td>13.434600</td>\n",
       "      <td>-6.011100</td>\n",
       "      <td>1.013300</td>\n",
       "      <td>13.076900</td>\n",
       "      <td>0.635100</td>\n",
       "      <td>-33.380200</td>\n",
       "      <td>-10.664200</td>\n",
       "      <td>-12.402500</td>\n",
       "      <td>-5.432200</td>\n",
       "      <td>-10.089000</td>\n",
       "      <td>-5.322500</td>\n",
       "      <td>1.209800</td>\n",
       "      <td>-0.678400</td>\n",
       "      <td>12.720000</td>\n",
       "      <td>-24.243100</td>\n",
       "      <td>-6.166800</td>\n",
       "      <td>2.089600</td>\n",
       "      <td>-4.787200</td>\n",
       "      <td>-34.798400</td>\n",
       "      <td>2.140600</td>\n",
       "      <td>-8.986100</td>\n",
       "      <td>1.508500</td>\n",
       "      <td>9.816900</td>\n",
       "      <td>-16.513600</td>\n",
       "      <td>-8.095100</td>\n",
       "      <td>-1.183400</td>\n",
       "      <td>-6.337100</td>\n",
       "      <td>-14.545700</td>\n",
       "      <td>-35.211700</td>\n",
       "      <td>-8.535900</td>\n",
       "      <td>8.859000</td>\n",
       "      <td>10.652800</td>\n",
       "      <td>-9.939600</td>\n",
       "      <td>-90.252500</td>\n",
       "      <td>1.206200</td>\n",
       "      <td>-47.686200</td>\n",
       "      <td>-23.902200</td>\n",
       "      <td>...</td>\n",
       "      <td>9.242000</td>\n",
       "      <td>-2.191500</td>\n",
       "      <td>-2.880000</td>\n",
       "      <td>11.030800</td>\n",
       "      <td>-8.196600</td>\n",
       "      <td>-21.840900</td>\n",
       "      <td>9.996500</td>\n",
       "      <td>-22.990400</td>\n",
       "      <td>-4.554400</td>\n",
       "      <td>-4.641600</td>\n",
       "      <td>-7.452200</td>\n",
       "      <td>4.852600</td>\n",
       "      <td>0.623100</td>\n",
       "      <td>-6.531700</td>\n",
       "      <td>-19.997700</td>\n",
       "      <td>3.816700</td>\n",
       "      <td>1.851200</td>\n",
       "      <td>-35.969500</td>\n",
       "      <td>-5.250200</td>\n",
       "      <td>4.258800</td>\n",
       "      <td>-14.506000</td>\n",
       "      <td>-22.479300</td>\n",
       "      <td>-11.453300</td>\n",
       "      <td>-22.748700</td>\n",
       "      <td>-2.995300</td>\n",
       "      <td>3.241500</td>\n",
       "      <td>-29.116500</td>\n",
       "      <td>4.952100</td>\n",
       "      <td>-29.273400</td>\n",
       "      <td>-7.856100</td>\n",
       "      <td>-22.037400</td>\n",
       "      <td>5.416500</td>\n",
       "      <td>-26.001100</td>\n",
       "      <td>-4.808200</td>\n",
       "      <td>-18.489700</td>\n",
       "      <td>-22.583300</td>\n",
       "      <td>-3.022300</td>\n",
       "      <td>-47.753600</td>\n",
       "      <td>4.412300</td>\n",
       "      <td>-2.554300</td>\n",
       "      <td>-14.093300</td>\n",
       "      <td>-2.691700</td>\n",
       "      <td>-3.814500</td>\n",
       "      <td>-11.783400</td>\n",
       "      <td>8.694400</td>\n",
       "      <td>-5.261000</td>\n",
       "      <td>-14.209600</td>\n",
       "      <td>5.960600</td>\n",
       "      <td>6.299300</td>\n",
       "      <td>-38.852800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.453850</td>\n",
       "      <td>-4.740025</td>\n",
       "      <td>8.722475</td>\n",
       "      <td>5.254075</td>\n",
       "      <td>9.883175</td>\n",
       "      <td>-11.200350</td>\n",
       "      <td>4.767700</td>\n",
       "      <td>13.943800</td>\n",
       "      <td>-2.317800</td>\n",
       "      <td>6.618800</td>\n",
       "      <td>-3.594950</td>\n",
       "      <td>-7.510600</td>\n",
       "      <td>13.894000</td>\n",
       "      <td>5.072800</td>\n",
       "      <td>5.781875</td>\n",
       "      <td>14.262800</td>\n",
       "      <td>7.452275</td>\n",
       "      <td>-10.476225</td>\n",
       "      <td>9.177950</td>\n",
       "      <td>6.276475</td>\n",
       "      <td>8.627800</td>\n",
       "      <td>11.551000</td>\n",
       "      <td>2.182400</td>\n",
       "      <td>2.634100</td>\n",
       "      <td>7.613000</td>\n",
       "      <td>13.456400</td>\n",
       "      <td>-8.321725</td>\n",
       "      <td>-2.307900</td>\n",
       "      <td>4.992100</td>\n",
       "      <td>3.171700</td>\n",
       "      <td>-13.766175</td>\n",
       "      <td>8.870000</td>\n",
       "      <td>-2.500875</td>\n",
       "      <td>11.456300</td>\n",
       "      <td>11.032300</td>\n",
       "      <td>0.116975</td>\n",
       "      <td>-0.007125</td>\n",
       "      <td>4.125475</td>\n",
       "      <td>7.591050</td>\n",
       "      <td>-2.199500</td>\n",
       "      <td>-12.831825</td>\n",
       "      <td>4.519575</td>\n",
       "      <td>10.713200</td>\n",
       "      <td>11.343800</td>\n",
       "      <td>5.313650</td>\n",
       "      <td>-28.730700</td>\n",
       "      <td>9.248750</td>\n",
       "      <td>-20.654525</td>\n",
       "      <td>6.351975</td>\n",
       "      <td>...</td>\n",
       "      <td>14.952200</td>\n",
       "      <td>7.064600</td>\n",
       "      <td>5.567900</td>\n",
       "      <td>15.233000</td>\n",
       "      <td>3.339900</td>\n",
       "      <td>-6.266025</td>\n",
       "      <td>12.475100</td>\n",
       "      <td>-8.939950</td>\n",
       "      <td>12.109200</td>\n",
       "      <td>7.243525</td>\n",
       "      <td>15.696125</td>\n",
       "      <td>5.470500</td>\n",
       "      <td>4.326100</td>\n",
       "      <td>7.029600</td>\n",
       "      <td>-7.094025</td>\n",
       "      <td>15.744550</td>\n",
       "      <td>2.699000</td>\n",
       "      <td>-9.643100</td>\n",
       "      <td>2.703200</td>\n",
       "      <td>5.374600</td>\n",
       "      <td>-3.258500</td>\n",
       "      <td>-4.720350</td>\n",
       "      <td>13.731775</td>\n",
       "      <td>-5.009525</td>\n",
       "      <td>15.064600</td>\n",
       "      <td>9.371600</td>\n",
       "      <td>-8.386500</td>\n",
       "      <td>9.808675</td>\n",
       "      <td>-7.395700</td>\n",
       "      <td>0.625575</td>\n",
       "      <td>-6.673900</td>\n",
       "      <td>9.084700</td>\n",
       "      <td>-6.064425</td>\n",
       "      <td>5.423100</td>\n",
       "      <td>5.663300</td>\n",
       "      <td>-7.360000</td>\n",
       "      <td>6.715200</td>\n",
       "      <td>-19.205125</td>\n",
       "      <td>12.501550</td>\n",
       "      <td>0.014900</td>\n",
       "      <td>-0.058825</td>\n",
       "      <td>5.157400</td>\n",
       "      <td>0.889775</td>\n",
       "      <td>0.584600</td>\n",
       "      <td>15.629800</td>\n",
       "      <td>-1.170700</td>\n",
       "      <td>-1.946925</td>\n",
       "      <td>8.252800</td>\n",
       "      <td>13.829700</td>\n",
       "      <td>-11.208475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.524750</td>\n",
       "      <td>-1.608050</td>\n",
       "      <td>10.580000</td>\n",
       "      <td>6.825000</td>\n",
       "      <td>11.108250</td>\n",
       "      <td>-4.833150</td>\n",
       "      <td>5.385100</td>\n",
       "      <td>16.456800</td>\n",
       "      <td>0.393700</td>\n",
       "      <td>7.629600</td>\n",
       "      <td>0.487300</td>\n",
       "      <td>-3.286950</td>\n",
       "      <td>14.025500</td>\n",
       "      <td>8.604250</td>\n",
       "      <td>7.520300</td>\n",
       "      <td>14.574100</td>\n",
       "      <td>9.232050</td>\n",
       "      <td>-5.666350</td>\n",
       "      <td>15.196250</td>\n",
       "      <td>12.453900</td>\n",
       "      <td>13.196800</td>\n",
       "      <td>17.234250</td>\n",
       "      <td>4.275150</td>\n",
       "      <td>3.008650</td>\n",
       "      <td>10.380350</td>\n",
       "      <td>13.662500</td>\n",
       "      <td>-4.196900</td>\n",
       "      <td>-1.132100</td>\n",
       "      <td>5.534850</td>\n",
       "      <td>4.950200</td>\n",
       "      <td>-7.411750</td>\n",
       "      <td>10.365650</td>\n",
       "      <td>-0.497650</td>\n",
       "      <td>14.576000</td>\n",
       "      <td>11.435200</td>\n",
       "      <td>3.917750</td>\n",
       "      <td>2.198000</td>\n",
       "      <td>5.900650</td>\n",
       "      <td>10.562700</td>\n",
       "      <td>0.672300</td>\n",
       "      <td>-6.617450</td>\n",
       "      <td>9.162650</td>\n",
       "      <td>11.243400</td>\n",
       "      <td>11.565000</td>\n",
       "      <td>9.437200</td>\n",
       "      <td>-12.547200</td>\n",
       "      <td>11.310750</td>\n",
       "      <td>-12.482400</td>\n",
       "      <td>14.559200</td>\n",
       "      <td>...</td>\n",
       "      <td>16.736950</td>\n",
       "      <td>10.127900</td>\n",
       "      <td>7.673700</td>\n",
       "      <td>16.649750</td>\n",
       "      <td>6.994050</td>\n",
       "      <td>-2.066100</td>\n",
       "      <td>13.184300</td>\n",
       "      <td>-4.868400</td>\n",
       "      <td>17.630450</td>\n",
       "      <td>10.217550</td>\n",
       "      <td>23.864500</td>\n",
       "      <td>5.633500</td>\n",
       "      <td>5.359700</td>\n",
       "      <td>10.788700</td>\n",
       "      <td>-2.637800</td>\n",
       "      <td>19.270800</td>\n",
       "      <td>2.960200</td>\n",
       "      <td>-4.011600</td>\n",
       "      <td>4.761600</td>\n",
       "      <td>5.634300</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>-0.807350</td>\n",
       "      <td>19.748000</td>\n",
       "      <td>-0.569750</td>\n",
       "      <td>20.206100</td>\n",
       "      <td>11.679800</td>\n",
       "      <td>-2.538450</td>\n",
       "      <td>11.737250</td>\n",
       "      <td>-0.942050</td>\n",
       "      <td>2.512300</td>\n",
       "      <td>-2.688800</td>\n",
       "      <td>10.036050</td>\n",
       "      <td>0.720200</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>12.521000</td>\n",
       "      <td>-3.946950</td>\n",
       "      <td>8.902150</td>\n",
       "      <td>-10.209750</td>\n",
       "      <td>15.239450</td>\n",
       "      <td>0.742600</td>\n",
       "      <td>3.203600</td>\n",
       "      <td>7.347750</td>\n",
       "      <td>1.901300</td>\n",
       "      <td>3.396350</td>\n",
       "      <td>17.957950</td>\n",
       "      <td>-0.172700</td>\n",
       "      <td>2.408900</td>\n",
       "      <td>8.888200</td>\n",
       "      <td>15.934050</td>\n",
       "      <td>-2.819550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.758200</td>\n",
       "      <td>1.358625</td>\n",
       "      <td>12.516700</td>\n",
       "      <td>8.324100</td>\n",
       "      <td>12.261125</td>\n",
       "      <td>0.924800</td>\n",
       "      <td>6.003000</td>\n",
       "      <td>19.102900</td>\n",
       "      <td>2.937900</td>\n",
       "      <td>8.584425</td>\n",
       "      <td>4.382925</td>\n",
       "      <td>0.852825</td>\n",
       "      <td>14.164200</td>\n",
       "      <td>12.274775</td>\n",
       "      <td>9.270425</td>\n",
       "      <td>14.874500</td>\n",
       "      <td>11.055900</td>\n",
       "      <td>-0.810775</td>\n",
       "      <td>21.013325</td>\n",
       "      <td>18.433300</td>\n",
       "      <td>17.879400</td>\n",
       "      <td>23.089050</td>\n",
       "      <td>6.293200</td>\n",
       "      <td>3.403800</td>\n",
       "      <td>13.479600</td>\n",
       "      <td>13.863700</td>\n",
       "      <td>-0.090200</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>6.093700</td>\n",
       "      <td>6.798925</td>\n",
       "      <td>-1.443450</td>\n",
