Pyckpocket_4_Recherche_du_meilleur_modèle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kaVArw2cLCH5"
   },
   "source": [
    "Import des fichiers dans google colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TFFWcFhsWqLZ",
    "outputId": "482bece9-a3fa-44a6-e69b-2377b4082e2e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6WEekJTfGYg4"
   },
   "source": [
    "Import des librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H3dpxxIIGVM5",
    "outputId": "59d77ca1-4295-43a0-b9f4-e13951ae72e2"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_validate, GridSearchCV\n",
    "from sklearn import svm\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier, StackingClassifier, BaggingClassifier\n",
    "#from xgboost import XGBClassifier\n",
    "\n",
    "from sklearn import metrics\n",
    "#from imblearn.metrics import classification_report_imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "9eb0b161"
   },
   "outputs": [],
   "source": [
    "# Import DataFrame pour modélisation\n",
    "df = pd.read_csv('df_model.csv', index_col=0)\n",
    "\n",
    "# La saison lue en tant que numérique est remise en chaine de caractère\n",
    "df['Saison'] = df['Saison'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Y59C7qfMM9v"
   },
   "source": [
    "# Sélection de variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ymRnuux6UEoM"
   },
   "source": [
    "Corrélation des variables entre elles et avec le résultat du match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "5oF1QYuQMJdW",
    "outputId": "35cbd9f4-c592-423f-c8c5-608341bb99cd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Resultat_match</th>\n",
       "      <th>Moy_cote_domicile</th>\n",
       "      <th>Max_cote_domicile</th>\n",
       "      <th>Min_cote_domicile</th>\n",
       "      <th>Moy_cote_exterieur</th>\n",
       "      <th>Max_cote_exterieur</th>\n",
       "      <th>Min_cote_exterieur</th>\n",
       "      <th>Moy_cote_nul</th>\n",
       "      <th>Max_cote_nul</th>\n",
       "      <th>Min_cote_nul</th>\n",
       "      <th>...</th>\n",
       "      <th>note_mil_dom</th>\n",
       "      <th>note_def_dom</th>\n",
       "      <th>note_gen_dom</th>\n",
       "      <th>note_att_ext</th>\n",
       "      <th>note_mil_ext</th>\n",
       "      <th>note_def_ext</th>\n",
       "      <th>note_gen_ext</th>\n",
       "      <th>Res_an_dernier_A</th>\n",
       "      <th>Res_an_dernier_D</th>\n",
       "      <th>Res_an_dernier_H</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Resultat_match</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.316128</td>\n",
       "      <td>-0.305735</td>\n",
       "      <td>-0.323177</td>\n",
       "      <td>0.287663</td>\n",
       "      <td>0.267276</td>\n",
       "      <td>0.303760</td>\n",
       "      <td>0.133529</td>\n",
       "      <td>0.136498</td>\n",
       "      <td>0.128379</td>\n",
       "      <td>...</td>\n",
       "      <td>0.253378</td>\n",
       "      <td>0.247336</td>\n",
       "      <td>0.253553</td>\n",
       "      <td>-0.239922</td>\n",
       "      <td>-0.267816</td>\n",
       "      <td>-0.283858</td>\n",
       "      <td>-0.282362</td>\n",
       "      <td>-0.084498</td>\n",
       "      <td>-0.028601</td>\n",
       "      <td>0.079533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_cote_domicile</th>\n",
       "      <td>-0.316128</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996338</td>\n",
       "      <td>0.993512</td>\n",
       "      <td>-0.415806</td>\n",
       "      <td>-0.373508</td>\n",
       "      <td>-0.457489</td>\n",
       "      <td>0.123811</td>\n",
       "      <td>0.106672</td>\n",
       "      <td>0.141168</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.312310</td>\n",
       "      <td>-0.315936</td>\n",
       "      <td>-0.324038</td>\n",
       "      <td>0.651118</td>\n",
       "      <td>0.651711</td>\n",
       "      <td>0.640279</td>\n",
       "      <td>0.695249</td>\n",
       "      <td>0.219029</td>\n",
       "      <td>-0.030618</td>\n",
       "      <td>-0.133929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max_cote_domicile</th>\n",
       "      <td>-0.305735</td>\n",
       "      <td>0.996338</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.983131</td>\n",
       "      <td>-0.394325</td>\n",
       "      <td>-0.353579</td>\n",
       "      <td>-0.435031</td>\n",
       "      <td>0.142350</td>\n",
       "      <td>0.125394</td>\n",
       "      <td>0.158528</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.296448</td>\n",
       "      <td>-0.301636</td>\n",
       "      <td>-0.308108</td>\n",
       "      <td>0.636409</td>\n",
       "      <td>0.635442</td>\n",
       "      <td>0.626767</td>\n",
       "      <td>0.679335</td>\n",
       "      <td>0.211368</td>\n",
       "      <td>-0.032695</td>\n",
       "      <td>-0.129895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min_cote_domicile</th>\n",
       "      <td>-0.323177</td>\n",
       "      <td>0.993512</td>\n",
       "      <td>0.983131</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.439346</td>\n",
       "      <td>-0.395719</td>\n",
       "      <td>-0.482073</td>\n",
       "      <td>0.099911</td>\n",
       "      <td>0.082286</td>\n",
       "      <td>0.120117</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.329679</td>\n",
       "      <td>-0.332261</td>\n",
       "      <td>-0.341783</td>\n",
       "      <td>0.662399</td>\n",
       "      <td>0.664862</td>\n",
       "      <td>0.649177</td>\n",
       "      <td>0.707484</td>\n",
       "      <td>0.228177</td>\n",
       "      <td>-0.025637</td>\n",
       "      <td>-0.139209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_cote_exterieur</th>\n",
       "      <td>0.287663</td>\n",
       "      <td>-0.415806</td>\n",
       "      <td>-0.394325</td>\n",
       "      <td>-0.439346</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987931</td>\n",
       "      <td>0.980986</td>\n",
       "      <td>0.821203</td>\n",
       "      <td>0.830920</td>\n",
       "      <td>0.785906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.653607</td>\n",
       "      <td>0.645174</td>\n",
       "      <td>0.690102</td>\n",
       "      <td>-0.305410</td>\n",
       "      <td>-0.325617</td>\n",
       "      <td>-0.324405</td>\n",
       "      <td>-0.335326</td>\n",
       "      <td>-0.148609</td>\n",
       "      <td>-0.030481</td>\n",
       "      <td>0.181058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max_cote_exterieur</th>\n",
       "      <td>0.267276</td>\n",
       "      <td>-0.373508</td>\n",
       "      <td>-0.353579</td>\n",
       "      <td>-0.395719</td>\n",
       "      <td>0.987931</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.950085</td>\n",
       "      <td>0.822893</td>\n",
       "      <td>0.838743</td>\n",
       "      <td>0.778465</td>\n",
       "      <td>...</td>\n",
       "      <td>0.619361</td>\n",
       "      <td>0.613223</td>\n",
       "      <td>0.655540</td>\n",
       "      <td>-0.273524</td>\n",
       "      <td>-0.293665</td>\n",
       "      <td>-0.293255</td>\n",
       "      <td>-0.302410</td>\n",
       "      <td>-0.138314</td>\n",
       "      <td>-0.031519</td>\n",
       "      <td>0.164760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min_cote_exterieur</th>\n",
       "      <td>0.303760</td>\n",
       "      <td>-0.457489</td>\n",
       "      <td>-0.435031</td>\n",
       "      <td>-0.482073</td>\n",
       "      <td>0.980986</td>\n",
       "      <td>0.950085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.793996</td>\n",
       "      <td>0.795763</td>\n",
       "      <td>0.777305</td>\n",
       "      <td>...</td>\n",
       "      <td>0.680856</td>\n",
       "      <td>0.664716</td>\n",
       "      <td>0.715625</td>\n",
       "      <td>-0.337090</td>\n",
       "      <td>-0.353072</td>\n",
       "      <td>-0.351092</td>\n",
       "      <td>-0.365159</td>\n",
       "      <td>-0.154075</td>\n",
       "      <td>-0.028376</td>\n",
       "      <td>0.205087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_cote_nul</th>\n",
       "      <td>0.133529</td>\n",
       "      <td>0.123811</td>\n",
       "      <td>0.142350</td>\n",
       "      <td>0.099911</td>\n",
       "      <td>0.821203</td>\n",
       "      <td>0.822893</td>\n",
       "      <td>0.793996</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995667</td>\n",
       "      <td>0.982713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.581348</td>\n",
       "      <td>0.545987</td>\n",
       "      <td>0.609485</td>\n",
       "      <td>0.099542</td>\n",
       "      <td>0.082116</td>\n",
       "      <td>0.055446</td>\n",
       "      <td>0.091265</td>\n",
       "      <td>-0.020007</td>\n",
       "      <td>-0.051025</td>\n",
       "      <td>0.151283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max_cote_nul</th>\n",
       "      <td>0.136498</td>\n",
       "      <td>0.106672</td>\n",
       "      <td>0.125394</td>\n",
       "      <td>0.082286</td>\n",
       "      <td>0.830920</td>\n",
       "      <td>0.838743</td>\n",
       "      <td>0.795763</td>\n",
       "      <td>0.995667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.966882</td>\n",
       "      <td>...</td>\n",
       "      <td>0.572437</td>\n",
       "      <td>0.541538</td>\n",
       "      <td>0.602739</td>\n",
       "      <td>0.085256</td>\n",
       "      <td>0.066730</td>\n",
       "      <td>0.041616</td>\n",
       "      <td>0.075396</td>\n",
       "      <td>-0.025497</td>\n",
       "      <td>-0.050220</td>\n",
       "      <td>0.144790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min_cote_nul</th>\n",
       "      <td>0.128379</td>\n",
       "      <td>0.141168</td>\n",
       "      <td>0.158528</td>\n",
       "      <td>0.120117</td>\n",
       "      <td>0.785906</td>\n",
       "      <td>0.778465</td>\n",
       "      <td>0.777305</td>\n",
       "      <td>0.982713</td>\n",
       "      <td>0.966882</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.586928</td>\n",
       "      <td>0.545336</td>\n",
       "      <td>0.614250</td>\n",
       "      <td>0.115549</td>\n",
       "      <td>0.098476</td>\n",
       "      <td>0.070701</td>\n",
       "      <td>0.109693</td>\n",
       "      <td>-0.010244</td>\n",
       "      <td>-0.051502</td>\n",
       "      <td>0.161562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nb_buts_equipe_dom_an_dernier</th>\n",
       "      <td>0.064225</td>\n",
       "      <td>-0.108913</td>\n",
       "      <td>-0.104315</td>\n",
       "      <td>-0.113953</td>\n",
       "      <td>0.197624</td>\n",
       "      <td>0.177173</td>\n",
       "      <td>0.222924</td>\n",
       "      <td>0.203273</td>\n",
       "      <td>0.193310</td>\n",
       "      <td>0.217452</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355383</td>\n",
       "      <td>0.319122</td>\n",
       "      <td>0.348353</td>\n",
       "      <td>0.035925</td>\n",
       "      <td>0.063561</td>\n",
       "      <td>0.059951</td>\n",
       "      <td>0.055566</td>\n",
       "      <td>-0.145754</td>\n",
       "      <td>0.017751</td>\n",
       "      <td>0.744862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nb_buts_equipe_ext_an_dernier</th>\n",
       "      <td>-0.083655</td>\n",
       "      <td>0.196362</td>\n",
       "      <td>0.189000</td>\n",
       "      <td>0.205911</td>\n",
       "      <td>-0.121397</td>\n",
       "      <td>-0.115146</td>\n",
       "      <td>-0.121336</td>\n",
       "      <td>0.021249</td>\n",
       "      <td>0.013189</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074681</td>\n",
       "      <td>0.073780</td>\n",
       "      <td>0.068571</td>\n",
       "      <td>0.304820</td>\n",
       "      <td>0.333384</td>\n",
       "      <td>0.312951</td>\n",
       "      <td>0.330754</td>\n",
       "      <td>0.690131</td>\n",
       "      <td>0.122756</td>\n",
       "      <td>-0.113253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Num_match_dom</th>\n",
       "      <td>-0.015986</td>\n",
       "      <td>0.008113</td>\n",
       "      <td>0.005972</td>\n",
       "      <td>0.012332</td>\n",
       "      <td>0.002369</td>\n",
       "      <td>-0.005728</td>\n",
       "      <td>0.011404</td>\n",
       "      <td>0.032744</td>\n",
       "      <td>0.029085</td>\n",
       "      <td>0.035457</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021865</td>\n",
       "      <td>-0.017392</td>\n",
       "      <td>-0.016604</td>\n",
       "      <td>-0.006180</td>\n",
       "      <td>-0.011588</td>\n",
       "      <td>-0.012289</td>\n",
       "      <td>-0.004747</td>\n",
       "      <td>-0.040721</td>\n",
       "      <td>-0.018244</td>\n",
       "      <td>0.002486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_points_5_match_dom</th>\n",
       "      <td>0.159726</td>\n",
       "      <td>-0.238280</td>\n",
       "      <td>-0.225482</td>\n",
       "      <td>-0.252119</td>\n",
       "      <td>0.432095</td>\n",
       "      <td>0.402842</td>\n",
       "      <td>0.448945</td>\n",
       "      <td>0.354691</td>\n",
       "      <td>0.353217</td>\n",
       "      <td>0.351054</td>\n",
       "      <td>...</td>\n",
       "      <td>0.505264</td>\n",
       "      <td>0.505060</td>\n",
       "      <td>0.515478</td>\n",
       "      <td>-0.008169</td>\n",
       "      <td>-0.009289</td>\n",
       "      <td>-0.004433</td>\n",
       "      <td>-0.011566</td>\n",
       "      <td>-0.036378</td>\n",
       "      <td>0.011330</td>\n",
       "      <td>0.128686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_buts_5_matchs_dom</th>\n",
       "      <td>0.179866</td>\n",
       "      <td>-0.224274</td>\n",
       "      <td>-0.212092</td>\n",
       "      <td>-0.234949</td>\n",
       "      <td>0.486004</td>\n",
       "      <td>0.457165</td>\n",
       "      <td>0.501475</td>\n",
       "      <td>0.457082</td>\n",
       "      <td>0.456341</td>\n",
       "      <td>0.448952</td>\n",
       "      <td>...</td>\n",
       "      <td>0.555243</td>\n",
       "      <td>0.510852</td>\n",
       "      <td>0.553554</td>\n",
       "      <td>-0.009789</td>\n",
       "      <td>-0.010092</td>\n",
       "      <td>-0.007812</td>\n",
       "      <td>-0.010576</td>\n",
       "      <td>-0.024735</td>\n",
       "      <td>-0.022639</td>\n",
       "      <td>0.137580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_tirs_cadres_5_matchs_dom</th>\n",
       "      <td>0.179153</td>\n",
       "      <td>-0.214699</td>\n",
       "      <td>-0.201208</td>\n",
       "      <td>-0.227142</td>\n",
       "      <td>0.459794</td>\n",
       "      <td>0.430236</td>\n",
       "      <td>0.471371</td>\n",
       "      <td>0.431608</td>\n",
       "      <td>0.431786</td>\n",
       "      <td>0.424031</td>\n",
       "      <td>...</td>\n",
       "      <td>0.517746</td>\n",
       "      <td>0.474901</td>\n",
       "      <td>0.515943</td>\n",
       "      <td>-0.005451</td>\n",
       "      <td>-0.005544</td>\n",
       "      <td>0.003170</td>\n",
       "      <td>-0.007482</td>\n",
       "      <td>-0.039581</td>\n",
       "      <td>-0.008059</td>\n",
       "      <td>0.147169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_tirs_5_matchs_dom</th>\n",
       "      <td>0.158741</td>\n",
       "      <td>-0.190919</td>\n",
       "      <td>-0.180645</td>\n",
       "      <td>-0.200378</td>\n",
       "      <td>0.399184</td>\n",
       "      <td>0.373554</td>\n",
       "      <td>0.409992</td>\n",
       "      <td>0.375578</td>\n",
       "      <td>0.376502</td>\n",
       "      <td>0.370032</td>\n",
       "      <td>...</td>\n",
       "      <td>0.450166</td>\n",
       "      <td>0.417119</td>\n",
