{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8750d15b",
   "metadata": {},
   "source": [
    "# Cours 4 : Machine Learning - Algorithmes supervisés (2/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c08ae5",
   "metadata": {},
   "source": [
    "## Préambule"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7ecb4b",
   "metadata": {},
   "source": [
    "Les objectifs de cette séance (3h) sont :\n",
    "* Préparation des bases de modélisation (sampling)\n",
    "* Construire un modèle de Machine Learning (cross-validation et hyperparamétrage) pour résoudre un problème de classification\n",
    "* Analyser les performances du modèle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e99c600",
   "metadata": {},
   "source": [
    "## Préparation du workspace"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b01045",
   "metadata": {},
   "source": [
    "### Import de librairies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97d58527",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Données\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Graphiques\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "import plotly.express as px\n",
    "\n",
    "# Machine Learning\n",
    "import sklearn.preprocessing as preproc\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# Statistiques\n",
    "from scipy.stats import chi2_contingency\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import (\n",
    "  GridSearchCV,\n",
    "  StratifiedKFold,\n",
    "  cross_val_score,\n",
    "  train_test_split,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06153286",
   "metadata": {},
   "source": [
    "### Définition des fonctions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c67db932",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cramers_V(var1,var2) :\n",
    "  crosstab = np.array(pd.crosstab(var1,var2, rownames=None, colnames=None)) # Cross table building\n",
    "  stat = chi2_contingency(crosstab)[0] # Keeping of the test statistic of the Chi2 test\n",
    "  obs = np.sum(crosstab) # Number of observations\n",
    "  mini = min(crosstab.shape)-1 # Take the minimum value between the colmns and the rows of the cross table\n",
    "  return (stat/(obs*mini))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "985e4e97",
   "metadata": {},
   "source": [
    "### Constantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "c9597b48",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_path = \"./1_inputs\"\n",
    "output_path = \"./2_outputs\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b035d2",
   "metadata": {},
   "source": [
    "### Import des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "8051b5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = input_path + '/base_retraitee.csv'\n",
    "data_retraitee = pd.read_csv(path, sep=\",\", decimal=\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2578ba1",
   "metadata": {},
   "source": [
    "## Préparation de la base de données"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3715c37",
   "metadata": {},
   "source": [
    "Dans cette partie nous souhaitons expliquer la survenance d'un sinistre en fonction des variables explicatives i.e. une variable binaire qui : \n",
    "* est égale à 1 si la personne a eu 1 ou plus de sinistres.\n",
    "* est égale à 0 le cas échéant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b9b98d36",
   "metadata": {},
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       "   ANNEE_CTR CONTRAT_ANCIENNETE FREQUENCE_PAIEMENT_COTISATION      GROUPE_KM  \\\n",
       "0       2019             (-1,0]                        ANNUEL  [20000;40000[   \n",
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       "2       2021              (1,2]                        ANNUEL      [0;20000[   \n",
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       "  ZONE_RISQUE  AGE_ASSURE_PRINCIPAL GENRE  DEUXIEME_CONDUCTEUR  \\\n",
       "0           B                    54     M                False   \n",
       "1           B                    88     F                 True   \n",
       "2           D                    35     F                 True   \n",
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       "4           A                    46     F                False   \n",
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       "   ANCIENNETE_PERMIS  ANNEE_CONSTRUCTION  ENERGIE EQUIPEMENT_SECURITE  \\\n",
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       "4                 31              2009.0   DIESEL                FAUX   \n",
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       "  VALEUR_DU_BIEN  SINISTRE  \n",
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     },
     "execution_count": 106,
     "metadata": {},
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   ],
   "source": [
    "data_model = data_retraitee\n",
    "\n",
    "# Calcul de la variable \"sinistré\" qui est vrai si la personne a eu un ou plusieurs sinistres ou faux le cas échéant\n",
    "data_model[\"SINISTRE\"] = data_model[\"NB\"] > 0\n",
    "data_model = data_model.drop([\"CHARGE\", \"NB\", \"EXPO\"], axis=1)\n",
    "data_model.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "657ebd89",
   "metadata": {},
   "source": [
    "**Exercice :** construisez les statistiques descriptives de la base utilisée. Notamment la distribution de la variable réponse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ba53b22e",
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       "      <td>14236.000000</td>\n",
       "      <td>14236</td>\n",
       "      <td>14236</td>\n",
       "      <td>14236</td>\n",
       "      <td>14236</td>\n",
       "      <td>14236.000000</td>\n",
       "      <td>14236</td>\n",
       "      <td>14236</td>\n",
       "      <td>14236.000000</td>\n",
       "      <td>14236.000000</td>\n",
       "      <td>14236</td>\n",
       "      <td>14236</td>\n",
       "      <td>14236</td>\n",
       "      <td>14236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>(0,1]</td>\n",
       "      <td>MENSUEL</td>\n",
       "      <td>[0;20000[</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ESSENCE</td>\n",
