{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_csv('xtraining_SC_GGP_AXA_FR.csv')\n",
    "y = pd.read_csv('ytraining_SC_GGP_AXA_FR.csv')\n",
    "Xscore = pd.read_csv('xscoring_SC_GGP_AXA_FR.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            target\n",
       "count  1000.000000\n",
       "mean      0.170000\n",
       "std       0.375821\n",
       "min       0.000000\n",
       "25%       0.000000\n",
       "50%       0.000000\n",
       "75%       0.000000\n",
       "max       1.000000"
      ],
      "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.170000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.375821</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "y.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               age      surface  montant_assure  \\\n",
       "count  1000.000000  1000.000000     1000.000000   \n",
       "mean     42.198000    86.863400    22461.069000   \n",
       "std      14.710853    28.498626     9076.273609   \n",
       "min      15.000000    31.270000     4478.000000   \n",
       "25%      31.000000    66.235000    15574.500000   \n",
       "50%      42.000000    80.605000    21201.000000   \n",
       "75%      52.000000   104.842500    27597.000000   \n",
       "max      94.000000   179.780000    55852.000000   \n",
       "\n",
       "       nb_sinistres_depuis_ouverture  cotisation_n-1  cotisation_n  \\\n",
       "count                    1000.000000     1000.000000   1000.000000   \n",
       "mean                        1.492000      272.762170    286.046570   \n",
       "std                         0.954383       76.378759     82.097481   \n",
       "min                         0.000000      175.380000    181.360000   \n",
       "25%                         1.000000      211.842500    220.230000   \n",
       "50%                         1.000000      227.525000    240.705000   \n",
       "75%                         2.000000      360.062500    372.647500   \n",
       "max                         4.000000      440.870000    510.490000   \n",
       "\n",
       "       duree_contrat  duree_dernier_sinistre  duree_zero_sinistre  \\\n",
       "count    1000.000000             1000.000000          1000.000000   \n",
       "mean     4883.704000               40.023000          3444.616000   \n",
       "std      2716.954916               41.793913          2826.615494   \n",
       "min        49.000000                0.000000             0.000000   \n",
       "25%      2637.750000                1.000000           565.000000   \n",
       "50%      4819.000000               29.000000          3111.500000   \n",
       "75%      7353.250000               68.000000          6037.000000   \n",
       "max      9526.000000              202.000000          9429.000000   \n",
       "\n",
       "           depa_01  ...  cate_Artisans,  cate_Cadres  cate_Employés  \\\n",
       "count  1000.000000  ...     1000.000000  1000.000000    1000.000000   \n",
       "mean      0.009000  ...        0.174000     0.162000       0.341000   \n",
       "std       0.094488  ...        0.379299     0.368635       0.474283   \n",
       "min       0.000000  ...        0.000000     0.000000       0.000000   \n",
       "25%       0.000000  ...        0.000000     0.000000       0.000000   \n",
       "50%       0.000000  ...        0.000000     0.000000       0.000000   \n",
       "75%       0.000000  ...        0.000000     0.000000       1.000000   \n",
       "max       1.000000  ...        1.000000     1.000000       1.000000   \n",
       "\n",
       "       cate_Inactifs  cate_Ouvriers  type_Appartement  type_Maison  \\\n",
       "count    1000.000000    1000.000000        1000.00000   1000.00000   \n",
       "mean        0.067000       0.125000           0.63400      0.36600   \n",
       "std         0.250147       0.330884           0.48195      0.48195   \n",
       "min         0.000000       0.000000           0.00000      0.00000   \n",
       "25%         0.000000       0.000000           0.00000      0.00000   \n",
       "50%         0.000000       0.000000           1.00000      0.00000   \n",
       "75%         0.000000       0.000000           1.00000      1.00000   \n",
       "max         1.000000       1.000000           1.00000      1.00000   \n",
       "\n",
       "       stat_Locataire  stat_Occupant  stat_Propriétaire  \n",
       "count     1000.000000    1000.000000        1000.000000  \n",
       "mean         0.586000       0.028000           0.386000  \n",
       "std          0.492795       0.165055           0.487074  \n",
       "min          0.000000       0.000000           0.000000  \n",
       "25%          0.000000       0.000000           0.000000  \n",
       "50%          1.000000       0.000000           0.000000  \n",
       "75%          1.000000       0.000000           1.000000  \n",
       "max          1.000000       1.000000           1.000000  \n",
       "\n",
       "[8 rows x 121 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>surface</th>\n      <th>montant_assure</th>\n      <th>nb_sinistres_depuis_ouverture</th>\n      <th>cotisation_n-1</th>\n      <th>cotisation_n</th>\n      <th>duree_contrat</th>\n      <th>duree_dernier_sinistre</th>\n      <th>duree_zero_sinistre</th>\n      <th>depa_01</th>\n      <th>...</th>\n      <th>cate_Artisans,</th>\n      <th>cate_Cadres</th>\n      <th>cate_Employés</th>\n      <th>cate_Inactifs</th>\n      <th>cate_Ouvriers</th>\n      <th>type_Appartement</th>\n      <th>type_Maison</th>\n      <th>stat_Locataire</th>\n      <th>stat_Occupant</th>\n      <th>stat_Propriétaire</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>...</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.00000</td>\n      <td>1000.00000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>42.198000</td>\n      <td>86.863400</td>\n      <td>22461.069000</td>\n      <td>1.492000</td>\n      <td>272.762170</td>\n      <td>286.046570</td>\n      <td>4883.704000</td>\n      <td>40.023000</td>\n      <td>3444.616000</td>\n      <td>0.009000</td>\n      <td>...</td>\n      <td>0.174000</td>\n      <td>0.162000</td>\n      <td>0.341000</td>\n      <td>0.067000</td>\n      <td>0.125000</td>\n      <td>0.63400</td>\n      <td>0.36600</td>\n      <td>0.586000</td>\n      <td>0.028000</td>\n      <td>0.386000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>14.710853</td>\n      <td>28.498626</td>\n      <td>9076.273609</td>\n      <td>0.954383</td>\n      <td>76.378759</td>\n      <td>82.097481</td>\n      <td>2716.954916</td>\n      <td>41.793913</td>\n      <td>2826.615494</td>\n      <td>0.094488</td>\n      <td>...</td>\n      <td>0.379299</td>\n      <td>0.368635</td>\n      <td>0.474283</td>\n      <td>0.250147</td>\n      <td>0.330884</td>\n      <td>0.48195</td>\n      <td>0.48195</td>\n      <td>0.492795</td>\n      <td>0.165055</td>\n      <td>0.487074</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>15.000000</td>\n      <td>31.270000</td>\n      <td>4478.000000</td>\n      <td>0.000000</td>\n      <td>175.380000</td>\n      <td>181.360000</td>\n      <td>49.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>31.000000</td>\n      <td>66.235000</td>\n      <td>15574.500000</td>\n      <td>1.000000</td>\n      <td>211.842500</td>\n      <td>220.230000</td>\n      <td>2637.750000</td>\n      <td>1.000000</td>\n      <td>565.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>42.000000</td>\n      <td>80.605000</td>\n      <td>21201.000000</td>\n      <td>1.000000</td>\n      <td>227.525000</td>\n      <td>240.705000</td>\n      <td>4819.000000</td>\n      <td>29.000000</td>\n      <td>3111.500000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.00000</td>\n      <td>0.00000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>52.000000</td>\n      <td>104.842500</td>\n      <td>27597.000000</td>\n      <td>2.000000</td>\n      <td>360.062500</td>\n      <td>372.647500</td>\n      <td>7353.250000</td>\n      <td>68.000000</td>\n      <td>6037.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.00000</td>\n      <td>1.00000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>94.000000</td>\n      <td>179.780000</td>\n      <td>55852.000000</td>\n      <td>4.000000</td>\n      <td>440.870000</td>\n      <td>510.490000</td>\n      <td>9526.000000</td>\n      <td>202.000000</td>\n      <td>9429.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.00000</td>\n      <td>1.00000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 121 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "             age     surface  montant_assure  nb_sinistres_depuis_ouverture  \\\n",
       "count  300.00000  300.000000      300.000000                     300.000000   \n",
       "mean    42.59000   86.704767    22894.856667                       1.466667   \n",
       "std     14.62949   30.453970     9374.614330                       0.992727   \n",
       "min     15.00000   29.360000     7217.000000                       0.000000   \n",