       "      <td>11.885000</td>\n",
       "      <td>1.469100</td>\n",
       "      <td>18.097125</td>\n",
       "      <td>11.844400</td>\n",
       "      <td>7.487725</td>\n",
       "      <td>4.460400</td>\n",
       "      <td>7.542400</td>\n",
       "      <td>13.598925</td>\n",
       "      <td>3.637825</td>\n",
       "      <td>-0.880875</td>\n",
       "      <td>13.754800</td>\n",
       "      <td>11.756900</td>\n",
       "      <td>11.804600</td>\n",
       "      <td>13.087300</td>\n",
       "      <td>3.150525</td>\n",
       "      <td>13.318300</td>\n",
       "      <td>-4.244525</td>\n",
       "      <td>23.028650</td>\n",
       "      <td>...</td>\n",
       "      <td>18.682500</td>\n",
       "      <td>13.057600</td>\n",
       "      <td>9.817300</td>\n",
       "      <td>18.263900</td>\n",
       "      <td>10.766350</td>\n",
       "      <td>1.891750</td>\n",
       "      <td>13.929300</td>\n",
       "      <td>-0.988575</td>\n",
       "      <td>23.875325</td>\n",
       "      <td>13.094525</td>\n",
       "      <td>32.622850</td>\n",
       "      <td>5.792000</td>\n",
       "      <td>6.371200</td>\n",
       "      <td>14.623900</td>\n",
       "      <td>1.323600</td>\n",
       "      <td>23.024025</td>\n",
       "      <td>3.241500</td>\n",
       "      <td>1.318725</td>\n",
       "      <td>7.020025</td>\n",
       "      <td>5.905400</td>\n",
       "      <td>3.096400</td>\n",
       "      <td>2.956800</td>\n",
       "      <td>25.907725</td>\n",
       "      <td>3.619900</td>\n",
       "      <td>25.641225</td>\n",
       "      <td>13.745500</td>\n",
       "      <td>2.704400</td>\n",
       "      <td>13.931300</td>\n",
       "      <td>5.338750</td>\n",
       "      <td>4.391125</td>\n",
       "      <td>0.996200</td>\n",
       "      <td>11.011300</td>\n",
       "      <td>7.499175</td>\n",
       "      <td>12.127425</td>\n",
       "      <td>19.456150</td>\n",
       "      <td>-0.590650</td>\n",
       "      <td>11.193800</td>\n",
       "      <td>-1.466000</td>\n",
       "      <td>18.345225</td>\n",
       "      <td>1.482900</td>\n",
       "      <td>6.406200</td>\n",
       "      <td>9.512525</td>\n",
       "      <td>2.949500</td>\n",
       "      <td>6.205800</td>\n",
       "      <td>20.396525</td>\n",
       "      <td>0.829600</td>\n",
       "      <td>6.556725</td>\n",
       "      <td>9.593300</td>\n",
       "      <td>18.064725</td>\n",
       "      <td>4.836800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.315000</td>\n",
       "      <td>10.376800</td>\n",
       "      <td>19.353000</td>\n",
       "      <td>13.188300</td>\n",
       "      <td>16.671400</td>\n",
       "      <td>17.251600</td>\n",
       "      <td>8.447700</td>\n",
       "      <td>27.691800</td>\n",
       "      <td>10.151300</td>\n",
       "      <td>11.150600</td>\n",
       "      <td>18.670200</td>\n",
       "      <td>17.188700</td>\n",
       "      <td>14.654500</td>\n",
       "      <td>22.331500</td>\n",
       "      <td>14.937700</td>\n",
       "      <td>15.863300</td>\n",
       "      <td>17.950600</td>\n",
       "      <td>19.025900</td>\n",
       "      <td>41.748000</td>\n",
       "      <td>35.183000</td>\n",
       "      <td>31.285900</td>\n",
       "      <td>49.044300</td>\n",
       "      <td>14.594500</td>\n",
       "      <td>4.875200</td>\n",
       "      <td>25.446000</td>\n",
       "      <td>14.654600</td>\n",
       "      <td>15.675100</td>\n",
       "      <td>3.243100</td>\n",
       "      <td>8.787400</td>\n",
       "      <td>13.143100</td>\n",
       "      <td>15.651500</td>\n",
       "      <td>20.171900</td>\n",
       "      <td>6.787100</td>\n",
       "      <td>29.546600</td>\n",
       "      <td>13.287800</td>\n",
       "      <td>21.528900</td>\n",
       "      <td>14.245600</td>\n",
       "      <td>11.863800</td>\n",
       "      <td>29.823500</td>\n",
       "      <td>15.322300</td>\n",
       "      <td>18.105600</td>\n",
       "      <td>26.165800</td>\n",
       "      <td>13.469600</td>\n",
       "      <td>12.577900</td>\n",
       "      <td>34.196100</td>\n",
       "      <td>62.084400</td>\n",
       "      <td>21.293900</td>\n",
       "      <td>20.685400</td>\n",
       "      <td>54.273800</td>\n",
       "      <td>...</td>\n",
       "      <td>25.435700</td>\n",
       "      <td>21.124500</td>\n",
       "      <td>18.384600</td>\n",
       "      <td>24.007500</td>\n",
       "      <td>23.242800</td>\n",
       "      <td>16.831600</td>\n",
       "      <td>16.497000</td>\n",
       "      <td>11.972100</td>\n",
       "      <td>44.779500</td>\n",
       "      <td>25.120000</td>\n",
       "      <td>58.394200</td>\n",
       "      <td>6.309900</td>\n",
       "      <td>10.134400</td>\n",
       "      <td>27.564800</td>\n",
       "      <td>12.119300</td>\n",
       "      <td>38.332200</td>\n",
       "      <td>4.220400</td>\n",
       "      <td>21.276600</td>\n",
       "      <td>14.886100</td>\n",
       "      <td>7.089000</td>\n",
       "      <td>16.731900</td>\n",
       "      <td>17.917300</td>\n",
       "      <td>53.591900</td>\n",
       "      <td>18.855400</td>\n",
       "      <td>43.546800</td>\n",
       "      <td>20.854800</td>\n",
       "      <td>20.245200</td>\n",
       "      <td>20.596500</td>\n",
       "      <td>29.841300</td>\n",
       "      <td>13.448700</td>\n",
       "      <td>12.750500</td>\n",
       "      <td>14.393900</td>\n",
       "      <td>29.248700</td>\n",
       "      <td>23.704900</td>\n",
       "      <td>44.363400</td>\n",
       "      <td>12.997500</td>\n",
       "      <td>21.739200</td>\n",
       "      <td>22.786100</td>\n",
       "      <td>29.330300</td>\n",
       "      <td>4.034100</td>\n",
       "      <td>18.440900</td>\n",
       "      <td>16.716500</td>\n",
       "      <td>8.402400</td>\n",
       "      <td>18.281800</td>\n",
       "      <td>27.928800</td>\n",
       "      <td>4.272900</td>\n",
       "      <td>18.321500</td>\n",
       "      <td>12.000400</td>\n",
       "      <td>26.079100</td>\n",
       "      <td>28.500700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 201 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              target          var_0          var_1          var_2  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.100490      10.679914      -1.627622      10.715192   \n",
       "std         0.300653       3.040051       4.050044       2.640894   \n",
       "min         0.000000       0.408400     -15.043400       2.117100   \n",
       "25%         0.000000       8.453850      -4.740025       8.722475   \n",
       "50%         0.000000      10.524750      -1.608050      10.580000   \n",
       "75%         0.000000      12.758200       1.358625      12.516700   \n",
       "max         1.000000      20.315000      10.376800      19.353000   \n",
       "\n",
       "               var_3          var_4          var_5          var_6  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        6.796529      11.078333      -5.065317       5.408949   \n",
       "std         2.043319       1.623150       7.863267       0.866607   \n",
       "min        -0.040200       5.074800     -32.562600       2.347300   \n",
       "25%         5.254075       9.883175     -11.200350       4.767700   \n",
       "50%         6.825000      11.108250      -4.833150       5.385100   \n",
       "75%         8.324100      12.261125       0.924800       6.003000   \n",
       "max        13.188300      16.671400      17.251600       8.447700   \n",
       "\n",
       "               var_7          var_8          var_9         var_10  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       16.545850       0.284162       7.567236       0.394340   \n",
       "std         3.418076       3.332634       1.235070       5.500793   \n",
       "min         5.349700     -10.505500       3.970500     -20.731300   \n",
       "25%        13.943800      -2.317800       6.618800      -3.594950   \n",
       "50%        16.456800       0.393700       7.629600       0.487300   \n",
       "75%        19.102900       2.937900       8.584425       4.382925   \n",
       "max        27.691800      10.151300      11.150600      18.670200   \n",
       "\n",
       "              var_11         var_12         var_13         var_14  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -3.245596      14.023978       8.530232       7.537606   \n",
       "std         5.970253       0.190059       4.639536       2.247908   \n",
       "min       -26.095000      13.434600      -6.011100       1.013300   \n",
       "25%        -7.510600      13.894000       5.072800       5.781875   \n",
       "50%        -3.286950      14.025500       8.604250       7.520300   \n",
       "75%         0.852825      14.164200      12.274775       9.270425   \n",
       "max        17.188700      14.654500      22.331500      14.937700   \n",
       "\n",
       "              var_15         var_16         var_17         var_18  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       14.573126       9.333264      -5.696731      15.244013   \n",
       "std         0.411711       2.557421       6.712612       7.851370   \n",
       "min        13.076900       0.635100     -33.380200     -10.664200   \n",
       "25%        14.262800       7.452275     -10.476225       9.177950   \n",
       "50%        14.574100       9.232050      -5.666350      15.196250   \n",
       "75%        14.874500      11.055900      -0.810775      21.013325   \n",
       "max        15.863300      17.950600      19.025900      41.748000   \n",
       "\n",
       "              var_19         var_20         var_21         var_22  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       12.438567      13.290894      17.257883       4.305430   \n",
       "std         7.996694       5.876254       8.196564       2.847958   \n",
       "min       -12.402500      -5.432200     -10.089000      -5.322500   \n",
       "25%         6.276475       8.627800      11.551000       2.182400   \n",
       "50%        12.453900      13.196800      17.234250       4.275150   \n",
       "75%        18.433300      17.879400      23.089050       6.293200   \n",
       "max        35.183000      31.285900      49.044300      14.594500   \n",
       "\n",
       "              var_23         var_24         var_25         var_26  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        3.019540      10.584400      13.667496      -4.055133   \n",
       "std         0.526893       3.777245       0.285535       5.922210   \n",
       "min         1.209800      -0.678400      12.720000     -24.243100   \n",
       "25%         2.634100       7.613000      13.456400      -8.321725   \n",
       "50%         3.008650      10.380350      13.662500      -4.196900   \n",
       "75%         3.403800      13.479600      13.863700      -0.090200   \n",
       "max         4.875200      25.446000      14.654600      15.675100   \n",
       "\n",
       "              var_27         var_28         var_29         var_30  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -1.137908       5.532980       5.053874      -7.687740   \n",
       "std         1.523714       0.783367       2.615942       7.965198   \n",
       "min        -6.166800       2.089600      -4.787200     -34.798400   \n",
       "25%        -2.307900       4.992100       3.171700     -13.766175   \n",
       "50%        -1.132100       5.534850       4.950200      -7.411750   \n",
       "75%         0.015625       6.093700       6.798925      -1.443450   \n",
