analyse.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "09b9459f",
   "metadata": {},
   "source": [
    "# Traitement, Visualisation et Analyse des données\n",
    "\n",
    "*Auteurs : Sylvia P. et Fabio G. C.* "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e397e0e7",
   "metadata": {},
   "source": [
    "## Import des bibliothèques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cfbe6f80",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d24dea9",
   "metadata": {},
   "source": [
    "## Importation des fichiers *crédit immo*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41da0486",
   "metadata": {},
   "source": [
    "### Format CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9059dda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_NOM</th>\n",
       "      <th>Niv_Etude_Bac</th>\n",
       "      <th>age</th>\n",
       "      <th>contrat_de_travail</th>\n",
       "      <th>Salaire</th>\n",
       "      <th>dette_anterieure</th>\n",
       "      <th>etat_civile</th>\n",
       "      <th>apport</th>\n",
       "      <th>enfant_a_Charge</th>\n",
       "      <th>Solvable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jean</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45</td>\n",
       "      <td>CDI</td>\n",
       "      <td>40000</td>\n",
       "      <td>4000</td>\n",
       "      <td>M</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VANESSA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28</td>\n",
       "      <td>CDI</td>\n",
       "      <td>30500</td>\n",
       "      <td>1320</td>\n",
       "      <td>M</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TARCISSE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55</td>\n",
       "      <td>CDI</td>\n",
       "      <td>28000</td>\n",
       "      <td>40000</td>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TIBAULT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23</td>\n",
       "      <td>CDD</td>\n",
       "      <td>15000</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GILES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33</td>\n",
       "      <td>CDD</td>\n",
       "      <td>27000</td>\n",
       "      <td>3000</td>\n",
       "      <td>C</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID_NOM  Niv_Etude_Bac  age contrat_de_travail  Salaire  dette_anterieure  \\\n",
       "0      jean            3.0   45                CDI    40000              4000   \n",
       "1   VANESSA            5.0   28                CDI    30500              1320   \n",
       "2  TARCISSE            0.0   55                CDI    28000             40000   \n",
       "3   TIBAULT            4.0   23                CDD    15000                 0   \n",
       "4     GILES            0.0   33                CDD    27000              3000   \n",
       "\n",
       "  etat_civile  apport  enfant_a_Charge Solvable  \n",
       "0           M     0.3              3.0      OUI  \n",
       "1           M     0.1              0.0      OUI  \n",
       "2           C     0.0              0.0      NON  \n",
       "3           M     0.1              NaN      OUI  \n",
       "4           C     0.1              2.0      NON  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cd_immo = pd.read_csv(\"./base_donnees/credit_immo/credit_immo.csv\")\n",
    "\n",
    "cd_immo.head() # afficher les 5 premières lignes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3201977",
   "metadata": {},
   "source": [
    "### Format XLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "73f155e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_NOM</th>\n",
       "      <th>Niv_Etude_Bac</th>\n",
       "      <th>age</th>\n",
       "      <th>contrat_de_travail</th>\n",
       "      <th>Salaire</th>\n",
       "      <th>dette_anterieure</th>\n",
       "      <th>etat_civile</th>\n",
       "      <th>apport</th>\n",
       "      <th>enfant_a_Charge</th>\n",
       "      <th>Solvable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jean</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45</td>\n",
       "      <td>CDI</td>\n",
       "      <td>40000</td>\n",
       "      <td>4000</td>\n",
       "      <td>M</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VANESSA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28</td>\n",
       "      <td>CDI</td>\n",
       "      <td>30500</td>\n",
       "      <td>1320</td>\n",
       "      <td>M</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TARCISSE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55</td>\n",
       "      <td>CDI</td>\n",
       "      <td>28000</td>\n",
       "      <td>40000</td>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TIBAULT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23</td>\n",
       "      <td>CDD</td>\n",
       "      <td>15000</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GILES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33</td>\n",
       "      <td>CDD</td>\n",
       "      <td>27000</td>\n",
       "      <td>3000</td>\n",
       "      <td>C</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID_NOM  Niv_Etude_Bac  age contrat_de_travail  Salaire  dette_anterieure  \\\n",
       "0      jean            3.0   45                CDI    40000              4000   \n",
       "1   VANESSA            5.0   28                CDI    30500              1320   \n",
       "2  TARCISSE            0.0   55                CDI    28000             40000   \n",
       "3   TIBAULT            4.0   23                CDD    15000                 0   \n",
       "4     GILES            0.0   33                CDD    27000              3000   \n",
       "\n",
       "  etat_civile  apport  enfant_a_Charge Solvable  \n",
       "0           M     0.3              3.0      OUI  \n",
       "1           M     0.1              0.0      OUI  \n",
       "2           C     0.0              0.0      NON  \n",
       "3           M     0.1              NaN      OUI  \n",
       "4           C     0.1              2.0      NON  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cd_immo_excel = pd.read_excel(\"./base_donnees/credit_immo/credit_immo.xls\")\n",
    "\n",
    "cd_immo_excel.head() # afficher les 5 premières lignes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3740f43d",
   "metadata": {},
   "source": [
    "###  Format JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e617875b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salaire</th>\n",
       "      <th>ID_NOM</th>\n",
       "      <th>dette_anterieure</th>\n",
       "      <th>apport</th>\n",
       "      <th>contrat_de_travail</th>\n",
       "      <th>enfant_a_Charge</th>\n",
       "      <th>etat_civile</th>\n",
       "      <th>Solvable</th>\n",
       "      <th>age</th>\n",
       "      <th>Niv_Etude_Bac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40000</td>\n",