       "      <td>0.448282</td>\n",
       "      <td>0.008520</td>\n",
       "      <td>0.021633</td>\n",
       "      <td>0.019142</td>\n",
       "      <td>0.015635</td>\n",
       "      <td>-0.017439</td>\n",
       "      <td>0.010166</td>\n",
       "      <td>0.146071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nb_jours_dernier_match_dom</th>\n",
       "      <td>-0.038109</td>\n",
       "      <td>-0.001443</td>\n",
       "      <td>-0.000288</td>\n",
       "      <td>-0.005518</td>\n",
       "      <td>-0.027239</td>\n",
       "      <td>-0.024894</td>\n",
       "      <td>-0.027361</td>\n",
       "      <td>-0.036333</td>\n",
       "      <td>-0.035778</td>\n",
       "      <td>-0.034878</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009829</td>\n",
       "      <td>-0.011811</td>\n",
       "      <td>-0.010300</td>\n",
       "      <td>0.010513</td>\n",
       "      <td>0.014201</td>\n",
       "      <td>0.011374</td>\n",
       "      <td>0.009539</td>\n",
       "      <td>0.025582</td>\n",
       "      <td>-0.008011</td>\n",
       "      <td>0.006649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_nb_fautes_5_matchs_dom</th>\n",
       "      <td>-0.029702</td>\n",
       "      <td>0.042259</td>\n",
       "      <td>0.041284</td>\n",
       "      <td>0.042167</td>\n",
       "      <td>-0.130639</td>\n",
       "      <td>-0.123110</td>\n",
       "      <td>-0.131919</td>\n",
       "      <td>-0.143170</td>\n",
       "      <td>-0.144669</td>\n",
       "      <td>-0.141934</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.160462</td>\n",
       "      <td>-0.136635</td>\n",
       "      <td>-0.149631</td>\n",
       "      <td>-0.001830</td>\n",
       "      <td>0.011561</td>\n",
       "      <td>0.018290</td>\n",
       "      <td>0.016655</td>\n",
       "      <td>-0.046320</td>\n",
       "      <td>0.021121</td>\n",
       "      <td>-0.084356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_corners_5_matchs_dom</th>\n",
       "      <td>0.123493</td>\n",
       "      <td>-0.135215</td>\n",
       "      <td>-0.129487</td>\n",
       "      <td>-0.141037</td>\n",
       "      <td>0.282409</td>\n",
       "      <td>0.265810</td>\n",
       "      <td>0.289258</td>\n",
       "      <td>0.265645</td>\n",
       "      <td>0.267980</td>\n",
       "      <td>0.259707</td>\n",
       "      <td>...</td>\n",
       "      <td>0.315504</td>\n",
       "      <td>0.301877</td>\n",
       "      <td>0.316416</td>\n",
       "      <td>-0.005639</td>\n",
       "      <td>0.001974</td>\n",
       "      <td>0.005980</td>\n",
       "      <td>0.001480</td>\n",
       "      <td>-0.046052</td>\n",
       "      <td>-0.003967</td>\n",
       "      <td>0.098388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_carton_J_5_matchs_dom</th>\n",
       "      <td>0.008185</td>\n",
       "      <td>0.045346</td>\n",
       "      <td>0.043475</td>\n",
       "      <td>0.045983</td>\n",
       "      <td>-0.065269</td>\n",
       "      <td>-0.060917</td>\n",
       "      <td>-0.061694</td>\n",
       "      <td>-0.033453</td>\n",
       "      <td>-0.034739</td>\n",
       "      <td>-0.027552</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.026048</td>\n",
       "      <td>-0.042432</td>\n",
       "      <td>-0.023424</td>\n",
       "      <td>0.015144</td>\n",
       "      <td>0.025124</td>\n",
       "      <td>0.029976</td>\n",
       "      <td>0.030465</td>\n",
       "      <td>-0.008550</td>\n",
       "      <td>-0.004459</td>\n",
       "      <td>-0.008544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_carton_R_5_matchs_dom</th>\n",
       "      <td>-0.008923</td>\n",
       "      <td>0.078010</td>\n",
       "      <td>0.075287</td>\n",
       "      <td>0.077214</td>\n",
       "      <td>-0.047835</td>\n",
       "      <td>-0.030242</td>\n",
       "      <td>-0.061747</td>\n",
       "      <td>-0.007577</td>\n",
       "      <td>-0.001545</td>\n",
       "      <td>-0.014289</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.054049</td>\n",
       "      <td>-0.071232</td>\n",
       "      <td>-0.050720</td>\n",
       "      <td>0.018233</td>\n",
       "      <td>0.019732</td>\n",
       "      <td>0.026398</td>\n",
       "      <td>0.024947</td>\n",
       "      <td>0.024550</td>\n",
       "      <td>0.020051</td>\n",
       "      <td>-0.063993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Num_match_ext</th>\n",
       "      <td>-0.015668</td>\n",
       "      <td>0.006744</td>\n",
       "      <td>0.004742</td>\n",
       "      <td>0.010758</td>\n",
       "      <td>0.003646</td>\n",
       "      <td>-0.004661</td>\n",
       "      <td>0.013009</td>\n",
       "      <td>0.033434</td>\n",
       "      <td>0.029771</td>\n",
       "      <td>0.036180</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019770</td>\n",
       "      <td>-0.015625</td>\n",
       "      <td>-0.014635</td>\n",
       "      <td>-0.008113</td>\n",
       "      <td>-0.013388</td>\n",
       "      <td>-0.013313</td>\n",
       "      <td>-0.005998</td>\n",
       "      <td>-0.040556</td>\n",
       "      <td>-0.019852</td>\n",
       "      <td>0.002997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_points_5_match_ext</th>\n",
       "      <td>-0.139063</td>\n",
       "      <td>0.426104</td>\n",
       "      <td>0.415782</td>\n",
       "      <td>0.435980</td>\n",
       "      <td>-0.204150</td>\n",
       "      <td>-0.180037</td>\n",
       "      <td>-0.230008</td>\n",
       "      <td>0.051119</td>\n",
       "      <td>0.046117</td>\n",
       "      <td>0.052192</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022829</td>\n",
       "      <td>0.006484</td>\n",
       "      <td>0.015140</td>\n",
       "      <td>0.473607</td>\n",
       "      <td>0.498111</td>\n",
       "      <td>0.492551</td>\n",
       "      <td>0.509176</td>\n",
       "      <td>0.107148</td>\n",
       "      <td>0.011430</td>\n",
       "      <td>-0.020270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_buts_5_matchs_ext</th>\n",
       "      <td>-0.146674</td>\n",
       "      <td>0.485015</td>\n",
       "      <td>0.476060</td>\n",
       "      <td>0.491003</td>\n",
       "      <td>-0.207348</td>\n",
       "      <td>-0.185640</td>\n",
       "      <td>-0.228445</td>\n",
       "      <td>0.111995</td>\n",
       "      <td>0.103934</td>\n",
       "      <td>0.116844</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002261</td>\n",
       "      <td>-0.009523</td>\n",
       "      <td>0.000411</td>\n",
       "      <td>0.505554</td>\n",
       "      <td>0.535118</td>\n",
       "      <td>0.489656</td>\n",
       "      <td>0.534417</td>\n",
       "      <td>0.123583</td>\n",
       "      <td>-0.005853</td>\n",
       "      <td>-0.019273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_tirs_cadres_5_matchs_ext</th>\n",
       "      <td>-0.128817</td>\n",
       "      <td>0.437068</td>\n",
       "      <td>0.428242</td>\n",
       "      <td>0.442804</td>\n",
       "      <td>-0.205558</td>\n",
       "      <td>-0.185678</td>\n",
       "      <td>-0.222878</td>\n",
       "      <td>0.086769</td>\n",
       "      <td>0.079631</td>\n",
       "      <td>0.092678</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023033</td>\n",
       "      <td>0.012161</td>\n",
       "      <td>0.019666</td>\n",
       "      <td>0.464450</td>\n",
       "      <td>0.506355</td>\n",
       "      <td>0.459587</td>\n",
       "      <td>0.503527</td>\n",
       "      <td>0.117844</td>\n",
       "      <td>0.033837</td>\n",
       "      <td>-0.005199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_tirs_5_matchs_ext</th>\n",
       "      <td>-0.120794</td>\n",
       "      <td>0.380244</td>\n",
       "      <td>0.371991</td>\n",
       "      <td>0.384546</td>\n",
       "      <td>-0.180279</td>\n",
       "      <td>-0.161446</td>\n",
       "      <td>-0.192775</td>\n",
       "      <td>0.071089</td>\n",
       "      <td>0.061784</td>\n",
       "      <td>0.080684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052485</td>\n",
       "      <td>0.032513</td>\n",
       "      <td>0.040864</td>\n",
       "      <td>0.401733</td>\n",
       "      <td>0.437962</td>\n",
       "      <td>0.400547</td>\n",
       "      <td>0.436979</td>\n",
       "      <td>0.120009</td>\n",
       "      <td>0.023532</td>\n",
       "      <td>0.029888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nb_jours_dernier_match_ext</th>\n",
       "      <td>-0.044709</td>\n",
       "      <td>0.027449</td>\n",
       "      <td>0.027680</td>\n",
       "      <td>0.024374</td>\n",
       "      <td>-0.032820</td>\n",
       "      <td>-0.029059</td>\n",
       "      <td>-0.035812</td>\n",
       "      <td>-0.026372</td>\n",
       "      <td>-0.027177</td>\n",
       "      <td>-0.023223</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010823</td>\n",
       "      <td>-0.014791</td>\n",
       "      <td>-0.009586</td>\n",
       "      <td>0.021116</td>\n",
       "      <td>0.021289</td>\n",
       "      <td>0.021040</td>\n",
       "      <td>0.015214</td>\n",
       "      <td>0.026492</td>\n",
       "      <td>0.018773</td>\n",
       "      <td>0.003104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_nb_fautes_5_matchs_ext</th>\n",
       "      <td>0.032632</td>\n",
       "      <td>-0.151639</td>\n",
       "      <td>-0.149908</td>\n",
       "      <td>-0.150724</td>\n",
       "      <td>0.054831</td>\n",
       "      <td>0.047231</td>\n",
       "      <td>0.056485</td>\n",
       "      <td>-0.060291</td>\n",
       "      <td>-0.055590</td>\n",
       "      <td>-0.067605</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001429</td>\n",
       "      <td>-0.000522</td>\n",
       "      <td>0.004898</td>\n",
       "      <td>-0.144901</td>\n",
       "      <td>-0.172632</td>\n",
       "      <td>-0.133799</td>\n",
       "      <td>-0.155243</td>\n",
       "      <td>-0.009493</td>\n",
       "      <td>-0.027983</td>\n",
       "      <td>-0.019396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_corners_5_matchs_ext</th>\n",
       "      <td>-0.096827</td>\n",
       "      <td>0.285665</td>\n",
       "      <td>0.282433</td>\n",
       "      <td>0.287547</td>\n",
       "      <td>-0.096143</td>\n",
       "      <td>-0.081763</td>\n",
       "      <td>-0.102330</td>\n",
       "      <td>0.090076</td>\n",
       "      <td>0.085667</td>\n",
       "      <td>0.095126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038935</td>\n",
       "      <td>0.035584</td>\n",
       "      <td>0.041596</td>\n",
       "      <td>0.286092</td>\n",
       "      <td>0.303818</td>\n",
       "      <td>0.284026</td>\n",
       "      <td>0.303876</td>\n",
       "      <td>0.067839</td>\n",
       "      <td>0.014042</td>\n",
       "      <td>0.007344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_carton_J_5_matchs_ext</th>\n",
       "      <td>0.021042</td>\n",
       "      <td>-0.070156</td>\n",
       "      <td>-0.073667</td>\n",
       "      <td>-0.060356</td>\n",
       "      <td>0.009337</td>\n",
       "      <td>0.008557</td>\n",
       "      <td>0.007588</td>\n",
       "      <td>-0.032464</td>\n",
       "      <td>-0.031936</td>\n",
       "      <td>-0.026164</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018768</td>\n",
       "      <td>-0.011339</td>\n",
       "      <td>-0.005452</td>\n",
       "      <td>-0.051662</td>\n",
       "      <td>-0.066615</td>\n",
       "      <td>-0.073712</td>\n",
       "      <td>-0.058278</td>\n",
       "      <td>0.055441</td>\n",
       "      <td>-0.024606</td>\n",
       "      <td>-0.034244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moy_carton_R_5_matchs_ext</th>\n",
       "      <td>-0.002225</td>\n",
       "      <td>-0.057478</td>\n",
       "      <td>-0.056534</td>\n",
       "      <td>-0.058362</td>\n",
       "      <td>0.011960</td>\n",
       "      <td>0.006338</td>\n",
       "      <td>0.015813</td>\n",
       "      <td>-0.021544</td>\n",
       "      <td>-0.021233</td>\n",
       "      <td>-0.022024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.055143</td>\n",
       "      <td>-0.053455</td>\n",
       "      <td>-0.053814</td>\n",
       "      <td>-0.048164</td>\n",
       "      <td>-0.079325</td>\n",
       "      <td>-0.101101</td>\n",
       "      <td>-0.076210</td>\n",
       "      <td>-0.009876</td>\n",
       "      <td>-0.019245</td>\n",
       "      <td>-0.030862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Points_moy_match_saison_dom</th>\n",
       "      <td>0.182228</td>\n",
       "      <td>-0.248777</td>\n",
       "      <td>-0.236238</td>\n",
       "      <td>-0.261218</td>\n",
       "      <td>0.493411</td>\n",
       "      <td>0.461859</td>\n",
       "      <td>0.512410</td>\n",
       "      <td>0.413305</td>\n",
       "      <td>0.407285</td>\n",
       "      <td>0.411077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.589135</td>\n",
       "      <td>0.597835</td>\n",
       "      <td>0.604469</td>\n",
       "      <td>-0.006984</td>\n",
       "      <td>-0.000430</td>\n",
       "      <td>-0.000387</td>\n",
       "      <td>-0.000243</td>\n",
       "      <td>-0.027710</td>\n",
       "      <td>-0.029454</td>\n",
       "      <td>0.140237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Points_moy_match_saison_ext</th>\n",
       "      <td>-0.170919</td>\n",
       "      <td>0.500993</td>\n",
       "      <td>0.489933</td>\n",
       "      <td>0.510184</td>\n",
       "      <td>-0.235346</td>\n",
       "      <td>-0.209761</td>\n",
       "      <td>-0.265505</td>\n",
       "      <td>0.059506</td>\n",
       "      <td>0.051199</td>\n",
       "      <td>0.063247</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002873</td>\n",
       "      <td>-0.003129</td>\n",
       "      <td>-0.000109</td>\n",
       "      <td>0.559410</td>\n",
       "      <td>0.590428</td>\n",
       "      <td>0.592374</td>\n",
       "      <td>0.603858</td>\n",
       "      <td>0.153304</td>\n",
       "      <td>-0.007596</td>\n",
       "      <td>-0.042246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pct_buts_par_tirs_cadres_5m_dom</th>\n",
       "      <td>0.089577</td>\n",
       "      <td>-0.115190</td>\n",
       "      <td>-0.110913</td>\n",
       "      <td>-0.117633</td>\n",
       "      <td>0.219894</td>\n",
       "      <td>0.208093</td>\n",
       "      <td>0.232122</td>\n",
       "      <td>0.204474</td>\n",
       "      <td>0.202867</td>\n",
       "      <td>0.202834</td>\n",
       "      <td>...</td>\n",
       "      <td>0.286498</td>\n",
       "      <td>0.265960</td>\n",
       "      <td>0.283343</td>\n",
       "      <td>-0.008253</td>\n",
       "      <td>-0.006289</td>\n",
       "      <td>-0.010418</td>\n",
       "      <td>-0.006281</td>\n",
       "      <td>-0.002056</td>\n",
       "      <td>-0.019197</td>\n",
       "      <td>0.057673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pct_buts_par_tirs_cadres_5m_ext</th>\n",
       "      <td>-0.082664</td>\n",
       "      <td>0.233966</td>\n",
       "      <td>0.229106</td>\n",
       "      <td>0.238831</td>\n",
       "      <td>-0.097247</td>\n",
       "      <td>-0.087746</td>\n",
       "      <td>-0.108402</td>\n",
       "      <td>0.059634</td>\n",
       "      <td>0.055328</td>\n",
       "      <td>0.060779</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005556</td>\n",
       "      <td>-0.018109</td>\n",
       "      <td>-0.008980</td>\n",
       "      <td>0.260941</td>\n",
       "      <td>0.267641</td>\n",
       "      <td>0.247159</td>\n",
       "      <td>0.265436</td>\n",
       "      <td>0.052337</td>\n",
       "      <td>-0.027406</td>\n",
       "      <td>-0.022992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>note_att_dom</th>\n",