       "      <td>FAUX</td>\n",
       "      <td>[10000;15000[</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4174</td>\n",
       "      <td>6854</td>\n",
       "      <td>7653</td>\n",
       "      <td>4867</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8387</td>\n",
       "      <td>11222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7294</td>\n",
       "      <td>9396</td>\n",
       "      <td>3629</td>\n",
       "      <td>13412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2018.769247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.023391</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.362391</td>\n",
       "      <td>2015.389646</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.655911</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.636799</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.875462</td>\n",
       "      <td>3.507688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1961.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2021.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ANNEE_CTR CONTRAT_ANCIENNETE FREQUENCE_PAIEMENT_COTISATION  \\\n",
       "count   14236.000000              14236                         14236   \n",
       "unique           NaN                  5                             3   \n",
       "top              NaN              (0,1]                       MENSUEL   \n",
       "freq             NaN               4174                          6854   \n",
       "mean     2018.769247                NaN                           NaN   \n",
       "std         1.655911                NaN                           NaN   \n",
       "min      2016.000000                NaN                           NaN   \n",
       "25%      2017.000000                NaN                           NaN   \n",
       "50%      2019.000000                NaN                           NaN   \n",
       "75%      2020.000000                NaN                           NaN   \n",
       "max      2021.000000                NaN                           NaN   \n",
       "\n",
       "        GROUPE_KM ZONE_RISQUE  AGE_ASSURE_PRINCIPAL  GENRE  \\\n",
       "count       14236       14236          14236.000000  14236   \n",
       "unique          4          17                   NaN      2   \n",
       "top     [0;20000[           C                   NaN      M   \n",
       "freq         7653        4867                   NaN   8387   \n",
       "mean          NaN         NaN             45.023391    NaN   \n",
       "std           NaN         NaN             13.636799    NaN   \n",
       "min           NaN         NaN             18.000000    NaN   \n",
       "25%           NaN         NaN             35.000000    NaN   \n",
       "50%           NaN         NaN             44.000000    NaN   \n",
       "75%           NaN         NaN             54.000000    NaN   \n",
       "max           NaN         NaN             94.000000    NaN   \n",
       "\n",
       "       DEUXIEME_CONDUCTEUR  ANCIENNETE_PERMIS  ANNEE_CONSTRUCTION  ENERGIE  \\\n",
       "count                14236       14236.000000        14236.000000    14236   \n",
       "unique                   2                NaN                 NaN        3   \n",
       "top                  False                NaN                 NaN  ESSENCE   \n",
       "freq                 11222                NaN                 NaN     7294   \n",
       "mean                   NaN          36.362391         2015.389646      NaN   \n",
       "std                    NaN          18.875462            3.507688      NaN   \n",
       "min                    NaN           1.000000         1961.000000      NaN   \n",
       "25%                    NaN          19.000000         2014.000000      NaN   \n",
       "50%                    NaN          35.000000         2016.000000      NaN   \n",
       "75%                    NaN          53.000000         2017.000000      NaN   \n",
       "max                    NaN          70.000000         2021.000000      NaN   \n",
       "\n",
       "       EQUIPEMENT_SECURITE VALEUR_DU_BIEN SINISTRE  \n",
       "count                14236          14236    14236  \n",
       "unique                   2              6        2  \n",
       "top                   FAUX  [10000;15000[    False  \n",
       "freq                  9396           3629    13412  \n",
       "mean                   NaN            NaN      NaN  \n",
       "std                    NaN            NaN      NaN  \n",
       "min                    NaN            NaN      NaN  \n",
       "25%                    NaN            NaN      NaN  \n",
       "50%                    NaN            NaN      NaN  \n",
       "75%                    NaN            NaN      NaN  \n",
       "max                    NaN            NaN      NaN  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_model.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "47cf4b69",
   "metadata": {},
   "outputs": [
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           "bgcolor": "#E5ECF6",
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            "ticks": ""
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          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
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           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
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           "title": {
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           },
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           "zerolinewidth": 2
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        },
        "title": {
         "text": "Distribution de la variable 'sinistré'"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
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         "title": {
          "text": "SINISTRE"
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        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
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         "title": {
          "text": "count"