       "max         3.243100       8.787400      13.143100      15.651500   \n",
       "\n",
       "              var_31         var_32         var_33         var_34  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       10.393046      -0.512886      14.774147      11.434250   \n",
       "std         2.159891       2.587830       4.322325       0.541614   \n",
       "min         2.140600      -8.986100       1.508500       9.816900   \n",
       "25%         8.870000      -2.500875      11.456300      11.032300   \n",
       "50%        10.365650      -0.497650      14.576000      11.435200   \n",
       "75%        11.885000       1.469100      18.097125      11.844400   \n",
       "max        20.171900       6.787100      29.546600      13.287800   \n",
       "\n",
       "              var_35         var_36         var_37         var_38  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        3.842499       2.187230       5.868899      10.642131   \n",
       "std         5.179559       3.119978       2.249730       4.278903   \n",
       "min       -16.513600      -8.095100      -1.183400      -6.337100   \n",
       "25%         0.116975      -0.007125       4.125475       7.591050   \n",
       "50%         3.917750       2.198000       5.900650      10.562700   \n",
       "75%         7.487725       4.460400       7.542400      13.598925   \n",
       "max        21.528900      14.245600      11.863800      29.823500   \n",
       "\n",
       "              var_39         var_40         var_41         var_42  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.662956      -6.725505       9.299858      11.222356   \n",
       "std         4.068845       8.279259       5.938088       0.695991   \n",
       "min       -14.545700     -35.211700      -8.535900       8.859000   \n",
       "25%        -2.199500     -12.831825       4.519575      10.713200   \n",
       "50%         0.672300      -6.617450       9.162650      11.243400   \n",
       "75%         3.637825      -0.880875      13.754800      11.756900   \n",
       "max        15.322300      18.105600      26.165800      13.469600   \n",
       "\n",
       "              var_43         var_44         var_45         var_46  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       11.569954       8.948289     -12.699667      11.326488   \n",
       "std         0.309599       5.903073      21.404912       2.860511   \n",
       "min        10.652800      -9.939600     -90.252500       1.206200   \n",
       "25%        11.343800       5.313650     -28.730700       9.248750   \n",
       "50%        11.565000       9.437200     -12.547200      11.310750   \n",
       "75%        11.804600      13.087300       3.150525      13.318300   \n",
       "max        12.577900      34.196100      62.084400      21.293900   \n",
       "\n",
       "              var_47         var_48  ...        var_150        var_151  \\\n",
       "count  200000.000000  200000.000000  ...  200000.000000  200000.000000   \n",
       "mean      -12.471737      14.704713  ...      16.817671      10.141542   \n",
       "std        10.579862      11.384332  ...       2.464157       3.962426   \n",
       "min       -47.686200     -23.902200  ...       9.242000      -2.191500   \n",
       "25%       -20.654525       6.351975  ...      14.952200       7.064600   \n",
       "50%       -12.482400      14.559200  ...      16.736950      10.127900   \n",
       "75%        -4.244525      23.028650  ...      18.682500      13.057600   \n",
       "max        20.685400      54.273800  ...      25.435700      21.124500   \n",
       "\n",
       "             var_152        var_153        var_154        var_155  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        7.633199      16.727902       6.974955      -2.074128   \n",
       "std         3.005373       2.014200       4.961678       5.771261   \n",
       "min        -2.880000      11.030800      -8.196600     -21.840900   \n",
       "25%         5.567900      15.233000       3.339900      -6.266025   \n",
       "50%         7.673700      16.649750       6.994050      -2.066100   \n",
       "75%         9.817300      18.263900      10.766350       1.891750   \n",
       "max        18.384600      24.007500      23.242800      16.831600   \n",
       "\n",
       "             var_156        var_157        var_158        var_159  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       13.209272      -4.813552      17.914591      10.223282   \n",
       "std         0.955140       5.570272       7.885579       4.122912   \n",
       "min         9.996500     -22.990400      -4.554400      -4.641600   \n",
       "25%        12.475100      -8.939950      12.109200       7.243525   \n",
       "50%        13.184300      -4.868400      17.630450      10.217550   \n",
       "75%        13.929300      -0.988575      23.875325      13.094525   \n",
       "max        16.497000      11.972100      44.779500      25.120000   \n",
       "\n",
       "             var_160        var_161        var_162        var_163  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       24.259300       5.633293       5.362896      11.002170   \n",
       "std        10.880263       0.217938       1.419612       5.262056   \n",
       "min        -7.452200       4.852600       0.623100      -6.531700   \n",
       "25%        15.696125       5.470500       4.326100       7.029600   \n",
       "50%        23.864500       5.633500       5.359700      10.788700   \n",
       "75%        32.622850       5.792000       6.371200      14.623900   \n",
       "max        58.394200       6.309900      10.134400      27.564800   \n",
       "\n",
       "             var_164        var_165        var_166        var_167  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -2.871906      19.315753       2.963335      -4.151155   \n",
       "std         5.457784       5.024182       0.369684       7.798020   \n",
       "min       -19.997700       3.816700       1.851200     -35.969500   \n",
       "25%        -7.094025      15.744550       2.699000      -9.643100   \n",
       "50%        -2.637800      19.270800       2.960200      -4.011600   \n",
       "75%         1.323600      23.024025       3.241500       1.318725   \n",
       "max        12.119300      38.332200       4.220400      21.276600   \n",
       "\n",
       "             var_168        var_169        var_170        var_171  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        4.937124       5.636008      -0.004962      -0.831777   \n",
       "std         3.105986       0.369437       4.424621       5.378008   \n",
       "min        -5.250200       4.258800     -14.506000     -22.479300   \n",
       "25%         2.703200       5.374600      -3.258500      -4.720350   \n",
       "50%         4.761600       5.634300       0.002800      -0.807350   \n",
       "75%         7.020025       5.905400       3.096400       2.956800   \n",
       "max        14.886100       7.089000      16.731900      17.917300   \n",
       "\n",
       "             var_172        var_173        var_174        var_175  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       19.817094      -0.677967      20.210677      11.640613   \n",
       "std         8.674171       5.966674       7.136427       2.892167   \n",
       "min       -11.453300     -22.748700      -2.995300       3.241500   \n",
       "25%        13.731775      -5.009525      15.064600       9.371600   \n",
       "50%        19.748000      -0.569750      20.206100      11.679800   \n",
       "75%        25.907725       3.619900      25.641225      13.745500   \n",
       "max        53.591900      18.855400      43.546800      20.854800   \n",
       "\n",
       "             var_176        var_177        var_178        var_179  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -2.799585      11.882933      -1.014064       2.591444   \n",
       "std         7.513939       2.628895       8.579810       2.798956   \n",
       "min       -29.116500       4.952100     -29.273400      -7.856100   \n",
       "25%        -8.386500       9.808675      -7.395700       0.625575   \n",
       "50%        -2.538450      11.737250      -0.942050       2.512300   \n",
       "75%         2.704400      13.931300       5.338750       4.391125   \n",
       "max        20.245200      20.596500      29.841300      13.448700   \n",
       "\n",
       "             var_180        var_181        var_182        var_183  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -2.741666      10.085518       0.719109       8.769088   \n",
       "std         5.261243       1.371862       8.963434       4.474924   \n",
       "min       -22.037400       5.416500     -26.001100      -4.808200   \n",
       "25%        -6.673900       9.084700      -6.064425       5.423100   \n",
       "50%        -2.688800      10.036050       0.720200       8.600000   \n",
       "75%         0.996200      11.011300       7.499175      12.127425   \n",
       "max        12.750500      14.393900      29.248700      23.704900   \n",
       "\n",
       "             var_184        var_185        var_186        var_187  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       12.756676      -3.983261       8.970274     -10.335043   \n",
       "std         9.318280       4.725167       3.189759      11.574708   \n",
       "min       -18.489700     -22.583300      -3.022300     -47.753600   \n",
       "25%         5.663300      -7.360000       6.715200     -19.205125   \n",
       "50%        12.521000      -3.946950       8.902150     -10.209750   \n",
       "75%        19.456150      -0.590650      11.193800      -1.466000   \n",
       "max        44.363400      12.997500      21.739200      22.786100   \n",
       "\n",
       "             var_188        var_189        var_190        var_191  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       15.377174       0.746072       3.234440       7.438408   \n",
       "std         3.944604       0.976348       4.559922       3.023272   \n",
       "min         4.412300      -2.554300     -14.093300      -2.691700   \n",
       "25%        12.501550       0.014900      -0.058825       5.157400   \n",
       "50%        15.239450       0.742600       3.203600       7.347750   \n",
       "75%        18.345225       1.482900       6.406200       9.512525   \n",
       "max        29.330300       4.034100      18.440900      16.716500   \n",
       "\n",
       "             var_192        var_193        var_194        var_195  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        1.927839       3.331774      17.993784      -0.142088   \n",
       "std         1.478423       3.992030       3.135162       1.429372   \n",
       "min        -3.814500     -11.783400       8.694400      -5.261000   \n",
       "25%         0.889775       0.584600      15.629800      -1.170700   \n",
       "50%         1.901300       3.396350      17.957950      -0.172700   \n",
       "75%         2.949500       6.205800      20.396525       0.829600   \n",
       "max         8.402400      18.281800      27.928800       4.272900   \n",
       "\n",
       "             var_196        var_197        var_198        var_199  \n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000  \n",
       "mean        2.303335       8.908158      15.870720      -3.326537  \n",
       "std         5.454369       0.921625       3.010945      10.438015  \n",
       "min       -14.209600       5.960600       6.299300     -38.852800  \n",
       "25%        -1.946925       8.252800      13.829700     -11.208475  \n",