       "      <td>0.246068</td>\n",
       "      <td>-0.302007</td>\n",
       "      <td>-0.286290</td>\n",
       "      <td>-0.318742</td>\n",
       "      <td>0.642370</td>\n",
       "      <td>0.611287</td>\n",
       "      <td>0.665907</td>\n",
       "      <td>0.574118</td>\n",
       "      <td>0.567780</td>\n",
       "      <td>0.577769</td>\n",
       "      <td>...</td>\n",
       "      <td>0.848907</td>\n",
       "      <td>0.817622</td>\n",
       "      <td>0.897168</td>\n",
       "      <td>-0.021320</td>\n",
       "      <td>-0.005745</td>\n",
       "      <td>-0.014784</td>\n",
       "      <td>-0.018302</td>\n",
       "      <td>-0.034280</td>\n",
       "      <td>0.030201</td>\n",
       "      <td>0.249991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>note_mil_dom</th>\n",
       "      <td>0.253378</td>\n",
       "      <td>-0.312310</td>\n",
       "      <td>-0.296448</td>\n",
       "      <td>-0.329679</td>\n",
       "      <td>0.653607</td>\n",
       "      <td>0.619361</td>\n",
       "      <td>0.680856</td>\n",
       "      <td>0.581348</td>\n",
       "      <td>0.572437</td>\n",
       "      <td>0.586928</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.891477</td>\n",
       "      <td>0.960738</td>\n",
       "      <td>-0.005677</td>\n",
       "      <td>0.003940</td>\n",
       "      <td>-0.006122</td>\n",
       "      <td>-0.008804</td>\n",
       "      <td>-0.017516</td>\n",
       "      <td>0.048059</td>\n",
       "      <td>0.278336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>note_def_dom</th>\n",
       "      <td>0.247336</td>\n",
       "      <td>-0.315936</td>\n",
       "      <td>-0.301636</td>\n",
       "      <td>-0.332261</td>\n",
       "      <td>0.645174</td>\n",
       "      <td>0.613223</td>\n",
       "      <td>0.664716</td>\n",
       "      <td>0.545987</td>\n",
       "      <td>0.541538</td>\n",
       "      <td>0.545336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.891477</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.944144</td>\n",
       "      <td>-0.021201</td>\n",
       "      <td>-0.011268</td>\n",
       "      <td>-0.025875</td>\n",
       "      <td>-0.024053</td>\n",
       "      <td>-0.001587</td>\n",
       "      <td>0.052088</td>\n",
       "      <td>0.258537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>note_gen_dom</th>\n",
       "      <td>0.253553</td>\n",
       "      <td>-0.324038</td>\n",
       "      <td>-0.308108</td>\n",
       "      <td>-0.341783</td>\n",
       "      <td>0.690102</td>\n",
       "      <td>0.655540</td>\n",
       "      <td>0.715625</td>\n",
       "      <td>0.609485</td>\n",
       "      <td>0.602739</td>\n",
       "      <td>0.614250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.960738</td>\n",
       "      <td>0.944144</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020507</td>\n",
       "      <td>-0.010257</td>\n",
       "      <td>-0.019936</td>\n",
       "      <td>-0.021005</td>\n",
       "      <td>-0.014123</td>\n",
       "      <td>0.042980</td>\n",
       "      <td>0.273694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>note_att_ext</th>\n",
       "      <td>-0.239922</td>\n",
       "      <td>0.651118</td>\n",
       "      <td>0.636409</td>\n",
       "      <td>0.662399</td>\n",
       "      <td>-0.305410</td>\n",
       "      <td>-0.273524</td>\n",
       "      <td>-0.337090</td>\n",
       "      <td>0.099542</td>\n",
       "      <td>0.085256</td>\n",
       "      <td>0.115549</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005677</td>\n",
       "      <td>-0.021201</td>\n",
       "      <td>-0.020507</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.849454</td>\n",
       "      <td>0.816317</td>\n",
       "      <td>0.896797</td>\n",
       "      <td>0.272224</td>\n",
       "      <td>0.042393</td>\n",
       "      <td>-0.035764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>note_mil_ext</th>\n",
       "      <td>-0.267816</td>\n",
       "      <td>0.651711</td>\n",
       "      <td>0.635442</td>\n",
       "      <td>0.664862</td>\n",
       "      <td>-0.325617</td>\n",
       "      <td>-0.293665</td>\n",
       "      <td>-0.353072</td>\n",
       "      <td>0.082116</td>\n",
       "      <td>0.066730</td>\n",
       "      <td>0.098476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003940</td>\n",
       "      <td>-0.011268</td>\n",
       "      <td>-0.010257</td>\n",
       "      <td>0.849454</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.890935</td>\n",
       "      <td>0.960840</td>\n",
       "      <td>0.287640</td>\n",
       "      <td>0.077948</td>\n",
       "      <td>-0.018424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>note_def_ext</th>\n",
       "      <td>-0.283858</td>\n",
       "      <td>0.640279</td>\n",
       "      <td>0.626767</td>\n",
       "      <td>0.649177</td>\n",
       "      <td>-0.324405</td>\n",
       "      <td>-0.293255</td>\n",
       "      <td>-0.351092</td>\n",
       "      <td>0.055446</td>\n",
       "      <td>0.041616</td>\n",
       "      <td>0.070701</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006122</td>\n",
       "      <td>-0.025875</td>\n",
       "      <td>-0.019936</td>\n",
       "      <td>0.816317</td>\n",
       "      <td>0.890935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.943518</td>\n",
       "      <td>0.280796</td>\n",
       "      <td>0.064023</td>\n",
       "      <td>-0.004345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>note_gen_ext</th>\n",
       "      <td>-0.282362</td>\n",
       "      <td>0.695249</td>\n",
       "      <td>0.679335</td>\n",
       "      <td>0.707484</td>\n",
       "      <td>-0.335326</td>\n",
       "      <td>-0.302410</td>\n",
       "      <td>-0.365159</td>\n",
       "      <td>0.091265</td>\n",
       "      <td>0.075396</td>\n",
       "      <td>0.109693</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008804</td>\n",
       "      <td>-0.024053</td>\n",
       "      <td>-0.021005</td>\n",
       "      <td>0.896797</td>\n",
       "      <td>0.960840</td>\n",
       "      <td>0.943518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.291696</td>\n",
       "      <td>0.062859</td>\n",
       "      <td>-0.017398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Res_an_dernier_A</th>\n",
       "      <td>-0.084498</td>\n",
       "      <td>0.219029</td>\n",
       "      <td>0.211368</td>\n",
       "      <td>0.228177</td>\n",
       "      <td>-0.148609</td>\n",
       "      <td>-0.138314</td>\n",
       "      <td>-0.154075</td>\n",
       "      <td>-0.020007</td>\n",
       "      <td>-0.025497</td>\n",
       "      <td>-0.010244</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017516</td>\n",
       "      <td>-0.001587</td>\n",
       "      <td>-0.014123</td>\n",
       "      <td>0.272224</td>\n",
       "      <td>0.287640</td>\n",
       "      <td>0.280796</td>\n",
       "      <td>0.291696</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.206498</td>\n",
       "      <td>-0.281773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Res_an_dernier_D</th>\n",
       "      <td>-0.028601</td>\n",
       "      <td>-0.030618</td>\n",
       "      <td>-0.032695</td>\n",
       "      <td>-0.025637</td>\n",
       "      <td>-0.030481</td>\n",
       "      <td>-0.031519</td>\n",
       "      <td>-0.028376</td>\n",
       "      <td>-0.051025</td>\n",
       "      <td>-0.050220</td>\n",
       "      <td>-0.051502</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048059</td>\n",
       "      <td>0.052088</td>\n",
       "      <td>0.042980</td>\n",
       "      <td>0.042393</td>\n",
       "      <td>0.077948</td>\n",
       "      <td>0.064023</td>\n",
       "      <td>0.062859</td>\n",
       "      <td>-0.206498</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.274820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Res_an_dernier_H</th>\n",
       "      <td>0.079533</td>\n",
       "      <td>-0.133929</td>\n",
       "      <td>-0.129895</td>\n",
       "      <td>-0.139209</td>\n",
       "      <td>0.181058</td>\n",
       "      <td>0.164760</td>\n",
       "      <td>0.205087</td>\n",
       "      <td>0.151283</td>\n",
       "      <td>0.144790</td>\n",
       "      <td>0.161562</td>\n",
       "      <td>...</td>\n",
       "      <td>0.278336</td>\n",
       "      <td>0.258537</td>\n",
       "      <td>0.273694</td>\n",
       "      <td>-0.035764</td>\n",
       "      <td>-0.018424</td>\n",
       "      <td>-0.004345</td>\n",
       "      <td>-0.017398</td>\n",
       "      <td>-0.281773</td>\n",
       "      <td>-0.274820</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Resultat_match  Moy_cote_domicile  \\\n",
       "Resultat_match                         1.000000          -0.316128   \n",
       "Moy_cote_domicile                     -0.316128           1.000000   \n",
       "Max_cote_domicile                     -0.305735           0.996338   \n",
       "Min_cote_domicile                     -0.323177           0.993512   \n",
       "Moy_cote_exterieur                     0.287663          -0.415806   \n",
       "Max_cote_exterieur                     0.267276          -0.373508   \n",
       "Min_cote_exterieur                     0.303760          -0.457489   \n",
       "Moy_cote_nul                           0.133529           0.123811   \n",
       "Max_cote_nul                           0.136498           0.106672   \n",
       "Min_cote_nul                           0.128379           0.141168   \n",
       "Nb_buts_equipe_dom_an_dernier          0.064225          -0.108913   \n",
       "Nb_buts_equipe_ext_an_dernier         -0.083655           0.196362   \n",
       "Num_match_dom                         -0.015986           0.008113   \n",
       "Moy_points_5_match_dom                 0.159726          -0.238280   \n",
       "Moy_buts_5_matchs_dom                  0.179866          -0.224274   \n",
       "Moy_tirs_cadres_5_matchs_dom           0.179153          -0.214699   \n",
       "Moy_tirs_5_matchs_dom                  0.158741          -0.190919   \n",
       "Nb_jours_dernier_match_dom            -0.038109          -0.001443   \n",
       "Moy_nb_fautes_5_matchs_dom            -0.029702           0.042259   \n",
       "Moy_corners_5_matchs_dom               0.123493          -0.135215   \n",
       "Moy_carton_J_5_matchs_dom              0.008185           0.045346   \n",
       "Moy_carton_R_5_matchs_dom             -0.008923           0.078010   \n",
       "Num_match_ext                         -0.015668           0.006744   \n",
       "Moy_points_5_match_ext                -0.139063           0.426104   \n",
       "Moy_buts_5_matchs_ext                 -0.146674           0.485015   \n",
       "Moy_tirs_cadres_5_matchs_ext          -0.128817           0.437068   \n",
       "Moy_tirs_5_matchs_ext                 -0.120794           0.380244   \n",
       "Nb_jours_dernier_match_ext            -0.044709           0.027449   \n",
       "Moy_nb_fautes_5_matchs_ext             0.032632          -0.151639   \n",
       "Moy_corners_5_matchs_ext              -0.096827           0.285665   \n",
       "Moy_carton_J_5_matchs_ext              0.021042          -0.070156   \n",
       "Moy_carton_R_5_matchs_ext             -0.002225          -0.057478   \n",
       "Points_moy_match_saison_dom            0.182228          -0.248777   \n",
       "Points_moy_match_saison_ext           -0.170919           0.500993   \n",
       "Pct_buts_par_tirs_cadres_5m_dom        0.089577          -0.115190   \n",
       "Pct_buts_par_tirs_cadres_5m_ext       -0.082664           0.233966   \n",
       "note_att_dom                           0.246068          -0.302007   \n",
       "note_mil_dom                           0.253378          -0.312310   \n",
       "note_def_dom                           0.247336          -0.315936   \n",
       "note_gen_dom                           0.253553          -0.324038   \n",
       "note_att_ext                          -0.239922           0.651118   \n",
       "note_mil_ext                          -0.267816           0.651711   \n",
       "note_def_ext                          -0.283858           0.640279   \n",
       "note_gen_ext                          -0.282362           0.695249   \n",
       "Res_an_dernier_A                      -0.084498           0.219029   \n",
       "Res_an_dernier_D                      -0.028601          -0.030618   \n",
       "Res_an_dernier_H                       0.079533          -0.133929   \n",
       "\n",
       "                                 Max_cote_domicile  Min_cote_domicile  \\\n",
       "Resultat_match                           -0.305735          -0.323177   \n",
       "Moy_cote_domicile                         0.996338           0.993512   \n",
       "Max_cote_domicile                         1.000000           0.983131   \n",
       "Min_cote_domicile                         0.983131           1.000000   \n",
       "Moy_cote_exterieur                       -0.394325          -0.439346   \n",
       "Max_cote_exterieur                       -0.353579          -0.395719   \n",
       "Min_cote_exterieur                       -0.435031          -0.482073   \n",
       "Moy_cote_nul                              0.142350           0.099911   \n",
       "Max_cote_nul                              0.125394           0.082286   \n",
       "Min_cote_nul                              0.158528           0.120117   \n",
       "Nb_buts_equipe_dom_an_dernier            -0.104315          -0.113953   \n",
       "Nb_buts_equipe_ext_an_dernier             0.189000           0.205911   \n",
       "Num_match_dom                             0.005972           0.012332   \n",
       "Moy_points_5_match_dom                   -0.225482          -0.252119   \n",
       "Moy_buts_5_matchs_dom                    -0.212092          -0.234949   \n",
       "Moy_tirs_cadres_5_matchs_dom             -0.201208          -0.227142   \n",
       "Moy_tirs_5_matchs_dom                    -0.180645          -0.200378   \n",
       "Nb_jours_dernier_match_dom               -0.000288          -0.005518   \n",
       "Moy_nb_fautes_5_matchs_dom                0.041284           0.042167   \n",
       "Moy_corners_5_matchs_dom                 -0.129487          -0.141037   \n",
       "Moy_carton_J_5_matchs_dom                 0.043475           0.045983   \n",
       "Moy_carton_R_5_matchs_dom                 0.075287           0.077214   \n",
       "Num_match_ext                             0.004742           0.010758   \n",
       "Moy_points_5_match_ext                    0.415782           0.435980   \n",
       "Moy_buts_5_matchs_ext                     0.476060           0.491003   \n",
       "Moy_tirs_cadres_5_matchs_ext              0.428242           0.442804   \n",
       "Moy_tirs_5_matchs_ext                     0.371991           0.384546   \n",
       "Nb_jours_dernier_match_ext                0.027680           0.024374   \n",
       "Moy_nb_fautes_5_matchs_ext               -0.149908          -0.150724   \n",
       "Moy_corners_5_matchs_ext                  0.282433           0.287547   \n",
       "Moy_carton_J_5_matchs_ext                -0.073667          -0.060356   \n",
       "Moy_carton_R_5_matchs_ext                -0.056534          -0.058362   \n",
       "Points_moy_match_saison_dom              -0.236238          -0.261218   \n",
       "Points_moy_match_saison_ext               0.489933           0.510184   \n",
       "Pct_buts_par_tirs_cadres_5m_dom          -0.110913          -0.117633   \n",
       "Pct_buts_par_tirs_cadres_5m_ext           0.229106           0.238831   \n",
       "note_att_dom                             -0.286290          -0.318742   \n",
       "note_mil_dom                             -0.296448          -0.329679   \n",