         }
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     },
     "metadata": {},
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   ],
   "source": [
    "# Observation de la distribution\n",
    "fig = px.histogram(\n",
    "    data_model, x=\"SINISTRE\", title=\"Distribution de la variable 'sinistré'\"\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d6156a",
   "metadata": {},
   "source": [
    "#### Etude des corrélations parmi les variables explicatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a0bc6278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14236, 13)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set = data_model.drop(\"SINISTRE\", axis=1)\n",
    "data_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "73d31ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Séparation en variables qualitatives ou catégorielles\n",
    "variables_na = []\n",
    "variables_numeriques = []\n",
    "variables_01 = []\n",
    "variables_categorielles = []\n",
    "for col in data_set.columns:\n",
    "    if True in data_model[col].isna().unique():\n",
    "        variables_na.append(data_model[col])\n",
    "    else:\n",
    "        if str(data_model[col].dtypes) in [\"int32\", \"int64\", \"float64\"]:\n",
    "            if len(data_model[col].unique()) == 2:\n",
    "                variables_categorielles.append(data_model[col])\n",
    "            else:\n",
    "                variables_numeriques.append(data_model[col])\n",
    "        else:\n",
    "            if len(data_model[col].unique()) == 2:\n",
    "                variables_categorielles.append(data_model[col])\n",
    "            else:\n",
    "                variables_categorielles.append(data_model[col])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e82fcade",
   "metadata": {},
   "source": [
    "##### Corrélation des variables catégorielles :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "30df8bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "vars_categorielles = pd.DataFrame(variables_categorielles).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "be7a7d00",
   "metadata": {},
   "outputs": [
    {
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         "name": "VALEUR_DU_BIEN",
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       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CONTRAT_ANCIENNETE</th>\n",
       "      <th>FREQUENCE_PAIEMENT_COTISATION</th>\n",
       "      <th>GROUPE_KM</th>\n",
       "      <th>ZONE_RISQUE</th>\n",
       "      <th>GENRE</th>\n",
       "      <th>DEUXIEME_CONDUCTEUR</th>\n",
       "      <th>ENERGIE</th>\n",
       "      <th>EQUIPEMENT_SECURITE</th>\n",
       "      <th>VALEUR_DU_BIEN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CONTRAT_ANCIENNETE</th>\n",
       "      <td>1.00</td>\n",
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       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
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       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GROUPE_KM</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZONE_RISQUE</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENRE</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEUXIEME_CONDUCTEUR</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENERGIE</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EQUIPEMENT_SECURITE</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALEUR_DU_BIEN</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               CONTRAT_ANCIENNETE  \\\n",
       "CONTRAT_ANCIENNETE                           1.00   \n",
       "FREQUENCE_PAIEMENT_COTISATION                0.00   \n",
       "GROUPE_KM                                    0.01   \n",
       "ZONE_RISQUE                                  0.02   \n",
       "GENRE                                        0.00   \n",
       "DEUXIEME_CONDUCTEUR                          0.00   \n",
       "ENERGIE                                      0.00   \n",
       "EQUIPEMENT_SECURITE                          0.01   \n",
       "VALEUR_DU_BIEN                               0.00   \n",
       "\n",
       "                               FREQUENCE_PAIEMENT_COTISATION  GROUPE_KM  \\\n",
       "CONTRAT_ANCIENNETE                                      0.00       0.01   \n",
       "FREQUENCE_PAIEMENT_COTISATION                           1.00       0.00   \n",
       "GROUPE_KM                                               0.00       1.00   \n",
       "ZONE_RISQUE                                             0.00       0.01   \n",
       "GENRE                                                   0.01       0.01   \n",
       "DEUXIEME_CONDUCTEUR                                     0.00       0.00   \n",
       "ENERGIE                                                 0.00       0.04   \n",
       "EQUIPEMENT_SECURITE                                     0.01       0.01   \n",
       "VALEUR_DU_BIEN                                          0.02       0.02   \n",
       "\n",
       "                               ZONE_RISQUE  GENRE  DEUXIEME_CONDUCTEUR  \\\n",
       "CONTRAT_ANCIENNETE                    0.02   0.00                  0.0   \n",
       "FREQUENCE_PAIEMENT_COTISATION         0.00   0.01                  0.0   \n",
       "GROUPE_KM                             0.01   0.01                  0.0   \n",
       "ZONE_RISQUE                           1.00   0.00                  0.0   \n",
       "GENRE                                 0.00   1.00                  0.0   \n",
       "DEUXIEME_CONDUCTEUR                   0.00   0.00                  1.0   \n",
       "ENERGIE                               0.01   0.02                  0.0   \n",
       "EQUIPEMENT_SECURITE                   0.03   0.01                  0.0   \n",
       "VALEUR_DU_BIEN                        0.00   0.07                  0.0   \n",
       "\n",
       "                               ENERGIE  EQUIPEMENT_SECURITE  VALEUR_DU_BIEN  \n",
       "CONTRAT_ANCIENNETE                0.00                 0.01            0.00  \n",
       "FREQUENCE_PAIEMENT_COTISATION     0.00                 0.01            0.02  \n",