       "50%         2.408900       8.888200      15.934050      -2.819550  \n",
       "75%         6.556725       9.593300      18.064725       4.836800  \n",
       "max        18.321500      12.000400      26.079100      28.500700  \n",
       "\n",
       "[8 rows x 201 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d2fea7-ebdd-4c84-b388-5407eb04e546",
   "metadata": {},
   "source": [
    "### Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "597de410-2652-4b5e-b2b5-3507317400ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=train.drop(columns=['ID_code','target'])\n",
    "y=train['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6c78b9d8-9396-43a2-9f23-87adcfb5da33",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_val, y_train, y_val=train_test_split(x,y, test_size=.20, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993991de-484b-4887-8e8f-47cdecf56d51",
   "metadata": {},
   "source": [
    "### 1th Model LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8c23b598-b304-4205-97d4-f84cb155443c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LogisticRegression(max_iter=1000,class_weight='balanced',n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "40906d80-747a-49e9-9071-dc3614f3e61b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;, max_iter=1000, n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">penalty&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">dual&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">C&nbsp;</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">class_weight&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;balanced&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">solver&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">1000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('multi_class',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">multi_class&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "LogisticRegression(class_weight='balanced', max_iter=1000, n_jobs=-1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7a22bd0a-fbcb-4178-a7a1-bc025bf2bc1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "lrtahmin=lr.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a968c77f-1e91-48b6-a5c2-65551cba652f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78105"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_val,lrtahmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e7e14850-45bf-441b-87f8-703cddf9054b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_val, lrtahmin).astype(int)\n",
    "\n",
    "sns.heatmap(cm, annot=True, fmt='d')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e26009c8-8c7e-4732-b27f-f9137b0e4873",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.78      0.87     35980\n",
      "           1       0.28      0.77      0.41      4020\n",
      "\n",
      "    accuracy                           0.78     40000\n",
      "   macro avg       0.63      0.78      0.64     40000\n",
      "weighted avg       0.90      0.78      0.82     40000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val,lrtahmin))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e5e882b6-11ea-4f54-a48a-c5b9431a2d23",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob=lr.predict_proba(x_val)[:, 1]\n",
    "threshold=0.30\n",
    "y_pred_thr=(y_prob >= threshold).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d480b8ab-0c07-409c-afa1-4fe2ba113281",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold: 0.3\n",
      "Accuracy: 0.6146\n",
      "[[20951 15029]\n",
      " [  387  3633]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.58      0.73     35980\n",
      "           1       0.19      0.90      0.32      4020\n",
      "\n",
      "    accuracy                           0.61     40000\n",
      "   macro avg       0.59      0.74      0.53     40000\n",
      "weighted avg       0.90      0.61      0.69     40000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Threshold:\", threshold)\n",
    "print(\"Accuracy:\", accuracy_score(y_val, y_pred_thr))\n",
    "print(confusion_matrix(y_val, y_pred_thr))\n",
    "print(classification_report(y_val, y_pred_thr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "23fa1151-4eaf-448a-afc5-dd262394633c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8577954377639319"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc=roc_auc_score(y_val, y_prob)\n",
    "roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e11f276a-23ac-40ef-8fc9-fc9a0748bc69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _=roc_curve(y_val, y_prob)\n",
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "plt.plot(fpr, tpr, label=f'AUC = {roc_auc:.4f}')\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve – Logistic Regression')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8766b532-b405-4f4d-9fca-10efacaed4b1",
   "metadata": {},
   "source": [
    "### 2th Model LGBMClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c5f469d6-dbc6-45cc-b3ab-4eee33a65541",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbc=lgb.LGBMClassifier(\n",
    "    n_estimators=500,\n",
    "    learning_rate=0.05,\n",
    "    num_leaves=31,\n",
    "    subsample=0.8,\n",
    "    colsample_bytree=0.8,\n",
    "    objective='binary',\n",
    "    class_weight='balanced',\n",
    "    random_state=42,\n",
    "    n_jobs=-1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9f82c5f6-bd5e-4a9e-8394-3874e4510cc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 16078, number of negative: 143922\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033101 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 51000\n",
      "[LightGBM] [Info] Number of data points in the train set: 160000, number of used features: 200\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n",
      "[LightGBM] [Info] Start training from score 0.000000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(class_weight=&#x27;balanced&#x27;, colsample_bytree=0.8,\n",
       "               learning_rate=0.05, n_estimators=500, n_jobs=-1,\n",
       "               objective=&#x27;binary&#x27;, random_state=42, subsample=0.8)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('boosting_type',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">boosting_type&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;gbdt&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('num_leaves',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">num_leaves&nbsp;</td>\n",
       "            <td class=\"value\">31</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_depth&nbsp;</td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">0.05</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
       "            <td class=\"value\">500</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample_for_bin',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample_for_bin&nbsp;</td>\n",
       "            <td class=\"value\">200000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('objective',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">objective&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;binary&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">class_weight&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;balanced&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_split_gain',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_split_gain&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_child_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_child_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_child_samples&nbsp;</td>\n",
       "            <td class=\"value\">20</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample&nbsp;</td>\n",
       "            <td class=\"value\">0.8</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample_freq',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample_freq&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
       "            <td class=\"value\">0.8</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_lambda',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('importance_type',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">importance_type&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;split&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "LGBMClassifier(class_weight='balanced', colsample_bytree=0.8,\n",
       "               learning_rate=0.05, n_estimators=500, n_jobs=-1,\n",
       "               objective='binary', random_state=42, subsample=0.8)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "94692678-ec0c-4568-99a3-bc59649e98d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbctahmin=lgbc.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ccc2d4fb-e0e1-4ee0-92c7-7a46cdaf98dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8529"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_val,lgbctahmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0b086f34-e988-4208-803e-54baca65b298",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob_lgbc=lgbc.predict_proba(x_val)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "23f7b097-b589-439d-80bf-2550ee8ccb4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold=0.30\n",
    "y_pred_lgbc=(y_prob_lgbc >= threshold).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96aef198-b5ac-43c7-8fac-ad36efc39bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "roc_auc_lgbc=roc_auc_score(y_val, y_val_prob_lgbc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f02e5cc5-b0f5-4b6a-b8df-78eb256301a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold: 0.3\n",
      "Accuracy: 0.69785\n",
      "[[24311 11669]\n",
      " [  417  3603]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.68      0.80     35980\n",
      "           1       0.24      0.90      0.37      4020\n",
      "\n",
      "    accuracy                           0.70     40000\n",
      "   macro avg       0.61      0.79      0.59     40000\n",
      "weighted avg       0.91      0.70      0.76     40000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Threshold:\", threshold)\n",
    "print(\"Accuracy:\", accuracy_score(y_val, y_pred_lgbc))\n",
    "print(confusion_matrix(y_val, y_pred_lgbc))\n",
    "print(classification_report(y_val, y_pred_lgbc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "844a5c3d-a2d1-4c01-a5c1-d492005ef79a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM ROC-AUC: 0.8888\n"
     ]
    }
   ],
   "source": [
    "print(f\"LightGBM ROC-AUC: {roc_auc_lgbc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5da56c2a-3945-41e5-91ad-1c475c2d1678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _=roc_curve(y_val, y_val_prob_lgbc)\n",
    "\n",
    "plt.figure(figsize=(6,5))\n",
    "plt.plot(fpr, tpr, label=f\"AUC = {roc_auc_lgbc:.4f}\")\n",
    "plt.plot([0,1],[0,1],'--')\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"ROC Curve – LightGBM\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3b41bf76-9c0b-43a2-b041-a3fd330b576b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_val, y_pred_lgbc).astype(int)\n",
    "\n",
    "sns.heatmap(cm, annot=True, fmt='d')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8ff65212-3af1-4513-97eb-3499fc22c15b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lightgbm_santander_save.pkl']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save={'model': lgbc,'features': x.columns.tolist(),'threshold': 0.30}\n",
    "joblib.dump(save,'lightgbm_santander_save.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b3c1ae12-6f9d-4273-b49e-c25625716f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp=pd.Series(lgbc.feature_importances_, index=x.columns)\n",