       "note_def_dom                             -0.301636          -0.332261   \n",
       "note_gen_dom                             -0.308108          -0.341783   \n",
       "note_att_ext                              0.636409           0.662399   \n",
       "note_mil_ext                              0.635442           0.664862   \n",
       "note_def_ext                              0.626767           0.649177   \n",
       "note_gen_ext                              0.679335           0.707484   \n",
       "Res_an_dernier_A                          0.211368           0.228177   \n",
       "Res_an_dernier_D                         -0.032695          -0.025637   \n",
       "Res_an_dernier_H                         -0.129895          -0.139209   \n",
       "\n",
       "                                 Moy_cote_exterieur  Max_cote_exterieur  \\\n",
       "Resultat_match                             0.287663            0.267276   \n",
       "Moy_cote_domicile                         -0.415806           -0.373508   \n",
       "Max_cote_domicile                         -0.394325           -0.353579   \n",
       "Min_cote_domicile                         -0.439346           -0.395719   \n",
       "Moy_cote_exterieur                         1.000000            0.987931   \n",
       "Max_cote_exterieur                         0.987931            1.000000   \n",
       "Min_cote_exterieur                         0.980986            0.950085   \n",
       "Moy_cote_nul                               0.821203            0.822893   \n",
       "Max_cote_nul                               0.830920            0.838743   \n",
       "Min_cote_nul                               0.785906            0.778465   \n",
       "Nb_buts_equipe_dom_an_dernier              0.197624            0.177173   \n",
       "Nb_buts_equipe_ext_an_dernier             -0.121397           -0.115146   \n",
       "Num_match_dom                              0.002369           -0.005728   \n",
       "Moy_points_5_match_dom                     0.432095            0.402842   \n",
       "Moy_buts_5_matchs_dom                      0.486004            0.457165   \n",
       "Moy_tirs_cadres_5_matchs_dom               0.459794            0.430236   \n",
       "Moy_tirs_5_matchs_dom                      0.399184            0.373554   \n",
       "Nb_jours_dernier_match_dom                -0.027239           -0.024894   \n",
       "Moy_nb_fautes_5_matchs_dom                -0.130639           -0.123110   \n",
       "Moy_corners_5_matchs_dom                   0.282409            0.265810   \n",
       "Moy_carton_J_5_matchs_dom                 -0.065269           -0.060917   \n",
       "Moy_carton_R_5_matchs_dom                 -0.047835           -0.030242   \n",
       "Num_match_ext                              0.003646           -0.004661   \n",
       "Moy_points_5_match_ext                    -0.204150           -0.180037   \n",
       "Moy_buts_5_matchs_ext                     -0.207348           -0.185640   \n",
       "Moy_tirs_cadres_5_matchs_ext              -0.205558           -0.185678   \n",
       "Moy_tirs_5_matchs_ext                     -0.180279           -0.161446   \n",
       "Nb_jours_dernier_match_ext                -0.032820           -0.029059   \n",
       "Moy_nb_fautes_5_matchs_ext                 0.054831            0.047231   \n",
       "Moy_corners_5_matchs_ext                  -0.096143           -0.081763   \n",
       "Moy_carton_J_5_matchs_ext                  0.009337            0.008557   \n",
       "Moy_carton_R_5_matchs_ext                  0.011960            0.006338   \n",
       "Points_moy_match_saison_dom                0.493411            0.461859   \n",
       "Points_moy_match_saison_ext               -0.235346           -0.209761   \n",
       "Pct_buts_par_tirs_cadres_5m_dom            0.219894            0.208093   \n",
       "Pct_buts_par_tirs_cadres_5m_ext           -0.097247           -0.087746   \n",
       "note_att_dom                               0.642370            0.611287   \n",
       "note_mil_dom                               0.653607            0.619361   \n",
       "note_def_dom                               0.645174            0.613223   \n",
       "note_gen_dom                               0.690102            0.655540   \n",
       "note_att_ext                              -0.305410           -0.273524   \n",
       "note_mil_ext                              -0.325617           -0.293665   \n",
       "note_def_ext                              -0.324405           -0.293255   \n",
       "note_gen_ext                              -0.335326           -0.302410   \n",
       "Res_an_dernier_A                          -0.148609           -0.138314   \n",
       "Res_an_dernier_D                          -0.030481           -0.031519   \n",
       "Res_an_dernier_H                           0.181058            0.164760   \n",
       "\n",
       "                                 Min_cote_exterieur  Moy_cote_nul  \\\n",
       "Resultat_match                             0.303760      0.133529   \n",
       "Moy_cote_domicile                         -0.457489      0.123811   \n",
       "Max_cote_domicile                         -0.435031      0.142350   \n",
       "Min_cote_domicile                         -0.482073      0.099911   \n",
       "Moy_cote_exterieur                         0.980986      0.821203   \n",
       "Max_cote_exterieur                         0.950085      0.822893   \n",
       "Min_cote_exterieur                         1.000000      0.793996   \n",
       "Moy_cote_nul                               0.793996      1.000000   \n",
       "Max_cote_nul                               0.795763      0.995667   \n",
       "Min_cote_nul                               0.777305      0.982713   \n",
       "Nb_buts_equipe_dom_an_dernier              0.222924      0.203273   \n",
       "Nb_buts_equipe_ext_an_dernier             -0.121336      0.021249   \n",
       "Num_match_dom                              0.011404      0.032744   \n",
       "Moy_points_5_match_dom                     0.448945      0.354691   \n",
       "Moy_buts_5_matchs_dom                      0.501475      0.457082   \n",
       "Moy_tirs_cadres_5_matchs_dom               0.471371      0.431608   \n",
       "Moy_tirs_5_matchs_dom                      0.409992      0.375578   \n",
       "Nb_jours_dernier_match_dom                -0.027361     -0.036333   \n",
       "Moy_nb_fautes_5_matchs_dom                -0.131919     -0.143170   \n",
       "Moy_corners_5_matchs_dom                   0.289258      0.265645   \n",
       "Moy_carton_J_5_matchs_dom                 -0.061694     -0.033453   \n",
       "Moy_carton_R_5_matchs_dom                 -0.061747     -0.007577   \n",
       "Num_match_ext                              0.013009      0.033434   \n",
       "Moy_points_5_match_ext                    -0.230008      0.051119   \n",
       "Moy_buts_5_matchs_ext                     -0.228445      0.111995   \n",
       "Moy_tirs_cadres_5_matchs_ext              -0.222878      0.086769   \n",
       "Moy_tirs_5_matchs_ext                     -0.192775      0.071089   \n",
       "Nb_jours_dernier_match_ext                -0.035812     -0.026372   \n",
       "Moy_nb_fautes_5_matchs_ext                 0.056485     -0.060291   \n",
       "Moy_corners_5_matchs_ext                  -0.102330      0.090076   \n",
       "Moy_carton_J_5_matchs_ext                  0.007588     -0.032464   \n",
       "Moy_carton_R_5_matchs_ext                  0.015813     -0.021544   \n",
       "Points_moy_match_saison_dom                0.512410      0.413305   \n",
       "Points_moy_match_saison_ext               -0.265505      0.059506   \n",
       "Pct_buts_par_tirs_cadres_5m_dom            0.232122      0.204474   \n",
       "Pct_buts_par_tirs_cadres_5m_ext           -0.108402      0.059634   \n",
       "note_att_dom                               0.665907      0.574118   \n",
       "note_mil_dom                               0.680856      0.581348   \n",
       "note_def_dom                               0.664716      0.545987   \n",
       "note_gen_dom                               0.715625      0.609485   \n",
       "note_att_ext                              -0.337090      0.099542   \n",
       "note_mil_ext                              -0.353072      0.082116   \n",
       "note_def_ext                              -0.351092      0.055446   \n",
       "note_gen_ext                              -0.365159      0.091265   \n",
       "Res_an_dernier_A                          -0.154075     -0.020007   \n",
       "Res_an_dernier_D                          -0.028376     -0.051025   \n",
       "Res_an_dernier_H                           0.205087      0.151283   \n",
       "\n",
       "                                 Max_cote_nul  Min_cote_nul  ...  \\\n",
       "Resultat_match                       0.136498      0.128379  ...   \n",
       "Moy_cote_domicile                    0.106672      0.141168  ...   \n",
       "Max_cote_domicile                    0.125394      0.158528  ...   \n",
       "Min_cote_domicile                    0.082286      0.120117  ...   \n",
       "Moy_cote_exterieur                   0.830920      0.785906  ...   \n",
       "Max_cote_exterieur                   0.838743      0.778465  ...   \n",
       "Min_cote_exterieur                   0.795763      0.777305  ...   \n",
       "Moy_cote_nul                         0.995667      0.982713  ...   \n",
       "Max_cote_nul                         1.000000      0.966882  ...   \n",
       "Min_cote_nul                         0.966882      1.000000  ...   \n",
       "Nb_buts_equipe_dom_an_dernier        0.193310      0.217452  ...   \n",
       "Nb_buts_equipe_ext_an_dernier        0.013189      0.034483  ...   \n",
       "Num_match_dom                        0.029085      0.035457  ...   \n",
       "Moy_points_5_match_dom               0.353217      0.351054  ...   \n",
       "Moy_buts_5_matchs_dom                0.456341      0.448952  ...   \n",
       "Moy_tirs_cadres_5_matchs_dom         0.431786      0.424031  ...   \n",
       "Moy_tirs_5_matchs_dom                0.376502      0.370032  ...   \n",
       "Nb_jours_dernier_match_dom          -0.035778     -0.034878  ...   \n",
       "Moy_nb_fautes_5_matchs_dom          -0.144669     -0.141934  ...   \n",
       "Moy_corners_5_matchs_dom             0.267980      0.259707  ...   \n",
       "Moy_carton_J_5_matchs_dom           -0.034739     -0.027552  ...   \n",
       "Moy_carton_R_5_matchs_dom           -0.001545     -0.014289  ...   \n",
       "Num_match_ext                        0.029771      0.036180  ...   \n",
       "Moy_points_5_match_ext               0.046117      0.052192  ...   \n",
       "Moy_buts_5_matchs_ext                0.103934      0.116844  ...   \n",
       "Moy_tirs_cadres_5_matchs_ext         0.079631      0.092678  ...   \n",
       "Moy_tirs_5_matchs_ext                0.061784      0.080684  ...   \n",
       "Nb_jours_dernier_match_ext          -0.027177     -0.023223  ...   \n",
       "Moy_nb_fautes_5_matchs_ext          -0.055590     -0.067605  ...   \n",
       "Moy_corners_5_matchs_ext             0.085667      0.095126  ...   \n",
       "Moy_carton_J_5_matchs_ext           -0.031936     -0.026164  ...   \n",
       "Moy_carton_R_5_matchs_ext           -0.021233     -0.022024  ...   \n",
       "Points_moy_match_saison_dom          0.407285      0.411077  ...   \n",
       "Points_moy_match_saison_ext          0.051199      0.063247  ...   \n",
       "Pct_buts_par_tirs_cadres_5m_dom      0.202867      0.202834  ...   \n",
       "Pct_buts_par_tirs_cadres_5m_ext      0.055328      0.060779  ...   \n",
       "note_att_dom                         0.567780      0.577769  ...   \n",
       "note_mil_dom                         0.572437      0.586928  ...   \n",
       "note_def_dom                         0.541538      0.545336  ...   \n",
       "note_gen_dom                         0.602739      0.614250  ...   \n",
       "note_att_ext                         0.085256      0.115549  ...   \n",
       "note_mil_ext                         0.066730      0.098476  ...   \n",
       "note_def_ext                         0.041616      0.070701  ...   \n",
       "note_gen_ext                         0.075396      0.109693  ...   \n",
       "Res_an_dernier_A                    -0.025497     -0.010244  ...   \n",
       "Res_an_dernier_D                    -0.050220     -0.051502  ...   \n",
       "Res_an_dernier_H                     0.144790      0.161562  ...   \n",
       "\n",
       "                                 note_mil_dom  note_def_dom  note_gen_dom  \\\n",
       "Resultat_match                       0.253378      0.247336      0.253553   \n",
       "Moy_cote_domicile                   -0.312310     -0.315936     -0.324038   \n",
       "Max_cote_domicile                   -0.296448     -0.301636     -0.308108   \n",
       "Min_cote_domicile                   -0.329679     -0.332261     -0.341783   \n",
       "Moy_cote_exterieur                   0.653607      0.645174      0.690102   \n",
       "Max_cote_exterieur                   0.619361      0.613223      0.655540   \n",
       "Min_cote_exterieur                   0.680856      0.664716      0.715625   \n",
       "Moy_cote_nul                         0.581348      0.545987      0.609485   \n",
       "Max_cote_nul                         0.572437      0.541538      0.602739   \n",
       "Min_cote_nul                         0.586928      0.545336      0.614250   \n",
       "Nb_buts_equipe_dom_an_dernier        0.355383      0.319122      0.348353   \n",
       "Nb_buts_equipe_ext_an_dernier        0.074681      0.073780      0.068571   \n",
       "Num_match_dom                       -0.021865     -0.017392     -0.016604   \n",
       "Moy_points_5_match_dom               0.505264      0.505060      0.515478   \n",
       "Moy_buts_5_matchs_dom                0.555243      0.510852      0.553554   \n",
       "Moy_tirs_cadres_5_matchs_dom         0.517746      0.474901      0.515943   \n",
       "Moy_tirs_5_matchs_dom                0.450166      0.417119      0.448282   \n",
       "Nb_jours_dernier_match_dom          -0.009829     -0.011811     -0.010300   \n",
       "Moy_nb_fautes_5_matchs_dom          -0.160462     -0.136635     -0.149631   \n",
       "Moy_corners_5_matchs_dom             0.315504      0.301877      0.316416   \n",
       "Moy_carton_J_5_matchs_dom           -0.026048     -0.042432     -0.023424   \n",
       "Moy_carton_R_5_matchs_dom           -0.054049     -0.071232     -0.050720   \n",
       "Num_match_ext                       -0.019770     -0.015625     -0.014635   \n",
       "Moy_points_5_match_ext               0.022829      0.006484      0.015140   \n",
       "Moy_buts_5_matchs_ext                0.002261     -0.009523      0.000411   \n",
       "Moy_tirs_cadres_5_matchs_ext         0.023033      0.012161      0.019666   \n",
       "Moy_tirs_5_matchs_ext                0.052485      0.032513      0.040864   \n",
       "Nb_jours_dernier_match_ext          -0.010823     -0.014791     -0.009586   \n",
       "Moy_nb_fautes_5_matchs_ext           0.001429     -0.000522      0.004898   \n",
       "Moy_corners_5_matchs_ext             0.038935      0.035584      0.041596   \n",
       "Moy_carton_J_5_matchs_ext           -0.018768     -0.011339     -0.005452   \n",
       "Moy_carton_R_5_matchs_ext           -0.055143     -0.053455     -0.053814   \n",
       "Points_moy_match_saison_dom          0.589135      0.597835      0.604469   \n",
       "Points_moy_match_saison_ext          0.002873     -0.003129     -0.000109   \n",
       "Pct_buts_par_tirs_cadres_5m_dom      0.286498      0.265960      0.283343   \n",
       "Pct_buts_par_tirs_cadres_5m_ext     -0.005556     -0.018109     -0.008980   \n",
       "note_att_dom                         0.848907      0.817622      0.897168   \n",