       "25%     31.00000   63.672500    15281.750000                       1.000000   \n",
       "50%     43.00000   78.530000    20810.500000                       1.000000   \n",
       "75%     54.00000  107.357500    28494.750000                       2.000000   \n",
       "max     86.00000  169.560000    50948.000000                       5.000000   \n",
       "\n",
       "       cotisation_n-1  cotisation_n  duree_contrat  duree_dernier_sinistre  \\\n",
       "count      300.000000    300.000000     300.000000              300.000000   \n",
       "mean       274.989200    288.086200    4810.156667               34.820000   \n",
       "std         80.104622     86.567491    2673.566074               39.671826   \n",
       "min        172.470000    180.700000      88.000000                0.000000   \n",
       "25%        212.580000    221.105000    2521.000000                1.000000   \n",
       "50%        227.590000    240.395000    5101.000000               19.500000   \n",
       "75%        364.185000    370.237500    7099.500000               62.000000   \n",
       "max        450.560000    599.500000    9530.000000              189.000000   \n",
       "\n",
       "       duree_zero_sinistre     depa_01  ...  cate_Artisans,  cate_Cadres  \\\n",
       "count           300.000000  300.000000  ...      300.000000   300.000000   \n",
       "mean           3174.016667    0.010000  ...        0.156667     0.186667   \n",
       "std            2745.057079    0.099665  ...        0.364094     0.390295   \n",
       "min               0.000000    0.000000  ...        0.000000     0.000000   \n",
       "25%             472.500000    0.000000  ...        0.000000     0.000000   \n",
       "50%            2566.500000    0.000000  ...        0.000000     0.000000   \n",
       "75%            5547.250000    0.000000  ...        0.000000     0.000000   \n",
       "max            9019.000000    1.000000  ...        1.000000     1.000000   \n",
       "\n",
       "       cate_Employés  cate_Inactifs  cate_Ouvriers  type_Appartement  \\\n",
       "count     300.000000     300.000000     300.000000        300.000000   \n",
       "mean        0.370000       0.023333       0.116667          0.626667   \n",
       "std         0.483611       0.151212       0.321559          0.484498   \n",
       "min         0.000000       0.000000       0.000000          0.000000   \n",
       "25%         0.000000       0.000000       0.000000          0.000000   \n",
       "50%         0.000000       0.000000       0.000000          1.000000   \n",
       "75%         1.000000       0.000000       0.000000          1.000000   \n",
       "max         1.000000       1.000000       1.000000          1.000000   \n",
       "\n",
       "       type_Maison  stat_Locataire  stat_Occupant  stat_Propriétaire  \n",
       "count   300.000000      300.000000     300.000000         300.000000  \n",
       "mean      0.373333        0.560000       0.016667           0.423333  \n",
       "std       0.484498        0.497216       0.128233           0.494913  \n",
       "min       0.000000        0.000000       0.000000           0.000000  \n",
       "25%       0.000000        0.000000       0.000000           0.000000  \n",
       "50%       0.000000        1.000000       0.000000           0.000000  \n",
       "75%       1.000000        1.000000       0.000000           1.000000  \n",
       "max       1.000000        1.000000       1.000000           1.000000  \n",
       "\n",
       "[8 rows x 121 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>surface</th>\n      <th>montant_assure</th>\n      <th>nb_sinistres_depuis_ouverture</th>\n      <th>cotisation_n-1</th>\n      <th>cotisation_n</th>\n      <th>duree_contrat</th>\n      <th>duree_dernier_sinistre</th>\n      <th>duree_zero_sinistre</th>\n      <th>depa_01</th>\n      <th>...</th>\n      <th>cate_Artisans,</th>\n      <th>cate_Cadres</th>\n      <th>cate_Employés</th>\n      <th>cate_Inactifs</th>\n      <th>cate_Ouvriers</th>\n      <th>type_Appartement</th>\n      <th>type_Maison</th>\n      <th>stat_Locataire</th>\n      <th>stat_Occupant</th>\n      <th>stat_Propriétaire</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>300.00000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>...</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>42.59000</td>\n      <td>86.704767</td>\n      <td>22894.856667</td>\n      <td>1.466667</td>\n      <td>274.989200</td>\n      <td>288.086200</td>\n      <td>4810.156667</td>\n      <td>34.820000</td>\n      <td>3174.016667</td>\n      <td>0.010000</td>\n      <td>...</td>\n      <td>0.156667</td>\n      <td>0.186667</td>\n      <td>0.370000</td>\n      <td>0.023333</td>\n      <td>0.116667</td>\n      <td>0.626667</td>\n      <td>0.373333</td>\n      <td>0.560000</td>\n      <td>0.016667</td>\n      <td>0.423333</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>14.62949</td>\n      <td>30.453970</td>\n      <td>9374.614330</td>\n      <td>0.992727</td>\n      <td>80.104622</td>\n      <td>86.567491</td>\n      <td>2673.566074</td>\n      <td>39.671826</td>\n      <td>2745.057079</td>\n      <td>0.099665</td>\n      <td>...</td>\n      <td>0.364094</td>\n      <td>0.390295</td>\n      <td>0.483611</td>\n      <td>0.151212</td>\n      <td>0.321559</td>\n      <td>0.484498</td>\n      <td>0.484498</td>\n      <td>0.497216</td>\n      <td>0.128233</td>\n      <td>0.494913</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>15.00000</td>\n      <td>29.360000</td>\n      <td>7217.000000</td>\n      <td>0.000000</td>\n      <td>172.470000</td>\n      <td>180.700000</td>\n      <td>88.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>31.00000</td>\n      <td>63.672500</td>\n      <td>15281.750000</td>\n      <td>1.000000</td>\n      <td>212.580000</td>\n      <td>221.105000</td>\n      <td>2521.000000</td>\n      <td>1.000000</td>\n      <td>472.500000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>43.00000</td>\n      <td>78.530000</td>\n      <td>20810.500000</td>\n      <td>1.000000</td>\n      <td>227.590000</td>\n      <td>240.395000</td>\n      <td>5101.000000</td>\n      <td>19.500000</td>\n      <td>2566.500000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>54.00000</td>\n      <td>107.357500</td>\n      <td>28494.750000</td>\n      <td>2.000000</td>\n      <td>364.185000</td>\n      <td>370.237500</td>\n      <td>7099.500000</td>\n      <td>62.000000</td>\n      <td>5547.250000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>86.00000</td>\n      <td>169.560000</td>\n      <td>50948.000000</td>\n      <td>5.000000</td>\n      <td>450.560000</td>\n      <td>599.500000</td>\n      <td>9530.000000</td>\n      <td>189.000000</td>\n      <td>9019.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 121 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "Xscore.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "Xscore = scaler.transform(Xscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               0            1            2            3            4    \\\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.000000   \n",
       "mean      0.344278     0.374341     0.350042     0.373000     0.366802   \n",
       "std       0.186213     0.191897     0.176671     0.238596     0.287690   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.202532     0.235439     0.215994     0.250000     0.137340   \n",
       "50%       0.341772     0.332200     0.325515     0.250000     0.196410   \n",
       "75%       0.468354     0.495404     0.450014     0.500000     0.695629   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "               5            6            7            8            9    ...  \\\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.000000  ...   \n",
       "mean      0.318071     0.510151     0.198134     0.365321     0.009000  ...   \n",
       "std       0.249438     0.286689     0.206901     0.299779     0.094488  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.118099     0.273161     0.004950     0.059922     0.000000  ...   \n",
       "50%       0.180309     0.503324     0.143564     0.329993     0.000000  ...   \n",
       "75%       0.581191     0.770734     0.336634     0.640259     0.000000  ...   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  ...   \n",
       "\n",
       "               111          112          113          114          115  \\\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.000000   \n",
       "mean      0.174000     0.162000     0.341000     0.067000     0.125000   \n",
       "std       0.379299     0.368635     0.474283     0.250147     0.330884   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     1.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "              116         117          118          119          120  \n",
       "count  1000.00000  1000.00000  1000.000000  1000.000000  1000.000000  \n",
       "mean      0.63400     0.36600     0.586000     0.028000     0.386000  \n",