    "imp.sort_values(ascending=False).head(20).plot(kind='barh', figsize=(8,6))\n",
    "plt.title(\"Top 20 Feature Importances – LightGBM\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dba81b9-4a65-4285-b39e-39f1854ec8f7",
   "metadata": {},
   "source": [
    "LightGBM showed the best overall performance based on ROC-AUC and was chosen as the final model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a87fbe73-ee65-44ef-900e-5e14e8d5a53a",
   "metadata": {},
   "source": [
    "### 3th Model XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "57ccd54e-794d-4dad-830e-676ad02e053c",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb=XGBClassifier(\n",
    "    n_estimators=500,\n",
    "    learning_rate=0.05,\n",
    "    max_depth=6,\n",
    "    subsample=0.8,\n",
    "    colsample_bytree=0.8,\n",
    "    objective='binary:logistic',\n",
    "    eval_metric='auc',\n",
    "    scale_pos_weight=(y_train == 0).sum() / (y_train == 1).sum(),\n",
    "    random_state=42,\n",
    "    n_jobs=-1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b306e53f-367f-4449-ad4a-0f76ee257b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;auc&#x27;, feature_types=None,\n",
       "              feature_weights=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=0.05, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=6,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=500,\n",
       "              n_jobs=-1, num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier\">?<span>Documentation for XGBClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('objective',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">objective&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;binary:logistic&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('base_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">base_score&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('booster',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">booster&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('callbacks',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">callbacks&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('colsample_bylevel',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">colsample_bylevel&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('colsample_bynode',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">colsample_bynode&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
       "            <td class=\"value\">0.8</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('device',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">device&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('early_stopping_rounds',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">early_stopping_rounds&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('enable_categorical',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">enable_categorical&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('eval_metric',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">eval_metric&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;auc&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('feature_types',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">feature_types&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('feature_weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">feature_weights&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('gamma',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">gamma&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('grow_policy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">grow_policy&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('importance_type',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">importance_type&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('interaction_constraints',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">interaction_constraints&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">0.05</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_bin',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_bin&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_cat_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_cat_threshold&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_cat_to_onehot',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_cat_to_onehot&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_delta_step',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_delta_step&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_depth&nbsp;</td>\n",
       "            <td class=\"value\">6</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_leaves',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_leaves&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_child_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('missing',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">missing&nbsp;</td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('monotone_constraints',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">monotone_constraints&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('multi_strategy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">multi_strategy&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
       "            <td class=\"value\">500</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('num_parallel_tree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">num_parallel_tree&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_lambda',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sampling_method',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">sampling_method&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('scale_pos_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">scale_pos_weight&nbsp;</td>\n",
       "            <td class=\"value\">np.float64(8.95148650329643)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample&nbsp;</td>\n",
       "            <td class=\"value\">0.8</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tree_method',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tree_method&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('validate_parameters',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">validate_parameters&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbosity',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbosity&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
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       "            element.style.width = originalWidth;\n",
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       "\n",
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       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric='auc', feature_types=None,\n",
       "              feature_weights=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=0.05, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=6,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=500,\n",
       "              n_jobs=-1, num_parallel_tree=None, ...)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f0657dbd-57b0-43a6-bb7b-c9679a0f6ab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgbtahmin=xgb.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6f33c407-6908-4941-ae5c-ff8b0e109556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.879075"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_val,xgbtahmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "657971fa-9dbd-44c0-afa3-c3321a5f0029",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob_xgb=xgb.predict_proba(x_val)[:, 1]\n",
    "roc_auc_xgb=roc_auc_score(y_val, y_prob_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9d049fb9-5a1f-4ace-bf27-c6602753a77e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM ROC-AUC: 0.8807\n"
     ]
    }
   ],
   "source": [
    "print(f\"LightGBM ROC-AUC: {roc_auc_xgb:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "34d8c9d6-acf4-4031-8f67-280271300402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TLgbq5CkiIiJup4AhIiIibqeAISIiIm5X7Ppg5IRhGFitVmw2m9mlSCHm6emJl5eXbocWkWJJAeMKaWlpxMTEkJSUZHYpUgQEBAQQERGBj4+P2aWIiOQrBYzL2O12Dhw4gKenJ5GRkfj4+OivT8kVwzBIS0vj1KlTHDhwgJtuuum6g9KIiBQlChiXSUtLw263U7FiRQICAswuRwo5f39/vL29OXToEGlpaZruW0SKFf1JlQ39pSnuop8lESmu9NtPRERE3E4BQ0RERNzO1ICxZs0aunbtSmRkJBaLhW+++ea6+6xevZro6Gj8/PyoWrUqH374Yd4XKiIiIi4xNWBcuHCBBg0a8P777+do+wMHDtC5c2dat27N1q1bee655xg+fDgLFy7M40oLj3Xr1uHp6UnHjh2zvPbLL79gsVg4d+5cltcaNmzICy+8kGnd1q1befDBBwkPD8fPz48aNWowdOhQ/v777zyq3mHKlClUqVIFPz8/oqOjWbt27XX3mTNnDg0aNHDeFjpw4EBOnz6daZvJkydTs2ZN/P39qVixIiNHjiQlJcWl97ZYLNk+3njjjRv/4CIiRYlRQADGokWLrrnNmDFjjFq1amVa98gjjxjNmzfP8fvEx8cbgBEfH5/lteTkZGPXrl1GcnJyjo9X0AwePNh46qmnjMDAQOPQoUOZXlu1apUBGGfPns2yX4MGDYzx48c7l7/77jvDx8fH6Nq1q7FixQpj//79xu+//248/fTTRo8ePfKs/vnz5xve3t7GJ598Yuzateuqn+Vya9euNTw8PIx33nnH2L9/v7F27Vqjbt26xr333uvc5vPPPzd8fX2NOXPmGAcOHDCWLVtmREREGCNGjHDpvWNiYjI9pk+fblgsFmPfvn3Z1lYUfqZERDJc6zv0SoXqNtX169fTvn37TOs6dOjAtGnTSE9Px9vbO8s+qamppKamOpcTEhJcek/DMEhON2dET39vT5fG4bhw4QJffPEFGzduJDY2lpkzZ/L888+7/L5JSUkMHDiQzp07s2jRIuf6KlWq0KxZs2xbQNxl0qRJDB48mCFDhgCOVodly5YxdepUJk6cmO0+v//+O1FRUQwfPtxZ5yOPPMLrr7/u3Gb9+vW0atWKPn36ABAVFUXv3r3ZsGGDS+9drly5TO/97bff0qZNG6pWreqmMyAiBYlhGCSmWolPSsduGNjsBnbDsd5ucHHZwDDgTFIaqem2i9uBzTCw2e3EJ6VzPD6FEr5e2OyOY9gMA7vdYPOhs0SU9He+lwFggIHjmMblz3Esk2nZcK7PWAY4fvYCtc7/j30lW/F81zq0rBaa7+euUAWM2NhYwsPDM60LDw/HarUSFxdHREREln0mTpzIhAkTcv2eyek26jy/LNf734hdL3YgwCfn/0QLFiygZs2a1KxZk4cffpgnn3yS//u//3N5sLBly5YRFxfHmDFjsn29ZMmSV9132LBhfP7559c8/q5du6hUqVKW9WlpaWzevJlnn3020/r27duzbt26qx6vZcuWjBs3jiVLltCpUydOnjzJV199RZcuXZzb3HrrrXz++eds2LCBpk2bsn//fpYsWUL//v1z/d4nTpzghx9+YNasWdf8vCJijvikdFKtNmyGgdVmOANC3Pk0ktNtrPn7FAnJ6aRa7azac5KqoYGk2QysNjt7T57Hx8uDNKs97ws9dNath/MnhTe9P6SL5wb+c2og51NquPX4OVWoAgZknYM+I61d7Ut07NixjBo1yrmckJBAxYoV865AE02bNo2HH34YgI4dO3L+/Hl+/vln2rZt69Jx9u7dC0CtWrVcruHFF1/kmWeeueY2kZGR2a6Pi4vDZrNlGyJjY2OveryWLVsyZ84cevbsSUpKClarlW7duvHee+85t+nVqxenTp3i1ltvdc418+ijjzoDRW7ee9asWQQFBXHfffdd8/OKyPWl2+wciLtAYooV+2WBwGo32H/qPN6eHhw7l8zJhFT8vD3YdPAse04kUibQJ1PLgtVux26HNJvrwWD70fhMy1eGixK+Xlgs4GGx4GEBTw9HHywP5zoLx84l06hSSbw9PfDysOB58XE+xYqvtwdVQgPx8vDAw2LBy9Oxz4mEFG4uH4LFAhYy+no5nmOxXFwHFiyXbeNY5vJ9gICUGFpseJKS8buxWbzo1eImIiqXyt0/yg0qVAGjXLlyWX7Znzx5Ei8vL8qUKZPtPr6+vvj6+ub6Pf29Pdn1Yodc738j/L09c7ztnj172LBhA19//TUAXl5e9OzZk+nTp7scMDJCW26EhYURFhaW6/0h+xB5rVaYXbt2MXz4cJ5//nk6dOhATEwMo0ePZtiwYUybNg1wdHB9+eWXmTJlCs2aNeOff/7hqaeeIiIigv/7v//L1XtPnz6dhx56SCN0igB2u0Hc+VT+OBbPsXPJpFntzksBNpvjv7uOJ2CzG6TZ7Gw/cg4vTw98PD2ITUi5/htcxekLadfdxtvz4he9xYLHxS/8c0npRFcuRXxyOr1uqYivtyeGYRBVJhAvTwvenh4E+3lTpoQPvl4eBPllvQRf4BzZCPP7wIWTEBCKZ8/PqVe5hWnlFKqA0aJFC7777rtM65YvX06TJk2y7X/hDhaLxaXLFGaZNm0aVquV8uXLO9cZhoG3tzdnz56lVKlSBAcHAxAfH5/lMse5c+cICQkBoEYNR3PaX3/9RYsWrv1w3sglktDQUDw9PbMNkVe2LFxu4sSJtGrVitGjRwNQv359AgMDad26NS+99JIzRPTt29fZv+Lmm2/mwoUL/Otf/2LcuHEuv/fatWvZs2cPCxYsuOZnFSnM7HaDC2lW0qx2dsUksO/kebYdOYeftydr98ZRsbQ/VptBQko6f58477b3rRoaiIeHBS8Px1/4nh4Wjp5N4rYaZUlJt1E60IdKpQOx2uzULR9MxVIBjuBwcduM58H+XoXi97dbbJ8Pi4eDLRXC60HveVAy6+/Z/GTqmT9//jz//POPc/nAgQNs27aN0qVLU6lSJcaOHcuxY8eYPXs24Pjyev/99xk1ahRDhw5l/fr1TJs2jXnz5pn1EQoEq9XK7Nmzeeutt7J0gr3//vuZM2cOTzzxhHPCrY0bN1K5cmXnNjExMRw7doyaNWsCjn4HoaGhvP7665k6eWY4d+7cVfth3MglEh8fH6Kjo1mxYgXdu3d3rl+xYgX33HPPVY+XlJSEl1fmH2VPT0frT0ZrTFJSUpZhuz09HX+xGIbh8ntPmzaN6OhoGjRocM3PKlIQ2OwGKek2YuJTOHzGcRkizWrn6Nlkzqda8fHywGa/dFli5rqDlC/pz7Fzydc8bnav+3p5kGq107JaGcqF+OF52aUAz4uBoX6FEHy8PLAbEFUmAD9vT3y9PJxBQVx06m9YNAwwoNbd0P0j8C1hdlXmBoxNmzbRpk0b53JGX4n+/fszc+ZMYmJiOHz4sPP1KlWqsGTJEkaOHMkHH3xAZGQk7777Lvfff3++116QfP/995w9e5bBgwc7WyEyPPDAA0ybNo0nnniCoKAgHnnkEZ5++mm8vLxo0KABx48fZ9y4cdSuXdsZTgIDA/n000958MEH6datG8OHD6d69erExcXxxRdfcPjwYebPn59