       "note_mil_dom                         1.000000      0.891477      0.960738   \n",
       "note_def_dom                         0.891477      1.000000      0.944144   \n",
       "note_gen_dom                         0.960738      0.944144      1.000000   \n",
       "note_att_ext                        -0.005677     -0.021201     -0.020507   \n",
       "note_mil_ext                         0.003940     -0.011268     -0.010257   \n",
       "note_def_ext                        -0.006122     -0.025875     -0.019936   \n",
       "note_gen_ext                        -0.008804     -0.024053     -0.021005   \n",
       "Res_an_dernier_A                    -0.017516     -0.001587     -0.014123   \n",
       "Res_an_dernier_D                     0.048059      0.052088      0.042980   \n",
       "Res_an_dernier_H                     0.278336      0.258537      0.273694   \n",
       "\n",
       "                                 note_att_ext  note_mil_ext  note_def_ext  \\\n",
       "Resultat_match                      -0.239922     -0.267816     -0.283858   \n",
       "Moy_cote_domicile                    0.651118      0.651711      0.640279   \n",
       "Max_cote_domicile                    0.636409      0.635442      0.626767   \n",
       "Min_cote_domicile                    0.662399      0.664862      0.649177   \n",
       "Moy_cote_exterieur                  -0.305410     -0.325617     -0.324405   \n",
       "Max_cote_exterieur                  -0.273524     -0.293665     -0.293255   \n",
       "Min_cote_exterieur                  -0.337090     -0.353072     -0.351092   \n",
       "Moy_cote_nul                         0.099542      0.082116      0.055446   \n",
       "Max_cote_nul                         0.085256      0.066730      0.041616   \n",
       "Min_cote_nul                         0.115549      0.098476      0.070701   \n",
       "Nb_buts_equipe_dom_an_dernier        0.035925      0.063561      0.059951   \n",
       "Nb_buts_equipe_ext_an_dernier        0.304820      0.333384      0.312951   \n",
       "Num_match_dom                       -0.006180     -0.011588     -0.012289   \n",
       "Moy_points_5_match_dom              -0.008169     -0.009289     -0.004433   \n",
       "Moy_buts_5_matchs_dom               -0.009789     -0.010092     -0.007812   \n",
       "Moy_tirs_cadres_5_matchs_dom        -0.005451     -0.005544      0.003170   \n",
       "Moy_tirs_5_matchs_dom                0.008520      0.021633      0.019142   \n",
       "Nb_jours_dernier_match_dom           0.010513      0.014201      0.011374   \n",
       "Moy_nb_fautes_5_matchs_dom          -0.001830      0.011561      0.018290   \n",
       "Moy_corners_5_matchs_dom            -0.005639      0.001974      0.005980   \n",
       "Moy_carton_J_5_matchs_dom            0.015144      0.025124      0.029976   \n",
       "Moy_carton_R_5_matchs_dom            0.018233      0.019732      0.026398   \n",
       "Num_match_ext                       -0.008113     -0.013388     -0.013313   \n",
       "Moy_points_5_match_ext               0.473607      0.498111      0.492551   \n",
       "Moy_buts_5_matchs_ext                0.505554      0.535118      0.489656   \n",
       "Moy_tirs_cadres_5_matchs_ext         0.464450      0.506355      0.459587   \n",
       "Moy_tirs_5_matchs_ext                0.401733      0.437962      0.400547   \n",
       "Nb_jours_dernier_match_ext           0.021116      0.021289      0.021040   \n",
       "Moy_nb_fautes_5_matchs_ext          -0.144901     -0.172632     -0.133799   \n",
       "Moy_corners_5_matchs_ext             0.286092      0.303818      0.284026   \n",
       "Moy_carton_J_5_matchs_ext           -0.051662     -0.066615     -0.073712   \n",
       "Moy_carton_R_5_matchs_ext           -0.048164     -0.079325     -0.101101   \n",
       "Points_moy_match_saison_dom         -0.006984     -0.000430     -0.000387   \n",
       "Points_moy_match_saison_ext          0.559410      0.590428      0.592374   \n",
       "Pct_buts_par_tirs_cadres_5m_dom     -0.008253     -0.006289     -0.010418   \n",
       "Pct_buts_par_tirs_cadres_5m_ext      0.260941      0.267641      0.247159   \n",
       "note_att_dom                        -0.021320     -0.005745     -0.014784   \n",
       "note_mil_dom                        -0.005677      0.003940     -0.006122   \n",
       "note_def_dom                        -0.021201     -0.011268     -0.025875   \n",
       "note_gen_dom                        -0.020507     -0.010257     -0.019936   \n",
       "note_att_ext                         1.000000      0.849454      0.816317   \n",
       "note_mil_ext                         0.849454      1.000000      0.890935   \n",
       "note_def_ext                         0.816317      0.890935      1.000000   \n",
       "note_gen_ext                         0.896797      0.960840      0.943518   \n",
       "Res_an_dernier_A                     0.272224      0.287640      0.280796   \n",
       "Res_an_dernier_D                     0.042393      0.077948      0.064023   \n",
       "Res_an_dernier_H                    -0.035764     -0.018424     -0.004345   \n",
       "\n",
       "                                 note_gen_ext  Res_an_dernier_A  \\\n",
       "Resultat_match                      -0.282362         -0.084498   \n",
       "Moy_cote_domicile                    0.695249          0.219029   \n",
       "Max_cote_domicile                    0.679335          0.211368   \n",
       "Min_cote_domicile                    0.707484          0.228177   \n",
       "Moy_cote_exterieur                  -0.335326         -0.148609   \n",
       "Max_cote_exterieur                  -0.302410         -0.138314   \n",
       "Min_cote_exterieur                  -0.365159         -0.154075   \n",
       "Moy_cote_nul                         0.091265         -0.020007   \n",
       "Max_cote_nul                         0.075396         -0.025497   \n",
       "Min_cote_nul                         0.109693         -0.010244   \n",
       "Nb_buts_equipe_dom_an_dernier        0.055566         -0.145754   \n",
       "Nb_buts_equipe_ext_an_dernier        0.330754          0.690131   \n",
       "Num_match_dom                       -0.004747         -0.040721   \n",
       "Moy_points_5_match_dom              -0.011566         -0.036378   \n",
       "Moy_buts_5_matchs_dom               -0.010576         -0.024735   \n",
       "Moy_tirs_cadres_5_matchs_dom        -0.007482         -0.039581   \n",
       "Moy_tirs_5_matchs_dom                0.015635         -0.017439   \n",
       "Nb_jours_dernier_match_dom           0.009539          0.025582   \n",
       "Moy_nb_fautes_5_matchs_dom           0.016655         -0.046320   \n",
       "Moy_corners_5_matchs_dom             0.001480         -0.046052   \n",
       "Moy_carton_J_5_matchs_dom            0.030465         -0.008550   \n",
       "Moy_carton_R_5_matchs_dom            0.024947          0.024550   \n",
       "Num_match_ext                       -0.005998         -0.040556   \n",
       "Moy_points_5_match_ext               0.509176          0.107148   \n",
       "Moy_buts_5_matchs_ext                0.534417          0.123583   \n",
       "Moy_tirs_cadres_5_matchs_ext         0.503527          0.117844   \n",
       "Moy_tirs_5_matchs_ext                0.436979          0.120009   \n",
       "Nb_jours_dernier_match_ext           0.015214          0.026492   \n",
       "Moy_nb_fautes_5_matchs_ext          -0.155243         -0.009493   \n",
       "Moy_corners_5_matchs_ext             0.303876          0.067839   \n",
       "Moy_carton_J_5_matchs_ext           -0.058278          0.055441   \n",
       "Moy_carton_R_5_matchs_ext           -0.076210         -0.009876   \n",
       "Points_moy_match_saison_dom         -0.000243         -0.027710   \n",
       "Points_moy_match_saison_ext          0.603858          0.153304   \n",
       "Pct_buts_par_tirs_cadres_5m_dom     -0.006281         -0.002056   \n",
       "Pct_buts_par_tirs_cadres_5m_ext      0.265436          0.052337   \n",
       "note_att_dom                        -0.018302         -0.034280   \n",
       "note_mil_dom                        -0.008804         -0.017516   \n",
       "note_def_dom                        -0.024053         -0.001587   \n",
       "note_gen_dom                        -0.021005         -0.014123   \n",
       "note_att_ext                         0.896797          0.272224   \n",
       "note_mil_ext                         0.960840          0.287640   \n",
       "note_def_ext                         0.943518          0.280796   \n",
       "note_gen_ext                         1.000000          0.291696   \n",
       "Res_an_dernier_A                     0.291696          1.000000   \n",
       "Res_an_dernier_D                     0.062859         -0.206498   \n",
       "Res_an_dernier_H                    -0.017398         -0.281773   \n",
       "\n",
       "                                 Res_an_dernier_D  Res_an_dernier_H  \n",
       "Resultat_match                          -0.028601          0.079533  \n",
       "Moy_cote_domicile                       -0.030618         -0.133929  \n",
       "Max_cote_domicile                       -0.032695         -0.129895  \n",
       "Min_cote_domicile                       -0.025637         -0.139209  \n",
       "Moy_cote_exterieur                      -0.030481          0.181058  \n",
       "Max_cote_exterieur                      -0.031519          0.164760  \n",
       "Min_cote_exterieur                      -0.028376          0.205087  \n",
       "Moy_cote_nul                            -0.051025          0.151283  \n",
       "Max_cote_nul                            -0.050220          0.144790  \n",
       "Min_cote_nul                            -0.051502          0.161562  \n",
       "Nb_buts_equipe_dom_an_dernier            0.017751          0.744862  \n",
       "Nb_buts_equipe_ext_an_dernier            0.122756         -0.113253  \n",
       "Num_match_dom                           -0.018244          0.002486  \n",
       "Moy_points_5_match_dom                   0.011330          0.128686  \n",
       "Moy_buts_5_matchs_dom                   -0.022639          0.137580  \n",
       "Moy_tirs_cadres_5_matchs_dom            -0.008059          0.147169  \n",
       "Moy_tirs_5_matchs_dom                    0.010166          0.146071  \n",
       "Nb_jours_dernier_match_dom              -0.008011          0.006649  \n",
       "Moy_nb_fautes_5_matchs_dom               0.021121         -0.084356  \n",
       "Moy_corners_5_matchs_dom                -0.003967          0.098388  \n",
       "Moy_carton_J_5_matchs_dom               -0.004459         -0.008544  \n",
       "Moy_carton_R_5_matchs_dom                0.020051         -0.063993  \n",
       "Num_match_ext                           -0.019852          0.002997  \n",
       "Moy_points_5_match_ext                   0.011430         -0.020270  \n",
       "Moy_buts_5_matchs_ext                   -0.005853         -0.019273  \n",
       "Moy_tirs_cadres_5_matchs_ext             0.033837         -0.005199  \n",
       "Moy_tirs_5_matchs_ext                    0.023532          0.029888  \n",
       "Nb_jours_dernier_match_ext               0.018773          0.003104  \n",
       "Moy_nb_fautes_5_matchs_ext              -0.027983         -0.019396  \n",
       "Moy_corners_5_matchs_ext                 0.014042          0.007344  \n",
       "Moy_carton_J_5_matchs_ext               -0.024606         -0.034244  \n",
       "Moy_carton_R_5_matchs_ext               -0.019245         -0.030862  \n",
       "Points_moy_match_saison_dom             -0.029454          0.140237  \n",
       "Points_moy_match_saison_ext             -0.007596         -0.042246  \n",
       "Pct_buts_par_tirs_cadres_5m_dom         -0.019197          0.057673  \n",
       "Pct_buts_par_tirs_cadres_5m_ext         -0.027406         -0.022992  \n",
       "note_att_dom                             0.030201          0.249991  \n",
       "note_mil_dom                             0.048059          0.278336  \n",
       "note_def_dom                             0.052088          0.258537  \n",
       "note_gen_dom                             0.042980          0.273694  \n",
       "note_att_ext                             0.042393         -0.035764  \n",
       "note_mil_ext                             0.077948         -0.018424  \n",
       "note_def_ext                             0.064023         -0.004345  \n",
       "note_gen_ext                             0.062859         -0.017398  \n",
       "Res_an_dernier_A                        -0.206498         -0.281773  \n",
       "Res_an_dernier_D                         1.000000         -0.274820  \n",
       "Res_an_dernier_H                        -0.274820          1.000000  \n",
       "\n",
       "[47 rows x 47 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_complet = df.copy()\n",
    "df_complet['Resultat_match'] = df_complet['Resultat_match'].replace(['H','D','A'], [2, 1,0])\n",
    "df_complet.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aa1U5-NXMal9"
   },
   "source": [
    "Les cotes à domicile sont très corrélées entre elles, on ne garde que celle qui est la plus corrélée avec le résultat du match : Min_cote_domicile.\n",
    "\n",
    "Idem pour les cotes à l'extérieur et nul, on ne garde que Min_cote_exterieur et Max_cote_nul.\n",
    "\n",
    "Les notes défense, milieu, attaque sont assez corrélées aussi, on garde note_def_ext et note_gen_dom.\n",
    "\n",
    "Le coefficient de cartons rouges sur 5 matches à l'extérieur est très proche de 0, nous allons donc supprimer cette variable.\n",
    "Idem pour Moy_nb_fautes_5_matchs_dom, Moy_carton_J_5_matchs_dom, Moy_carton_R_5_matchs_dom, Moy_nb_fautes_5_matchs_ext,\tMoy_corners_5_matchs_ext, Moy_carton_J_5_matchs_ext, Pct_buts_par_tirs_cadres_5m_dom, Pct_buts_par_tirs_cadres_5m_ext, Num_match_ext, Num_match_dom, Nb_jours_dernier_match_dom, Nb_jours_dernier_match_ext."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "iNXyTswFU1rr"
   },
   "outputs": [],
   "source": [
    "# Suppression des variables corrélées\n",
    "to_drop = [ 'Moy_cote_domicile', 'Max_cote_domicile','Moy_cote_exterieur','Max_cote_exterieur','Moy_cote_nul','Min_cote_nul','note_att_dom','note_mil_dom','note_def_dom','note_att_ext', 'note_mil_ext', 'note_gen_ext',\n",
    "  'Moy_carton_R_5_matchs_ext','Moy_nb_fautes_5_matchs_dom', 'Moy_carton_J_5_matchs_dom','Moy_carton_R_5_matchs_dom','Moy_nb_fautes_5_matchs_ext',\n",
    "   'Moy_corners_5_matchs_ext', 'Moy_carton_J_5_matchs_ext','Res_an_dernier_A','Res_an_dernier_H','Res_an_dernier_D','Pct_buts_par_tirs_cadres_5m_dom',\n",
    "   'Pct_buts_par_tirs_cadres_5m_ext','Num_match_ext','Num_match_dom','Nb_jours_dernier_match_dom','Nb_jours_dernier_match_ext']\n",
    "df.drop(to_drop, axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CjQIC7ERBB-g",
    "outputId": "db8eed6b-f362-45b0-8ba5-d2bd96b52d35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2178, 18)\n",
      "(220, 18)\n",
      "(2178, 1)\n",
      "(220, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DARIDOR\\AppData\\Local\\Temp/ipykernel_1176/1442730602.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  target['Resultat_match'] = target['Resultat_match'].replace(['H','D','A'], [2, 1,0])\n"
     ]
    }
   ],
   "source": [
    "# Création des dataframes de cible et de données\n",
    "target = df[['Resultat_match','Saison']]\n",
    "data=df.drop(['Resultat_match'],axis=1)\n",
    "\n",
    "# Remplacement des modalités de target par des nombres pour la modélisation\n",
    "target['Resultat_match'] = target['Resultat_match'].replace(['H','D','A'], [2, 1,0])\n",
    "\n",
    "# Création des dataframes d'entraînement et de test : entraînement sur les saisons 2015 à 2020 et test sur 2021 (saison non encore terminée)\n",
    "X_train_ = data.loc[data['Saison'] != '2021', :].drop('Saison',axis=1)\n",