       "GROUPE_KM                         0.04                 0.01            0.02  \n",
       "ZONE_RISQUE                       0.01                 0.03            0.00  \n",
       "GENRE                             0.02                 0.01            0.07  \n",
       "DEUXIEME_CONDUCTEUR               0.00                 0.00            0.00  \n",
       "ENERGIE                           1.00                 0.02            0.08  \n",
       "EQUIPEMENT_SECURITE               0.02                 1.00            0.07  \n",
       "VALEUR_DU_BIEN                    0.08                 0.07            1.00  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test du V de Cramer\n",
    "rows = []\n",
    "\n",
    "for var1 in vars_categorielles:\n",
    "    col = []\n",
    "    for var2 in vars_categorielles:\n",
    "        cramers = cramers_V(\n",
    "            vars_categorielles[var1], vars_categorielles[var2]\n",
    "        )  # V de Cramer\n",
    "        col.append(round(cramers, 2))  # arrondi du résultat\n",
    "    rows.append(col)\n",
    "\n",
    "cramers_results = np.array(rows)\n",
    "v_cramer_resultats = pd.DataFrame(\n",
    "    cramers_results,\n",
    "    columns=vars_categorielles.columns,\n",
    "    index=vars_categorielles.columns,\n",
    ")\n",
    "\n",
    "v_cramer_resultats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "b3297dca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# On repère les variables trop corrélées\n",
    "for i in range(v_cramer_resultats.shape[0]):\n",
    "    for j in range(i + 1, v_cramer_resultats.shape[0]):\n",
    "        if v_cramer_resultats.iloc[i, j] > 0.7:\n",
    "            print(\n",
    "                f\"{v_cramer_resultats.index.to_numpy()[i]} et {v_cramer_resultats.colmns[j]} sont trop dépendantes, V-CRAMER = {v_cramer_resultats.iloc[i, j]}\"\n",
    "            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f615121",
   "metadata": {},
   "source": [
    "##### Corrélation des variables numériques :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "d1fa12fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "vars_numeriques = pd.DataFrame(variables_numeriques).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5777d20f",
   "metadata": {},
   "source": [
    "**Question :** quels sont vos commentaires ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c70946b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "ANNEE_CTR",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "AGE_ASSURE_PRINCIPAL",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ANCIENNETE_PERMIS",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "ANNEE_CONSTRUCTION",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "4d9d1f6e-75c3-46f7-aef5-41fca2b9955d",
       "rows": [
        [
         "ANNEE_CTR",
         "1.0",
         "0.048023234802924315",
         "0.043983174120495815",
         "0.3615499864845018"
        ],
        [
         "AGE_ASSURE_PRINCIPAL",
         "0.048023234802924315",
         "1.0",
         "0.4987430846753776",
         "-0.0591835157827114"
        ],
        [
         "ANCIENNETE_PERMIS",
         "0.043983174120495815",
         "0.4987430846753776",
         "1.0",
         "-0.0298138263902136"
        ],
        [
         "ANNEE_CONSTRUCTION",
         "0.3615499864845018",
         "-0.0591835157827114",
         "-0.0298138263902136",
         "1.0"
        ]
       ],
       "shape": {
        "columns": 4,
        "rows": 4
       }
      },
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>ANNEE_CTR</th>\n",
       "      <th>AGE_ASSURE_PRINCIPAL</th>\n",
       "      <th>ANCIENNETE_PERMIS</th>\n",
       "      <th>ANNEE_CONSTRUCTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ANNEE_CTR</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.048023</td>\n",
       "      <td>0.043983</td>\n",
       "      <td>0.361550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE_ASSURE_PRINCIPAL</th>\n",
       "      <td>0.048023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498743</td>\n",
       "      <td>-0.059184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANCIENNETE_PERMIS</th>\n",
       "      <td>0.043983</td>\n",
       "      <td>0.498743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.029814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANNEE_CONSTRUCTION</th>\n",
       "      <td>0.361550</td>\n",
       "      <td>-0.059184</td>\n",
       "      <td>-0.029814</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ANNEE_CTR  AGE_ASSURE_PRINCIPAL  ANCIENNETE_PERMIS  \\\n",
       "ANNEE_CTR              1.000000              0.048023           0.043983   \n",
       "AGE_ASSURE_PRINCIPAL   0.048023              1.000000           0.498743   \n",
       "ANCIENNETE_PERMIS      0.043983              0.498743           1.000000   \n",
       "ANNEE_CONSTRUCTION     0.361550             -0.059184          -0.029814   \n",
       "\n",
       "                      ANNEE_CONSTRUCTION  \n",
       "ANNEE_CTR                       0.361550  \n",
       "AGE_ASSURE_PRINCIPAL           -0.059184  \n",
       "ANCIENNETE_PERMIS              -0.029814  \n",
       "ANNEE_CONSTRUCTION              1.000000  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Corrélation de Pearson\n",
    "correlations_num = vars_numeriques.corr(method=\"pearson\")\n",
    "correlations_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "4c29f1f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# On repère les variables trop corrélées\n",
    "nb_variables = correlations_num.shape[0]\n",
    "for i in range(nb_variables):\n",
    "    for j in range(i + 1, nb_variables):\n",
    "        if abs(correlations_num.iloc[i, j]) > 0.7:\n",
    "            print(\n",
    "                f\"{correlations_num.index.to_numpy()[i]} et {correlations_num.columns[j]} sont trop dépendantes, corr = {correlations_num.iloc[i, j]}\"\n",
    "            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "212209ec",
   "metadata": {},
   "source": [
    "#### Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65aca700",