       "std       0.48195     0.48195     0.492795     0.165055     0.487074  \n",
       "min       0.00000     0.00000     0.000000     0.000000     0.000000  \n",
       "25%       0.00000     0.00000     0.000000     0.000000     0.000000  \n",
       "50%       1.00000     0.00000     1.000000     0.000000     0.000000  \n",
       "75%       1.00000     1.00000     1.000000     0.000000     1.000000  \n",
       "max       1.00000     1.00000     1.000000     1.000000     1.000000  \n",
       "\n",
       "[8 rows x 121 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>111</th>\n      <th>112</th>\n      <th>113</th>\n      <th>114</th>\n      <th>115</th>\n      <th>116</th>\n      <th>117</th>\n      <th>118</th>\n      <th>119</th>\n      <th>120</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>...</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.00000</td>\n      <td>1000.00000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.344278</td>\n      <td>0.374341</td>\n      <td>0.350042</td>\n      <td>0.373000</td>\n      <td>0.366802</td>\n      <td>0.318071</td>\n      <td>0.510151</td>\n      <td>0.198134</td>\n      <td>0.365321</td>\n      <td>0.009000</td>\n      <td>...</td>\n      <td>0.174000</td>\n      <td>0.162000</td>\n      <td>0.341000</td>\n      <td>0.067000</td>\n      <td>0.125000</td>\n      <td>0.63400</td>\n      <td>0.36600</td>\n      <td>0.586000</td>\n      <td>0.028000</td>\n      <td>0.386000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.186213</td>\n      <td>0.191897</td>\n      <td>0.176671</td>\n      <td>0.238596</td>\n      <td>0.287690</td>\n      <td>0.249438</td>\n      <td>0.286689</td>\n      <td>0.206901</td>\n      <td>0.299779</td>\n      <td>0.094488</td>\n      <td>...</td>\n      <td>0.379299</td>\n      <td>0.368635</td>\n      <td>0.474283</td>\n      <td>0.250147</td>\n      <td>0.330884</td>\n      <td>0.48195</td>\n      <td>0.48195</td>\n      <td>0.492795</td>\n      <td>0.165055</td>\n      <td>0.487074</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.202532</td>\n      <td>0.235439</td>\n      <td>0.215994</td>\n      <td>0.250000</td>\n      <td>0.137340</td>\n      <td>0.118099</td>\n      <td>0.273161</td>\n      <td>0.004950</td>\n      <td>0.059922</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.341772</td>\n      <td>0.332200</td>\n      <td>0.325515</td>\n      <td>0.250000</td>\n      <td>0.196410</td>\n      <td>0.180309</td>\n      <td>0.503324</td>\n      <td>0.143564</td>\n      <td>0.329993</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.00000</td>\n      <td>0.00000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.468354</td>\n      <td>0.495404</td>\n      <td>0.450014</td>\n      <td>0.500000</td>\n      <td>0.695629</td>\n      <td>0.581191</td>\n      <td>0.770734</td>\n      <td>0.336634</td>\n      <td>0.640259</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.00000</td>\n      <td>1.00000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.00000</td>\n      <td>1.00000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 121 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "pd.DataFrame(X).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              0           1           2           3           4           5    \\\n",
       "count  300.000000  300.000000  300.000000  300.000000  300.000000  300.000000   \n",
       "mean     0.349241    0.373273    0.358486    0.366667    0.375190    0.324268   \n",
       "std      0.185183    0.205063    0.182478    0.248182    0.301724    0.263019   \n",
       "min      0.000000   -0.012861    0.053315    0.000000   -0.010961   -0.002005   \n",
       "25%      0.202532    0.218184    0.210296    0.250000    0.140118    0.120758   \n",
       "50%      0.354430    0.318228    0.317914    0.250000    0.196655    0.179367   \n",
       "75%      0.493671    0.512339    0.467488    0.500000    0.711157    0.573869   \n",
       "max      0.898734    0.931183    0.904543    1.250000    1.036499    1.270440   \n",
       "\n",
       "              6           7           8           9    ...         111  \\\n",
       "count  300.000000  300.000000  300.000000  300.000000  ...  300.000000   \n",
       "mean     0.502391    0.172376    0.336623    0.010000  ...    0.156667   \n",
       "std      0.282111    0.196395    0.291129    0.099665  ...    0.364094   \n",
       "min      0.004115    0.000000    0.000000    0.000000  ...    0.000000   \n",
       "25%      0.260842    0.004950    0.050111    0.000000  ...    0.000000   \n",
       "50%      0.533080    0.096535    0.272192    0.000000  ...    0.000000   \n",
       "75%      0.743959    0.306931    0.588318    0.000000  ...    0.000000   \n",
       "max      1.000422    0.935644    0.956517    1.000000  ...    1.000000   \n",
       "\n",
       "              112         113         114         115         116         117  \\\n",
       "count  300.000000  300.000000  300.000000  300.000000  300.000000  300.000000   \n",
       "mean     0.186667    0.370000    0.023333    0.116667    0.626667    0.373333   \n",
       "std      0.390295    0.483611    0.151212    0.321559    0.484498    0.484498   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      0.000000    0.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "75%      0.000000    1.000000    0.000000    0.000000    1.000000    1.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "              118         119         120  \n",
       "count  300.000000  300.000000  300.000000  \n",
       "mean     0.560000    0.016667    0.423333  \n",
       "std      0.497216    0.128233    0.494913  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000  \n",
       "50%      1.000000    0.000000    0.000000  \n",
       "75%      1.000000    0.000000    1.000000  \n",
       "max      1.000000    1.000000    1.000000  \n",
       "\n",
       "[8 rows x 121 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>111</th>\n      <th>112</th>\n      <th>113</th>\n      <th>114</th>\n      <th>115</th>\n      <th>116</th>\n      <th>117</th>\n      <th>118</th>\n      <th>119</th>\n      <th>120</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>...</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n      <td>300.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.349241</td>\n      <td>0.373273</td>\n      <td>0.358486</td>\n      <td>0.366667</td>\n      <td>0.375190</td>\n      <td>0.324268</td>\n      <td>0.502391</td>\n      <td>0.172376</td>\n      <td>0.336623</td>\n      <td>0.010000</td>\n      <td>...</td>\n      <td>0.156667</td>\n      <td>0.186667</td>\n      <td>0.370000</td>\n      <td>0.023333</td>\n      <td>0.116667</td>\n      <td>0.626667</td>\n      <td>0.373333</td>\n      <td>0.560000</td>\n      <td>0.016667</td>\n      <td>0.423333</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.185183</td>\n      <td>0.205063</td>\n      <td>0.182478</td>\n      <td>0.248182</td>\n      <td>0.301724</td>\n      <td>0.263019</td>\n      <td>0.282111</td>\n      <td>0.196395</td>\n      <td>0.291129</td>\n      <td>0.099665</td>\n      <td>...</td>\n      <td>0.364094</td>\n      <td>0.390295</td>\n      <td>0.483611</td>\n      <td>0.151212</td>\n      <td>0.321559</td>\n      <td>0.484498</td>\n      <td>0.484498</td>\n      <td>0.497216</td>\n      <td>0.128233</td>\n      <td>0.494913</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>-0.012861</td>\n      <td>0.053315</td>\n      <td>0.000000</td>\n      <td>-0.010961</td>\n      <td>-0.002005</td>\n      <td>0.004115</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.202532</td>\n      <td>0.218184</td>\n      <td>0.210296</td>\n      <td>0.250000</td>\n      <td>0.140118</td>\n      <td>0.120758</td>\n      <td>0.260842</td>\n      <td>0.004950</td>\n      <td>0.050111</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.354430</td>\n      <td>0.318228</td>\n      <td>0.317914</td>\n      <td>0.250000</td>\n      <td>0.196655</td>\n      <td>0.179367</td>\n      <td>0.533080</td>\n      <td>0.096535</td>\n      <td>0.272192</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.493671</td>\n      <td>0.512339</td>\n      <td>0.467488</td>\n      <td>0.500000</td>\n      <td>0.711157</td>\n      <td>0.573869</td>\n      <td>0.743959</td>\n      <td>0.306931</td>\n      <td>0.588318</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>0.898734</td>\n      <td>0.931183</td>\n      <td>0.904543</td>\n      <td>1.250000</td>\n      <td>1.036499</td>\n      <td>1.270440</td>\n      <td>1.000422</td>\n      <td>0.935644</td>\n      <td>0.956517</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 121 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "pd.DataFrame(Xscore).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.2"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split  \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "len(X_test)/len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              0           1           2           3           4           5    \\\n",