    "X_test_ = data.loc[data['Saison'] == '2021', :].drop('Saison',axis=1)\n",
    "y_train = target.loc[target['Saison'] != '2021', :].drop('Saison',axis=1)\n",
    "y_test = target.loc[target['Saison'] == '2021', :].drop('Saison',axis=1)\n",
    "print(X_train_.shape)\n",
    "print(X_test_.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "# Standardisation des données\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train_)\n",
    "X_test = scaler.transform(X_test_)\n",
    "\n",
    "y_test = y_test.to_numpy().reshape(220,)\n",
    "y_train = y_train.to_numpy().reshape(2178,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u16nSbWQWb_5"
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   "source": [
    "Pourcentage de matchs par résultat"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% de matchs par résultat sur l'échantillon d'entrainement\n"
     ]
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       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-62672100-b34e-47aa-876f-88014d94f268 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-62672100-b34e-47aa-876f-88014d94f268');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   Resultat_match\n",
       "2        0.413636\n",
       "0        0.300000\n",
       "1        0.286364"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train\n",
    "df_y_train=pd.DataFrame(y_train, columns=['Resultat_match'])\n",
    "print(\"% de matchs par résultat sur l'échantillon d'entrainement\")\n",
    "display(df_y_train.Resultat_match.value_counts(normalize=True).to_frame())\n",
    "\n",
    "# Test\n",
    "df_y_test=pd.DataFrame(y_test, columns=['Resultat_match'])\n",
    "print(\"% de matchs par résultat sur l'échantillon de test\")\n",
    "display(df_y_test.Resultat_match.value_counts(normalize=True).to_frame())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pZ90f3w6WkAJ"
   },
   "source": [
    "Le résultat le plus courant est la victoire à domicile (44% sur l'échantillon d'entrainement et 41,3 % su l'échantillon de test. \n",
    "\n",
    "Notre modèle devra avoir un pourcentage de bonne modélisation au moins supérieur à 41,3 % pour avoir un intérêt par rapport à miser toujours sur la victoire de l'équipe à domicile."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5RtUHsUXtu3Z"
   },
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "id": "qKKQI71AtugU",
    "outputId": "3aa96c9f-c3c8-4f63-896c-6dee9798339a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.4727272727272727\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Modèle SVM avec les paramètres par défaut\n",
    "svm = svm.SVC()\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# Matrice de confusion et scores\n",
    "y_pred_svm = svm.predict(X_test)\n",
    "confusion_matrix = pd.crosstab(y_test, y_pred_svm, rownames = ['Classe réelle'], colnames = ['Classe prédite par SVM'])\n",
    "sns.heatmap(confusion_matrix, annot = True)\n",
    "print('Accuracy: ',metrics.accuracy_score(y_test, y_pred_svm));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "19BkXhKHaua-",
    "outputId": "9f827e9e-4e4b-4abd-cfd7-131a9d7f3ae7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.50      0.47      0.80      0.48      0.61      0.36        66\n",
      "          1       0.00      0.00      0.97      0.00      0.00      0.00        63\n",
      "          2       0.48      0.80      0.38      0.60      0.55      0.32        91\n",
      "\n",
      "avg / total       0.35      0.47      0.67      0.39      0.41      0.24       220\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Rapport de classification\n",
    "print(classification_report_imbalanced(y_test, y_pred_svm)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8R2-zC2AEdEZ",
    "outputId": "8dc716ec-fffc-4dd2-92ac-8b0d583da852"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "# Modèle SVM avec un Grid Search\n",
    "param_svm = {'C':[0.1,1,10], 'kernel':['rbf','linear', 'poly'], 'gamma':[0.001, 0.1, 0.5]}\n",
    "\n",
    "grid_svm = GridSearchCV(estimator=svm, param_grid=param_svm)\n",
    "grid_svm.fit(X_train,y_train)\n",
    "\n",
    "# Meilleurs paramètres\n",
    "print(grid_svm.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "E2IZ5AKME4up",
    "outputId": "588d040a-2659-4f7c-f5b7-22a8d6ef899d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.4727272727272727\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matrice de confusion et scores\n",
    "y_pred_grid_svm = grid_svm.predict(X_test)\n",
    "confusion_matrix = pd.crosstab(y_test,y_pred_grid_svm, rownames = ['Classe réelle'], colnames = ['Classe prédite par Grid_SVM'])\n",
    "sns.heatmap(confusion_matrix, annot = True)\n",
    "print('Accuracy: ',metrics.accuracy_score(y_test, y_pred_grid_svm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qls2WBtQZXeM"
   },
   "source": [
    "Le score est le même qu'avec les paramètres par défaut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I2jTjvBna8aq",
    "outputId": "1b372603-1a12-4a04-846f-6f31375ad69e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.50      0.38      0.84      0.43      0.56      0.30        66\n",
      "          1       0.00      0.00      1.00      0.00      0.00      0.00        63\n",
      "          2       0.46      0.87      0.29      0.61      0.51      0.27        91\n",
      "\n",
      "avg / total       0.34      0.47      0.66      0.38      0.38      0.20       220\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "# Rapport de classification\n",
    "print(classification_report_imbalanced(y_test, y_pred_grid_svm)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bPcuHGR3U9P8"
   },
   "source": [
    "#Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "id": "yI8NfchRYq3y",
    "outputId": "542e2433-0675-4e33-9774-d975fa875b18"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.4590909090909091\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Modèle Random Forest avec les paramètres par défaut\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Matrice de confusion et scores\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "confusion_matrix = pd.crosstab(y_test, y_pred_rf, rownames = ['Classe réelle'], colnames = ['Classe prédite par Random Forest'])\n",
    "sns.heatmap(confusion_matrix, annot = True)\n",
    "print('Accuracy: ',metrics.accuracy_score(y_test, y_pred_rf));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uazpkOkkZ17W"
   },
   "source": [
    "Score changeant à chaque compilation (compris entre 0.43 et 0.49)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0d7oLxfTbZrx",
    "outputId": "a066eb7c-2feb-468b-ca59-7e026e678eb6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.49      0.47      0.79      0.48      0.61      0.36        66\n",
      "          1       0.27      0.11      0.88      0.16      0.31      0.09        63\n",
      "          2       0.48      0.69      0.47      0.57      0.57      0.33        91\n",
      "\n",
      "avg / total       0.42      0.46      0.68      0.42      0.51      0.27       220\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Rapport de classification\n",
    "print(classification_report_imbalanced(y_test, y_pred_rf)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gON368_XU_aV",
    "outputId": "b19f8469-7472-4019-a181-3f5dc6f0a0ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_features': 'log2', 'min_samples_split': 56}\n"
     ]
    }
   ],
   "source": [
    "# Modèle Random Forest avec Grid Search \n",
    "\n",
    "param_rf = {'max_features': [\"sqrt\", \"log2\" ], 'min_samples_split': range(2, 60, 3)}\n",
    "\n",
    "grid_rf = GridSearchCV(estimator = rf, param_grid = param_rf)\n",
    "grid_rf.fit(X_train, y_train)\n",
    "\n",
    "# Meilleurs paramètres\n",
    "print(grid_rf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 303
    },
    "id": "xIKWegojaOj-",
    "outputId": "31a5b5ec-b5a8-427a-90bb-784080a07bba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.4681818181818182\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matrice de confusion et scores\n",
    "y_pred_grid_rf = grid_rf.predict(X_test)\n",
    "confusion_matrix = pd.crosstab(y_test,y_pred_grid_rf, rownames = ['Classe réelle'], colnames = ['Classe prédite par Grid_RF'])\n",
    "sns.heatmap(confusion_matrix, annot = True)\n",
    "print('Accuracy: ',metrics.accuracy_score(y_test, y_pred_grid_rf));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AT-mdwE5Vqm9",
    "outputId": "ce9f52b5-abaf-4a2b-d4c4-7706da0197e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.48      0.47      0.78      0.47      0.60      0.35        66\n",
      "          1       0.18      0.03      0.94      0.05      0.17      0.03        63\n",
      "          2       0.49      0.77      0.43      0.60      0.57      0.34        91\n",
      "\n",
      "avg / total       0.40      0.47      0.68      0.40      0.47      0.25       220\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Rapport de classification\n",
    "print(classification_report_imbalanced(y_test, y_pred_grid_rf)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2-cZ2zpJYgdf"
   },
   "source": [
    "Les résultats sont mauvais surtout pour modéliser les matchs nuls (modalité 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aEeVd5KvqQHJ"
   },
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "id": "_n4v0Kq8qTdi",
    "outputId": "aa8b54f9-03c9-4f3c-acb5-d5dc5b7e8710"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.4\n"
     ]
    },
    {
     "data": {
      "image/png": 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9HHjIfoy5Z2za4VWEW/9wNXvs/mV69NiQN1+fxDm/uZibb7kz7bAqSlW37qz/szOhqgokVjw+npXPPEX3MY9QP28uXS/NLVy54l+Ps/z2USlHW1pZ/DWkKPDbUdLEiNhJ0vMRsUOybXJE9G/inJHAzclqeKvvuz0iChYR2+oIui2ZseidtEOoeHP23jLtENYKPf7+zxYPf5/8zGFF55zd5txVluF2MSPo+ZK2IPkFI+kw4N2mToiIRp8UUExyNjMrtzI+rLtoxSToE8jVhftJeht4A/h+SaMyMyuzoA3WoJMFPvaRtB5QFREflD4sM7Pyqs9gUbXgNDtJQyVtACwDLpP0nKT9Sh+amVn51KOiW7kUMw/6fyLifWA/oDtwFHB+SaMyMyuzQEW3cimmBr0qmgOA0RExTVL2ijVmZi1Q1xZr0MCzkh4C+gLDJa1PNi94mpmtsSwmtWIS9LFAf+D1iFgmaUPgh6UNy8ysvLKYoIupQX8ZeDkiFkn6PvArYHFpwzIzK68s1qCLSdDXAsskbQ+cRm4B/9EljcrMrMzqVXwrl2ISdG3k7gf/BnBVRFyNn6hiZhUmi9PsiqlBfyBpOLm7B3eXVAW0L21YZmbllcUnLhYzgj4cWAEcGxFzgBrgopJGZWZWZvVS0a1cirnVew5wad77t3AN2swqTAbv9C7qVu+BkiZKWiLpI0l1kjyLw8wqSn0zWrkUU4O+Cvgu8GdgAHA08LlSBmVmVm4ZfGZscc8kjIgZQHVE1CUPfB1c2rDMzMqrDhXdyqWYEfQySR2AyZIuJLdYfxoPmzUzK5m2OoI+CqgGTgSWApsCh5YyKDOzcmutGrSkTSWNk/SSpGmShibbz5b0tqTJSTugUEzFzOKYmbxcDpxT6Hgzs7aoFWdx1AKnRcRzyeJyz0p6ONl3WURcXGxHjSZoSS/SRMwRsV2xH2JmlnWtVeKIiHdJntsaER9Img70XpO+mhpBf31NOjQza4tKMX1OUh9gB2ACsBtwoqSjgUnkRtkLmzq/qRp0e6AmImbmN3J3EhZzcdHMrM2oU/FN0hBJk/LakNX7k9QZuBs4JXkq1bXAFuSWb34XuKRQTE0l6MuB9xvY/n6yz8ysYjTnImFEjIiIAXltRH5fktqTS863RcRfyJ0zN5mqXA/cAOxcKKamEnTPiHhx9Y3Jtj6FOjYza0tacRaHgJHA9Ii4NG97r7zDDgGmFoqpqVJF1yb2dSrUsZlZW9KKszh2Izc9+UVJk5NtZwLfk9Q/+ag3geMLddRUgp4k6biIuCF/o6QfAc+uSdRmZlnVirM4noAGbzd8oLl9NZWgTwHukXQk/03IA4AO5IbnZmYVI4vPJGw0QUfEXGBXSXsB2yab74+IR8sSmZlZGWVxwf5i7iQcB4wrQyxmZqnJ4locns9sZkYbK3Gk7U/rfibtECresA7d0g6h4nX5w3Vph2BFyuITVTKboM3Myqk+gynaCdrMjDZ6kdDMbG3gGrSZWUZ5FoeZWUa5Bm1mllHZS89O0GZmgGvQZmaZVZfBMbQTtJkZHkGbmWWWLxKamWVU9tKzE7SZGeASh5lZZvkioZlZRrkGbWaWUdlLz07QZmaAR9BmZpnli4RmZhkVHkGbmWWTZ3GYmWWUSxxmZhlVHx5Bm5llUvbSsxO0mRngaXZmZpmVxVkcVWkHYGaWBbVE0a0pkjaVNE7SS5KmSRqabN9Q0sOSXk3+7VYoJidoMzNyI+hi/yugFjgtIrYBBgInSNoGOAN4JCK2Ah5J3jfJCdrMjNw0u2JbUyLi3Yh4Lnn9ATAd6A18AxiVHDYK+GahmJygzcyAiCi6SRoiaVJeG9JQn5L6ADsAE4CeEfFusmsO0LNQTL5IaGZG82ZxRMQIYERTx0jqDNwNnBIR70vKPz8kFfxAJ2gzM1r3Vm9J7ckl59si4i/J5rmSekXEu5J6AfMK9eMSh5kZuRF0sa0pyg2VRwLTI+LSvF33Acckr48B7i0Uk0fQa6B9rx5seukw2vXoCgEL7niQBTf/jY5b96H3eSdQtW5HVs6ex1unXEz9kuVph9tm/eziUxm4z0AWzV/Ej/bJlfg233pzhp1/Mh3X68TcWXP57Unns2zJspQjbfvq6uo4/NiT2XijHlxz0Tmc9bvLmPbvV4kI+mzam/N+eRrrrtsp7TBLKlrvVu/dgKOAFyVNTradCZwP/EnSscBM4DuFOvIIeg1EbR3vnnsTr+x7AjMO+Rk9jjqQdbbclJrzT2bOBaN4dfBJLB77FBsN+VbaobZpY//8MMO/f+Yntp120TBu+N1IjtvneJ548Em+8+NvpxRdZbn1z/eyeZ/Pfvz+9JOH8JdR13DP6Gvp1XNjbr/7bylGVx6tOIvjiYhQRGwXEf2T9kBELIiIvSNiq4jYJyL+UygmJ+g1UPveQpZPew2A+qXL+fC1WbT/THfW6bsJSydMBWDJE5Pp8rVd0wyzzXtxwou8v+iDT2yr2byGKU+/CMCzjz3H7gd8JY3QKsqcee/x2L+e4dCD9v94W+f11gNyo8oPV6wg7/pWxWrFedCtxgm6hdrXbEynbbZg2eSX+fDVt9hgv4EAdDlgN9r36pFydJVn5itvstv+uV98e3x9dzbaZKOUI2r7Lrjiek796bFIn0wHvzrvUvY46AjemDmbIw47OKXoyqe1atCtqWQJWlI/SXsnU03ytw8u1WeWW9W6Hdns2uG885sbqF+ynNm/+D3dv38AW/7tMqo6dyJW1qYdYsW56LRLOfjog7j2gavp1LkTtf6OW2T8kxPYsFtXvtBvq0/tO/eXpzLu3lvZvM+mPPjIYylEV151UV90K5eSXCSUdDJwArk7aEZKGhoRq65Y/hZ4sJHzhgBDAM7a8Isctv5mpQivdbSrZrPrhrPor+N5f+xTAKx4bTZvHP2/AHTouwkb7LVTmhFWpFmvzeL0I4cDUNO3NwP33jnliNq256e8xPgnnubxpyay4qOVLF26jNPPuZALfv0LAKqrq/naPntw0213cciB+6UcbWllcbGkUs3iOA7YMSKWJHfS3CWpT0RcATRazcqf/D2lz0HZ+7bybHrByXw4YxbzR/53pkx19y7ULVgMEj1PPJwFt/09xQgrU9fuXVm0YBGSOHLoEfztD/enHVKbNuwnP2TYT34IwDPPTeGWO+7m/P/9OW/NfofP1mxCRDDuiafpu1lNypGW3tq0YH9VRCwBiIg3Je1JLklvRhMJuq1Yd8A2dDt0EMunv8FWD1wBwJwLR9Oh7yb0OOpAABaPfYqFf/5HmmG2eb+8ajjbf3k7umzYhTsn3saoS/5Ap/U68o1jcvXQx//+BA/+cWzKUVaeiODMcy9h6dJlRASf37IvZ/38xLTDKrnspWdQK879+2+n0qPAqRExOW9bO+Am4MiIqC7UR9ZH0JVgWO2KtEOoeA9Ovi7tENYK7Xts3uKB3269BxWdc558+9GyDDRLNYI+mtySex+LiFrgaEnXl+gzzczW2FrzRJWImN3EvidL8ZlmZi1RztkZxfKt3mZmrF2zOMzM2pRSXI9rKSdoMzPWohq0mVlb4xG0mVlG1RVcp678nKDNzFi77iQ0M2tTPIvDzCyjPII2M8soj6DNzDLKI2gzs4zyrd5mZhnlEoeZWUaFR9BmZtnkW73NzDLKt3qbmWWUR9BmZhlVV+8atJlZJnkWh5lZRmWxBl2VdgBmZllQTxTdCpF0k6R5kqbmbTtb0tuSJiftgEL9OEGbmZEbQRfbinALMLiB7ZdFRP+kPVCoE5c4zMxo3YuEEfGYpD4t7ccjaDMzWrfE0YQTJU1JSiDdCh3sBG1mRvNKHJKGSJqU14YU8RHXAlsA/YF3gUsKneASh5kZzVtuNCJGACOa039EzF31WtINwJhC53gEbWZGbh50sf+tCUm98t4eAkxt7NhVPII2M6N1F+yXdAewJ9BD0mzg18CekvoDAbwJHF+oHydoMzOgvhWXG42I7zWweWRz+3GCNjMjm3cSOkGbmZHNBK0sBtVWSRqSXN21EvF3XHr+jrPDszhaVzFzIa1l/B2Xnr/jjHCCNjPLKCdoM7OMcoJuXa7blZ6/49Lzd5wRvkhoZpZRHkGbmWWUE7SZWUY5QbcCSYMlvSxphqQz0o6nEjX0CCFrXZI2lTRO0kuSpkkamnZMazvXoFtIUjXwCrAvMBuYCHwvIl5KNbAKI2l3YAkwOiK2TTueSpSsttYrIp6TtD7wLPBN/7+cHo+gW25nYEZEvB4RHwF3At9IOaaKExGPAf9JO45KFhHvRsRzyesPgOlA73SjWrs5Qbdcb2BW3vvZ+H9qa+OS5+ntAExIN5K1mxO0mX2CpM7A3cApEfF+2vGszZygW+5tYNO89zXJNrM2R1J7csn5toj4S9rxrO2coFtuIrCVpL6SOgDfBe5LOSazZpMkcovKT4+IS9OOx5ygWywiaoETgbHkLqr8KSKmpRtV5UkeIfQU8HlJsyUdm3ZMFWg34ChgkKTJSTsg7aDWZp5mZ2aWUR5Bm5lllBO0mVlGOUGbmWWUE7SZWUY5QVtmSDpC0mfTjsMsK5ygK4ikz0i6U9Jrkp6V9ICkz0nqk/VV4JJpcxtHxFuN7L9F0mHJ6xslbZO8PrOMYTYU1ye+W0nHJd99tyTmtyWtk+zrIenNvPNC0kl5514l6Qfl/hksu5ygK0Ryk8E9wPiI2CIidgSGAz3Tjaxxktqteh0RIyPi8mLOi4gf5a2wVtYEnR9zA/uOAk4C9o+IhcnmOuB/GjllHjA0ucHJ7FOcoCvHXsDKiLhu1YaIeCEiHs8/KBm5PS7puaTtmmzvJemx5OaEqZK+Kqk6GQVOlfSipGHJsVtIejAZKT4uqd/qwUg6W9IfJD0l6VVJxyXb90zOuQ94KfmMiyRNlDRF0vHJcUpGlC9L+gewcV7f4yUNkHQ+0CmJ+bZk3/clPZNsuz5ZDnb12N6UdGHyMz0jactk+0GSJkh6XtI/JPVc7Wd5EvhDQ1++pO8AZwD7RcT8vF2XA8MaSezvAY8AxzTUp1mjowFrc7Ylt35vIfOAfSPiQ0lbAXcAA4AjgLERcV6S1NYF+gO9V62/LKlr0scI4McR8aqkXYBrgEENfNZ2wEBgPeB5Sfcn278EbBsRb0gaAiyOiJ2SUsCTkh4it5La54FtyP0V8BJwU37nEXGGpBMjon8S39bA4cBuEbFS0jXAkcDoBmJbHBFflHQ0uST6deAJYGBEhKQfAb8ATkuO3wb4SkQsb6CvzYCrgB0iYs5q+95K+j0K+FsD514A/F3STQ3ss7WcE/Tapz1wlaT+5P78/lyyfSJwk3KL5fw1IiZLeh3YXNKVwP3AQ8qtdLYr8OdcVQWAdRr5rHuThLZc0jhya2cvAp6JiDeSY/YDtltVXwa6AFsBuwN3REQd8I6kR4v42fYGdgQmJrF1IvcLqSF35P17WfK6BvijcgvXdwDeyDv+vkaSM+RGwv8BvpPXV77fAfeS+w4/ISJelzSB3C9Is09wiaNyTCOXnAoZBswFtic3cu4AHy+Ivzu5lfhukXR0UkfdHhgP/Bi4kdz/M4sion9e27qRz1p9HYFV75fmbRNwUl5ffSPioSJ+joYIGJXX1+cj4uwiYlv1+krgqoj4InA80DHvmPyYV7cMOAD4saQjP/VBEa8Ck8kl8Ib8Fjg9id/sY07QleNRYJ2kZACApO0kfXW147oA70ZEPbk/u6uTYzcD5kbEDeQS8Zck9QCqIuJu4FfAl5L1gd+Q9O3kPEnavpGYviGpo6TuwJ7kRumrGwv8JBm5o9ysk/WAx4DDkxp1L3I19oasXHUuuXruYZI2TvraMPm5GnJ43r9P5X03q5aKbVZdOCLmAYOB30rav4FDzgN+1si5/yZXwjmoOZ9plc8JukJEbtWrQ4B9lJtmN43cn9ar10SvAY6R9ALQj/+ODPcEXpD0PLmkdQW5J8OMlzQZuJXcrBDI1XWPTfqYRuOP+JoCjAOeBv4vIt5p4JgbySWn55SbrnY9udLbPcCryb7R/DeJrm4EMEXSbcnMjl+RK8VMAR4GejVyXrfkmKHk/qoAOJtc6eZZYH4j5zUqKdscTK5UtPNq+6YBzzVx+nnkSixmH/NqdlYSks4GlslOIAAAAABESURBVETExWnHsjrl5iIPWG22hVnmeARtZpZRHkGbmWWUR9BmZhnlBG1mllFO0GZmGeUEbWaWUU7QZmYZ5QRtZpZR/w8LPzs18aosvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Modèle KNN avec les paramètres par défaut\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# Matrice de confusion et scores\n",