   "metadata": {},
   "source": [
    "Deux étapes sont nécessaires avant de lancer l'apprentissage d'un modèle, c'est ce qu'on connait comme le *Preprocessing* :\n",
    "\n",
    "* Les modèles proposés par la librairie \"sklearn\" ne gèrent que des variables numériques. Il est donc nécessaire de transformer les variables catégorielles en variables numériques : ce processus s'appelle le *One Hot Encoding*.\n",
    "* Normaliser les données numériques"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c23d236",
   "metadata": {},
   "source": [
    "**Exercice :** proposez un bout de code permettant de réaliser le One Hot Encoding des variables catégorielles. Vous pourrez utiliser la fonction \"preproc.OneHotEncoder\" de la librairie sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "b8530717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "CONTRAT_ANCIENNETE_(0,1]",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CONTRAT_ANCIENNETE_(1,2]",
         "rawType": "float64",
         "type": "float"
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       "      <th>CONTRAT_ANCIENNETE_(0,1]</th>\n",
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       "   CONTRAT_ANCIENNETE_(0,1]  CONTRAT_ANCIENNETE_(1,2]  \\\n",
       "0                       0.0                       0.0   \n",
       "1                       0.0                       0.0   \n",
       "2                       0.0                       1.0   \n",
       "3                       0.0                       0.0   \n",
       "4                       0.0                       0.0   \n",
       "\n",
       "   CONTRAT_ANCIENNETE_(2,5]  CONTRAT_ANCIENNETE_(5,10]  \\\n",
       "0                       0.0                        0.0   \n",
       "1                       0.0                        0.0   \n",
       "2                       0.0                        0.0   \n",
       "3                       1.0                        0.0   \n",
       "4                       1.0                        0.0   \n",
       "\n",
       "   FREQUENCE_PAIEMENT_COTISATION_MENSUEL  \\\n",
       "0                                    0.0   \n",
       "1                                    0.0   \n",
       "2                                    0.0   \n",
       "3                                    0.0   \n",
       "4                                    1.0   \n",
       "\n",
       "   FREQUENCE_PAIEMENT_COTISATION_TRIMESTRIEL  GROUPE_KM_[20000;40000[  \\\n",
       "0                                        0.0                      1.0   \n",
       "1                                        0.0                      1.0   \n",
       "2                                        0.0                      0.0   \n",
       "3                                        0.0                      0.0   \n",
       "4                                        0.0                      1.0   \n",
       "\n",
       "   GROUPE_KM_[40000;60000[  GROUPE_KM_[60000;99999[  ZONE_RISQUE_B  ...  \\\n",
       "0                      0.0                      0.0            1.0  ...   \n",
       "1                      0.0                      0.0            1.0  ...   \n",
       "2                      0.0                      0.0            0.0  ...   \n",
       "3                      0.0                      0.0            0.0  ...   \n",
       "4                      0.0                      0.0            0.0  ...   \n",
       "\n",
       "   GENRE_M  DEUXIEME_CONDUCTEUR_True  ENERGIE_DIESEL  ENERGIE_ESSENCE  \\\n",
       "0      1.0                       0.0             0.0              1.0   \n",
       "1      0.0                       1.0             1.0              0.0   \n",
       "2      0.0                       1.0             0.0              1.0   \n",
       "3      1.0                       0.0             0.0              1.0   \n",
       "4      0.0                       0.0             1.0              0.0   \n",
       "\n",
       "   EQUIPEMENT_SECURITE_VRAI  VALEUR_DU_BIEN_[10000;15000[  \\\n",
       "0                       0.0                           1.0   \n",
       "1                       1.0                           0.0   \n",
       "2                       0.0                           0.0   \n",
       "3                       1.0                           0.0   \n",
       "4                       0.0                           1.0   \n",
       "\n",
       "   VALEUR_DU_BIEN_[15000;20000[  VALEUR_DU_BIEN_[20000;25000[  \\\n",
       "0                           0.0                           0.0   \n",
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       "2                           1.0                           0.0   \n",
       "3                           0.0                           0.0   \n",
       "4                           0.0                           0.0   \n",
       "\n",
       "   VALEUR_DU_BIEN_[25000;35000[  VALEUR_DU_BIEN_[35000;99999[  \n",
       "0                           0.0                           0.0  \n",
       "1                           0.0                           0.0  \n",
       "2                           0.0                           0.0  \n",
       "3                           0.0                           1.0  \n",
       "4                           0.0                           0.0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One hot encoding des variables catégorielles\n",
    "preproc_ohe = preproc.OneHotEncoder(handle_unknown=\"ignore\")\n",
    "preproc_ohe = preproc.OneHotEncoder(drop=\"first\", sparse_output=False).fit(\n",
    "    vars_categorielles\n",
    ")\n",
    "\n",
    "variables_categorielles_ohe = preproc_ohe.transform(vars_categorielles)\n",
    "variables_categorielles_ohe = pd.DataFrame(\n",
    "    variables_categorielles_ohe,\n",
    "    columns=preproc_ohe.get_feature_names_out(vars_categorielles.columns),\n",
    ")\n",
    "variables_categorielles_ohe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be6a3e4",
   "metadata": {},
   "source": [
    "**Exercice :** proposez un bout de code permettant noramliser les variables numériques présentes dans la base. Vous pourrez utiliser la fonction \"preproc.StandardScaler\" de la librairie sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ff3847d",