       "count  800.000000  800.000000  800.000000  800.000000  800.000000  800.000000   \n",
       "mean     0.344747    0.373978    0.352949    0.368750    0.368131    0.318731   \n",
       "std      0.185341    0.189740    0.180912    0.234917    0.286502    0.249066   \n",
       "min      0.000000    0.010774    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.202532    0.235439    0.215002    0.250000    0.138178    0.119444   \n",
       "50%      0.341772    0.335903    0.328045    0.250000    0.199066    0.180445   \n",
       "75%      0.468354    0.488570    0.460938    0.500000    0.694037    0.581207   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "              6           7           8           9    ...         111  \\\n",
       "count  800.000000  800.000000  800.000000  800.000000  ...  800.000000   \n",
       "mean     0.506860    0.197333    0.358045    0.006250  ...    0.172500   \n",
       "std      0.288273    0.201852    0.298163    0.078859  ...    0.378051   \n",
       "min      0.001688    0.000000    0.000000    0.000000  ...    0.000000   \n",
       "25%      0.266092    0.004950    0.057376    0.000000  ...    0.000000   \n",
       "50%      0.499156    0.153465    0.312971    0.000000  ...    0.000000   \n",
       "75%      0.768756    0.336634    0.627559    0.000000  ...    0.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000  ...    1.000000   \n",
       "\n",
       "              112         113         114         115        116        117  \\\n",
       "count  800.000000  800.000000  800.000000  800.000000  800.00000  800.00000   \n",
       "mean     0.165000    0.342500    0.073750    0.122500    0.63375    0.36625   \n",
       "std      0.371413    0.474843    0.261527    0.328068    0.48208    0.48208   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.00000    0.00000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.00000    0.00000   \n",
       "50%      0.000000    0.000000    0.000000    0.000000    1.00000    0.00000   \n",
       "75%      0.000000    1.000000    0.000000    0.000000    1.00000    1.00000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.00000    1.00000   \n",
       "\n",
       "              118         119         120  \n",
       "count  800.000000  800.000000  800.000000  \n",
       "mean     0.580000    0.027500    0.392500  \n",
       "std      0.493867    0.163637    0.488613  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000  \n",
       "50%      1.000000    0.000000    0.000000  \n",
       "75%      1.000000    0.000000    1.000000  \n",
       "max      1.000000    1.000000    1.000000  \n",
       "\n",
       "[8 rows x 121 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>111</th>\n      <th>112</th>\n      <th>113</th>\n      <th>114</th>\n      <th>115</th>\n      <th>116</th>\n      <th>117</th>\n      <th>118</th>\n      <th>119</th>\n      <th>120</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>...</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>800.00000</td>\n      <td>800.00000</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n      <td>800.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.344747</td>\n      <td>0.373978</td>\n      <td>0.352949</td>\n      <td>0.368750</td>\n      <td>0.368131</td>\n      <td>0.318731</td>\n      <td>0.506860</td>\n      <td>0.197333</td>\n      <td>0.358045</td>\n      <td>0.006250</td>\n      <td>...</td>\n      <td>0.172500</td>\n      <td>0.165000</td>\n      <td>0.342500</td>\n      <td>0.073750</td>\n      <td>0.122500</td>\n      <td>0.63375</td>\n      <td>0.36625</td>\n      <td>0.580000</td>\n      <td>0.027500</td>\n      <td>0.392500</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.185341</td>\n      <td>0.189740</td>\n      <td>0.180912</td>\n      <td>0.234917</td>\n      <td>0.286502</td>\n      <td>0.249066</td>\n      <td>0.288273</td>\n      <td>0.201852</td>\n      <td>0.298163</td>\n      <td>0.078859</td>\n      <td>...</td>\n      <td>0.378051</td>\n      <td>0.371413</td>\n      <td>0.474843</td>\n      <td>0.261527</td>\n      <td>0.328068</td>\n      <td>0.48208</td>\n      <td>0.48208</td>\n      <td>0.493867</td>\n      <td>0.163637</td>\n      <td>0.488613</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.010774</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.001688</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.202532</td>\n      <td>0.235439</td>\n      <td>0.215002</td>\n      <td>0.250000</td>\n      <td>0.138178</td>\n      <td>0.119444</td>\n      <td>0.266092</td>\n      <td>0.004950</td>\n      <td>0.057376</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.341772</td>\n      <td>0.335903</td>\n      <td>0.328045</td>\n      <td>0.250000</td>\n      <td>0.199066</td>\n      <td>0.180445</td>\n      <td>0.499156</td>\n      <td>0.153465</td>\n      <td>0.312971</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.00000</td>\n      <td>0.00000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.468354</td>\n      <td>0.488570</td>\n      <td>0.460938</td>\n      <td>0.500000</td>\n      <td>0.694037</td>\n      <td>0.581207</td>\n      <td>0.768756</td>\n      <td>0.336634</td>\n      <td>0.627559</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.00000</td>\n      <td>1.00000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.00000</td>\n      <td>1.00000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 121 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "pd.DataFrame(X_train).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              0           1           2           3           4           5    \\\n",
       "count  200.000000  200.000000  200.000000  200.000000  200.000000  200.000000   \n",
       "mean     0.342405    0.375793    0.338414    0.390000    0.361484    0.315430   \n",
       "std      0.190125    0.200777    0.158461    0.252674    0.293059    0.251531   \n",
       "min      0.000000    0.000000    0.053295    0.000000    0.016498    0.028742   \n",
       "25%      0.215190    0.235506    0.231868    0.250000    0.129421    0.111909   \n",
       "50%      0.341772    0.319541    0.310498    0.500000    0.185242    0.179564   \n",
       "75%      0.443038    0.518955    0.424106    0.500000    0.707051    0.580143   \n",
       "max      0.949367    0.982291    0.834294    1.000000    0.890956    0.831921   \n",
       "\n",
       "              6           7           8           9    ...         111  \\\n",
       "count  200.000000  200.000000  200.000000  200.000000  ...  200.000000   \n",
       "mean     0.523317    0.201337    0.394428    0.020000  ...    0.180000   \n",
       "std      0.280589    0.226485    0.305180    0.140351  ...    0.385152   \n",
       "min      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
       "25%      0.287802    0.004950    0.077871    0.000000  ...    0.000000   \n",
       "50%      0.532605    0.123762    0.390550    0.000000  ...    0.000000   \n",
       "75%      0.772475    0.329208    0.671148    0.000000  ...    0.000000   \n",
       "max      0.996307    0.930693    0.968607    1.000000  ...    1.000000   \n",
       "\n",
       "              112         113         114         115         116         117  \\\n",
       "count  200.000000  200.000000  200.000000  200.000000  200.000000  200.000000   \n",
       "mean     0.150000    0.335000    0.040000    0.135000    0.635000    0.365000   \n",
       "std      0.357967    0.473175    0.196451    0.342581    0.482638    0.482638   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      0.000000    0.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "75%      0.000000    1.000000    0.000000    0.000000    1.000000    1.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "              118         119         120  \n",
       "count  200.000000  200.000000  200.000000  \n",
       "mean     0.610000    0.030000    0.360000  \n",
       "std      0.488974    0.171015    0.481205  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000  \n",
       "50%      1.000000    0.000000    0.000000  \n",
       "75%      1.000000    0.000000    1.000000  \n",
       "max      1.000000    1.000000    1.000000  \n",
       "\n",