    "y_pred_knn = knn.predict(X_test)\n",
    "confusion_matrix = pd.crosstab(y_test, y_pred_knn, rownames = ['Classe réelle'], colnames = ['Classe prédite par KNN'])\n",
    "sns.heatmap(confusion_matrix, annot = True)\n",
    "print('Accuracy: ',metrics.accuracy_score(y_test, y_pred_knn));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bHVGjrUofV8O"
   },
   "source": [
    "Le score est inférieur à SVM et à miser toujours sur une victoire à domicile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A-bAUy6WqZzt",
    "outputId": "3260248c-7013-49a7-df3d-e71c87f13f6d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.40      0.47      0.70      0.43      0.57      0.32        66\n",
      "          1       0.29      0.22      0.78      0.25      0.42      0.16        63\n",
      "          2       0.45      0.47      0.60      0.46      0.53      0.28        91\n",
      "\n",
      "avg / total       0.39      0.40      0.68      0.39      0.51      0.26       220\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Rapport de classification\n",
    "print(classification_report_imbalanced(y_test, y_pred_knn)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kV28sPW_rFf_",
    "outputId": "e696492b-6651-4146-c1a8-c4caae8d87f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'algorithm': 'auto', 'metric': 'manhattan', 'n_neighbors': 55, 'weights': 'distance'}\n"
     ]
    }
   ],
   "source": [
    "# Modèle KNN avec GridSearch\n",
    "\n",
    "param_knn = {'n_neighbors' : [k for k in range(5,60,5)],\n",
    "                  'weights' : ['uniform', 'distance'],\n",
    "                 'algorithm' : ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
    "                 'metric' : ['minkowski', 'manhattan', 'chebyshev', 'euclidean']}\n",
    "\n",
    "grid_knn = GridSearchCV(estimator = knn, param_grid = param_knn)\n",
    "grid_knn.fit(X_train, y_train)\n",
    "\n",
    "# Meilleurs paramètres\n",
    "print(grid_knn.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "fjuw52UAr5KH",
    "outputId": "27dd62a7-80c1-42d2-fc15-00d4dd6be41b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.4727272727272727\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matrice de confusion et scores\n",
    "y_pred_grid_knn = grid_knn.predict(X_test)\n",
    "confusion_matrix = pd.crosstab(y_test,y_pred_grid_knn, rownames = ['Classe réelle'], colnames = ['Classe prédite par Grid_KNN'])\n",
    "sns.heatmap(confusion_matrix, annot = True)\n",
    "print('Accuracy: ',metrics.accuracy_score(y_test, y_pred_grid_knn));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RJ8KRTYSgXLc"
   },
   "source": [
    "Score identique au SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DN7tiL2dr8FE",
    "outputId": "ea5f3b32-1219-4d5f-c4af-957828c7ee3d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.53      0.42      0.84      0.47      0.60      0.34        66\n",
      "          1       0.33      0.16      0.87      0.22      0.37      0.13        63\n",
      "          2       0.48      0.73      0.45      0.58      0.57      0.34        91\n",
      "\n",
      "avg / total       0.45      0.47      0.69      0.44      0.52      0.28       220\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Rapport de classification\n",
    "print(classification_report_imbalanced(y_test, y_pred_grid_knn)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hMXQqbSqtKdl"
   },
   "source": [
    "# Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ooWrYYXLtQD3",
    "outputId": "4e0c0dcd-4ef5-4c15-d9b2-5e769ea050af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(n_estimators=1000, oob_score=True)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc = BaggingClassifier(n_estimators = 1000, oob_score = True)\n",
    "bc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wXlC2w3ttTSZ",
    "outputId": "da6df79b-0256-4b74-81db-4346e0321d62"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4921946740128558"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Erreur Out Of Bag\n",
    "bc.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B_EhjvLNtp3o",
    "outputId": "199f90c4-56fe-457b-e0ad-13085ed0453c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4727272727272727"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Score\n",
    "bc.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n_QlhxPEt2D0"
   },
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w_8Gj0z0t5bW",
    "outputId": "aa6f7cdc-bb17-4d96-a1e8-017876ac272e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(objective='multi:softprob')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boost = XGBClassifier()\n",
    "boost.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "id": "_DwTyVmZuLEw",
    "outputId": "19907d99-ae25-4dcf-f4d5-b094d75b7192"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.4863636363636364\n"
     ]
    },
    {
     "data": {
      "image/png": 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fLGPsU+P4dv++aYdSUeqWrWDZcy/T6aDdAKjpvAkb7dqLpaPWezhvq9DSc3G0hGJeebWDpFGSXk7Wd5Z0SYFzpjVSpgNdGjsvIgZHRJ+I6LPzJj2b/cuU03FX/ozFs+bz1K2Pr922Rfet1i5//dA+LH7j7TRCq0hbbNGZTp06AtCuXTsO6XcgM2dm+494a1DTuSPVHTsAoHZt6HjgLnwyax4AnY/ch6VPTiJWrkozxLLJYgu6mD7oPwH/CfwRICKmSfoz8OsmzulCfoKldceYiQqY7L9Hn97s+a8H8vaMufzi8SsAePx/72Hv4w7iS1/ZmsjleH/+Eh64+JaUI60cXbt24bZbr6W6uoqqqioeeOAxhj/+ZNphtXq1XTaj53Wno6oqqKrivcfGsfTJfIt586P25+3rh6UcYflk8ZZzMQm6Q0RMWOeVV6sbOzjxF2Dj5P2FnyFpbPHhZdObk2ZyTvcffm77jLGf+3WthUyfPoM993Kffkv7eMZcXu5/boP7ZhzzX2WOJl11uTR6fJtWTIJeIqknyR8YSccAC5o6ISIanUYvIk5oVoRmZmVQxpd1F62YBH0q+WfOd5Q0H3gT+FFJozIzK7Mge8PsinlQZTZwiKSNgKqIWF76sMzMyiuXwU7oYkZxnCmpI7ACuEbSZEn9Sx+amVn55FDRpVyK6RX/t4hYBvQnP+n0ScAVJY3KzKzMAhVdyqWYPug10RwB3BkRr2idIR1mZq1dXWvsgwZelPQE0AO4UNImZPOGp5nZF5bFpFZMgh5I/jVXsyNihaTOQMXMp2FmBtlM0MX0Qe8DzIyIpZJ+BFwCfFDasMzMyiuLfdDFJOibgBWSdgF+QX7y/jtLGpWZWZnlVHwpl2IS9OrIz+B9FHB9RNyA36xiZhUmi8PsiumDXi7pQvJPDx4oqQqoLW1YZmblVZd2AA0opgV9HLASGBgRC8m/Lvx3JY3KzKzMclLRpVyKedR7IXB1vfV/4D5oM6swGXzSu6hHvb8paaKkDyX9U1KdJI/iMLOKkmtGKZdi+qCvB34I3A/0AU4GdihlUGZm5ZbBd8YW907CiJgFVEdEXUTcDhxW2rDMzMqrDhVdmiKpnaQJkl6S9IqkXybbe0gaL2mWpHsltSkUUzEJekVS0VRJ/yvp7CLPMzNrNVpwHPRK4OCI2IX8U9iHSfomcCVwTURsT/51gI2+2GSNYhLtSUA1cBrwEbAt8K9FnGdm1mq0VB905H2YrNYmJYCDgQeS7UOAowvFVMwojrnJ4sfALwsdb2bWGjVnFIekQcCgepsGR8TgevurgReB7YEbyD+BvTQi1rzPdR7QrdB1Gk3QkqY3FXNE7FyocjOz1qI5NwmTZDy4if11wK6SNgWGATt+kZiaakF/54tUaGbWGpVi+FwyydwY8pPObSqpJmlFbwPML3R+U33QtcA2ETG3fkkqLmZ4nplZq1Gn4ktTJH0paTkjqT1wKDADGAMckxx2CvBIoZiaStDXAssa2L4s2WdmVjFa8EGVrsAYSdOAicDIiPgLcD5wjqRZ5F8feGuhippqCXeJiOnrboyI6ZK6F47RzKz1aKkujoiYBuzWwPbZwF7NqaupBL1pE/vaN+ciZmZZ19rm4pgk6afrbpT0E/LDR8zMKkYWJ+xvqgV9FjBM0ol8mpD7AG2A75U6MDOzcsriOwkbTdARsQjYV9JBwNeTzcMjYnRZIjMzK6MsTthfzJOEY8gPDzEzq1hZnM3O45nNzGhlXRxpe2n1e2mHUPGqqzwpYantPu2qtEOwImVxFEdmE7SZWTnlMpiinaDNzGilNwnNzDYE7oM2M8soj+IwM8so90GbmWVU9tKzE7SZGeA+aDOzzKrLYBvaCdrMDLegzcwyyzcJzcwyKnvp2QnazAxwF4eZWWb5JqGZWUa5D9rMLKOyl56bfmmsmdkGI0cUXZoiaVtJYyS9KukVSWcm2ztLGinp9eTnZoVicoI2MyN/k7DYUsBq4BcRsRPwTeBUSTsBFwCjIqIXMCpZb5ITtJkZEM34X5P1RCyIiMnJ8nJgBtANOAoYkhw2BDi6UEzugzYzozSjOCR1B3YDxgNdImJBsmsh0KXQ+W5Bm5nRvC4OSYMkTapXBq1bn6SNgQeBsyJiWf19EREUcV/SLWgzMyAXxbegI2IwMLix/ZJqySfnuyPioWTzIkldI2KBpK7A4kLXcQvazIx8c7bY0hRJAm4FZkTE1fV2PQqckiyfAjxSKCa3oM3MaNEHVfYDTgKmS5qabLsIuAK4T9JAYC5wbKGKnKDNzKDg6Iyi64l4FmjsDYf9mlOXE7SZGbA6g88SOkGbmdFyLeiW5ARtZoanGzUzy6xoxjC7cnGCNjPD042amWWWJ+w3M8sot6AryLlXncM3D/kmS5cs5SeH5B/DP/mckzjyhMNZ+u4HANx65W1MGD0xzTArSlVVFc8/N5y3317I974/IO1wKsKbc+dx7n/9z9r1eW8v4LSfnMSid97lqXHjqamtYdtuXfn1RefQcZONU4y09NwHXUH+dv9IHrnjUc6/9rzPbH/gTw9x/x8fSCmqynb6aQN5beasik8U5dRju214cMgNANTV1XHw0SfR71v78ubceZz18wHU1FRz9Y23cstd93LOfwxMOdrSyuIoDs/F8QVNHz+dZUuXpx3GBqNbt604/PCDuf32oWmHUrFemDSVbbt1ZeuturDf3ntQU1MNwM5f25FFi5ekHF3ptdR80C3JCbqFHf3j7/KnkTdz7lXnsHEnt/RaylW/u5wLL/otuVwW2zmV4a+jnuKIQ771ue3Dhj/B/vvsmUJE5dVSr7xqSSVL0JJ2lNQvmRO1/vbDSnXNtD1252OctN+PGdT/33lv8Xv8/NLPTRFrX8ARh/fjnXfeZcqU6WmHUrFWrVrF2GfH0//gAz6z/Y9DhlJdXc13+h+UUmTlUxe5oku5lCRBSzqD/FR6pwMvSzqq3u7fNnHe2kmw5380rxShldT7S5aSy+WICIb/+a/suOuOaYdUEfbZtw9HHnkoM2c+x1133kDfvvtx++3XpR1WRXnmhUl8dYeebNH50/eYPjx8JE+Pm8CVl51HfgbNyrYhdXH8FNgjIo4G+gKXrnmzLY3P8kREDI6IPhHRp9tG25QotNLpvGXntcv7H7Yfc2bOSS+YCnLppVfSc/u96N17X046+VTGjh3HgAFnFj7Rivb4yLEccWjftevPvjCJ2/58P3+48jLat2uXXmBllIsoupRLqUZxVEXEhwARMUdSX+ABSdvRRIJuTS6+/kJ22WdnOnXuxD0T72bI7+9il312pufXekIEC99axDUXuJVn2bfi4094fuIULjvvjLXbfnP1jfxz1Sp+etbFQP5G4WXnnZ5WiGWRvUF2oFKM/ZM0GjgnIqbW21YD3AacGBHVherot03/LH5eFeXZJTPSDqHifTjvqbRD2CDUbvGV9W747dft4KJzzrj5o8vS0CxVC/pkYHX9DRGxGjhZ0h9LdE0zsy9sg3mSMCIavcMXEeNKcU0zs/VRztEZxfKThGZmeMJ+M7PM8lwcZmYZtcH0QZuZtTZuQZuZZVRdBuez82RJZma07JOEkm6TtFjSy/W2dZY0UtLryc/NmqoDnKDNzIAWn4vjDmDdieEuAEZFRC9gVLLeJCdoMzNatgUdEU8D762z+ShgSLI8BDi6UD1O0GZmNK8FXX/mzaQUM7dwl4hYkCwvBLoUOsE3Cc3MoFmz1EXEYGDwF71WRISkghd0gjYzoyyPei+S1DUiFkjqCiwudIK7OMzMKMuE/Y8CpyTLp5B/qUmT3II2MwOiBVvQkoaSf1nJFpLmAZcBVwD3SRoIzAWOLVSPE7SZGS37qHdEHN/Irn7NqccJ2swMP+ptZpZZnizJzCyj6nLZm4vDCdrMDE/Yb2aWWe6DNjPLKPdBm5lllFvQZmYZ5ZuEZmYZ5S4OM7OMcheHmVlGNWe60XJxgjYzw+Ogzcwyyy1oM7OMypV+wv5mc4I2M8M3Cc3MMiuLCVpZDKq1kjQoeZmklYg/49LzZ5wdfidhyyrm1eu2fvwZl54/44xwgjYzyygnaDOzjHKCblnutys9f8al5884I3yT0Mwso9yCNjPLKCdoM7OMcoJuAZIOkzRT0ixJF6QdTyWSdJukxZJeTjuWSiVpW0ljJL0q6RVJZ6Yd04bOfdDrSVI18HfgUGAeMBE4PiJeTTWwCiPpQOBD4M6I+Hra8VQiSV2BrhExWdImwIvA0f4up8ct6PW3FzArImZHxD+Be4CjUo6p4kTE08B7acdRySJiQURMTpaXAzOAbulGtWFzgl5/3YC36q3Pw19qa+UkdQd2A8anG8mGzQnazD5D0sbAg8BZEbEs7Xg2ZE7Q628+sG299W2SbWatjqRa8sn57oh4KO14NnRO0OtvItBLUg9JbYAfAo+mHJNZs0kScCswIyKuTjsec4JebxGxGjgN+Bv5myr3RcQr6UZVeSQNBZ4HekuaJ2lg2jFVoP2Ak4CDJU1NyhFpB7Uh8zA7M7OMcgvazCyjnKDNzDLKCdrMLKOcoM3MMsoJ2jJD0gmSvpx2HGZZ4QRdQSRtJekeSW9IelHS45J2kNQ967PAJcPmtoyIfzSy/w5JxyTLt0jaKVm+qIxhNhRXn2TmtzbJek9JsyV1TNb3kjRW0uuSJksaLukbyb7LJc1PhrO9JukmSf7/pK3lL0OFSB4yGAaMjYieEbEHcCHQJd3IGiepZs1yRNwaEdcWc15E/KTeDGtlTdD1Y05imQQ8BZybbLoBuDgilknqAtwHXBQRvSJid+B/gJ71qrgmInYFdgK+AXyr1L+DtR5O0JXjIGBVRNy8ZkNEvBQRz9Q/KGlNP5O05iZL2jfZ3lXS00lr7mVJB0iqTlquL0uaLuns5NiekkYkrfRnJO24bjBJ6/AuSc8nrcefJtv7Juc8CryaXON3kiZKmibpZ8lxknR9Ms/2k8CW9eoem7RcrwDaJzHfnez7kaQJybY/JtPBrhvbHEn/m/xOEyRtn2z/F0njJU2R9GSSYOv/LuOAuxr47C8CfirpPKAmIoYm208DhkTEc/X+TZ6NiIcbqKMN0A54v4F9tqGKCJcKKMAZ5FtjDe3rDrycLHcA2iXLvYBJyfIvyLf8AKqBTYA9gJH16tk0+TkK6JUs7w2MbuCalwMvAe2BLcjP+Lc10Bf4COiRHDcIuCRZbgtMAnoA3wdGJrFsDSwFjkmOGwv0SZY/rHfNrwKPAbXJ+o3AyQ3ENqfe73oy8JdkeTM+fXjrJ8Dv6/0uLwLtm/j8fwbUAb3rbXsIOKqJcy4nP2/LVPKJ+c9pf49cslU+859rtkGoBa6XtCv5hLJDsn0icFsyWc7DETFV0mzgK5L+AAwHnkhmOtsXuD/fqwLkE2tDHomIj4GPJY0hP3f2UmBCRLyZHNMf2HlN/zLQifwfjgOBoRFRB7wtaXQRv1s/8n9UJiaxtQcWN3Ls0Ho/r0mWtwHuVX7i+jbAm/WOfzT5XRpzOLCIfFfFzIYOkDQe6Ag8ERFr3lZyTURclXzuD0j6YUTc08R1bAPiLo7K8Qr55FTI2eQTyS5AH/KJiMhPiH8g+RbdHZJOjoj3k+PGAj8HbiH/nVkaEbvWK19t5FrrziOwZv2jetsEnF6vrh4R8UQRv0dDRL5LYU1dvSPi8iJiW7P8B+D6iPgG+RZxu3rH1I/5sxeVvkP+D8u3gd9J6pDsegXYfe1FIvYGLk2O/WwwEauAEeT/DcwAJ+hKMhpoK2nQmg2SdpZ0wDrHdQIWRESO/MQ41cmx2wGLIuJP5BPx7pK2AKoi4kHgEmD3yM8P/KakHyTnSdIujcR0lKR2kjYn37UxsYFj/gb8e9KCJBl1shHwNHBc0kfdlXwfe0NWrTmXfNfLMZK2TOrqnPxeDTmu3s/n6302a6aKPaWR8z5DUnvgauDUiJgOPAJcnOy+Afjxmn7+RAcakNzk3Q94o5jr2obBXRwVIiJC0veAayWdD3xCvq/1rHUOvRF4UNLJ5Ftsa1qGfYH/lLSK/Lv/Tib/Zpjb6w39ujD5eSJwk6RLyHeZ3EO+v3ld04Ax5Pug/zsi3pa0wzrH3EK+j3xykqTeAY4mPyLlYOBV4B98mkTXNfzwX9MAAAClSURBVBiYJmlyRJyYxPREEvMq4FRgbgPnbSZpGrASOD7Zdjn5rpv3yf/B69HINeu7FBgWn44quRx4SdIdEfG6pOOAKyV1I9/dsgT4Vb3zz5b0I/Kf4zTy/z5mgGezsxKRdDn5G3hXpR3LuiTNIX+TcUnasZg1xV0cZmYZ5Ra0mVlGuQVtZpZRTtBmZhnlBG1mllFO0GZmGeUEbWaWUU7QZmYZ9f8BiBPBiwu+WXAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matrice de confusion et scores\n",
    "y_pred_boost = boost.predict(X_test)\n",
    "confusion_matrix = pd.crosstab(y_test,y_pred_boost, rownames = ['Classe réelle'], colnames = ['Classe prédite par XGB'])\n",
    "sns.heatmap(confusion_matrix, annot = True)\n",
    "print('Accuracy: ',metrics.accuracy_score(y_test, y_pred_boost));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gnmxi_3xvJaH"
   },
   "source": [
    "Meilleur score obtenu jusqu'ici"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uU6CYhmgcn8r"
   },
   "source": [
    "#Réduction de dimension - PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u5jHDtI9eU97"
   },
   "source": [
    "Test sur KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kbN7veRecszl",
    "outputId": "4197810e-109f-474c-baf7-b5aa1257615c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5045454545454545\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf=KNeighborsClassifier(algorithm='auto', metric='manhattan', n_neighbors= 55, weights= 'distance')\n",
    "\n",
    "pca = PCA(n_components = 0.9)\n",
    "X_train_pca = pca.fit_transform(X_train)\n",
    "X_test_pca = pca.transform(X_test)\n",
    "clf.fit(X_train_pca, y_train)\n",
    "print(clf.score(X_test_pca, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4yFeXrkQgcC_",
    "outputId": "2da8fc81-7819-4421-98cb-7e6f559e4656"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "print(pca.n_components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.39607167e-01, 3.72917448e-01, 2.77255185e-01, 1.26082786e-01,\n",
       "        3.25450431e-02, 3.01333950e-01, 3.29784634e-01, 3.30669240e-01,\n",
       "        2.82082782e-01, 1.90448820e-01, 1.10925910e-01, 1.17360801e-01,\n",
       "        1.13517026e-01, 8.80036881e-02, 3.02775386e-01, 1.16376652e-01,\n",
       "        3.41327934e-01, 1.28533068e-01],\n",
       "       [2.71535447e-01, 2.93422480e-02, 1.59046221e-01, 9.02614916e-02,\n",
       "        1.51172123e-01, 1.27189197e-01, 1.38205340e-01, 1.41680064e-01,\n",
       "        1.27673752e-01, 8.41398343e-02, 3.63029225e-01, 3.83482342e-01,\n",
       "        3.71458239e-01, 3.05435369e-01, 1.25342561e-01, 3.54444092e-01,\n",
       "        1.32052901e-01, 3.48540220e-01],\n",
       "       [2.80754749e-02, 5.20260834e-02, 1.42446304e-02, 1.70320825e-01,\n",
       "        1.56315623e-01, 3.17267374e-01, 1.58496243e-01, 2.18475167e-01,\n",
       "        4.79733556e-01, 6.27406163e-01, 8.43808966e-02, 3.59875405e-02,\n",
       "        7.99576959e-02, 1.31994969e-01, 3.08368137e-01, 7.95283582e-02,\n",
       "        1.09167581e-01, 4.37732358e-04],\n",
       "       [2.17256740e-02, 2.22706006e-02, 4.48208398e-02, 6.09636838e-01,\n",
       "        6.05130389e-01, 2.18721822e-01, 1.74389516e-01, 5.38283642e-02,\n",
       "        7.38278044e-02, 1.36363648e-01, 2.02926007e-01, 1.40295216e-01,\n",
       "        8.49742369e-03, 1.09933881e-01, 1.77565725e-01, 1.52947493e-01,\n",
       "        1.20692979e-01, 1.07147242e-01],\n",
       "       [9.18257655e-02, 4.19611909e-01, 5.28602808e-01, 2.24224706e-02,\n",
       "        3.24452666e-01, 3.00385312e-01, 2.38650074e-01, 2.50679601e-01,\n",
       "        1.72723362e-01, 9.87894539e-03, 4.35560170e-04, 1.37254986e-01,\n",
       "        1.94707828e-01, 1.81751796e-01, 1.31038159e-01, 1.07270936e-01,\n",
       "        1.75166998e-01, 2.02974706e-01],\n",
       "       [2.75486810e-01, 1.60780326e-01, 3.26315676e-01, 5.43757257e-02,\n",
       "        1.93770751e-01, 5.64293369e-02, 1.85691650e-02, 8.97101323e-02,\n",
       "        1.22076544e-01, 1.01857825e-01, 1.52445470e-01, 3.39862856e-02,\n",
       "        4.31227175e-01, 5.88307960e-01, 1.06424421e-02, 3.27390861e-01,\n",
       "        5.00734676e-02, 2.19438051e-01],\n",
       "       [4.27921614e-01, 7.26060509e-03, 2.76175307e-01, 4.83433538e-01,\n",
       "        1.96581412e-01, 4.97768269e-02, 1.22863679e-01, 1.32872348e-01,\n",
       "        8.17718382e-02, 4.67362234e-02, 4.19329277e-01, 2.35379709e-01,\n",
       "        2.58227868e-02, 2.48653642e-01, 5.39051259e-02, 2.35135247e-01,\n",
       "        1.64228506e-01, 2.12613980e-01],\n",
       "       [2.96004020e-01, 1.22975461e-01, 4.14989794e-02, 5.35707232e-01,\n",
       "        5.96845077e-01, 2.70033076e-03, 1.34207863e-01, 1.78472508e-01,\n",
       "        7.40602602e-02, 1.71336276e-01, 1.47923638e-01, 1.63542623e-01,\n",
       "        1.16314873e-01, 1.95500721e-02, 1.03453393e-01, 2.13285160e-02,\n",
       "        1.56624017e-01, 2.63420094e-01],\n",