tLTd6iWTUqFH07duXJk2a0KJFCz7++GMOHz7MsGHDnNtcGTy7du3K0KFDmTp1qvMSyYgRI2jatKkzzHTt2pVJkybRqFEj5yWS//u//6Nbt27OMJKT9wZHP54vv/ySt956K9efU8Qd0m12jp9LJjY+hfjkdE4kpPDnsQROnU/l9PnULH0KXHFleMjo8Nj6plCC/b2pF+n4XVMlNABvTw+8PT1oULEkIf6F4FJCUVO2BrSbACkJ0GYcFJA5kEwNGHfcccc1r/fPnDkzy7rbb7+dLVu25GFVhc+0adNo27ZtlnABjhaMV155hS1bttC4cWPefvttIiIieO655zh48CBhYWG0adOG+fPnZ2oFuOeee1i3bh0TJ06kT58+zs6xd955Jy+99FKefZaePXty+vRpXnzxRWJiYqhXrx5LlizJ0uJyefAcMGAAiYmJvP/++zz99NOULFmSO++8k9dee825zX/+8x8sFgv/+c9/OHbsGGXLlqVr1668/PLLLr03wPz58zEMg969e+fZeRABR4CIOZfCnhOJXEi1sv3oOQA2HDjDycRUTiWmXvsA19CyWhkCfb2IT0onsqQfYcF+eF68LOHpYcGChU43l6N8SX/8vT3VslDQnD0IWKDUxd9PrZ4ys5psWYwb6dFXCCUkJBASEkJ8fLyzT0KGlJQUDhw44BzJUeRG6WdKrsZmd4yxk2513BK5bl8c6/ed5u8TiXh5ergUHkoFeHM2KZ1bohx3CwT5eVMnIpiKpf2pUCqA2hHB+Ht74uPlgaeCQuF38DdY8DAElYPBy8E3KN/e+lrfoVcqJr1fRETMlZJuY9+p83y4ej+/7DlJYorV5WO0rR3OqfOptK4eip+3B7dElaZJVGmFhuJk80z44WmwWx2dONOS8jVguEIBQ0TEzdJtdvbEJvLT7hMkp9v4aPX+a27v6WHBZjfofHM5Qkv4cnP5EKqHlaBS6QCC/b3x9iwY19TFRDYrLB8H/7s4wWfd++CeD8AnwNy6rkEBQ0QkG6lWGwfjkjh9PpXUi7dp+nl7EnMumZj4FAJ8PDl6Npmj55IoE+iLzW6QbrMTm5DCuaT0ax67elgJHrujGm3rhBPo46UWCLm25LPw5UDYv8qx3OY/cNszjtG2CjAFDBEp9k4kpHA+1cqWQ2f5cvNRNhw449L+R85kfzunhwXqRobQsnoZqpctwQPRFVweul+EH55xhAvvAMctqHW6mV1RjihgZKOY9XuVPKSfpYLHZjdYu/cUu2MSWbozlu1Hzl13n3rlgx0jTsancOtNoaRZ7QT7e1O5TCB2u0GpQB/KlPDBy8OCl4cHvt4e3Fw+RJc2xD3avwTnDkGXSRBR3+xqckwB4zIZo4EmJSXh7+9vcjVSFCQlJQHk2UizkpVhGJxISOVkYgonElKJjU/m223H8fHyYN2+09fcNyzIF4sFoiuXon+LKJpWKa0WB8l/hgHHtkCFaMdycAQMXlHgL4lcSQHjMp6enpQsWZKTJ08CEBAQoF8ukiuGYZCUlMTJkycpWbKkczAvcb8zF9L48c8YJv+01+VxITrfXI4GFUrSq2klDRAlBYMtHZaMhs0z4IHpUO/iQJKF8LtIAeMK5cqVA3CGDJEbUbJkSefPlLjHiYQU3l/5D4u2HuN86vVv9axVLogQf29CS/jSrWEkQb5eREeVwtdLoU8KmKQz8EU/OLgWsMD5wv09pIBxBYvFQkREBGFhYaSnX7snuMi1eHt7q+XiBsWdT2XGbwfYeOAsu2MSSLxOoBjVrgb3NIwkPNgPPxdmIxYx3cndMK+XY4ROnyB4YBrUMGcmb3dRwLgKT09PfTmI5BOb3WD/qfMcPpPEwdNJLPkjhs2Hzl5zn7JBvrzYrS633hRaOKbSFrmaPUth4RBIS4RSUdB7PoTVNruqG6aAISL5zjAMFm45xss/7MJuQHzytVsLG1QI4f7oCtxeoyylA30UKKToOLUH5vcGww6Vb4UesyGwjNlVuYUChojkOavNzk+7T7L18Fk2HTp7zdaJ0BK+VCsbSLkQP+5tVJ5W1ULx8dLtnlJEla0JLZ6A1ATo9AZ4+ZhdkdsoYIiIW1ltdrYdOceavXHY7HY+WLXvmts/3qYaXRtEUq1sCY0bIcVD4gmweECJso7lthMcd4kUwjtFrkUBQ0RybcWuE+yOSWDR1mMAHDmThNV+7cHFWlYrQ8tqZeh0cwTVypbIjzJFCo6Y7TCvD4RUgP6LwcsXPIpmsFbAEJEcMwyDd37ey7S1B657R0doCV/Cgny5rUZZAnw8+ddtVXVnhxRvu76FRcMgPQm8/eDCKUfQKKIUMETkqtJtdv6KSeTHP2P4afcJ/j5xPtvtBrSMwmY3uK1GWcKCfKkVEaRxJkQyGAaseQNWvexYrnYnPDAD/EuaWlZeU8AQEU6fT2Xx9uOcvZBGfHI6n/1+iPBgP2LiU666z+v31+eu2mGUKeGbj5WKFDJpSfDt47Dza8dys0cdc4t4Fv2v36L/CUUki5R0G3P/d5j//rCLq83Hdnm4CA/2Jd1m0KdpJfq1rExYkF8+VSpSyH033BEuPLyhy1sQ3d/sivKNAoZIMbH0z1g+//0Qv/4Td9Vt/Lw96FQvggql/Anx96ZFtTLcFBak20RFcuuOsXB8K3R9F6JamV1NvlLAECnilu+M5V+fbc72NW9PC0NaV2Vo66qUCvDW5H4i7nB6H5Sp5nhepho8vgE8il+fJAUMkSLoxz9i+HDNfrYfOZfltQEto2hTK4yW1cpo3AkRd7LbYeV/4bd34OGvHJ05oViGC1DAECky4s6nMm7RHyzbeSLb1z98uDEd60Xkc1UixURqInz9COz5wbF8dPOlgFFMKWCIFFJ2u8G55HRW/32SrYfPMXv9oSzbjGxbg3Z1wqkTGWxChSLFxNlDMK83nNwJnr7Q7T1o0NPsqkyngCFSCH277RhPzd+W7Ws3lw/hzQcbULNcUP4WJVIcHVoHCx6GpNMQGAa95kLFW8yuqkBQwBApJJbvjOWFxTs5fsXYFIE+nlxIs9H55nIMbFWFW6JKm1ShSDFzYhfM6gb2dChXH3rPK9Ijc7pKAUOkADufauXF73byxaaj2b4+Y+AttKkZls9ViQgAYbWhfk9IOw/3TgGfQLMrKlAUMEQKqCGzNvHT7qwdNge1qsKjd1SjbJBG0BTJdynxgAX8gh2zn3adDBbPIjth2Y1QwBApAI6eTWLx9uN8vGY/Pp4enExMzfR6/QohfNy3CeVCNIKmiGlO74N5vaBUFcflEA9P8PQ2u6oCSwFDxCR/Hovn/779k62Hz111m9Y3hTKt/y0aSVPEbPtXwxf9IOUcpF2AhGNQspLZVRVoChgi+Wjh5qN8tGbfVWclvSWqFLUjgunRpCJlg3wJD1aLhYjpNnwCP/4bDBuUbwK95kBQObOrKvAUMETywWfrD/J/3+7M9rXujcoz7PZquq1UpKCxpTuCxaZpjuX6PR1zingr+OeEAoZIHvllz0lmrz/Eyr9OZnltVLsatK8bTs3wIM3/IVJQffsE7JgPWKDteGg1wtGxU3JEAUPEzY6fS6blqyuzrPfx9ODLYS1oULFk/hclIq5rPgz2/exotajV2exqCh0FDBE3+edkIne/9ysp6fZM63s0qcDd9SNpfVOoWitECrrzp6BEWcfzyEbw1A7wCTC3pkJKAUPEDZ6av5Vvtx3PtK5bg0je6dVQoUKkMDAMWP8BrHoZ+n8PFaId6xUuck0BQ+QGvLV8D++t/CfTug51w/mgT2O8NBW6SOFgTYXvR8G2zx3Lf313KWBIrilgiOTC1fpZ/PXfjvh5e5pQkYjkyvlTjsnKjvwOFg/o8Ao0G2Z2VUWCAoaIC1bsOsHs9QdZuzcu0/pvHm9FQ3XeFClcYv90jMwZfwR8Q+DB6VC9rdlVFRkKGCLXsPdEItN/O8j2I+fYFZOQ5fV+LSrz4j31TKhMRG7IiV0wrT2kX4DS1aDPAgi9yeyqihQFDJFs/HMykbaT1lz19f/eW48OdcIJ00ibIoVT2VpQ9Q5HwHhwJviXMruiIkcBQ+QK2Y262aBCCJ1ujuDOWmHUCNeImyKFUnoyYHGMxOnhAfd/Cp4+4KmvwrygsyrFXrrNztGzyczfeJhvtx4nNiHF+VqneuWY8lBj3WoqUtglxMD83hBaE7p/6BiRU7eg5ikFDCm2ktNsDJixgf8dOJPt6zsndCDQV/+LiBR6xzbD/IcgMQbOHnR06tRMqHlOvz2l2ElOs/H+qr18sGpfpvV+3h7ULBfMoFZRdGsQqVYLkaLgj6/g28fBmuLod9F7nsJFPlHAkGIj7nwqr/34F19uPpppfYVS/vzyzB0aGEukKLHb4ZdXYM0bjuWb2sP908Av2Ny6ihEFDCnyDMPg8/8d5v+++TPT+prhQbzXp5E6bYoURd8/BVtmO563fBLaTgAPDYKXnxQwpEhLTrNR+/mlmdZFlQngs8HNqFhaHbxEiqybH4Q/FkKXN6FhH7OrKZYUMKTISrPaaff26kzrfhh+K3UjQ0yqSETyVNoF8Al0PK9yG4z4AwLLmFtTMaaLzlIkzVp3kBr/+ZGjZ5MBaF8nnH2vdFa4ECmqts2FdxrAqb8vrVO4MJVaMKRI2XzoLPdPXZdp3cBWUYzvWtekikQkT9lt8NN4WPeeY3nzDOg40dyaBFDAkCLiz2Px3P3er1nWzxnSjFbVQ02oSETyXEoCLBwMe5c7lm8bA3eMNbcmcVLAkEIvISU9S7i4o2ZZPu3XRLeeihRVZ/bDvN5w6i/w8oN7p0C9+82uSi6jgCGFWnxyOg0mLHcuj+1Ui0dur2ZiRSKS507uhhmdIPksBEVAr7lQvrHZVckVFDCk0DEMg1//iePdn/ey8eBZ5/rpA5pwZ61wEysTkXxRuhqE1XFMXtZrLgRHmF2RZEMBQwqVY+eSafXqyizrH29TTeFCpCizWR3/9fQCLx/o+Tl4+zseUiApYEihcSoxNUu4+NdtVRnZtgb+PhqhT6TISj4LXw6AsLrQ8RXHuoDSppYk16eAIYXCwbgL3PHmL87lJ++szqh2NTQhmUhRd+pvmNcLzuyDIxuh+aNQsqLZVUkOKGBIgbf9yDnu+eA35/IbD9TnwSb6BSNS5P3zE3w5CFLjIaTSxZlQ9f9+YWH6PXxTpkyhSpUq+Pn5ER0dzdq1a6+5/Zw5c2jQoAEBAQFEREQwcOBATp8+nU/VSn6b+su+TOHiv/fWU7gQKeoMA36fCnMedISLSi1g6EooV8/sysQFpgaMBQsWMGLECMaNG8fWrVtp3bo1nTp14vDhw9lu/+uvv9KvXz8GDx7Mzp07+fLLL9m4cSNDhgzJ58olrxmGwegvt/Pa0r+c68Z3rUPf5pVNrEpE8sXSsbD0WTDs0PBh6PctlChrdlXiIothGIZZb96sWTMaN27M1KlTnetq167Nvffey8SJWYd6ffPNN5k6dSr79u1zrnvvvfd4/fXXOXLkSI7eMyEhgZCQEOLj4wkODr7xDyFul5CSTv0Xlmdat+35dpQM8DGpIhHJV7sWOzp1tv8vNH8M1NeqwHDlO9S0Foy0tDQ2b95M+/btM61v374969aty3afli1bcvToUZYsWYJhGJw4cYKvvvqKLl26XPV9UlNTSUhIyPSQgskwDCZ8tzNLuNj7cieFC5GiLuM2VIA63WD4FmjxuMJFIWZawIiLi8NmsxEennnsgvDwcGJjY7Pdp2XLlsyZM4eePXvi4+NDuXLlKFmyJO+9995V32fixImEhIQ4HxUr6vp9QWS3G9R5fhkzfjvoXDe2Uy0OvtoFbw33LVK0/bUEPrgF4o9eWlcqyrRyxD1M/8195W2GhmFc9dbDXbt2MXz4cJ5//nk2b97M0qVLOXDgAMOGDbvq8ceOHUt8fLzzkdNLKZJ/0m12Rn+1g+R0GwABPp6sHdNGQ36LFHWGAWsnwfw+jrlFfnvH7IrEjUy7TTU0NBRPT88srRUnT57M0qqRYeLEibRq1YrRo0cDUL9+fQIDA2ndujUvvfQSERFZh4v19fXF19fX/R9A3OKv2AQ6Tr5051Drm0L5bHAzEysSkXyRngKLn4Q/vnAsNxkMHV4xtyZxK9NaMHx8fIiOjmbFihWZ1q9YsYKWLVtmu09SUhIeHplL9vR0jOBoYl9VyaX/7T+dKVwE+3nxwUOasEikyEuMhZldHOHC4gmd34S7J4Gnt9mViRuZOtDWqFGj6Nu3L02aNKFFixZ8/PHHHD582HnJY+zYsRw7dozZs2cD0LVrV4YOHcrUqVPp0KEDMTExjBgxgqZNmxIZGWnmRxEXfbHpCGO+2uFcnjWoKbfX0G1oIkVe3F6YfQ8kHAO/ktBjNlS93eyqJA+YGjB69uzJ6dOnefHFF4mJiaFevXosWbKEypUdYx3ExMRkGhNjwIABJCYm8v777/P0009TsmRJ7rzzTl577TWzPoLkwuLtxzOFi8VPtKJ+hZLmFSQi+Sc4EvxLg08g9J4PZdTXqqgydRwMM2gcDHNdOez3jhfaE+ynZlGRIs1ud9xumtGBP+G4I2D4hZhbl7isUIyDIcXPyYSUTOHif8/dpXAhUtSlXYCvBsDaty6tC45UuCgGFDAkX5xPtdL0lZ+dy58PbkZ4sJ+JFYlInos/CtM7wq5vYfXrEH/M7IokH2k2VckX90+5NDrrlIcac+tNoSZWIyJ57shGx/gWF05CQCj0/BxCyptdleQjBQzJc898uZ09JxIBuLV6KJ1vzjpeiYgUIdvnw+LhYEuFsLqOadZLaaLC4kYBQ/JUx8lr+CvWES7qRATz2eCmJlckInlq5Uuw5g3H85pd4L6PwbeEuTWJKRQwJE8cPZvEra+tci5XLhPAkqdam1iRiOSLkIvzPbV+Gtr8BzzU1a+4UsAQt0tJt2UKF7XKBbFkuMKFSJFlGJduQY3uDxH1IbKRuTWJ6RQtxe1uf+NSuHjyzuosHXEbHh6aclmkSDr4K3x6F1w4fWmdwoWggCFu9vjcLZxISAUcE5c93b6myRWJSJ7ZPNMx7PexzbD6VbOrkQJGl0jEbbYdOccPO2Kcy5/2b2JiNSKSZ2xWWD4O/vehY7nufdB2grk1SYGjgCFuYbXZuffiKJ2eHhb+ebkTFosui4gUOcln4cuBsP/ipdA2/4HbnrnUB0PkIgUMcYtP1h5wPv+gTyOFC5Gi6MwBmPMAnP4HvAOg+0dQp5vZVUkBpYAhNyzNaue1pX8B0DSqNB3raSAtkSLJLwTsNgiu4Bg8K6K+2RVJAaaAITfEMIxMd42821u9x0WKrIDS8NBX4BcMJcLMrkYKON1FIjfk4zX7iYlPAeC/99ajXIgmMBMpMqxp8N1TsGnGpXWh1RUuJEfUgiG5lpiSzsQfHZdGmlQuRd/mmmtApMi4cBq+6AeHfgVPX6jREYJ1+VNyTgFDciU+OZ0GE5Y7l9/RpRGRouPkbpjbE84dAp8guP9ThQtxmQKG5Mqby/Y4n/dvUZnyJf1NrEZE3GbPUlg4BNISoVQU9J4PYbXNrkoKIQUMcdnmQ2f57PdDAPS6pSIT7qlnckUi4ha/vQsrngcMqHwr9JgNgWXMrkoKKQUMcdnDn/7P+XzifTebWImIuJXdChgQPRA6vQ5ePmZXJIWYAoa4ZNhnm0lOtwHwQZ/GGlBLpCi5daRjbItqd2lkTrlhuk1VcuzImSSW7owF4M5aYXSpr05fIoVazA5HZ87U845liwWqt1W4ELdQwJAcm7vhMABRZQKYponMRAq3XYthegf4eyms/K/Z1UgRpEskkiMx8clM/WUfAA81q6xLIyKFlWHAmjdg1cuO5Wp3wh1jza1JiiQFDMmRZxf+4Xzet4UG1BIplNKS4NvHYefXjuVmj0L7l8BTXwXifvqpkuvadPAMq/8+BcATbarj5+1pckUi4rKE4zCvN8RsAw8v6PIWRA8wuyopwhQw5Lr6fOK4LbVskC/PdKhpcjUikiuGAYkx4F8aen4GUbeaXZEUcQoYck0frd5Hms0OwAtd65pcjYjkWkh56LMA/Es5RugUyWO6i0SuymY3nJOZWSzQ+eZyJlckIjlmt8NPE2DnN5fWRTZSuJB8oxYMuarnv/3T+Xz9s3fpzhGRwiI1Eb5+BPb8AN4BUKk5BOkPBMlfChiSrT2xicz5n2Pci2G3V6NciJ/JFYlIjpw95OjMeXInePrA3W8rXIgpFDAkW89+vQOAWuWCeLZTLZOrEZEcObQeFjwMSXEQGAa95kLFW8yuSoopBQzJYta6g2w9fA6A57vWMbcYEcmZrZ/DdyPAng7l6kPveRBSweyqpBhTwJBMrDY74xfvBKBGeAlaVgs1uSIRyZGTux3hos49cO9U8Ak0uyIp5hQwxMkwDJq+8rNzee7Q5iZWIyIuafeio+Xi5gfBQzcIivn0UyhOvT/5nTMX0gB4vE01Qkv4mlyRiFzV6X2OYb+tjv9n8fCEBj0VLqTAUAuGAPDW8j38vv8MAMF+XjzTXiN2ihRY+3+BL/pDyjkIKONovRApYBQwhNV/n+K9lf84lzf/XzuNeSFSUG34BH78Nxg2KN8Emj9mdkUi2cpVW5rVauWnn37io48+IjExEYDjx49z/vx5txYn+eOZL7c7n++c0AFvTzWxihQ4tnT4fhQsecYRLur3hAE/aIwLKbBcbsE4dOgQHTt25PDhw6SmptKuXTuCgoJ4/fXXSUlJ4cMPP8yLOiWPbD50llOJqQB8/+StBPqqUUukwEk6A1/0g4NrAQu0HQ+tRjjG8BcpoFz+U/Wpp56iSZMmnD17Fn9/f+f67t278/PPP19jTyloTiWmcv/UdQDUiQimXvkQkysSkWwlnYaYHeBTwjG+xa0jFS6kwHP5z9Vff/2V3377DR8fn0zrK1euzLFjx9xWmOSt7UfOcc8HvzmXh99V3cRqROSaQm9yTLEeWBbCNfidFA4ut2DY7XZsNluW9UePHiUoKMgtRUneSrPaGTxrk3P5kdur0rFehIkViUgmhgHr3nfcLZKh6u0KF1KouBww2rVrx+TJk53LFouF8+fPM378eDp37uzO2iQPGIbBvR/8Rtx5R7+LL4e1YGyn2iZXJSJO1lT49glYPs5xK+r5k2ZXJJIrLl8iefvtt2nTpg116tQhJSWFPn36sHfvXkJDQ5k3b15e1ChudM8Hv7ErJgGAB6IrcEtUaZMrEhGn86cck5Ud+R0sHnDHWMdlEZFCyOWAERkZybZt25g/fz6bN2/GbrczePBgHnrooUydPqXgWb/vNDuOxgMQHuzL6/fXN7kiEXGK/cMxzXr8EfANgQdnQPW7zK5KJNcshmEYruywZs0aWrZsiZdX5mxitVpZt24dt912m1sLdLeEhARCQkKIj48nODjY7HLy1X1TfmPLxVlSD77axdxiROSS3d/D1/+C9AtQuhr0WeDo2ClSwLjyHepyH4w2bdpw5syZLOvj4+Np06aNq4eTfHIuKc0ZLl7pfrO5xYhIZnuWOMJF1Ttg6M8KF1IkuHyJxDCMbIeRPn36NIGBmh64oHpr+d8AlA3ypXfTiiZXIyKZdJkEYXWg2TDw1GB3UjTk+Cf5vvvuAxx3jQwYMABf30szbdpsNnbs2EHLli3dX6HcsFOJqSzYdASArvUjNc+IiNkSYuB/H8JdzztmQfX2g5ZPmF2ViFvlOGCEhDhGeTQMg6CgoEwdOn18fGjevDlDhw51f4Vyw178fhdpVjuhJXz5dyfNkipiqmNbYH4fSIwBLz9oM9bsikTyRI4DxowZMwCIiorimWee0eWQQuJg3AW+234cgCGtq+Dr5WlyRSLF2J8L4ZvHwJoCZWtBg55mVySSZ1y+2Dd+/Pi8qEPygM1ucMebvwDg6+VBvxaVzS1IpLiy2+GXV2DNG47lm9rD/dPAr3jdySbFS656E3311Vd88cUXHD58mLS0tEyvbdmyxS2FyY3rO+1/zuevP1CfAB91HhPJd2kXYNEjsPs7x3LLJ6HtBEffC5EizOXbVN99910GDhxIWFgYW7dupWnTppQpU4b9+/fTqVOnvKhRciE2PoV1+04DUKtcEPc0LG9yRSLF1Ol9sHcFePrAvVOh/UsKF1IsuPwn7ZQpU/j444/p3bs3s2bNYsyYMVStWpXnn38+2/ExxBw//hnjfL50RMEe/EykSIuoD/d9DCXKQaVmZlcjkm9cbsE4fPiw83ZUf39/EhMTAejbt6/mIilAJny3C4DH7qhmciUixdC2uY67RTLUuUfhQoodlwNGuXLlOH3a0fReuXJlfv/9dwAOHDiAi6OOSx6Z8N1O5/Oet2hQLZF8Y7fBsnHwzaOOW1EvxJldkYhpXA4Yd955J9995+isNHjwYEaOHEm7du3o2bMn3bt3d3uB4poLqVZm/HYQgJIB3lQuo9uJRfJFSjzM6wXr33csN+oL/pqtWIovlwPGxx9/zLhx4wAYNmwYM2fOpHbt2kyYMIGpU6e6XMCUKVOoUqUKfn5+REdHs3bt2mtun5qayrhx46hcuTK+vr5Uq1aN6dOnu/y+RdVHa/Y7n//4VGsTKxEpRs7sh0/bwd7ljsGz7p8Gd44DD5d/xYoUGS538vTw8MDjsv9pevToQY8ePQA4duwY5cvn/G6FBQsWMGLECKZMmUKrVq346KOP6NSpE7t27aJSpUrZ7tOjRw9OnDjBtGnTqF69OidPnsRqtbr6MYqsd3/eC8A9DSOJCPG/ztYicsMOrIEv+kHyWQiKgF5zoXxjs6sSMZ1bBkaIjY3l5Zdf5tNPPyU5OTnH+02aNInBgwczZMgQACZPnsyyZcuYOnUqEydOzLL90qVLWb16Nfv376d0aUfTY1RUlDs+QpHw4ep9zuf9WkSZV4hIcfK/jxzhIrKxI1wER5hdkUiBkOP2u3PnzvHQQw9RtmxZIiMjeffdd7Hb7Tz//PNUrVqV33//3aVLFWlpaWzevJn27dtnWt++fXvWrVuX7T6LFy+mSZMmvP7665QvX54aNWrwzDPPXDPUpKamkpCQkOlRFKWk23j1x78AqBIaSHTlUiZXJFJM3DsVbh0JA5coXIhcJsctGM899xxr1qyhf//+LF26lJEjR7J06VJSUlL48ccfuf32211647i4OGw2G+Hh4ZnWh4eHExsbm+0++/fv59dff8XPz49FixYRFxfHY489xpkzZ64abiZOnMiECRNcqq0w+vz3Q87nswc1NbESkSIu+azjNtTmj4HF4hjuu+0LZlclUuDkuAXjhx9+YMaMGbz55pssXrwYwzCoUaMGK1eudDlcXO7KqcMNw7jqdOJ2ux2LxcKcOXNo2rQpnTt3ZtKkScycOfOqrRhjx44lPj7e+Thy5Eiuay3IZq0/CECIvzcVSweYW4xIUXXqb/jkLlj2HPw+xexqRAq0HLdgHD9+nDp16gBQtWpV/Pz8nH0nciM0NBRPT88srRUnT57M0qqRISIigvLlyzunjgeoXbs2hmFw9OhRbrrppiz7+Pr64uvrm+s6C4N1/8Rx5IwjYE3oVtfkakSKqH9+gi8HQWo8hFSCKrn/w0qkOMhxC4bdbsfb29u57OnpeUNTtvv4+BAdHc2KFSsyrV+xYoVzpNArtWrViuPHj3P+/Hnnur///hsPDw8qVKiQ61oKuz6fOiY1Cw/25d5GmnNExK0MA9ZPgTkPOsJFpRYwdCWUq2d2ZSIFWo5bMAzDYMCAAc7WgJSUFIYNG5YlZHz99dc5fvNRo0bRt29fmjRpQosWLfj44485fPgww4YNAxyXN44dO8bs2bMB6NOnD//9738ZOHAgEyZMIC4ujtGjRzNo0CD8/YvnLZnJaTbn89EdaplYiUgRZE2DJU/DFsfvIBo+DHdPAq+i3Soq4g45Dhj9+/fPtPzwww/f8Jv37NmT06dP8+KLLxITE0O9evVYsmQJlStXBiAmJobDhw87ty9RogQrVqzgySefpEmTJpQpU4YePXrw0ksv3XAthZFhGHR+99LAZF0bqAe7iFvFbIetc8Di4ZgFNaNjp4hcl8UoZhOIJCQkEBISQnx8PMHBwWaXc0O+33GcJ+ZuBRyTmo3pqBYMEbfbPBOCy8NN7cyuRMR0rnyHumWgLcl/hmEwafnfANQIL8HoDjVNrkikiNjzI5SpDqEXO41HDzC1HJHCSgPlF1Iz1x1kf9wFAD7o0/iqt/aKSA4ZBqydBPN6OyYtSz5ndkUihZpaMAqphVuOAtCmZlluCg8yuRqRQi49BRY/CX984Viuegf4aCZikRuhgFEIbThwhj+POYY8f/KurGN/iIgLEmNh/kNwbBNYPKHTa9B0qNlViRR6ChiF0ITvdgLQNKo0jStpzhGRXDu+Deb3gYRj4FcSesyGqhpAS8QdctUH47PPPqNVq1ZERkZy6JBjDozJkyfz7bffurU4yer0+VR2Hne0Xgy9rarJ1YgUcj9PcISL0BqOwbMULkTcxuWAMXXqVEaNGkXnzp05d+4cNptjoKeSJUsyefJkd9cnV5j26wHn87a1w0ysRKQI6P4xNOoLQ36CMtXMrkakSHE5YLz33nt88sknjBs3Dk9PT+f6Jk2a8Mcff7i1OMnqi02Ozp2j2tXQnSMirkq7ADu+uLRcoizc8z74hVx9HxHJFZf7YBw4cIBGjRplWe/r68uFCxfcUpRkb8vhs8SdTwWgu+YcEXFN/FHHLaixO8CWDo0eMrsikSLN5RaMKlWqsG3btizrf/zxR+dsq5I3Mi6P3Fo9VFOyi7jiyEb4uI0jXASEQmn1XxLJay63YIwePZrHH3+clJQUDMNgw4YNzJs3j4kTJ/Lpp5/mRY0CnE+18sOOGAAG3RplbjEihcn2+bB4ONhSIawu9J4HpSqbXZVIkedywBg4cCBWq5UxY8aQlJREnz59KF++PO+88w69evXKixoF+GDVP87nd9RQ506R67Lb4OcX4bfJjuWaXeC+j8G3hKlliRQXuRoHY+jQoQwdOpS4uDjsdjthYfrCy0t2u8HUX/YBcF/j8nh4qHOnyHUd/v1SuGj9NLT5D3hodgSR/OLy/20TJkxg3z7Hl11oaKjCRT5YtPWY8/kjt+lWOpEciWoFdz0P933q+K/ChUi+cvn/uIULF1KjRg2aN2/O+++/z6lTp/KiLrnMpxc7d1YtG0jNcpp3ROSqDq2DhOOXlls/DfUfNK8ekWLM5YCxY8cOduzYwZ133smkSZMoX748nTt3Zu7cuSQlJeVFjcVacpqNvScSAXjsjuomVyNSgG2eCbO6Om5FTdPvIhGz5arNsG7durzyyivs37+fVatWUaVKFUaMGEG5cuXcXV+xN3X1Pqx2gyA/L7o1iDS7HJGCx2aFH/8N3z0FdiuUrmJ2RSKCGyY7CwwMxN/fHx8fHxITE91Rk1xm8TZH/4vbapTFx0vXkEUyST4LXw6E/ascy23GwW2jQaPcipguV99YBw4c4OWXX6ZOnTo0adKELVu28MILLxAbG+vu+oq12PgUDp52NPWO76pBzEQyifsHPm3rCBfeAY6ZUG8fo3AhUkC43ILRokULNmzYwM0338zAgQOd42CI+31zsfWiamggYUF+JlcjUsB89xSc/geCKzgGz4qob3ZFInIZlwNGmzZt+PTTT6lbt25e1COXWfO34w4dzTsiko3uUx19L7q+AyV0u7xIQeNywHjllVfyog65QqrVxqaDZwFoXaOsydWIFADWNDi4Bqq3dSyXrORouRCRAilHAWPUqFH897//JTAwkFGjRl1z20mTJrmlsOJu9Z5TpNnsBPt5US8y2OxyRMx14TR80Q8O/eYIFTU7mV2RiFxHjgLG1q1bSU9Pdz6XvPftNsdgQRVKBeDlqbtHpBg7uRvm9oRzh8AnCCyeZlckIjmQo4CxatWqbJ9L3ki32fnhD8fMqcPv0uBaUoztWQoLh0BaIpSKgt7zIay22VWJSA64/KfxoEGDsh3v4sKFCwwaNMgtRRV3i7ZcmnvkrtrhJlYiYhLDgN/egXm9HOGi8q0wZKXChUgh4nLAmDVrFsnJyVnWJycnM3v2bLcUVdzNWn8QgH4tKuOtyyNSHB1YDSueBwyIHgB9F0FgGbOrEhEX5PgukoSEBAzDwDAMEhMT8fO7NC6DzWZjyZIlmlnVDWx2g53HEwC1XkgxVvUOaPYolK4KTYdq8CyRQijHAaNkyZJYLBYsFgs1atTI8rrFYmHChAluLa44Wrv30uy0zauWNrESkXwW+wcEl4eAiz/3nV41tx4RuSE5DhirVq3CMAzuvPNOFi5cSOnSl778fHx8qFy5MpGRmozrRn256SgAdSOD8fVSb3kpJnZ9C4uGQYUm8PDX4OltdkUicoNyHDBuv/12wDEPSaVKlbCoyTJP7Dh2DoBeTSuZW4hIfjAMWPMGrHrZsezhBdYUBQyRIiBHAWPHjh3Uq1cPDw8P4uPj+eOPP666bf36mg8gt/48Fs+RM44OtHfVUn8WKeLSkuDbx2DnIsdys0eh/UvgecOTPItIAZCj/5MbNmxIbGwsYWFhNGzYEIvFgmEYWbazWCzYbDa3F1lcDJixAYCa4UFElvQ3uRqRPJRwHOb1hpht4OENXd6C6P5mVyUibpSjgHHgwAHKli3rfC7uZxgGcefTAGhRTbfjSRFmGPDlQEe4CCgDPT6DqFZmVyUibpajgFG5cuVsn4v7bD8a73w+ou1NJlYikscsFug62THd+n0fO0boFJEiJ1cDbf3www/O5TFjxlCyZElatmzJoUOH3FpccTLjN0fL0B01y1IywMfkakTczG6HY5svLYfVhkHLFC5EijCXA8Yrr7yCv7+jf8D69et5//33ef311wkNDWXkyJFuL7C4yJjc7NbqoSZXIuJmqedhwcMwrQMc/O3Set2JJlKkudxd+8iRI1Sv7piA65tvvuGBBx7gX//6F61ateKOO+5wd33FQtz5VOfz5lXV/0KKkHOHHZ05T/wJnr5wPtbsikQkn7jcglGiRAlOnz4NwPLly2nbti0Afn5+2c5RItf3+tK/nM/rlQ8xsRIRNzq0Hj5u4wgXgWEw4Aeod7/ZVYlIPnG5BaNdu3YMGTKERo0a8ffff9OlSxcAdu7cSVRUlLvrKxZ+2eMYHrxbA42EKkXEls/g+5FgT4dy9aH3PAipYHZVIpKPXG7B+OCDD2jRogWnTp1i4cKFlCnjaNLfvHkzvXv3dnuBRd2FVCsnEx2XSPq20B06UgTsWwWLn3CEizr3wKClChcixZDFyG7ErCIsISGBkJAQ4uPjCQ4ONrscVv51gkEzNwFwYGJnDcEuhZ9hwMIhEHoT3DYGPFz+O0ZECihXvkNzNSbvuXPnmDZtGrt378ZisVC7dm0GDx5MSIj6D7hqy6FzgGPmVIULKbTO7IcS4eAT6Lg75L5PFCxEijmXfwNs2rSJatWq8fbbb3PmzBni4uJ4++23qVatGlu2bMmLGou0XTEJANxWo6zJlYjk0v5fHJ05Fw1zjHcBChci4noLxsiRI+nWrRuffPIJXl6O3a1WK0OGDGHEiBGsWbPG7UUWZSv/OglAWJCfyZWI5MKGT+DHf4Nhc8wvkpYIfmrJFJFcBIxNmzZlChcAXl5ejBkzhiZNmri1uKIuISXd+bxVdY1/IYWILd0RLDZNcyzf3AO6vQfeCsoi4uByO2ZwcDCHDx/Osv7IkSMEBQW5pajiYucxx+WRMoE+RIRo9lQpJJLOwGfdL4YLC9w13jGniMKFiFzG5RaMnj17MnjwYN58801atmyJxWLh119/ZfTo0bpN1UXr9sUBUCNcwUwKCcOAeb3gyP/Ap4SjM2etzmZXJSIFkMsB480338RisdCvXz+sVisA3t7ePProo7z66qtuL7Aom/arY4KzqNBAkysRySGLBdr9F759HHrMgvC6ZlckIgVUrsfBSEpKYt++fRiGQfXq1QkICHB3bXmiII2DEfWsY1bal7vX46FmGmRLCijDcNyGWqbapXU2K3jm6i53ESnEXPkOzXEfjKSkJB5//HHKly9PWFgYQ4YMISIigvr16xeacFGQ/HMy0fm8Y91yJlYicg3WVEdrxYetIfaPS+sVLkTkOnIcMMaPH8/MmTPp0qULvXr1YsWKFTz66KN5WVuRNn/DEQDqlQ+mTAlfk6sRycb5UzCrK2ybA9ZkiNludkUiUojk+M+Qr7/+mmnTptGrVy8AHn74YVq1aoXNZsPT0zPPCiyq1u93zEhrtRWrkdqlsIj9wzHNevwR8A2BB6dD9bZmVyUihUiOWzCOHDlC69atnctNmzbFy8uL48eP50lhRd3O445bVB9rU93kSkSusPs7mNbBES5KV4OhPytciIjLctyCYbPZ8PHxybyzl5fzThLJucOnk5zP29UON7ESkSvsWwkLHnY8r3oHPDgT/EuZWZGIFFI5DhiGYTBgwAB8fS/1F0hJSWHYsGEEBl66zfLrr792b4VF0O8HTjuf+/vo8pIUIFG3QbU7ocxN0OEVdeYUkVzL8W+P/v37Z1n38MMPu7WY4uKTNfsB6NYg0uRKRIDEE45WCi8fR6DovcDxXETkBuQ4YMyYMSMv6yg2zlxIY+/J8wDc20gBQ0x2bAvM7wM1OsDdkx0DaSlciIgbaE7lfLZ27ynn8zY1w0ysRIq9PxfCjE6QGAOHf4fUBLMrEpEixPSAMWXKFKpUqYKfnx/R0dGsXbs2R/v99ttveHl50bBhw7wt0M2+2x4DwB01y2KxWEyuRoolux1WvgRfDQJrCtzUAQav0DTrIuJWpgaMBQsWMGLECMaNG8fWrVtp3bo1nTp1yna21svFx8fTr18/7rrrrnyq1H0yMkXpADVDiwnSLsCX/WDNG47llsOh9zzwM3fYfBEpekwNGJMmTWLw4MEMGTKE2rVrM3nyZCpWrMjUqVOvud8jjzxCnz59aNGiRT5V6j6HTl8AoGM9DQ8u+cwwYE4PxzgXnj5w71Ro/1/w0J1MIuJ+pgWMtLQ0Nm/eTPv27TOtb9++PevWrbvqfjNmzGDfvn2MHz8+R++TmppKQkJCpodZbHaD/accAaNaWAnT6pBiymKBW0dAiXLQ/3to2MfsikSkCMtVwPjss89o1aoVkZGRHDp0CIDJkyfz7bff5vgYcXFx2Gw2wsMzDzQVHh5ObGxstvvs3buXZ599ljlz5uDllbMbYCZOnEhISIjzUbFixRzX6G77Tp3HajewWKBSaU0QJ/nk/KWOxdzUDoZvhUrNzKtHRIoFlwPG1KlTGTVqFJ07d+bcuXPYbDYASpYsyeTJk10u4MqOjoZhZNv50Waz0adPHyZMmECNGjVyfPyxY8cSHx/vfBw5csTlGt1l3T9xALSoWgZvT9P710pRZ7fB8v/AB7c4plvP4KNwKyJ5z+Vvuffee49PPvmEcePGZZrkrEmTJvzxxx/X2DOz0NBQPD09s7RWnDx5MkurBkBiYiKbNm3iiSeewMvLCy8vL1588UW2b9+Ol5cXK1euzPZ9fH19CQ4OzvQwy6tL/wKgQcWSptUgxURKAszrBeveg+Sz8M/PZlckIsWMy+MAHzhwgEaNGmVZ7+vry4ULF3J8HB8fH6Kjo1mxYgXdu3d3rl+xYgX33HNPlu2Dg4OzBJgpU6awcuVKvvrqK6pUqeLCp8h/hmGQkm4HoLIuj0heOrPfMRPqqb/Ayw/unQL17je7KhEpZlwOGFWqVGHbtm1Urlw50/off/yROnXquHSsUaNG0bdvX5o0aUKLFi34+OOPOXz4MMOGDQMclzeOHTvG7Nmz8fDwoF69epn2DwsLw8/PL8v6guhkYqrz+T0Ny5tYiRRpB9bCF30drRZBEdBrLpRvbHZVIlIMuRwwRo8ezeOPP05KSgqGYbBhwwbmzZvHxIkT+fTTT106Vs+ePTl9+jQvvvgiMTEx1KtXjyVLljjDS0xMzHXHxCgsth8553yuCc4kT+z/BT6/H+xWiGzsCBfBEWZXJSLFlMUwDMPVnT755BNeeuklZ4fJ8uXL88ILLzB48GC3F+huCQkJhISEEB8fn6/9MSYu2c1HFyc5O/hql3x7XylG0lNgZmcoVQXueR+8/c2uSESKGFe+Q3M1F/PQoUMZOnQocXFx2O12wsI0p8b1HD6TBECr6mVMrkSKlJR48AkCDw/w9oO+34Bv0KUhY0VETHJD90qGhoYqXOTQ0bPJADTUHSTiLqf+ho/vgFUvX1rnF6xwISIFQq46eV5rkq79+/df9bXi7I9j8QA0qVza5EqkSNj7k2OystR42PEFtHpK84mISIHicsAYMWJEpuX09HS2bt3K0qVLGT16tLvqKlJSrTbn81oRQSZWIoWeYcDvU2H5ODDsULE59Pxc4UJEChyXA8ZTTz2V7foPPviATZs23XBBRdGRi/0vAMoF+5lYiRRq1jT4YRRs/cyx3PBhuHsSePmaW5eISDbcNl51p06dWLhwobsOV6QcO5cCQICP5zUvL4lclWHA3B6OcGHxgPYvO+4UUbgQkQLKbQHjq6++onRp9S/Izh9HzwHQJErnR3LJYnHMfuobDH2+gJZPqDOniBRoLl8iadSoUaa/wg3DIDY2llOnTjFlyhS3FldULN91AoCKpTQugbgo7QL4BDqe1+8B1e6CQN3qLCIFn8sB495778207OHhQdmyZbnjjjuoVauWu+oqUkoF+ADg6aG/OCWHDAN+fRs2TYchP0PQxQkAFS5EpJBwKWBYrVaioqLo0KED5cqVy6uaipxj5xxjYLSqHmpyJVIopKfA4ifhjy8cy39+BS0eN7cmEREXudQHw8vLi0cffZTU1NTrbywA2O0G/5w8D0AFXSKR60mMhZldHOHC4gmd31S4EJFCyeVOns2aNWPr1q15UUuRtD/u0hT21cNKmFiJFHjHt8LHbeDYJvArCX0XQdOhZlclIpIrLvfBeOyxx3j66ac5evQo0dHRBAYGZnq9fv36biuuKDhwMWD4e3vi66VZVOUqDv4Knz8A1mQIrQm950GZamZXJSKSazkOGIMGDWLy5Mn07NkTgOHDhztfs1gsGIaBxWLBZrNd7RDF0pq/TwHQvKpuUZVrKHczlKzkeDwwDfxCzK5IROSG5DhgzJo1i1dffZUDBw7kZT1FTnK6I3AF+OZq4lopyqyp4OnjGM/CLwT6fweBoeChli4RKfxy/K1nGAYAlStXzrNiiqK9Fzt41o3UXBFymfijMK83NOgNLR5zrMu4FVVEpAhw6c9qDXPtuox5SMqX1B0kctGRjTC/D1w4CedPQOO+4KtJ8ESkaHEpYNSoUeO6IePMmTM3VFBRc+ZCGqBbVOWi7fNh8XCwpUJYXUdnToULESmCXAoYEyZMICREnc9yym43nM/DgjSLarFmt8PPE+C3yY7lml3gvo/BV7cui0jR5FLA6NWrF2FhYXlVS5Fz5Oxl07SHKGAUW4YBX/SFv753LLd+Gtr8BzzcNtegiEiBk+PfcOp/4bpdxxMAR/8Lb099mRRbFgtE3QqevnDfp3DX8woXIlLkuXwXieTc3yccd5DcElXK5ErEFDYreF78X6zZMKjREUpXMbcmEZF8kuM/o+x2uy6PuOjMBcecLZG6g6T42TwLProNks85li0WhQsRKVbUTpuH4s477iAJ9vc2uRLJNzYr/PgsfDccTu6ELbPMrkhExBQaXjIPrdh9AtAtqsVG8ln4ciDsX+VYbjMOWg6/9j4iIkWUAkYeKunvzcnEVEJL+JpdiuS1uH9gXk84/Q94B0D3D6HOPWZXJSJiGgWMPJKcZuNkoqMPRrWyGuugSDuyAeY8ACnxEFzBMXhWhGYVFpHiTQEjj+w75biDpGSAN6ElfEyuRvJUqSjwCXJMs95rDpRQZ2gREQWMPJIxB0nl0gEaQ6QostsvjWVRIgwGfAfB5cFLl8NEREB3keSZP4/HAxARog6eRc6F0zCrq2NekQylqypciIhcRi0YeSQpzQaATQOUFS0nd8PcnnDuEJz6C2rdrflERESyoYCRRw6fdlwiqRoaaHIl4jZ7lsLCIZCW6Oh30Xu+woWIyFUoYOSRn/86CcCtN4WaXIncMMOAde/CivGAAZVvhR6zIbCM2ZWJiBRYChh54PJ5W8prmPDCzTDgm8dg+1zHcvQA6PQGeOnOIBGRa1HAyAPnU63O5+rkWchZLBBSHiye0PFVaDrUsU5ERK5JASMPZMxBAuDv42liJZJrhnEpSNzxnKMzZ2RDU0sSESlMdJtqHtgdkwBoDpJCa9e3jttQ05Mdyx4eChciIi5SwMgDGYNsqSW9kDEM+OU1+KIfHFwLGz42uyIRkUJLl0jywD8nHcOElwpQR8BCIy0Jvn0Mdi5yLDd/DJo/bm5NIiKFmAJGHsjod6GAUUgkHId5vSFmG3h4Q5e3ILq/2VWJiBRqChh5YNPBswC0qKZxEgq849scI3Oej4WAMtDjM4hqZXZVIiKFngJGHvD2cnRtSbPaTa5ErssvBGxpEFbHMc16qSizKxIRKRIUMPLA8XOOuw+iNEx4wVe6CvT7xjFZmW+Q2dWIiBQZuoskD6RcnOgsxN/b5Eoki9TzsOBh+HvZpXURDRQuRETcTAEjDyReHMmztDp5FixnD8G09rD7O8fw32kXzK5IRKTI0iUSNzMMAx9PD9JsdkqXUMAoMA6thwUPQdJpCAyDXnPBR5ewRETyigKGmyWkWEmzOTp3lgrQJZICYcts+H4U2NOhXH1HZ86QCmZXJSJSpClguNnBOEeze2gJHwJ8dHpNZbfD8v/A7x84luvcA/dOVcuFiEg+0Degm51MTAUgLMjP5EoEDw+wpjie3zEWbhvjWCciInlOAcPNth1xDLKlic4KiE6vQZ1uUPUOsysRESlW9OecmyWmOO4gqVVOtz2aYv9q+HIA2Bz/Dnh6K1yIiJhALRhudv5iwPBX/4v8t+ET+PHfYNigfDS0fNLsikREii19C7pZfHI6ACV1B0n+saU7gsWmaY7l+j3hlqHm1iQiUswpYLjZmaQ0AEr46tTmi6Qz8GV/OLAGsEDb8dBqBFgsZlcmIlKs6VvQzU4mOO4iCQ/WXSR57tQex0yoZw+ATwm47xOo1dnsqkREBAUMtztzwdGCEapRPPOe3QoXTkHJStB7PoTXNbsiERG5SAHDjaw2O8npjonOSmoekrwXXhf6LICytSAw1OxqRETkMrpN1Y1OX2y9APXByBPWVFg8HA7/fmld1K0KFyIiBZAChhvFxqc4n/t46dS61flTMKsrbJkFX/SHtCSzKxIRkWsw/VtwypQpVKlSBT8/P6Kjo1m7du1Vt/36669p164dZcuWJTg4mBYtWrBs2bJ8rPba9sedByAiRB083Sr2D/ikDRz5H/iGwL0fgE+A2VWJiMg1mBowFixYwIgRIxg3bhxbt26ldevWdOrUicOHD2e7/Zo1a2jXrh1Llixh8+bNtGnThq5du7J169Z8rjx7x84mA5BmtZtcSRGy+3uY1gHij0DpqjDkJ6je1uyqRETkOiyGYRhmvXmzZs1o3LgxU6dOda6rXbs29957LxMnTszRMerWrUvPnj15/vnnc7R9QkICISEhxMfHExwcnKu6r+bTtft56Yfd1AgvwfKRt7v12MWOYcDat2Dlfx3LVW6HB2dCQGlTyxIRKc5c+Q41rQUjLS2NzZs30759+0zr27dvz7p163J0DLvdTmJiIqVLX/1LJzU1lYSEhEyPvHL24iBb0ZVL5dl7FBuG4bg0AtD0X/DwQoULEZFCxLSAERcXh81mIzw8PNP68PBwYmNjc3SMt956iwsXLtCjR4+rbjNx4kRCQkKcj4oVK95Q3ddy+IzjEomHRpG8cR4ecO9UeGA6dH7DMWmZiIgUGqZ38rRc8WVsGEaWddmZN28eL7zwAgsWLCAsLOyq240dO5b4+Hjn48iRIzdc89UE+zluTU1VH4zcObYFloxxtF6AoyNnvfvNrUlERHLFtMEaQkND8fT0zNJacfLkySytGldasGABgwcP5ssvv6Rt22t3+PP19cXX1/eG682JTQfPAnBTWIl8eb8i5Y+v4NvHwZoCoTdBU01WJiJSmJnWguHj40N0dDQrVqzItH7FihW0bNnyqvvNmzePAQMGMHfuXLp06ZLXZboksqTj9tQLaTaTKylE7HZY+RIsHOwIFzd1cMyGKiIihZqpw02OGjWKvn370qRJE1q0aMHHH3/M4cOHGTZsGOC4vHHs2DFmz54NOMJFv379eOedd2jevLmz9cPf35+QkBDTPkeG86lWAGqGB5lcSSGReh4WPQJ/fe9Ybjkc2r4AHp6mliUiIjfO1IDRs2dPTp8+zYsvvkhMTAz16tVjyZIlVK5cGYCYmJhMY2J89NFHWK1WHn/8cR5//HHn+v79+zNz5sz8Lj+LjKHCg/w0TPh1nTsM8/rAiT/A0we6vgMN+5hdlYiIuImp42CYIS/HwWj+ys/EJqTw/ZO3Uq+8+S0qBdrh32Hm3eBfEnrNhYpNza5IRESuw5XvUP2p7UaJKekABGqis+ur1BwenAERDaFk3t06LCIi5jD9NtWiwjAM51TtgT7qQ5CF3QY/vwgndl5aV7urwoWISBGlgOEmaTY79osXm/wUMDJLSYB5vRxDf8/rDenJZlckIiJ5TG35bpKYYnU+D/TRaXU6s98RKk79BV5+0HY8ePubXZWIiOQxfRO6SfLFsS88PSx4emiocAAOrIEv+kHyWQiKcHTmLN/Y7KpERCQfKGC4ScYtqgHeujwCwKbpsGQ02K0Q2dgRLoIjzK5KRETyiQKGm6TbHPOPJKZar7NlMWC3w65vHeHi5geh23u6LCIiUswoYLhJysU7SGqV0yieeHjAgzMd84vcMgQ0u6yISLGju0jcJDY+BQBvz2J6Sk/9DavfuLTsX8oxYZnChYhIsaQWDDeJT3YMsnUyMcXkSkzwz0/w5SBIjYegctC4r9kViYiIyRQw3MR6cRCMyJLFqK+BYcD/PoRlz4Fhh4rNoUZHs6sSEZECQAHDzcoE+phdQv6wpsGSp2GLY6ZbGj4Md08CL19z6xIRkQJBAcNNrBfvIgktUQy+YC/EwYK+cHgdWDyg3X+hxePqbyEiIk4KGG6SbnNcIvHyLAZfsjHb4PB68A2GB6bDTe3MrkhERAoYBQw3OZfkGGjLy6MY3EVSvS10fccxI2rZmmZXIyIiBVAx+DbMH2eTHHeRWO12kyvJA4YB6z+AswcvrYvur3AhIiJXpYDhJqUCvAFITS9iASM9Bb7+l+NOkXm9HcsiIiLXoUskbpJ+8TbVCqUCTK7EjRJjYX4fOLYZLJ7QZBB4+5ldlYiIFAIKGG6SbnW0XHh7FZFOnse3wrw+kHgc/EpCj1lQ9Q6zqxIRkUJCAcNNYjKGCi8KnTz//Bq+eQysyRBaA3rPhzLVzK5KREQKEQUMN0m72IKRarWZXMkNsttg/fuOcFG9reM2VL8Qs6sSEZFCRgHDTUIudvL08SrkLRgentBzDmyeCbePcSyLiIi4qJB/GxYcGSN5lgwohEOFxx91BIoMwRHQZqzChYiI5JpaMNwkY7IzL49C1snzyEbHnSIXTjo6c9a91+yKRESkCFDAcBOrc6jwQtQotH0+LB4OtlQIrwflG5tdkYiIFBEKGG5iK0wtGHYb/Pwi/DbZsVzrbuj+EfiWMLUsEREpOhQw3CQ53XH3iHdBb8FITYSFQ+HvHx3LrZ+BNuOgKNxeKyIiBYYChptcSLUCEOxXwE/pvlWOcOHpC/d8APUfNLsiEREpggr4t2HhsT/uAgABPgX8lNbpBneNhyq3Q4Vos6sREZEiSu3ibpLRcuHlWQD7YGyfD+dPXlpuPUrhQkRE8pQChpukXRwHo4RvAWrBsFnhx3/DokdgwcNgTTO7IhERKSYK0Ldh4ZZycZr2AjOSZ/JZ+HIg7F/lWK7eDjy9za1JRESKDQUMN0i/2HoB4FMQ7iKJ+wfm9YTT/4B3gOMW1DrdzK5KRESKEQUMN0hKuzTBWaDZl0j2rYQvB0BKPARXgN7zIKK+uTWJiEixo4DhBtbLWjC8zezkabfB0ucc4aJCU+g1B0qEmVePiIgUWwWgPb/wS7ddGsXTYjExYHh4OkLFLUNhwPcKFyIiYhoFDDdIMXMUzwunYdfiS8tlqkGXN8HLN/9rERERuUgBww2sdsclkozhwvPNyd3wSRv4sj/883P+vreIiMg1qA+GG2RcIikd6JN/b7pnKSwcAmmJUCoKgiPz771FRESuQwHDDTKmavfNjzEwDAPWvQsrxgMGVL4VesyGwDJ5/94iIiI5pIDhBukXL5Hk+TDh6Snw/QjYPs+xHD0AOr0BXvnYciIiIpIDChhukG51BAzvvJ7yfPdiR7iweELHV6HpUDDzrhUREZGrUMBwg4x5SPJ8mPCbH4Tj2+CmtlDtzrx9LxERkRuggOEGGX0w8iRg/L0cKjUHv2BHa0XHV9z/HiIiIm6m21TdwGZ3BAwPd16uMAxY/TrMfRAWDnaM0ikiIlJIqAXDDexGRsBw0wHTkuDbx2DnIsdy6WqOwCEiIlJIKGC4waWA4YaEkXAc5vWGmG3g4QVd3nLcLSIiIlKIKGC4wcUrJHjcaBPG0U0wvw+cPwH+paHnZxB1640XKCIiks8UMNzgUh+MGzmIFb4e6ggXYXUc06yXinJLfSIiIvlNnTzdIOMSieeNJAxPL3hgBtS9DwYvV7gQEZFCTQHDDXLdByM1EQ6svbQc2RAenAG+Qe4rTkRExAQKGG5wcZwt1wLG2UMwrQN8fj8c3Zw3hYmIiJhEfTDc4EKqFXChD8ah9bDgIUg6DYFhgG5BFRGRokUBww2S0x2DYJ1LTr/+xls+g+9Hgj0dytV3dOYMqZDHFYqIiOQvBQw3CPTxBC7drpotuw1WPA/r33cs17kH7p0KPoF5X6CIiEg+U8Bwg4zbVCuU9L/6RtvnXQoXd4yF28ZAXs++KiIiYhIFDDfI0UBbDfrAvlVQ+26o2z1/ChMRETGJAoYbXHUukiMbHP0svP0crRUPTMv/4kREREygNno3yLhE4nn5baobPoHpHeG74ZqoTEREih3TA8aUKVOoUqUKfn5+REdHs3bt2mtuv3r1aqKjo/Hz86Nq1ap8+OGH+VTp1dkyWjA8LGBLh+9HwZJnwLg4xbrdamJ1IiIi+c/UgLFgwQJGjBjBuHHj2Lp1K61bt6ZTp04cPnw42+0PHDhA586dad26NVu3buW5555j+PDhLFy4MJ8rzyyjgSLIlgCfdYdN0wALtH0Bun8Ent5mliciIpLvLIZhXvt9s2bNaNy4MVOnTnWuq127Nvfeey8TJ07Msv2///1vFi9ezO7du53rhg0bxvbt21m/fn2O3jMhIYGQkBDi4+MJDg6+8Q8BvPvzXr79aRVfBE2mTNox8CkB938KNTu55fgiIiIFgSvfoaa1YKSlpbF582bat2+faX379u1Zt25dtvusX78+y/YdOnRg06ZNpKdnP8hVamoqCQkJmR7uZljTmeH9uiNclKwEg1coXIiISLFmWsCIi4vDZrMRHh6eaX14eDixsbHZ7hMbG5vt9larlbi4uGz3mThxIiEhIc5HxYoV3fMBLmO1ePJv67/YV6IJDF0F4XXc/h4iIiKFiemdPC1XTBBmGEaWddfbPrv1GcaOHUt8fLzzceTIkRusOKsHoyvyxKBBGH2/gcBQtx9fRESksDFtHIzQ0FA8PT2ztFacPHkySytFhnLlymW7vZeXF2XKlMl2H19fX3x9fd1T9FVUKhNApTIBefoeIiIihYlpLRg+Pj5ER0ezYsWKTOtXrFhBy5Yts92nRYsWWbZfvnw5TZo0wdtbd2qIiIgUFKZeIhk1ahSffvop06dPZ/fu3YwcOZLDhw8zbNgwwHF5o1+/fs7thw0bxqFDhxg1ahS7d+9m+vTpTJs2jWeeecasjyAiIiLZMHWo8J49e3L69GlefPFFYmJiqFevHkuWLKFy5coAxMTEZBoTo0qVKixZsoSRI0fywQcfEBkZybvvvsv9999v1kcQERGRbJg6DoYZ8mIcDBERkeKgUIyDISIiIkWXAoaIiIi4nQKGiIiIuJ0ChoiIiLidAoaIiIi4nQKGiIiIuJ0ChoiIiLidAoaIiIi4nQKGiIiIuJ0ChoiIiLidqXORmCFjZPSEhASTKxERESlcMr47czLLSLELGImJiQBUrFjR5EpEREQKp8TEREJCQq65TbGb7Mxut3P8+HGCgoKwWCxuO25CQgIVK1bkyJEjmkTNDXQ+3U/n1L10Pt1P59S98uJ8GoZBYmIikZGReHhcu5dFsWvB8PDwoEKFCnl2/ODgYP2P4UY6n+6nc+peOp/up3PqXu4+n9drucigTp4iIiLidgoYIiIi4nYKGG7i6+vL+PHj8fX1NbuUIkHn0/10Tt1L59P9dE7dy+zzWew6eYqIiEjeUwuGiIiIuJ0ChoiIiLidAoaIiIi4nQKGiIiIuJ0CRg5NmTKFKlWq4OfnR3R0NGvXrr3m9qtXryY6Oho/Pz+qVq3Khx9+mE+VFh6unNOvv/6adu3aUbZsWYKDg2nRogXLli3Lx2oLPld/RjP89ttveHl50bBhw7wtsBBy9ZympqYybtw4KleujK+vL9WqVWP69On5VG3h4Oo5nTNnDg0aNCAgIICIiAgGDhzI6dOn86nagm3NmjV07dqVyMhILBYL33zzzXX3ydfvJkOua/78+Ya3t7fxySefGLt27TKeeuopIzAw0Dh06FC22+/fv98ICAgwnnrqKWPXrl3GJ598Ynh7extfffVVPldecLl6Tp966injtddeMzZs2GD8/fffxtixYw1vb29jy5Yt+Vx5weTq+cxw7tw5o2rVqkb79u2NBg0a5E+xhURuzmm3bt2MZs2aGStWrDAOHDhg/O9//zN+++23fKy6YHP1nK5du9bw8PAw3nnnHWP//v3G2rVrjbp16xr33ntvPldeMC1ZssQYN26csXDhQgMwFi1adM3t8/u7SQEjB5o2bWoMGzYs07patWoZzz77bLbbjxkzxqhVq1amdY888ojRvHnzPKuxsHH1nGanTp06xoQJE9xdWqGU2/PZs2dP4z//+Y8xfvx4BYwruHpOf/zxRyMkJMQ4ffp0fpRXKLl6Tt944w2jatWqmda9++67RoUKFfKsxsIqJwEjv7+bdInkOtLS0ti8eTPt27fPtL59+/asW7cu233Wr1+fZfsOHTqwadMm0tPT86zWwiI35/RKdrudxMRESpcunRclFiq5PZ8zZsxg3759jB8/Pq9LLHRyc04XL15MkyZNeP311ylfvjw1atTgmWeeITk5OT9KLvByc05btmzJ0aNHWbJkCYZhcOLECb766iu6dOmSHyUXOfn93VTsJjtzVVxcHDabjfDw8Ezrw8PDiY2NzXaf2NjYbLe3Wq3ExcURERGRZ/UWBrk5p1d66623uHDhAj169MiLEguV3JzPvXv38uyzz7J27Vq8vPRr4Eq5Oaf79+/n119/xc/Pj0WLFhEXF8djjz3GmTNn1A+D3J3Tli1bMmfOHHr27ElKSgpWq5Vu3brx3nvv5UfJRU5+fzepBSOHrpza3TCMa073nt322a0vzlw9pxnmzZvHCy+8wIIFCwgLC8ur8gqdnJ5Pm81Gnz59mDBhAjVq1Miv8golV35G7XY7FouFOXPm0LRpUzp37sykSZOYOXOmWjEu48o53bVrF8OHD+f5559n8+bNLF26lAMHDjBs2LD8KLVIys/vJv3pch2hoaF4enpmSdgnT57MkgQzlCtXLtvtvby8KFOmTJ7VWljk5pxmWLBgAYMHD+bLL7+kbdu2eVlmoeHq+UxMTGTTpk1s3bqVJ554AnB8ORqGgZeXF8uXL+fOO+/Ml9oLqtz8jEZERFC+fPlMU1nXrl0bwzA4evQoN910U57WXNDl5pxOnDiRVq1aMXr0aADq169PYGAgrVu35qWXXir2rcGuyu/vJrVgXIePjw/R0dGsWLEi0/oVK1bQsmXLbPdp0aJFlu2XL19OkyZN8Pb2zrNaC4vcnFNwtFwMGDCAuXPn6hrsZVw9n8HBwfzxxx9s27bN+Rg2bBg1a9Zk27ZtNGvWLL9KL7By8zPaqlUrjh8/zvnz553r/v77bzw8PKhQoUKe1lsY5OacJiUl4eGR+WvK09MTuPSXt+Rcvn835UnX0SIm49aqadOmGbt27TJGjBhhBAYGGgcPHjQMwzCeffZZo2/fvs7tM24FGjlypLFr1y5j2rRpuk31Cq6e07lz5xpeXl7GBx98YMTExDgf586dM+sjFCiuns8r6S6SrFw9p4mJiUaFChWMBx54wNi5c6exevVq46abbjKGDBli1kcocFw9pzNmzDC8vLyMKVOmGPv27TN+/fVXo0mTJkbTpk3N+ggFSmJiorF161Zj69atBmBMmjTJ2Lp1q/O2X7O/mxQwcuiDDz4wKleubPj4+BiNGzc2Vq9e7Xytf//+xu23355p+19++cVo1KiR4ePjY0RFRRlTp07N54oLPlfO6e23324AWR79+/fP/8ILKFd/Ri+ngJE9V8/p7t27jbZt2xr+/v5GhQoVjFGjRhlJSUn5XHXB5uo5fffdd406deoY/v7+RkREhPHQQw8ZR48ezeeqC6ZVq1Zd8/ei2d9Nmq5dRERE3E59MERERMTtFDBERETE7RQwRERExO0UMERERMTtFDBERETE7RQwRERExO0UMERERMTtFDBERETE7RQwRIqYmTNnUrJkSbPLyLWoqCgmT558zW1eeOEFGjZsmC/1iEjuKGCIFEADBgzAYrFkefzzzz9ml8bMmTMz1RQREUGPHj04cOCAW46/ceNG/vWvfzmXLRYL33zzTaZtnnnmGX7++We3vN/VXPk5w8PD6dq1Kzt37nT5OIU58InklgKGSAHVsWNHYmJiMj2qVKlidlmAY0bWmJgYjh8/zty5c9m2bRvdunXDZrPd8LHLli1LQEDANbcpUaJEnkwvfaXLP+cPP/zAhQsX6NKlC2lpaXn+3iKFnQKGSAHl6+tLuXLlMj08PT2ZNGkSN998M4GBgVSsWJHHHnss0xThV9q+fTtt2rQhKCiI4OBgoqOj2bRpk/P1devWcdttt+Hv70/FihUZPnw4Fy5cuGZtFouFcuXKERERQZs2bRg/fjx//vmns4Vl6tSpVKtWDR8fH2rWrMlnn32Waf8XXniBSpUq4evrS2RkJMOHD3e+dvklkqioKAC6d++OxWJxLl9+iWTZsmX4+flx7ty5TO8xfPhwbr/9drd9ziZNmjBy5EgOHTrEnj17nNtc69/jl19+YeDAgcTHxztbQl544QUA0tLSGDNmDOXLlycwMJBmzZrxyy+/XLMekcJEAUOkkPHw8ODdd9/lzz//ZNasWaxcuZIxY8ZcdfuHHnqIChUqsHHjRjZv3syzzz6Lt7c3AH/88QcdOnTgvvvuY8eOHSxYsIBff/2VJ554wqWa/P39AUhPT2fRokU89dRTPP300/z555888sgjDBw4kFWrVgHw1Vdf8fbbb/PRRx+xd+9evvnmG26++eZsj7tx40YAZsyYQUxMjHP5cm3btqVkyZIsXLjQuc5ms/HFF1/w0EMPue1znjt3jrlz5wI4zx9c+9+jZcuWTJ482dkSEhMTwzPPPAPAwIED+e2335g/fz47duzgwQcfpGPHjuzduzfHNYkUaHk2T6uI5Fr//v0NT09PIzAw0Pl44IEHst32iy++MMqUKeNcnjFjhhESEuJcDgoKMmbOnJntvn379jX+9a9/ZVq3du1aw8PDw0hOTs52nyuPf+TIEaN58+ZGhQoVjNTUVKNly5bG0KFDM+3z4IMPGp07dzYMwzDeeusto0aNGkZaWlq2x69cubLx9ttvO5cBY9GiRZm2uXJ6+eHDhxt33nmnc3nZsmWGj4+PcebMmRv6nIARGBhoBAQEOKfC7tatW7bbZ7jev4dhGMY///xjWCwW49ixY5nW33XXXcbYsWOveXyRwsLL3HgjIlfTpk0bpk6d6lwODAwEYNWqVbzyyivs2rWLhIQErFYrKSkpXLhwwbnN5UaNGsWQIUP47LPPaNu2LQ8++CDVqlUDYPPmzfzzzz/MmTPHub1hGNjtdg4cOEDt2rWzrS0+Pp4SJUpgGAZJSUk0btyYr7/+Gh8fH3bv3p2pkyZAq1ateOeddwB48MEHmTx5MlWrVqVjx4507tyZrl274uWV+19HDz30EC1atOD48eNERkYyZ84cOnfuTKlSpW7ocwYFBbFlyxasViurV6/mjTfe4MMPP8y0jav/HgBbtmzBMAxq1KiRaX1qamq+9C0RyQ8KGCIFVGBgINWrV8+07tChQ3Tu3Jlhw4bx3//+l9KlS/Prr78yePBg0tPTsz3OCy+8QJ8+ffjhhx/48ccfGT9+PPPnz6d79+7Y7XYeeeSRTH0gMlSqVOmqtWV88Xp4eBAeHp7li9RisWRaNgzDua5ixYrs2bOHFStW8NNPP/HYY4/xxhtvsHr16kyXHlzRtGlTqlWrxvz583n00UdZtGgRM2bMcL6e28/p4eHh/DeoVasWsbGx9OzZkzVr1gC5+/fIqMfT05PNmzfj6emZ6bUSJUq49NlFCioFDJFCZNOmTVitVt566y08PBxdqL744ovr7lejRg1q1KjByJEj6d27NzNmzKB79+40btyYnTt3Zgky13P5F++Vateuza+//kq/fv2c69atW5eplcDf359u3brRrVs3Hn/8cWrVqsUff/xB48aNsxzP29s7R3en9OnThzlz5lChQgU8PDzo0qWL87Xcfs4rjRw5kkmTJrFo0SK6d++eo38PHx+fLPU3atQIm83GyZMnad269Q3VJFJQqZOnSCFSrVo1rFYr7733Hvv37+ezzz7L0mR/ueTkZJ544gl++eUXDh06xG+//cbGjRudX/b//ve/Wb9+PY8//jjbtm1j7969LF68mCeffDLXNY4ePZqZM2fy4YcfsnfvXiZNmsTXX3/t7Nw4c+ZMpk2bxp9//un8DP7+/lSuXDnb40VFRfHzzz8TGxvL2bNnr/q+Dz30EFu2bOHll1/mgQcewM/Pz/mauz5ncHAwQ4YMYfz48RiGkaN/j6ioKM6fP8/PP/9MXFwcSUlJ1KhRg4ceeoh+/frx9ddfc+DAATZu3Mhrr73GkiVLXKpJpMAyswOIiGSvf//+xj333JPta5MmTTIiIiIMf39/o0OHDsbs2bMNwDh79qxhGJk7Faamphq9evUyKlasaPj4+BiRkZHGE088kalj44YNG4x27doZJUqUMAIDA4369esbL7/88lVry67T4pWmTJliVK1a1fD29jZq1KhhzJ492/naokWLjGbNmhnBwcFGYGCg0bx5c+Onn35yvn5lJ8/Fixcb1atXN7y8vIzKlSsbhpG1k2eGW265xQCMlStXZnnNXZ/z0KFDhpeXl7FgwQLDMK7/72EYhjFs2DCjTJkyBmCMHz/eMAzDSEtLM55//nkjKirK8Pb2NsqVK2d0797d2LFjx1VrEilMLIZhGOZGHBERESlqdIlERERE3E4BQ0RERNxOAUNERETcTgFDRERE3E4BQ0RERNxOAUNERETcTgFDRERE3E4BQ0RERNxOAUNERETcTgFDRERE3E4BQ0RERNzu/wEQJZ9JWmvwCAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _=roc_curve(y_val, y_prob_xgb)\n",
    "\n",
    "plt.figure(figsize=(6,5))\n",
    "plt.plot(fpr, tpr, label=f\"AUC = {roc_auc_xgb:.4f}\")\n",
    "plt.plot([0,1],[0,1],'--')\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"ROC Curve – LightGBM\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df5a0c13-9be1-43fd-8b9d-fca35342d894",
   "metadata": {},
   "source": [
    "### Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ad74a4c7-ed33-4e88-8182-b7e38e329ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "testids=test[\"ID_code\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d50759a8-4735-4106-91d8-d2fb0d90881d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test=test.drop(columns=[\"ID_code\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f4ee41cc-7aaf-4707-a574-a45e10e2eb74",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_prob=lgbc.predict_proba(test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "75276791-d1a6-4c65-89ad-c6ef93db9ed9",
   "metadata": {},
   "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_code</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test_0</td>\n",
       "      <td>0.454843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test_1</td>\n",
       "      <td>0.563838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test_2</td>\n",
       "      <td>0.552455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>test_3</td>\n",
       "      <td>0.408692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>test_4</td>\n",
       "      <td>0.232621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ID_code    target\n",
       "0  test_0  0.454843\n",
       "1  test_1  0.563838\n",
       "2  test_2  0.552455\n",
       "3  test_3  0.408692\n",
       "4  test_4  0.232621"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission=pd.DataFrame({'ID_code': testids,'target': test_prob})\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7d00a49a-8815-4aba-a4fe-318046b9a3bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(\"submission_lightgbm.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "69654a14-d91f-4bb3-83ae-3279c7a7e59b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 2)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4328a789-07b2-40c3-b9d4-038ecb0897fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID_code    0\n",
       "target     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "fb667c97-6ff6-4ef1-b940-64e668fb4ea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_sample=test.sample(5000, random_state=42)\n",
    "test_sample.to_csv('test_sample.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cfd419d-1731-4f97-b0dd-f368d3ee979f",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "In this project, different classification models were tested to predict customer transactions. Logistic Regression was used as a baseline model and achieved a ROC-AUC score of about 0.86.\n",
    "\n",
    "The gradient boosting models performed better. LightGBM achieved the best result with a ROC-AUC score of 0.8888, which was higher than both Logistic Regression and XGBoost. Even though XGBoost had a slightly higher accuracy, ROC-AUC was chosen as the main metric because the dataset is imbalanced.\n",
    "\n",
    "Therefore, LightGBM was selected as the final model. The model was applied to the test dataset, and probability predictions were created for Kaggle submission. This project shows that choosing the right evaluation metric and model is very important for tabular data."
   ]
  }
 ],
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