   "metadata": {},
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       "        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>ANNEE_CTR</th>\n",
       "      <th>AGE_ASSURE_PRINCIPAL</th>\n",
       "      <th>ANCIENNETE_PERMIS</th>\n",
       "      <th>ANNEE_CONSTRUCTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.139356</td>\n",
       "      <td>0.658287</td>\n",
       "      <td>0.563588</td>\n",
       "      <td>0.174011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.139356</td>\n",
       "      <td>3.151628</td>\n",
       "      <td>0.987434</td>\n",
       "      <td>0.744207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.347192</td>\n",
       "      <td>-0.735051</td>\n",
       "      <td>-1.078814</td>\n",
       "      <td>0.459109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.347192</td>\n",
       "      <td>0.071618</td>\n",
       "      <td>0.404646</td>\n",
       "      <td>0.744207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.464562</td>\n",
       "      <td>0.071618</td>\n",
       "      <td>-0.284103</td>\n",
       "      <td>-1.821676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ANNEE_CTR  AGE_ASSURE_PRINCIPAL  ANCIENNETE_PERMIS  ANNEE_CONSTRUCTION\n",
       "0   0.139356              0.658287           0.563588            0.174011\n",
       "1   0.139356              3.151628           0.987434            0.744207\n",
       "2   1.347192             -0.735051          -1.078814            0.459109\n",
       "3   1.347192              0.071618           0.404646            0.744207\n",
       "4  -0.464562              0.071618          -0.284103           -1.821676"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalisation des varibales numériques\n",
    "preproc_scale = preproc.StandardScaler(with_mean=True, with_std=True)\n",
    "preproc_scale.fit(vars_numeriques)\n",
    "\n",
    "vars_numeriques_scaled = preproc_scale.transform(vars_numeriques)\n",
    "vars_numeriques_scaled = pd.DataFrame(\n",
    "    vars_numeriques_scaled, columns=vars_numeriques.columns\n",
    ")\n",
    "vars_numeriques_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ecba832",
   "metadata": {},
   "source": [
    "## Algorithme supervisé : Gradient Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efcb8987",
   "metadata": {},
   "source": [
    "A ce stade, nous avons vu les différentes étapes pour lancer un algorithme de Machine Learning. Néanmoins, ces étapes ne sont pas suffisantes pour construire un modèle performant.  \n",
    "En effet, afin de construire un modèle performant le Data Scientist doit agir sur l'apprentissage du modèle. Dans ce qui suit nous :\n",
    "* Changerons d'algorithme pour utiliser un algorithme plus performant (Gradient Boosting)\n",
    "* Raliserons un *grid search* sur les paramètres du modèle\n",
    "* Appliquerons l'apprentissage par validation croisée\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3feaff44",
   "metadata": {},
   "source": [
    "**Exercice :** Implémentez l'algorithme du Gradient Boosting en appliquant les techniques vues lors des derniers cours (sampling, Grid search et Cross Validation)  \n",
    "**Remarques :**\n",
    "* Vous pouvez utiliser les modèles \"GradientBoostingClassifier\" et \"GridSearchCV\" de la libraire Sklearn.  \n",
    "* Pensez à utiliser les métriques relatives aux problèmes de classification."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a6adbfe",
   "metadata": {},
   "source": [
    "#### Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9342ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_global = vars_numeriques_scaled.merge(\n",
    "    variables_categorielles_ohe, left_index=True, right_index=True\n",
    ")\n",
    "# Réorganisation des données\n",
    "X = X_global.to_numpy()\n",
    "Y = np.array(data_model[\"SINISTRE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "177643ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sampling en 80% train et 20% test\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, Y, test_size=0.2, random_state=42, stratify=Y\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76ece01f",
   "metadata": {},
   "source": [
    "#### Fitting avec Cross-Validation et *Grid Search*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb60fe19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Définir la grille d'hyperparamètres à rechercher\n",
    "param_grid = {\n",
    "    \"n_estimators\": [100, 200, 250],\n",
    "    \"learning_rate\": [0.5, 0.7, 0.9],\n",
    "}\n",
    "\n",
    "# Nombre de folds pour la validation croisée\n",
    "num_folds = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b976720e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meilleurs hyperparamètres :  {'learning_rate': 0.9, 'n_estimators': 250}\n"
     ]
    }
   ],
   "source": [
    "# Initialisation du modèle GradientBoostingClassifier\n",
    "gbc = GradientBoostingClassifier(random_state=42)\n",
    "\n",
    "# Création de l'objet GridSearchCV pour la recherche sur grille avec validation croisée\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=gbc,\n",
    "    param_grid=param_grid,\n",
    "    cv=StratifiedKFold(\n",
    "        n_splits=num_folds, shuffle=True, random_state=42\n",
    "    ),  # Validation croisée avec 5 folds\n",
    "    scoring='recall',  # Métrique d'évaluation (moins c'est mieux)\n",
    "    n_jobs=-1,  # Utiliser tous les cœurs du processeur\n",
    ")\n",
    "\n",
    "# Exécution de la recherche sur grille\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Afficher les meilleurs hyperparamètres\n",
    "best_params = grid_search.best_params_\n",
    "print(\"Meilleurs hyperparamètres : \", best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "0a35a4bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialiser le modèle final avec les meilleurs hyperparamètres\n",
    "best_gbc = GradientBoostingClassifier(random_state=42, **best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "e12177a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall pour le fold 1: 0.015151515151515152\n",