       "       [4.27308774e-02, 6.29772780e-02, 9.75487587e-02, 2.64855879e-02,\n",
       "        1.79632998e-01, 1.20213021e-01, 2.77467315e-01, 3.50121746e-01,\n",
       "        1.86447216e-01, 4.90170819e-01, 1.59054649e-01, 2.29439508e-01,\n",
       "        5.81951523e-02, 8.70903198e-02, 4.72006451e-01, 7.55587933e-02,\n",
       "        1.61936053e-01, 3.45012151e-01],\n",
       "       [2.55979069e-01, 5.82342608e-02, 1.11080794e-01, 1.88827486e-01,\n",
       "        5.30764622e-02, 3.52605813e-01, 1.37030010e-01, 1.32923513e-01,\n",
       "        2.00295970e-01, 3.80037488e-01, 4.25921272e-03, 3.41668822e-01,\n",
       "        1.01513719e-01, 2.78121465e-01, 1.70180580e-02, 3.48358246e-01,\n",
       "        4.07035890e-01, 2.07880484e-01]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(pca.components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Min_cote_domicile</th>\n",
       "      <th>Min_cote_exterieur</th>\n",
       "      <th>Max_cote_nul</th>\n",
       "      <th>Nb_buts_equipe_dom_an_dernier</th>\n",
       "      <th>Nb_buts_equipe_ext_an_dernier</th>\n",
       "      <th>Moy_points_5_match_dom</th>\n",
       "      <th>Moy_buts_5_matchs_dom</th>\n",
       "      <th>Moy_tirs_cadres_5_matchs_dom</th>\n",
       "      <th>Moy_tirs_5_matchs_dom</th>\n",
       "      <th>Moy_corners_5_matchs_dom</th>\n",
       "      <th>Moy_points_5_match_ext</th>\n",
       "      <th>Moy_buts_5_matchs_ext</th>\n",
       "      <th>Moy_tirs_cadres_5_matchs_ext</th>\n",
       "      <th>Moy_tirs_5_matchs_ext</th>\n",
       "      <th>Points_moy_match_saison_dom</th>\n",
       "      <th>Points_moy_match_saison_ext</th>\n",
       "      <th>note_gen_dom</th>\n",
       "      <th>note_def_ext</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC-1</th>\n",
       "      <td>-0.239607</td>\n",
       "      <td>0.372917</td>\n",
       "      <td>0.277255</td>\n",
       "      <td>0.126083</td>\n",
       "      <td>-0.032545</td>\n",
       "      <td>0.301334</td>\n",
       "      <td>0.329785</td>\n",
       "      <td>0.330669</td>\n",
       "      <td>0.282083</td>\n",
       "      <td>0.190449</td>\n",
       "      <td>-0.110926</td>\n",
       "      <td>-0.117361</td>\n",
       "      <td>-0.113517</td>\n",
       "      <td>-0.088004</td>\n",
       "      <td>0.302775</td>\n",
       "      <td>-0.116377</td>\n",
       "      <td>0.341328</td>\n",
       "      <td>-0.128533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-2</th>\n",
       "      <td>0.271535</td>\n",
       "      <td>-0.029342</td>\n",
       "      <td>0.159046</td>\n",
       "      <td>0.090261</td>\n",
       "      <td>0.151172</td>\n",
       "      <td>0.127189</td>\n",
       "      <td>0.138205</td>\n",
       "      <td>0.141680</td>\n",
       "      <td>0.127674</td>\n",
       "      <td>0.084140</td>\n",
       "      <td>0.363029</td>\n",
       "      <td>0.383482</td>\n",
       "      <td>0.371458</td>\n",
       "      <td>0.305435</td>\n",
       "      <td>0.125343</td>\n",
       "      <td>0.354444</td>\n",
       "      <td>0.132053</td>\n",
       "      <td>0.348540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-3</th>\n",
       "      <td>0.028075</td>\n",
       "      <td>-0.052026</td>\n",
       "      <td>-0.014245</td>\n",
       "      <td>-0.170321</td>\n",
       "      <td>-0.156316</td>\n",
       "      <td>-0.317267</td>\n",
       "      <td>-0.158496</td>\n",
       "      <td>0.218475</td>\n",
       "      <td>0.479734</td>\n",
       "      <td>0.627406</td>\n",
       "      <td>0.084381</td>\n",
       "      <td>0.035988</td>\n",
       "      <td>-0.079958</td>\n",
       "      <td>-0.131995</td>\n",
       "      <td>-0.308368</td>\n",
       "      <td>0.079528</td>\n",
       "      <td>-0.109168</td>\n",
       "      <td>-0.000438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-4</th>\n",
       "      <td>0.021726</td>\n",
       "      <td>0.022271</td>\n",
       "      <td>0.044821</td>\n",
       "      <td>0.609637</td>\n",
       "      <td>0.605130</td>\n",
       "      <td>-0.218722</td>\n",
       "      <td>-0.174390</td>\n",
       "      <td>-0.053828</td>\n",
       "      <td>0.073828</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>-0.202926</td>\n",
       "      <td>-0.140295</td>\n",
       "      <td>-0.008497</td>\n",
       "      <td>0.109934</td>\n",
       "      <td>-0.177566</td>\n",
       "      <td>-0.152947</td>\n",
       "      <td>0.120693</td>\n",
       "      <td>0.107147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-5</th>\n",
       "      <td>0.091826</td>\n",
       "      <td>0.419612</td>\n",
       "      <td>0.528603</td>\n",
       "      <td>0.022422</td>\n",
       "      <td>-0.324453</td>\n",
       "      <td>-0.300385</td>\n",
       "      <td>-0.238650</td>\n",
       "      <td>-0.250680</td>\n",
       "      <td>-0.172723</td>\n",
       "      <td>0.009879</td>\n",
       "      <td>-0.000436</td>\n",
       "      <td>0.137255</td>\n",
       "      <td>0.194708</td>\n",
       "      <td>0.181752</td>\n",
       "      <td>-0.131038</td>\n",
       "      <td>-0.107271</td>\n",
       "      <td>0.175167</td>\n",
       "      <td>-0.202975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-6</th>\n",
       "      <td>0.275487</td>\n",
       "      <td>0.160780</td>\n",
       "      <td>0.326316</td>\n",
       "      <td>-0.054376</td>\n",
       "      <td>0.193771</td>\n",
       "      <td>-0.056429</td>\n",
       "      <td>-0.018569</td>\n",
       "      <td>-0.089710</td>\n",
       "      <td>-0.122077</td>\n",
       "      <td>-0.101858</td>\n",
       "      <td>0.152445</td>\n",
       "      <td>-0.033986</td>\n",
       "      <td>-0.431227</td>\n",
       "      <td>-0.588308</td>\n",
       "      <td>-0.010642</td>\n",
       "      <td>0.327391</td>\n",
       "      <td>0.050073</td>\n",
       "      <td>0.219438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-7</th>\n",
       "      <td>0.427922</td>\n",
       "      <td>0.007261</td>\n",
       "      <td>0.276175</td>\n",
       "      <td>-0.483434</td>\n",
       "      <td>0.196581</td>\n",
       "      <td>0.049777</td>\n",
       "      <td>0.122864</td>\n",
       "      <td>0.132872</td>\n",
       "      <td>0.081772</td>\n",
       "      <td>-0.046736</td>\n",
       "      <td>-0.419329</td>\n",
       "      <td>-0.235380</td>\n",
       "      <td>0.025823</td>\n",
       "      <td>0.248654</td>\n",
       "      <td>-0.053905</td>\n",
       "      <td>-0.235135</td>\n",
       "      <td>-0.164229</td>\n",
       "      <td>0.212614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-8</th>\n",
       "      <td>-0.296004</td>\n",
       "      <td>0.122975</td>\n",
       "      <td>-0.041499</td>\n",
       "      <td>-0.535707</td>\n",
       "      <td>0.596845</td>\n",
       "      <td>-0.002700</td>\n",
       "      <td>-0.134208</td>\n",
       "      <td>-0.178473</td>\n",
       "      <td>-0.074060</td>\n",
       "      <td>0.171336</td>\n",
       "      <td>0.147924</td>\n",
       "      <td>0.163543</td>\n",
       "      <td>0.116315</td>\n",
       "      <td>-0.019550</td>\n",
       "      <td>0.103453</td>\n",
       "      <td>-0.021329</td>\n",
       "      <td>0.156624</td>\n",
       "      <td>-0.263420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-9</th>\n",
       "      <td>0.042731</td>\n",
       "      <td>-0.062977</td>\n",
       "      <td>-0.097549</td>\n",
       "      <td>-0.026486</td>\n",
       "      <td>-0.179633</td>\n",
       "      <td>0.120213</td>\n",
       "      <td>-0.277467</td>\n",
       "      <td>-0.350122</td>\n",
       "      <td>-0.186447</td>\n",
       "      <td>0.490171</td>\n",
       "      <td>-0.159055</td>\n",
       "      <td>-0.229440</td>\n",
       "      <td>-0.058195</td>\n",
       "      <td>0.087090</td>\n",
       "      <td>0.472006</td>\n",
       "      <td>0.075559</td>\n",
       "      <td>0.161936</td>\n",
       "      <td>0.345012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-10</th>\n",
       "      <td>-0.255979</td>\n",
       "      <td>0.058234</td>\n",
       "      <td>-0.111081</td>\n",
       "      <td>-0.188827</td>\n",
       "      <td>-0.053076</td>\n",
       "      <td>-0.352606</td>\n",
       "      <td>-0.137030</td>\n",
       "      <td>0.132924</td>\n",
       "      <td>0.200296</td>\n",
       "      <td>-0.380037</td>\n",
       "      <td>0.004259</td>\n",
       "      <td>-0.341669</td>\n",
       "      <td>-0.101514</td>\n",
       "      <td>0.278121</td>\n",
       "      <td>-0.017018</td>\n",
       "      <td>0.348358</td>\n",
       "      <td>0.407036</td>\n",
       "      <td>0.207880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Min_cote_domicile  Min_cote_exterieur  Max_cote_nul  \\\n",
       "PC-1           -0.239607            0.372917      0.277255   \n",
       "PC-2            0.271535           -0.029342      0.159046   \n",
       "PC-3            0.028075           -0.052026     -0.014245   \n",
       "PC-4            0.021726            0.022271      0.044821   \n",
       "PC-5            0.091826            0.419612      0.528603   \n",
       "PC-6            0.275487            0.160780      0.326316   \n",
       "PC-7            0.427922            0.007261      0.276175   \n",
       "PC-8           -0.296004            0.122975     -0.041499   \n",
       "PC-9            0.042731           -0.062977     -0.097549   \n",
       "PC-10          -0.255979            0.058234     -0.111081   \n",
       "\n",
       "       Nb_buts_equipe_dom_an_dernier  Nb_buts_equipe_ext_an_dernier  \\\n",
       "PC-1                        0.126083                      -0.032545   \n",
       "PC-2                        0.090261                       0.151172   \n",
       "PC-3                       -0.170321                      -0.156316   \n",
       "PC-4                        0.609637                       0.605130   \n",
       "PC-5                        0.022422                      -0.324453   \n",
       "PC-6                       -0.054376                       0.193771   \n",
       "PC-7                       -0.483434                       0.196581   \n",
       "PC-8                       -0.535707                       0.596845   \n",
       "PC-9                       -0.026486                      -0.179633   \n",
       "PC-10                      -0.188827                      -0.053076   \n",
       "\n",
       "       Moy_points_5_match_dom  Moy_buts_5_matchs_dom  \\\n",
       "PC-1                 0.301334               0.329785   \n",
       "PC-2                 0.127189               0.138205   \n",
       "PC-3                -0.317267              -0.158496   \n",
       "PC-4                -0.218722              -0.174390   \n",
       "PC-5                -0.300385              -0.238650   \n",
       "PC-6                -0.056429              -0.018569   \n",
       "PC-7                 0.049777               0.122864   \n",
       "PC-8                -0.002700              -0.134208   \n",
       "PC-9                 0.120213              -0.277467   \n",
       "PC-10               -0.352606              -0.137030   \n",
       "\n",
       "       Moy_tirs_cadres_5_matchs_dom  Moy_tirs_5_matchs_dom  \\\n",
       "PC-1                       0.330669               0.282083   \n",
       "PC-2                       0.141680               0.127674   \n",
       "PC-3                       0.218475               0.479734   \n",
       "PC-4                      -0.053828               0.073828   \n",
       "PC-5                      -0.250680              -0.172723   \n",
       "PC-6                      -0.089710              -0.122077   \n",
       "PC-7                       0.132872               0.081772   \n",
       "PC-8                      -0.178473              -0.074060   \n",
       "PC-9                      -0.350122              -0.186447   \n",
       "PC-10                      0.132924               0.200296   \n",
       "\n",
       "       Moy_corners_5_matchs_dom  Moy_points_5_match_ext  \\\n",
       "PC-1                   0.190449               -0.110926   \n",
       "PC-2                   0.084140                0.363029   \n",
       "PC-3                   0.627406                0.084381   \n",
       "PC-4                   0.136364               -0.202926   \n",
       "PC-5                   0.009879               -0.000436   \n",
       "PC-6                  -0.101858                0.152445   \n",
       "PC-7                  -0.046736               -0.419329   \n",
       "PC-8                   0.171336                0.147924   \n",
       "PC-9                   0.490171               -0.159055   \n",
       "PC-10                 -0.380037                0.004259   \n",
       "\n",
       "       Moy_buts_5_matchs_ext  Moy_tirs_cadres_5_matchs_ext  \\\n",
       "PC-1               -0.117361                     -0.113517   \n",
       "PC-2                0.383482                      0.371458   \n",
       "PC-3                0.035988                     -0.079958   \n",
       "PC-4               -0.140295                     -0.008497   \n",
       "PC-5                0.137255                      0.194708   \n",
       "PC-6               -0.033986                     -0.431227   \n",
       "PC-7               -0.235380                      0.025823   \n",
       "PC-8                0.163543                      0.116315   \n",
       "PC-9               -0.229440                     -0.058195   \n",
       "PC-10              -0.341669                     -0.101514   \n",
       "\n",
       "       Moy_tirs_5_matchs_ext  Points_moy_match_saison_dom  \\\n",
       "PC-1               -0.088004                     0.302775   \n",
       "PC-2                0.305435                     0.125343   \n",
       "PC-3               -0.131995                    -0.308368   \n",
       "PC-4                0.109934                    -0.177566   \n",
       "PC-5                0.181752                    -0.131038   \n",
       "PC-6               -0.588308                    -0.010642   \n",
       "PC-7                0.248654                    -0.053905   \n",
       "PC-8               -0.019550                     0.103453   \n",
       "PC-9                0.087090                     0.472006   \n",
       "PC-10               0.278121                    -0.017018   \n",
       "\n",
       "       Points_moy_match_saison_ext  note_gen_dom  note_def_ext  \n",
       "PC-1                     -0.116377      0.341328     -0.128533  \n",
       "PC-2                      0.354444      0.132053      0.348540  \n",
       "PC-3                      0.079528     -0.109168     -0.000438  \n",
       "PC-4                     -0.152947      0.120693      0.107147  \n",
       "PC-5                     -0.107271      0.175167     -0.202975  \n",
       "PC-6                      0.327391      0.050073      0.219438  \n",
       "PC-7                     -0.235135     -0.164229      0.212614  \n",
       "PC-8                     -0.021329      0.156624     -0.263420  \n",
       "PC-9                      0.075559      0.161936      0.345012  \n",
       "PC-10                     0.348358      0.407036      0.207880  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(pca.components_,columns=X_train_.columns, index = ['PC-1','PC-2', 'PC-3','PC-4','PC-5',\n",
    "                                                                     'PC-6','PC-7','PC-8','PC-9','PC-10'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QoNlrpYzfsuo"
   },
   "source": [
    "La réduction de dimension augmente le score du KNN en ne gardant que 10 composantes au lieu des 18 variables de départ et donne le meilleur résultat sur cet ensemble de variables."
   ]
  }
 ],
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