       "[8 rows x 121 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>111</th>\n      <th>112</th>\n      <th>113</th>\n      <th>114</th>\n      <th>115</th>\n      <th>116</th>\n      <th>117</th>\n      <th>118</th>\n      <th>119</th>\n      <th>120</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>...</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n      <td>200.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.342405</td>\n      <td>0.375793</td>\n      <td>0.338414</td>\n      <td>0.390000</td>\n      <td>0.361484</td>\n      <td>0.315430</td>\n      <td>0.523317</td>\n      <td>0.201337</td>\n      <td>0.394428</td>\n      <td>0.020000</td>\n      <td>...</td>\n      <td>0.180000</td>\n      <td>0.150000</td>\n      <td>0.335000</td>\n      <td>0.040000</td>\n      <td>0.135000</td>\n      <td>0.635000</td>\n      <td>0.365000</td>\n      <td>0.610000</td>\n      <td>0.030000</td>\n      <td>0.360000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.190125</td>\n      <td>0.200777</td>\n      <td>0.158461</td>\n      <td>0.252674</td>\n      <td>0.293059</td>\n      <td>0.251531</td>\n      <td>0.280589</td>\n      <td>0.226485</td>\n      <td>0.305180</td>\n      <td>0.140351</td>\n      <td>...</td>\n      <td>0.385152</td>\n      <td>0.357967</td>\n      <td>0.473175</td>\n      <td>0.196451</td>\n      <td>0.342581</td>\n      <td>0.482638</td>\n      <td>0.482638</td>\n      <td>0.488974</td>\n      <td>0.171015</td>\n      <td>0.481205</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.053295</td>\n      <td>0.000000</td>\n      <td>0.016498</td>\n      <td>0.028742</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.215190</td>\n      <td>0.235506</td>\n      <td>0.231868</td>\n      <td>0.250000</td>\n      <td>0.129421</td>\n      <td>0.111909</td>\n      <td>0.287802</td>\n      <td>0.004950</td>\n      <td>0.077871</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.341772</td>\n      <td>0.319541</td>\n      <td>0.310498</td>\n      <td>0.500000</td>\n      <td>0.185242</td>\n      <td>0.179564</td>\n      <td>0.532605</td>\n      <td>0.123762</td>\n      <td>0.390550</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.443038</td>\n      <td>0.518955</td>\n      <td>0.424106</td>\n      <td>0.500000</td>\n      <td>0.707051</td>\n      <td>0.580143</td>\n      <td>0.772475</td>\n      <td>0.329208</td>\n      <td>0.671148</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>0.949367</td>\n      <td>0.982291</td>\n      <td>0.834294</td>\n      <td>1.000000</td>\n      <td>0.890956</td>\n      <td>0.831921</td>\n      <td>0.996307</td>\n      <td>0.930693</td>\n      <td>0.968607</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 121 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "pd.DataFrame(X_test).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           target\n",
       "count  800.000000\n",
       "mean     0.170000\n",
       "std      0.375868\n",
       "min      0.000000\n",
       "25%      0.000000\n",
       "50%      0.000000\n",
       "75%      0.000000\n",
       "max      1.000000"
      ],
      "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>800.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.170000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.375868</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "pd.DataFrame(y_train).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           target\n",
       "count  200.000000\n",
       "mean     0.170000\n",
       "std      0.376575\n",
       "min      0.000000\n",
       "25%      0.000000\n",
       "50%      0.000000\n",
       "75%      0.000000\n",
       "max      1.000000"
      ],
      "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>200.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.170000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.376575</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "pd.DataFrame(y_test).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "target\n0.0       664\n1.0       136\ndtype: int64 target\n1.0       664\n0.0       664\ndtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "sm = SMOTE(random_state=42)\n",
    "x_train_res, y_train_res = sm.fit_sample(X_train, y_train)\n",
    "\n",
    "print(y_train.value_counts(), y_train_res.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n non résilié       0.93      0.92      0.92       166\n     resilié       0.62      0.68      0.65        34\n\n    accuracy                           0.88       200\n   macro avg       0.78      0.80      0.79       200\nweighted avg       0.88      0.88      0.88       200\n\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "model_RF = RandomForestClassifier(n_estimators=1000)\n",
    "model_RF.fit(x_train_res , y_train_res)\n",
    "y_pred = model_RF.predict(X_test)\n",
    "target_names = ['non résilié', 'resilié'] # 1 résilié, 0 pas résilié\n",
    "print(classification_report(y_test, y_pred,target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n non résilié       0.94      0.91      0.92       166\n     resilié       0.62      0.71      0.66        34\n\n    accuracy                           0.88       200\n   macro avg       0.78      0.81      0.79       200\nweighted avg       0.88      0.88      0.88       200\n\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "model_GB = GradientBoostingClassifier(n_estimators=1000)\n",
    "model_GB.fit(x_train_res , y_train_res)\n",
    "y_pred = model_GB.predict(X_test)\n",
    "target_names = ['non résilié', 'resilié'] \n",
    "print(classification_report(y_test, y_pred,target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n non résilié       0.93      0.88      0.90       166\n     resilié       0.53      0.68      0.60        34\n\n    accuracy                           0.84       200\n   macro avg       0.73      0.78      0.75       200\nweighted avg       0.86      0.84      0.85       200\n\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "model_AB = AdaBoostClassifier()\n",
    "model_AB.fit(x_train_res , y_train_res)\n",
    "y_pred = model_AB.predict(X_test)\n",
    "target_names = ['non résilié', 'resilié'] \n",
    "print(classification_report(y_test, y_pred,target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "|   iter    |  target   | learni... | n_esti... |\n",
      "-------------------------------------------------\n",
      "| \u001b[0m 1       \u001b[0m | \u001b[0m 0.7089  \u001b[0m | \u001b[0m-0.008298\u001b[0m | \u001b[0m 24.41   \u001b[0m |\n",
      "| \u001b[0m 2       \u001b[0m | \u001b[0m 0.6923  \u001b[0m | \u001b[0m-0.04999 \u001b[0m | \u001b[0m 16.05   \u001b[0m |\n",
      "| \u001b[0m 3       \u001b[0m | \u001b[0m 0.7073  \u001b[0m | \u001b[0m-0.03532 \u001b[0m | \u001b[0m 11.85   \u001b[0m |\n",
      "| \u001b[0m 4       \u001b[0m | \u001b[0m 0.6897  \u001b[0m | \u001b[0m-0.03137 \u001b[0m | \u001b[0m 16.91   \u001b[0m |\n",
      "| \u001b[0m 5       \u001b[0m | \u001b[0m 0.6829  \u001b[0m | \u001b[0m-0.01032 \u001b[0m | \u001b[0m 20.78   \u001b[0m |\n",
      "| \u001b[95m 6       \u001b[0m | \u001b[95m 0.72    \u001b[0m | \u001b[95m-0.008081\u001b[0m | \u001b[95m 23.7    \u001b[0m |\n",
      "| \u001b[0m 7       \u001b[0m | \u001b[0m 0.6753  \u001b[0m | \u001b[0m-0.02955 \u001b[0m | \u001b[0m 27.56   \u001b[0m |\n",
      "| \u001b[0m 8       \u001b[0m | \u001b[0m 0.7105  \u001b[0m | \u001b[0m-0.04726 \u001b[0m | \u001b[0m 23.41   \u001b[0m |\n",
      "| \u001b[95m 9       \u001b[0m | \u001b[95m 0.7273  \u001b[0m | \u001b[95m-0.00827 \u001b[0m | \u001b[95m 21.17   \u001b[0m |\n",
      "| \u001b[0m 10      \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.03596 \u001b[0m | \u001b[0m 13.96   \u001b[0m |\n",
      "| \u001b[0m 11      \u001b[0m | \u001b[0m 0.7179  \u001b[0m | \u001b[0m-0.007011\u001b[0m | \u001b[0m 21.28   \u001b[0m |\n",
      "| \u001b[0m 12      \u001b[0m | \u001b[0m 0.7073  \u001b[0m | \u001b[0m 0.01039 \u001b[0m | \u001b[0m 13.78   \u001b[0m |\n",
      "| \u001b[95m 13      \u001b[0m | \u001b[95m 0.7317  \u001b[0m | \u001b[95m-0.03507 \u001b[0m | \u001b[95m 22.58   \u001b[0m |\n",
      "| \u001b[0m 14      \u001b[0m | \u001b[0m 0.7013  \u001b[0m | \u001b[0m 0.01136 \u001b[0m | \u001b[0m 22.72   \u001b[0m |\n",
      "| \u001b[0m 15      \u001b[0m | \u001b[0m 0.6977  \u001b[0m | \u001b[0m-0.01044 \u001b[0m | \u001b[0m 13.23   \u001b[0m |\n",
      "| \u001b[0m 16      \u001b[0m | \u001b[0m 0.7105  \u001b[0m | \u001b[0m-0.05    \u001b[0m | \u001b[0m 22.49   \u001b[0m |\n",
      "| \u001b[0m 17      \u001b[0m | \u001b[0m 0.6829  \u001b[0m | \u001b[0m-0.01468 \u001b[0m | \u001b[0m 21.17   \u001b[0m |\n",
      "| \u001b[0m 18      \u001b[0m | \u001b[0m 0.72    \u001b[0m | \u001b[0m 0.01099 \u001b[0m | \u001b[0m 17.86   \u001b[0m |\n",
      "| \u001b[0m 19      \u001b[0m | \u001b[0m 0.6923  \u001b[0m | \u001b[0m 0.00532 \u001b[0m | \u001b[0m 24.04   \u001b[0m |\n",
      "| \u001b[0m 20      \u001b[0m | \u001b[0m 0.6966  \u001b[0m | \u001b[0m-0.03516 \u001b[0m | \u001b[0m 18.13   \u001b[0m |\n",
      "| \u001b[0m 21      \u001b[0m | \u001b[0m 0.7073  \u001b[0m | \u001b[0m 0.01034 \u001b[0m | \u001b[0m 13.78   \u001b[0m |\n",
      "| \u001b[0m 22      \u001b[0m | \u001b[0m 0.7273  \u001b[0m | \u001b[0m-0.03031 \u001b[0m | \u001b[0m 22.57   \u001b[0m |\n",
      "| \u001b[0m 23      \u001b[0m | \u001b[0m 0.6829  \u001b[0m | \u001b[0m-0.01265 \u001b[0m | \u001b[0m 21.18   \u001b[0m |\n",
      "| \u001b[0m 24      \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m-0.03232 \u001b[0m | \u001b[0m 22.58   \u001b[0m |\n",
      "| \u001b[0m 25      \u001b[0m | \u001b[0m 0.6835  \u001b[0m | \u001b[0m-0.03319 \u001b[0m | \u001b[0m 23.68   \u001b[0m |\n",
      "| \u001b[0m 26      \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.03413 \u001b[0m | \u001b[0m 13.97   \u001b[0m |\n",