      "Recall pour le fold 2: 0.022727272727272728\n",
      "Recall pour le fold 3: 0.030303030303030304\n",
      "Recall pour le fold 4: 0.007633587786259542\n",
      "Recall pour le fold 5: 0.022727272727272728\n",
      "\n",
      "\n",
      "Accuracy pour le fold 1: 0.9293239683933274\n",
      "Accuracy pour le fold 2: 0.9218612818261633\n",
      "Accuracy pour le fold 3: 0.9341527655838455\n",
      "Accuracy pour le fold 4: 0.9336846728151076\n",
      "Accuracy pour le fold 5: 0.9358805445761967\n",
      "\n",
      "\n",
      "Precision pour le fold 1: 0.06060606060606061\n",
      "Precision pour le fold 2: 0.057692307692307696\n",
      "Precision pour le fold 3: 0.15384615384615385\n",
      "Precision pour le fold 4: 0.045454545454545456\n",
      "Precision pour le fold 5: 0.15\n"
     ]
    }
   ],
   "source": [
    "# Recall de chaque fold\n",
    "recall_scores = cross_val_score(best_gbc, X_train, y_train, cv=num_folds, scoring='recall')\n",
    "\n",
    "# Afficher les scores pour chaque fold\n",
    "for i, score in enumerate(recall_scores):\n",
    "    print(f\"Recall pour le fold {i + 1}: {score}\")\n",
    "\n",
    "# Accuracy de chaque fold\n",
    "accuracy_scores = cross_val_score(best_gbc, X_train, y_train, cv=num_folds, scoring='accuracy')\n",
    "\n",
    "# Afficher les scores pour chaque fold\n",
    "print(\"\\n\")\n",
    "for i, score in enumerate(accuracy_scores):\n",
    "    print(f\"Accuracy pour le fold {i + 1}: {score}\")\n",
    "\n",
    "# Precision de chaque fold\n",
    "precision_scores = cross_val_score(best_gbc, X_train, y_train, cv=num_folds, scoring='precision')\n",
    "\n",
    "# Afficher les scores pour chaque fold\n",
    "print(\"\\n\")\n",
    "for i, score in enumerate(precision_scores):\n",
    "    print(f\"Precision pour le fold {i + 1}: {score}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "5683862c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exactitude : 0.024242424242424242\n"
     ]
    }
   ],
   "source": [
    "# Entraîner le modèle final sur la base entière\n",
    "best_gbc.fit(X_train, y_train)\n",
    "\n",
    "# Faire des prédictions sur l'ensemble de test\n",
    "y_pred = best_gbc.predict(X_test)\n",
    "\n",
    "# Calculer le recall des prédictions\n",
    "recall = metrics.recall_score(y_test, y_pred)\n",
    "print(f\"Exactitude : {recall}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "525aef5a",
   "metadata": {},
   "source": [
    "### Fitting avec *Oversampling*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76077b9d",
   "metadata": {},
   "source": [
    "#### *Oversampling*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "764406bd",
   "metadata": {},
   "outputs": [
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       "config": {
        "plotlyServerURL": "https://plot.ly"
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         "bingroup": "x",
         "hovertemplate": "SINISTRE=%{x}<br>count=%{y}<extra></extra>",
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          "geo": {
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          "mapbox": {
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            "linecolor": "white",
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           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
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          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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          },
          "shapedefaults": {
           "line": {
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          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
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          },
          "title": {
           "x": 0.05
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          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
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           "zerolinecolor": "white",
           "zerolinewidth": 2
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          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
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           "title": {
            "standoff": 15
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           "zerolinewidth": 2
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    "# Observation de la distribution sur Y_train\n",
    "df = pd.DataFrame(y_train, columns=[\"SINISTRE\"])\n",
    "fig = px.histogram(df, x=\"SINISTRE\", title=\"Distribution de la variable Y_train\")\n",
    "fig.show()\n"
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  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "13b7f0ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Oversampling\n",
    "# Appliquer le suréchantillonnage à la classe minoritaire\n",
    "sampler = RandomOverSampler(random_state=42)\n",
    "X_train_resampled, y_train_resampled = sampler.fit_resample(X_train, y_train)"
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  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "50cb20db",
   "metadata": {},
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          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Distribution de la variable Y_train_resampled"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "SINISTRE"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "count"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Observation de la distribution sur Y_train\n",
    "df = pd.DataFrame(y_train_resampled, columns=[\"SINISTRE\"])\n",
    "fig = px.histogram(\n",
    "    df, x=\"SINISTRE\", title=\"Distribution de la variable Y_train_resampled\"\n",
    ")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd1eb242",
   "metadata": {},
   "source": [
    "#### Fitting sur le nouvel échantillon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "7f2b0273",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Définir la grille d'hyperparamètres à rechercher\n",
    "param_grid = {\n",
    "    \"n_estimators\": [475, 500, 525],\n",