      "| \u001b[0m 27      \u001b[0m | \u001b[0m 0.7297  \u001b[0m | \u001b[0m-0.02612 \u001b[0m | \u001b[0m 22.58   \u001b[0m |\n",
      "| \u001b[0m 28      \u001b[0m | \u001b[0m 0.7013  \u001b[0m | \u001b[0m 0.000315\u001b[0m | \u001b[0m 21.18   \u001b[0m |\n",
      "| \u001b[0m 29      \u001b[0m | \u001b[0m 0.7073  \u001b[0m | \u001b[0m-0.02756 \u001b[0m | \u001b[0m 22.59   \u001b[0m |\n",
      "| \u001b[0m 30      \u001b[0m | \u001b[0m 0.72    \u001b[0m | \u001b[0m 0.000387\u001b[0m | \u001b[0m 23.7    \u001b[0m |\n",
      "| \u001b[0m 31      \u001b[0m | \u001b[0m 0.6923  \u001b[0m | \u001b[0m 0.003145\u001b[0m | \u001b[0m 23.71   \u001b[0m |\n",
      "| \u001b[0m 32      \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.02833 \u001b[0m | \u001b[0m 13.96   \u001b[0m |\n",
      "| \u001b[0m 33      \u001b[0m | \u001b[0m 0.7105  \u001b[0m | \u001b[0m-0.04397 \u001b[0m | \u001b[0m 22.58   \u001b[0m |\n",
      "| \u001b[0m 34      \u001b[0m | \u001b[0m 0.6842  \u001b[0m | \u001b[0m-0.003856\u001b[0m | \u001b[0m 19.76   \u001b[0m |\n",
      "| \u001b[0m 35      \u001b[0m | \u001b[0m 0.7059  \u001b[0m | \u001b[0m-0.03591 \u001b[0m | \u001b[0m 22.57   \u001b[0m |\n",
      "| \u001b[0m 36      \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.01857 \u001b[0m | \u001b[0m 14.29   \u001b[0m |\n",
      "| \u001b[0m 37      \u001b[0m | \u001b[0m 0.72    \u001b[0m | \u001b[0m-0.00727 \u001b[0m | \u001b[0m 23.7    \u001b[0m |\n",
      "| \u001b[95m 38      \u001b[0m | \u001b[95m 0.7368  \u001b[0m | \u001b[95m-0.02643 \u001b[0m | \u001b[95m 14.29   \u001b[0m |\n",
      "| \u001b[0m 39      \u001b[0m | \u001b[0m 0.6923  \u001b[0m | \u001b[0m-0.01537 \u001b[0m | \u001b[0m 22.58   \u001b[0m |\n",
      "| \u001b[0m 40      \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.02017 \u001b[0m | \u001b[0m 14.28   \u001b[0m |\n",
      "| \u001b[0m 41      \u001b[0m | \u001b[0m 0.7143  \u001b[0m | \u001b[0m-0.02964 \u001b[0m | \u001b[0m 14.29   \u001b[0m |\n",
      "| \u001b[0m 42      \u001b[0m | \u001b[0m 0.7143  \u001b[0m | \u001b[0m-0.03763 \u001b[0m | \u001b[0m 14.28   \u001b[0m |\n",
      "| \u001b[0m 43      \u001b[0m | \u001b[0m 0.6977  \u001b[0m | \u001b[0m-0.02414 \u001b[0m | \u001b[0m 14.28   \u001b[0m |\n",
      "| \u001b[0m 44      \u001b[0m | \u001b[0m 0.72    \u001b[0m | \u001b[0m-0.004892\u001b[0m | \u001b[0m 23.7    \u001b[0m |\n",
      "| \u001b[0m 45      \u001b[0m | \u001b[0m 0.725   \u001b[0m | \u001b[0m-0.006783\u001b[0m | \u001b[0m 27.31   \u001b[0m |\n",
      "| \u001b[0m 46      \u001b[0m | \u001b[0m 0.7143  \u001b[0m | \u001b[0m-0.0317  \u001b[0m | \u001b[0m 14.29   \u001b[0m |\n",
      "| \u001b[0m 47      \u001b[0m | \u001b[0m 0.7059  \u001b[0m | \u001b[0m-0.03789 \u001b[0m | \u001b[0m 22.58   \u001b[0m |\n",
      "| \u001b[0m 48      \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.02697 \u001b[0m | \u001b[0m 13.96   \u001b[0m |\n",
      "| \u001b[0m 49      \u001b[0m | \u001b[0m 0.7273  \u001b[0m | \u001b[0m-0.02878 \u001b[0m | \u001b[0m 22.58   \u001b[0m |\n",
      "| \u001b[0m 50      \u001b[0m | \u001b[0m 0.725   \u001b[0m | \u001b[0m-0.004436\u001b[0m | \u001b[0m 27.31   \u001b[0m |\n",
      "| \u001b[0m 51      \u001b[0m | \u001b[0m 0.7179  \u001b[0m | \u001b[0m-0.006697\u001b[0m | \u001b[0m 21.28   \u001b[0m |\n",
      "| \u001b[0m 52      \u001b[0m | \u001b[0m 0.7273  \u001b[0m | \u001b[0m-0.02933 \u001b[0m | \u001b[0m 22.59   \u001b[0m |\n",
      "| \u001b[0m 53      \u001b[0m | \u001b[0m 0.7143  \u001b[0m | \u001b[0m-0.01339 \u001b[0m | \u001b[0m 14.28   \u001b[0m |\n",
      "| \u001b[0m 54      \u001b[0m | \u001b[0m 0.6835  \u001b[0m | \u001b[0m-0.03413 \u001b[0m | \u001b[0m 23.39   \u001b[0m |\n",
      "| \u001b[0m 55      \u001b[0m | \u001b[0m 0.6923  \u001b[0m | \u001b[0m-0.02074 \u001b[0m | \u001b[0m 29.96   \u001b[0m |\n",
      "| \u001b[0m 56      \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.03278 \u001b[0m | \u001b[0m 13.96   \u001b[0m |\n",
      "| \u001b[0m 57      \u001b[0m | \u001b[0m 0.6977  \u001b[0m | \u001b[0m-0.02405 \u001b[0m | \u001b[0m 14.29   \u001b[0m |\n",
      "| \u001b[0m 58      \u001b[0m | \u001b[0m 0.7297  \u001b[0m | \u001b[0m-0.02543 \u001b[0m | \u001b[0m 22.58   \u001b[0m |\n",
      "| \u001b[0m 59      \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.03681 \u001b[0m | \u001b[0m 13.97   \u001b[0m |\n",
      "| \u001b[0m 60      \u001b[0m | \u001b[0m 0.6494  \u001b[0m | \u001b[0m-0.009551\u001b[0m | \u001b[0m 27.31   \u001b[0m |\n",
      "| \u001b[0m 61      \u001b[0m | \u001b[0m 0.6829  \u001b[0m | \u001b[0m 0.006223\u001b[0m | \u001b[0m 16.56   \u001b[0m |\n",
      "| \u001b[0m 62      \u001b[0m | \u001b[0m 0.6933  \u001b[0m | \u001b[0m 0.02765 \u001b[0m | \u001b[0m 13.92   \u001b[0m |\n",
      "| \u001b[0m 63      \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m-0.03269 \u001b[0m | \u001b[0m 22.59   \u001b[0m |\n",
      "| \u001b[0m 64      \u001b[0m | \u001b[0m 0.6753  \u001b[0m | \u001b[0m-0.02335 \u001b[0m | \u001b[0m 22.58   \u001b[0m |\n",
      "| \u001b[0m 65      \u001b[0m | \u001b[0m 0.7073  \u001b[0m | \u001b[0m-0.03869 \u001b[0m | \u001b[0m 13.97   \u001b[0m |\n",
      "| \u001b[0m 66      \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.03417 \u001b[0m | \u001b[0m 13.96   \u001b[0m |\n",
      "| \u001b[0m 67      \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.0304  \u001b[0m | \u001b[0m 13.96   \u001b[0m |\n",
      "| \u001b[0m 68      \u001b[0m | \u001b[0m 0.7105  \u001b[0m | \u001b[0m 0.02672 \u001b[0m | \u001b[0m 23.46   \u001b[0m |\n",
      "| \u001b[0m 69      \u001b[0m | \u001b[0m 0.7073  \u001b[0m | \u001b[0m-0.03992 \u001b[0m | \u001b[0m 13.97   \u001b[0m |\n",
      "| \u001b[0m 70      \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m-0.03472 \u001b[0m | \u001b[0m 22.58   \u001b[0m |\n",
      "| \u001b[0m 71      \u001b[0m | \u001b[0m 0.6753  \u001b[0m | \u001b[0m 0.01585 \u001b[0m | \u001b[0m 19.81   \u001b[0m |\n",
      "| \u001b[0m 72      \u001b[0m | \u001b[0m 0.7297  \u001b[0m | \u001b[0m-0.02563 \u001b[0m | \u001b[0m 22.57   \u001b[0m |\n",
      "| \u001b[0m 73      \u001b[0m | \u001b[0m 0.7297  \u001b[0m | \u001b[0m-0.02604 \u001b[0m | \u001b[0m 22.58   \u001b[0m |\n",
      "| \u001b[0m 74      \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m-0.03239 \u001b[0m | \u001b[0m 22.57   \u001b[0m |\n",
      "| \u001b[0m 75      \u001b[0m | \u001b[0m 0.7105  \u001b[0m | \u001b[0m-0.02205 \u001b[0m | \u001b[0m 22.57   \u001b[0m |\n",
      "| \u001b[0m 76      \u001b[0m | \u001b[0m 0.7209  \u001b[0m | \u001b[0m-0.02868 \u001b[0m | \u001b[0m 19.62   \u001b[0m |\n",
      "| \u001b[0m 77      \u001b[0m | \u001b[0m 0.7059  \u001b[0m | \u001b[0m-0.03574 \u001b[0m | \u001b[0m 22.57   \u001b[0m |\n",
      "| \u001b[0m 78      \u001b[0m | \u001b[0m 0.725   \u001b[0m | \u001b[0m-0.002359\u001b[0m | \u001b[0m 27.31   \u001b[0m |\n",
      "| \u001b[0m 79      \u001b[0m | \u001b[0m 0.7273  \u001b[0m | \u001b[0m-0.02864 \u001b[0m | \u001b[0m 22.57   \u001b[0m |\n",
      "| \u001b[0m 80      \u001b[0m | \u001b[0m 0.6753  \u001b[0m | \u001b[0m 0.03513 \u001b[0m | \u001b[0m 20.67   \u001b[0m |\n",
      "| \u001b[0m 81      \u001b[0m | \u001b[0m 0.7143  \u001b[0m | \u001b[0m-0.02885 \u001b[0m | \u001b[0m 14.29   \u001b[0m |\n",
      "| \u001b[0m 82      \u001b[0m | \u001b[0m 0.72    \u001b[0m | \u001b[0m-0.000691\u001b[0m | \u001b[0m 23.7    \u001b[0m |\n",
      "| \u001b[0m 83      \u001b[0m | \u001b[0m 0.7143  \u001b[0m | \u001b[0m-0.02838 \u001b[0m | \u001b[0m 14.28   \u001b[0m |\n",
      "| \u001b[0m 84      \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.0215  \u001b[0m | \u001b[0m 14.27   \u001b[0m |\n",
      "| \u001b[0m 85      \u001b[0m | \u001b[0m 0.725   \u001b[0m | \u001b[0m-0.001135\u001b[0m | \u001b[0m 27.31   \u001b[0m |\n",
      "| \u001b[0m 86      \u001b[0m | \u001b[0m 0.7273  \u001b[0m | \u001b[0m-0.02949 \u001b[0m | \u001b[0m 22.58   \u001b[0m |\n",
      "| \u001b[0m 87      \u001b[0m | \u001b[0m 0.7143  \u001b[0m | \u001b[0m-0.01783 \u001b[0m | \u001b[0m 14.27   \u001b[0m |\n",
      "| \u001b[0m 88      \u001b[0m | \u001b[0m 0.7143  \u001b[0m | \u001b[0m-0.01541 \u001b[0m | \u001b[0m 14.28   \u001b[0m |\n",
      "| \u001b[0m 89      \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.03371 \u001b[0m | \u001b[0m 13.97   \u001b[0m |\n",
      "| \u001b[0m 90      \u001b[0m | \u001b[0m 0.6667  \u001b[0m | \u001b[0m 0.03289 \u001b[0m | \u001b[0m 29.7    \u001b[0m |\n",
      "| \u001b[0m 91      \u001b[0m | \u001b[0m 0.7013  \u001b[0m | \u001b[0m-0.004525\u001b[0m | \u001b[0m 21.17   \u001b[0m |\n",
      "| \u001b[0m 92      \u001b[0m | \u001b[0m 0.725   \u001b[0m | \u001b[0m-0.006537\u001b[0m | \u001b[0m 27.3    \u001b[0m |\n",
      "| \u001b[0m 93      \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m-0.03363 \u001b[0m | \u001b[0m 22.58   \u001b[0m |\n",
      "| \u001b[0m 94      \u001b[0m | \u001b[0m 0.72    \u001b[0m | \u001b[0m 0.01181 \u001b[0m | \u001b[0m 17.85   \u001b[0m |\n",
      "| \u001b[0m 95      \u001b[0m | \u001b[0m 0.7273  \u001b[0m | \u001b[0m-0.02811 \u001b[0m | \u001b[0m 22.56   \u001b[0m |\n",
      "| \u001b[0m 96      \u001b[0m | \u001b[0m 0.7179  \u001b[0m | \u001b[0m 0.03583 \u001b[0m | \u001b[0m 11.03   \u001b[0m |\n",
      "| \u001b[0m 97      \u001b[0m | \u001b[0m 0.6988  \u001b[0m | \u001b[0m-0.04844 \u001b[0m | \u001b[0m 11.03   \u001b[0m |\n",
      "| \u001b[0m 98      \u001b[0m | \u001b[0m 0.72    \u001b[0m | \u001b[0m-0.005421\u001b[0m | \u001b[0m 23.69   \u001b[0m |\n",
      "| \u001b[0m 99      \u001b[0m | \u001b[0m 0.725   \u001b[0m | \u001b[0m-0.003373\u001b[0m | \u001b[0m 27.31   \u001b[0m |\n",
      "| \u001b[0m 100     \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.02875 \u001b[0m | \u001b[0m 13.97   \u001b[0m |\n",
      "| \u001b[0m 101     \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m-0.0317  \u001b[0m | \u001b[0m 22.57   \u001b[0m |\n",
      "| \u001b[0m 102     \u001b[0m | \u001b[0m 0.725   \u001b[0m | \u001b[0m-0.003994\u001b[0m | \u001b[0m 27.3    \u001b[0m |\n",
      "| \u001b[0m 103     \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m-0.02606 \u001b[0m | \u001b[0m 13.97   \u001b[0m |\n",
      "| \u001b[0m 104     \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m-0.03203 \u001b[0m | \u001b[0m 22.56   \u001b[0m |\n",
      "| \u001b[0m 105     \u001b[0m | \u001b[0m 0.6667  \u001b[0m | \u001b[0m-0.01108 \u001b[0m | \u001b[0m 23.7    \u001b[0m |\n",
      "| \u001b[0m 106     \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.03034 \u001b[0m | \u001b[0m 13.97   \u001b[0m |\n",
      "| \u001b[0m 107     \u001b[0m | \u001b[0m 0.7273  \u001b[0m | \u001b[0m-0.02879 \u001b[0m | \u001b[0m 22.56   \u001b[0m |\n",
      "| \u001b[0m 108     \u001b[0m | \u001b[0m 0.725   \u001b[0m | \u001b[0m 0.001284\u001b[0m | \u001b[0m 27.3    \u001b[0m |\n",
      "| \u001b[0m 109     \u001b[0m | \u001b[0m 0.6977  \u001b[0m | \u001b[0m-0.02414 \u001b[0m | \u001b[0m 13.96   \u001b[0m |\n",
      "| \u001b[0m 110     \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.02835 \u001b[0m | \u001b[0m 13.96   \u001b[0m |\n",
      "| \u001b[0m 111     \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m-0.02569 \u001b[0m | \u001b[0m 13.97   \u001b[0m |\n",
      "| \u001b[0m 112     \u001b[0m | \u001b[0m 0.7059  \u001b[0m | \u001b[0m-0.03629 \u001b[0m | \u001b[0m 22.56   \u001b[0m |\n",
      "| \u001b[0m 113     \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m-0.03362 \u001b[0m | \u001b[0m 22.55   \u001b[0m |\n",
      "| \u001b[0m 114     \u001b[0m | \u001b[0m 0.7297  \u001b[0m | \u001b[0m-0.0264  \u001b[0m | \u001b[0m 22.55   \u001b[0m |\n",
      "| \u001b[0m 115     \u001b[0m | \u001b[0m 0.7105  \u001b[0m | \u001b[0m-0.02175 \u001b[0m | \u001b[0m 22.55   \u001b[0m |\n",
      "| \u001b[0m 116     \u001b[0m | \u001b[0m 0.6753  \u001b[0m | \u001b[0m 0.02889 \u001b[0m | \u001b[0m 20.23   \u001b[0m |\n",
      "| \u001b[0m 117     \u001b[0m | \u001b[0m 0.7105  \u001b[0m | \u001b[0m-0.03839 \u001b[0m | \u001b[0m 22.55   \u001b[0m |\n",
      "| \u001b[0m 118     \u001b[0m | \u001b[0m 0.6494  \u001b[0m | \u001b[0m-0.00918 \u001b[0m | \u001b[0m 27.3    \u001b[0m |\n",
      "| \u001b[0m 119     \u001b[0m | \u001b[0m 0.7027  \u001b[0m | \u001b[0m 0.02998 \u001b[0m | \u001b[0m 27.67   \u001b[0m |\n",
      "| \u001b[0m 120     \u001b[0m | \u001b[0m 0.6818  \u001b[0m | \u001b[0m-0.01368 \u001b[0m | \u001b[0m 10.26   \u001b[0m |\n",
      "| \u001b[0m 121     \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.02186 \u001b[0m | \u001b[0m 13.98   \u001b[0m |\n",
      "| \u001b[0m 122     \u001b[0m | \u001b[0m 0.7273  \u001b[0m | \u001b[0m-0.02985 \u001b[0m | \u001b[0m 22.55   \u001b[0m |\n",
      "| \u001b[0m 123     \u001b[0m | \u001b[0m 0.72    \u001b[0m | \u001b[0m 0.01326 \u001b[0m | \u001b[0m 17.85   \u001b[0m |\n",
      "| \u001b[0m 124     \u001b[0m | \u001b[0m 0.7073  \u001b[0m | \u001b[0m-0.03763 \u001b[0m | \u001b[0m 12.63   \u001b[0m |\n",
      "| \u001b[0m 125     \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m-0.02649 \u001b[0m | \u001b[0m 13.98   \u001b[0m |\n",
      "| \u001b[0m 126     \u001b[0m | \u001b[0m 0.725   \u001b[0m | \u001b[0m 0.000708\u001b[0m | \u001b[0m 27.31   \u001b[0m |\n",
      "| \u001b[0m 127     \u001b[0m | \u001b[0m 0.7073  \u001b[0m | \u001b[0m-0.04029 \u001b[0m | \u001b[0m 13.49   \u001b[0m |\n",
      "| \u001b[0m 128     \u001b[0m | \u001b[0m 0.6818  \u001b[0m | \u001b[0m-0.0342  \u001b[0m | \u001b[0m 19.62   \u001b[0m |\n",
      "| \u001b[0m 129     \u001b[0m | \u001b[0m 0.6977  \u001b[0m | \u001b[0m-0.02281 \u001b[0m | \u001b[0m 13.97   \u001b[0m |\n",
      "| \u001b[0m 130     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.04295 \u001b[0m | \u001b[0m 20.19   \u001b[0m |\n",
      "| \u001b[0m 131     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.04592 \u001b[0m | \u001b[0m 20.19   \u001b[0m |\n",
      "| \u001b[0m 132     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.0392  \u001b[0m | \u001b[0m 20.18   \u001b[0m |\n",
      "| \u001b[0m 133     \u001b[0m | \u001b[0m 0.6842  \u001b[0m | \u001b[0m 0.002835\u001b[0m | \u001b[0m 11.47   \u001b[0m |\n",
      "| \u001b[0m 134     \u001b[0m | \u001b[0m 0.7105  \u001b[0m | \u001b[0m-0.022   \u001b[0m | \u001b[0m 22.56   \u001b[0m |\n",
      "| \u001b[0m 135     \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m-0.03169 \u001b[0m | \u001b[0m 22.55   \u001b[0m |\n",
      "| \u001b[0m 136     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.04505 \u001b[0m | \u001b[0m 20.19   \u001b[0m |\n",
      "| \u001b[0m 137     \u001b[0m | \u001b[0m 0.6923  \u001b[0m | \u001b[0m-0.01419 \u001b[0m | \u001b[0m 22.83   \u001b[0m |\n",
      "| \u001b[0m 138     \u001b[0m | \u001b[0m 0.6966  \u001b[0m | \u001b[0m-0.03732 \u001b[0m | \u001b[0m 18.91   \u001b[0m |\n",
      "| \u001b[0m 139     \u001b[0m | \u001b[0m 0.6977  \u001b[0m | \u001b[0m-0.03507 \u001b[0m | \u001b[0m 20.18   \u001b[0m |\n",
      "| \u001b[0m 140     \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.02949 \u001b[0m | \u001b[0m 13.97   \u001b[0m |\n",
      "| \u001b[0m 141     \u001b[0m | \u001b[0m 0.7143  \u001b[0m | \u001b[0m-0.01192 \u001b[0m | \u001b[0m 14.29   \u001b[0m |\n",
      "| \u001b[95m 142     \u001b[0m | \u001b[95m 0.7436  \u001b[0m | \u001b[95m-0.04955 \u001b[0m | \u001b[95m 20.19   \u001b[0m |\n",
      "| \u001b[0m 143     \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m-0.02579 \u001b[0m | \u001b[0m 13.98   \u001b[0m |\n",
      "| \u001b[0m 144     \u001b[0m | \u001b[0m 0.7059  \u001b[0m | \u001b[0m-0.03712 \u001b[0m | \u001b[0m 22.55   \u001b[0m |\n",
      "| \u001b[0m 145     \u001b[0m | \u001b[0m 0.7436  \u001b[0m | \u001b[0m-0.04984 \u001b[0m | \u001b[0m 20.19   \u001b[0m |\n",
      "| \u001b[0m 146     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.04167 \u001b[0m | \u001b[0m 20.18   \u001b[0m |\n",
      "| \u001b[0m 147     \u001b[0m | \u001b[0m 0.7105  \u001b[0m | \u001b[0m-0.03834 \u001b[0m | \u001b[0m 22.59   \u001b[0m |\n",
      "| \u001b[0m 148     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.04779 \u001b[0m | \u001b[0m 20.2    \u001b[0m |\n",
      "| \u001b[0m 149     \u001b[0m | \u001b[0m 0.6494  \u001b[0m | \u001b[0m-0.0114  \u001b[0m | \u001b[0m 27.3    \u001b[0m |\n",
      "| \u001b[0m 150     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.04754 \u001b[0m | \u001b[0m 20.18   \u001b[0m |\n",
      "| \u001b[0m 151     \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.02047 \u001b[0m | \u001b[0m 11.35   \u001b[0m |\n",
      "| \u001b[0m 152     \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.02718 \u001b[0m | \u001b[0m 13.98   \u001b[0m |\n",
      "| \u001b[0m 153     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.04512 \u001b[0m | \u001b[0m 20.18   \u001b[0m |\n",
      "| \u001b[0m 154     \u001b[0m | \u001b[0m 0.7294  \u001b[0m | \u001b[0m-0.0198  \u001b[0m | \u001b[0m 19.62   \u001b[0m |\n",
      "| \u001b[0m 155     \u001b[0m | \u001b[0m 0.7294  \u001b[0m | \u001b[0m-0.02184 \u001b[0m | \u001b[0m 19.61   \u001b[0m |\n",
      "| \u001b[95m 156     \u001b[0m | \u001b[95m 0.7654  \u001b[0m | \u001b[95m-0.02304 \u001b[0m | \u001b[95m 19.61   \u001b[0m |\n",
      "| \u001b[0m 157     \u001b[0m | \u001b[0m 0.7294  \u001b[0m | \u001b[0m-0.01931 \u001b[0m | \u001b[0m 19.61   \u001b[0m |\n",
      "| \u001b[0m 158     \u001b[0m | \u001b[0m 0.6966  \u001b[0m | \u001b[0m-0.03566 \u001b[0m | \u001b[0m 18.6    \u001b[0m |\n",
      "| \u001b[0m 159     \u001b[0m | \u001b[0m 0.7436  \u001b[0m | \u001b[0m-0.04827 \u001b[0m | \u001b[0m 20.2    \u001b[0m |\n",
      "| \u001b[0m 160     \u001b[0m | \u001b[0m 0.6923  \u001b[0m | \u001b[0m-0.02572 \u001b[0m | \u001b[0m 19.61   \u001b[0m |\n",
      "| \u001b[0m 161     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.04471 \u001b[0m | \u001b[0m 20.18   \u001b[0m |\n",
      "| \u001b[0m 162     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.04209 \u001b[0m | \u001b[0m 20.18   \u001b[0m |\n",