    "    \"learning_rate\": [0, 7, 0.9, 1.5],\n",
    "}\n",
    "\n",
    "# Nombre de folds pour la validation croisée\n",
    "num_folds = 5\n",
    "\n",
    "# Initialisation du modèle GradientBoostingClassifier\n",
    "gb = GradientBoostingClassifier(random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "1d02b400",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meilleurs hyperparamètres :  {'learning_rate': 0.9, 'n_estimators': 500}\n"
     ]
    }
   ],
   "source": [
    "# Création de l'objet GridSearchCV pour la recherche sur grille avec validation croisée\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=gb,\n",
    "    param_grid=param_grid,\n",
    "    cv=StratifiedKFold(\n",
    "        n_splits=num_folds, shuffle=True, random_state=42\n",
    "    ),  # Validation croisée stratifiée avec 5 plis\n",
    "    scoring=\"recall\",  # Métrique d'évaluation\n",
    "    n_jobs=-1,  # Utiliser tous les cœurs du processeur\n",
    ")\n",
    "\n",
    "# Exécution de la recherche sur grille\n",
    "grid_search.fit(X_train_resampled, y_train_resampled)\n",
    "\n",
    "# Afficher les meilleurs hyperparamètres\n",
    "best_params = grid_search.best_params_\n",
    "print(\"Meilleurs hyperparamètres : \", best_params)\n",
    "\n",
    "# Initialiser un modèle avec les meilleurs hyperparamètres\n",
    "best_gbc = GradientBoostingClassifier(random_state=42, **best_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "28956389",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall pour le fold 1: 0.9948741845293569\n",
      "Recall pour le fold 2: 0.9972041006523765\n",
      "Recall pour le fold 3: 0.9972041006523765\n",
      "Recall pour le fold 4: 0.9981351981351981\n",
      "Recall pour le fold 5: 0.9958061509785647\n",
      "\n",
      "\n",
      "Accuracy pour le fold 1: 0.945479962721342\n",
      "Accuracy pour le fold 2: 0.9452469711090401\n",
      "Accuracy pour le fold 3: 0.9391891891891891\n",
      "Accuracy pour le fold 4: 0.9461663947797716\n",
      "Accuracy pour le fold 5: 0.9370776042880448\n",
      "\n",
      "\n",
      "Precision pour le fold 1: 0.9054283290924512\n",
      "Precision pour le fold 2: 0.9033347403967918\n",
      "Precision pour le fold 3: 0.8935281837160751\n",
      "Precision pour le fold 4: 0.9041385135135135\n",
      "Precision pour le fold 5: 0.8911592994161801\n"
     ]
    }
   ],
   "source": [
    "# Zoom sur la CV\n",
    "# Recall de chaque fold\n",
    "recall_scores = cross_val_score(\n",
    "    best_gbc, X_train_resampled, y_train_resampled, cv=num_folds, scoring=\"recall\"\n",
    ")\n",
    "\n",
    "# Afficher les scores pour chaque fold\n",
    "for i, score in enumerate(recall_scores):\n",
    "    print(f\"Recall pour le fold {i + 1}: {score}\")\n",
    "\n",
    "# Accuracy de chaque fold\n",
    "accuracy_scores = cross_val_score(\n",
    "    best_gbc, X_train_resampled, y_train_resampled, cv=num_folds, scoring=\"accuracy\"\n",
    ")\n",
    "\n",
    "# Afficher les scores pour chaque fold\n",
    "print(\"\\n\")\n",
    "for i, score in enumerate(accuracy_scores):\n",
    "    print(f\"Accuracy pour le fold {i + 1}: {score}\")\n",
    "\n",
    "# Precision de chaque fold\n",
    "precision_scores = cross_val_score(\n",
    "    best_gbc, X_train_resampled, y_train_resampled, cv=num_folds, scoring=\"precision\"\n",
    ")\n",
    "\n",
    "# Afficher les scores pour chaque fold\n",
    "print(\"\\n\")\n",
    "for i, score in enumerate(precision_scores):\n",
    "    print(f\"Precision pour le fold {i + 1}: {score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "5ab7fedd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.9, n_estimators=500, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(learning_rate=0.9, n_estimators=500, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.9, n_estimators=500, random_state=42)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Entraîner le modèle final sur toute la base\n",
    "best_gbc.fit(X_train_resampled, y_train_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "7df7f223",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Faire des prédictions sur l'ensemble de test\n",
    "y_pred = best_gbc.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a723cbc",
   "metadata": {},
   "source": [
    "#### Validation du modèle -  métriques"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c0312d",
   "metadata": {},
   "source": [
    "**Exercice :** \n",
    "* Construisez la matrice de confusion (metrics.confusion_matrix).\n",
    "* Calculez les métriques : accuracy, recall & precision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "5d9ef448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x11a6d52b0>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "[encoded data, 21452 characters]",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matrice de confusion\n",
    "confusion_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "cm_display = metrics.ConfusionMatrixDisplay(\n",
    "    confusion_matrix=confusion_matrix, display_labels=[False, True]\n",
    ")\n",
    "\n",
    "cm_display.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "037188fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall : 0.1696969696969697\n"
     ]
    }
   ],
   "source": [
    "# Calculer le recall des prédictions\n",
    "recall = metrics.recall_score(y_test, y_pred)\n",
    "print(f\"Recall : {recall}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "b9d20ebf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exactitude : 0.8599016853932584\n"
     ]
    }
   ],
   "source": [
    "# Calculer l'accuracy des prédictions\n",
    "acc = metrics.accuracy_score(y_test, y_pred)\n",
    "print(f\"Exactitude : {acc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "7f867d05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision : 0.09655172413793103\n"
     ]
    }
   ],
   "source": [
    "# Calculer le precision des prédictions\n",
    "precision = metrics.precision_score(y_test, y_pred)\n",
    "print(f\"Precision : {precision}\")"
   ]
  }
 ],
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