      "| \u001b[0m 163     \u001b[0m | \u001b[0m 0.6316  \u001b[0m | \u001b[0m-0.01572 \u001b[0m | \u001b[0m 24.41   \u001b[0m |\n",
      "| \u001b[0m 164     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.04472 \u001b[0m | \u001b[0m 20.17   \u001b[0m |\n",
      "| \u001b[0m 165     \u001b[0m | \u001b[0m 0.6966  \u001b[0m | \u001b[0m-0.02401 \u001b[0m | \u001b[0m 19.62   \u001b[0m |\n",
      "| \u001b[0m 166     \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.01921 \u001b[0m | \u001b[0m 13.98   \u001b[0m |\n",
      "| \u001b[0m 167     \u001b[0m | \u001b[0m 0.7229  \u001b[0m | \u001b[0m-0.03052 \u001b[0m | \u001b[0m 13.98   \u001b[0m |\n",
      "| \u001b[0m 168     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.04705 \u001b[0m | \u001b[0m 20.2    \u001b[0m |\n",
      "| \u001b[0m 169     \u001b[0m | \u001b[0m 0.7     \u001b[0m | \u001b[0m 0.04646 \u001b[0m | \u001b[0m 12.87   \u001b[0m |\n",
      "| \u001b[0m 170     \u001b[0m | \u001b[0m 0.6923  \u001b[0m | \u001b[0m-0.02636 \u001b[0m | \u001b[0m 19.62   \u001b[0m |\n",
      "| \u001b[0m 171     \u001b[0m | \u001b[0m 0.7436  \u001b[0m | \u001b[0m-0.04914 \u001b[0m | \u001b[0m 20.17   \u001b[0m |\n",
      "| \u001b[0m 172     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.04669 \u001b[0m | \u001b[0m 20.17   \u001b[0m |\n",
      "| \u001b[0m 173     \u001b[0m | \u001b[0m 0.7342  \u001b[0m | \u001b[0m-0.04572 \u001b[0m | \u001b[0m 20.2    \u001b[0m |\n",
      "| \u001b[0m 174     \u001b[0m | \u001b[0m 0.7273  \u001b[0m | \u001b[0m-0.04561 \u001b[0m | \u001b[0m 18.47   \u001b[0m |\n",
      "| \u001b[95m 175     \u001b[0m | \u001b[95m 0.7848  \u001b[0m | \u001b[95m 0.04772 \u001b[0m | \u001b[95m 17.78   \u001b[0m |\n",
      "| \u001b[0m 176     \u001b[0m | \u001b[0m 0.7848  \u001b[0m | \u001b[0m 0.04406 \u001b[0m | \u001b[0m 17.78   \u001b[0m |\n",
      "| \u001b[0m 177     \u001b[0m | \u001b[0m 0.7848  \u001b[0m | \u001b[0m 0.04471 \u001b[0m | \u001b[0m 17.78   \u001b[0m |\n",
      "| \u001b[0m 178     \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m 0.03386 \u001b[0m | \u001b[0m 18.93   \u001b[0m |\n",
      "| \u001b[0m 179     \u001b[0m | \u001b[0m 0.7632  \u001b[0m | \u001b[0m 0.04011 \u001b[0m | \u001b[0m 18.93   \u001b[0m |\n",
      "| \u001b[0m 180     \u001b[0m | \u001b[0m 0.7848  \u001b[0m | \u001b[0m 0.04769 \u001b[0m | \u001b[0m 17.78   \u001b[0m |\n",
      "| \u001b[0m 181     \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m 0.03206 \u001b[0m | \u001b[0m 18.94   \u001b[0m |\n",
      "| \u001b[0m 182     \u001b[0m | \u001b[0m 0.7532  \u001b[0m | \u001b[0m 0.04485 \u001b[0m | \u001b[0m 18.92   \u001b[0m |\n",
      "| \u001b[0m 183     \u001b[0m | \u001b[0m 0.7632  \u001b[0m | \u001b[0m 0.042   \u001b[0m | \u001b[0m 18.93   \u001b[0m |\n",
      "| \u001b[0m 184     \u001b[0m | \u001b[0m 0.72    \u001b[0m | \u001b[0m 0.01095 \u001b[0m | \u001b[0m 17.86   \u001b[0m |\n",
      "| \u001b[0m 185     \u001b[0m | \u001b[0m 0.7143  \u001b[0m | \u001b[0m-0.04837 \u001b[0m | \u001b[0m 15.47   \u001b[0m |\n",
      "| \u001b[0m 186     \u001b[0m | \u001b[0m 0.7848  \u001b[0m | \u001b[0m 0.0473  \u001b[0m | \u001b[0m 17.78   \u001b[0m |\n",
      "| \u001b[0m 187     \u001b[0m | \u001b[0m 0.6829  \u001b[0m | \u001b[0m-0.0407  \u001b[0m | \u001b[0m 23.65   \u001b[0m |\n",
      "| \u001b[0m 188     \u001b[0m | \u001b[0m 0.6923  \u001b[0m | \u001b[0m-0.01221 \u001b[0m | \u001b[0m 26.04   \u001b[0m |\n",
      "| \u001b[0m 189     \u001b[0m | \u001b[0m 0.7532  \u001b[0m | \u001b[0m 0.04529 \u001b[0m | \u001b[0m 18.94   \u001b[0m |\n",
      "| \u001b[0m 190     \u001b[0m | \u001b[0m 0.7848  \u001b[0m | \u001b[0m 0.04348 \u001b[0m | \u001b[0m 17.79   \u001b[0m |\n",
      "| \u001b[0m 191     \u001b[0m | \u001b[0m 0.7     \u001b[0m | \u001b[0m 0.04061 \u001b[0m | \u001b[0m 12.46   \u001b[0m |\n",
      "| \u001b[0m 192     \u001b[0m | \u001b[0m 0.7848  \u001b[0m | \u001b[0m 0.04312 \u001b[0m | \u001b[0m 17.79   \u001b[0m |\n",
      "| \u001b[0m 193     \u001b[0m | \u001b[0m 0.7532  \u001b[0m | \u001b[0m 0.04991 \u001b[0m | \u001b[0m 18.93   \u001b[0m |\n",
      "| \u001b[0m 194     \u001b[0m | \u001b[0m 0.7848  \u001b[0m | \u001b[0m 0.04101 \u001b[0m | \u001b[0m 17.79   \u001b[0m |\n",
      "| \u001b[0m 195     \u001b[0m | \u001b[0m 0.7073  \u001b[0m | \u001b[0m 0.0367  \u001b[0m | \u001b[0m 17.79   \u001b[0m |\n",
      "| \u001b[0m 196     \u001b[0m | \u001b[0m 0.7848  \u001b[0m | \u001b[0m 0.0435  \u001b[0m | \u001b[0m 17.78   \u001b[0m |\n",
      "| \u001b[0m 197     \u001b[0m | \u001b[0m 0.6923  \u001b[0m | \u001b[0m 0.02793 \u001b[0m | \u001b[0m 24.72   \u001b[0m |\n",
      "| \u001b[0m 198     \u001b[0m | \u001b[0m 0.7013  \u001b[0m | \u001b[0m 0.01218 \u001b[0m | \u001b[0m 22.56   \u001b[0m |\n",
      "| \u001b[0m 199     \u001b[0m | \u001b[0m 0.7073  \u001b[0m | \u001b[0m 0.007552\u001b[0m | \u001b[0m 13.13   \u001b[0m |\n",
      "| \u001b[0m 200     \u001b[0m | \u001b[0m 0.6761  \u001b[0m | \u001b[0m 0.03671 \u001b[0m | \u001b[0m 28.28   \u001b[0m |\n",
      "| \u001b[0m 201     \u001b[0m | \u001b[0m 0.7105  \u001b[0m | \u001b[0m 0.03921 \u001b[0m | \u001b[0m 18.93   \u001b[0m |\n",
      "| \u001b[0m 202     \u001b[0m | \u001b[0m 0.7848  \u001b[0m | \u001b[0m 0.04867 \u001b[0m | \u001b[0m 17.78   \u001b[0m |\n",
      "| \u001b[0m 203     \u001b[0m | \u001b[0m 0.7436  \u001b[0m | \u001b[0m 0.03745 \u001b[0m | \u001b[0m 18.93   \u001b[0m |\n",
      "| \u001b[0m 204     \u001b[0m | \u001b[0m 0.7532  \u001b[0m | \u001b[0m 0.04703 \u001b[0m | \u001b[0m 18.93   \u001b[0m |\n",
      "| \u001b[0m 205     \u001b[0m | \u001b[0m 0.7317  \u001b[0m | \u001b[0m 0.02761 \u001b[0m | \u001b[0m 18.92   \u001b[0m |\n",
      "| \u001b[0m 206     \u001b[0m | \u001b[0m 0.7532  \u001b[0m | \u001b[0m 0.04951 \u001b[0m | \u001b[0m 18.93   \u001b[0m |\n",
      "| \u001b[0m 207     \u001b[0m | \u001b[0m 0.7848  \u001b[0m | \u001b[0m 0.04445 \u001b[0m | \u001b[0m 17.77   \u001b[0m |\n",
      "| \u001b[0m 208     \u001b[0m | \u001b[0m 0.7848  \u001b[0m | \u001b[0m 0.04138 \u001b[0m | \u001b[0m 17.79   \u001b[0m |\n",
      "| \u001b[0m 209     \u001b[0m | \u001b[0m 0.6667  \u001b[0m | \u001b[0m-0.02362 \u001b[0m | \u001b[0m 23.7    \u001b[0m |\n",
      "| \u001b[0m 210     \u001b[0m | \u001b[0m 0.7632  \u001b[0m | \u001b[0m 0.04247 \u001b[0m | \u001b[0m 18.93   \u001b[0m |\n",
      "=================================================\n",
      "max\n",
      "{'target': 0.7848101265822784, 'params': {'learning_rate': 0.047715766463314324, 'n_estimators': 17.779424609189796}}\n"
     ]
    }
   ],
   "source": [
    "from bayes_opt import BayesianOptimization\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "# Basic Example : https://github.com/fmfn/BayesianOptimization/blob/master/examples/basic-tour.ipynb\n",
    "\n",
    "def black_box_function(n_estimators, learning_rate=0):\n",
    "    model = AdaBoostClassifier(n_estimators = int(n_estimators),\n",
    "                               learning_rate = 10**learning_rate,\n",
    "                               random_state = 42)\n",
    "    model.fit(x_train_res , y_train_res)\n",
    "    y_pred = model.predict(X_test)\n",
    "    return f1_score(y_test, y_pred)\n",
    "\n",
    "pbounds = {'n_estimators':(10, 30), 'learning_rate':(-0.05, 0.05)}\n",
    "\n",
    "optimizer = BayesianOptimization(\n",
    "    f=black_box_function,\n",
    "    pbounds=pbounds,\n",
    "    verbose=2, # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent\n",
    "    random_state=1,\n",
    ")\n",
    "\n",
    "optimizer.maximize(\n",
    "    init_points=10,\n",
    "    n_iter=200,\n",
    ")\n",
    "\n",
    "print('max')\n",
    "\n",
    "print(optimizer.max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "F1 0.7848101265822784\n              precision    recall  f1-score   support\n\n non résilié       0.98      0.92      0.95       166\n     resilié       0.69      0.91      0.78        34\n\n    accuracy                           0.92       200\n   macro avg       0.83      0.91      0.87       200\nweighted avg       0.93      0.92      0.92       200\n\n"
     ]
    }
   ],
   "source": [
    "model = AdaBoostClassifier(n_estimators = int(optimizer.max['params']['n_estimators']),\n",
    "                            learning_rate = 10**optimizer.max['params']['learning_rate'],\n",
    "                            random_state = 42)\n",
    "model.fit(x_train_res , y_train_res)\n",
    "y_pred = model.predict(X_test)\n",
    "print(\"F1\", f1_score(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred,target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.2733333333333333"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "yscore = model.predict(Xscore)\n",
    "np.mean(yscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "yDF = pd.DataFrame(data = yscore.astype(int), columns=['target'])\n",
    "yDF.index+=1000\n",
    "yDF.to_csv(\"result_schindler_hugo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}