       "      <td>jean</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.3</td>\n",
       "      <td>CDI</td>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>OUI</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30500</td>\n",
       "      <td>VANESSA</td>\n",
       "      <td>1320</td>\n",
       "      <td>0.1</td>\n",
       "      <td>CDI</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>OUI</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28000</td>\n",
       "      <td>TARCISSE</td>\n",
       "      <td>40000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CDI</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>NON</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15000</td>\n",
       "      <td>TIBAULT</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>CDD</td>\n",
       "      <td></td>\n",
       "      <td>M</td>\n",
       "      <td>OUI</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27000</td>\n",
       "      <td>GILES</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>CDD</td>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>NON</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Salaire    ID_NOM  dette_anterieure  apport contrat_de_travail  \\\n",
       "0    40000      jean              4000     0.3                CDI   \n",
       "1    30500   VANESSA              1320     0.1                CDI   \n",
       "2    28000  TARCISSE             40000     0.0                CDI   \n",
       "3    15000   TIBAULT                 0     0.1                CDD   \n",
       "4    27000     GILES              3000     0.1                CDD   \n",
       "\n",
       "  enfant_a_Charge etat_civile Solvable  age Niv_Etude_Bac  \n",
       "0               3           M      OUI   45             3  \n",
       "1               0           M      OUI   28             5  \n",
       "2               0           C      NON   55             0  \n",
       "3                           M      OUI   23             4  \n",
       "4               2           C      NON   33             0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cd_immo_json = pd.read_json(\"./base_donnees/credit_immo/credit_immo.json\")\n",
    "\n",
    "cd_immo_json.head() # afficher les 5 premières lignes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82ad0361",
   "metadata": {},
   "source": [
    "## Création d'une base de données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ab9174f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.23937838 -0.35652145 -0.10057653  0.05984021]\n",
      " [ 0.43888805 -0.70140765  0.95793994  0.28180915]\n",
      " [-0.35182986  1.92518739 -0.89334489 -0.32859121]\n",
      " [ 1.77746331  0.40091402 -2.25102984  0.75019376]\n",
      " [-0.12191749  0.68831089  1.12328754 -0.83024441]\n",
      " [ 0.4118052   0.57449959 -0.54360617 -0.68386513]]\n"
     ]
    }
   ],
   "source": [
    "data_bd = np.random.randn(6, 4) # données aléatoire suivant une loi normal sur 6 lignes et 4 colonnes\n",
    "\n",
    "print(data_bd) # affichage de data_bd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "90a1f1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_col = [\"taux_de_ventes\", \"croissance_vente\", \"ratio_benefice\", \"ratio_perte\"] # nom des colonnes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "729fa00a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>taux_de_ventes</th>\n",
       "      <th>croissance_vente</th>\n",
       "      <th>ratio_benefice</th>\n",
       "      <th>ratio_perte</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.239378</td>\n",
       "      <td>-0.356521</td>\n",
       "      <td>-0.100577</td>\n",
       "      <td>0.059840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.438888</td>\n",
       "      <td>-0.701408</td>\n",
       "      <td>0.957940</td>\n",
       "      <td>0.281809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.351830</td>\n",
       "      <td>1.925187</td>\n",
       "      <td>-0.893345</td>\n",
       "      <td>-0.328591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.777463</td>\n",
       "      <td>0.400914</td>\n",
       "      <td>-2.251030</td>\n",
       "      <td>0.750194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.121917</td>\n",
       "      <td>0.688311</td>\n",
       "      <td>1.123288</td>\n",
       "      <td>-0.830244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   taux_de_ventes  croissance_vente  ratio_benefice  ratio_perte\n",
       "0       -1.239378         -0.356521       -0.100577     0.059840\n",
       "1        0.438888         -0.701408        0.957940     0.281809\n",
       "2       -0.351830          1.925187       -0.893345    -0.328591\n",
       "3        1.777463          0.400914       -2.251030     0.750194\n",
       "4       -0.121917          0.688311        1.123288    -0.830244"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bd = pd.DataFrame(data_bd, columns=label_col) # Création du dataframe\n",
    "\n",
    "bd.head() # afficher les 5 premières lignes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39730920",
   "metadata": {},
   "source": [
    "### Ajout de deux index vide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c258111d",
   "metadata": {},
   "outputs": [],
   "source": [
    "bd = bd.reindex([7,0,1,2,3,4,5,6]) # Ajout d'un index 7 en début de dataframe et d'un index 6 en fin de dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8df7d58",
   "metadata": {},
   "source": [
    "### Localisation des données null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9e45a91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>taux_de_ventes</th>\n",
       "      <th>croissance_vente</th>\n",
       "      <th>ratio_benefice</th>\n",
       "      <th>ratio_perte</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   taux_de_ventes  croissance_vente  ratio_benefice  ratio_perte\n",
       "7            True              True            True         True\n",
       "0           False             False           False        False\n",
       "1           False             False           False        False\n",
       "2           False             False           False        False\n",
       "3           False             False           False        False\n",
       "4           False             False           False        False\n",
       "5           False             False           False        False\n",
       "6            True              True            True         True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bd.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88ae07ca",
   "metadata": {},
   "source": [
    "### Remplacement des valeurs nulles par 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ba2f363a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>taux_de_ventes</th>\n",
       "      <th>croissance_vente</th>\n",
       "      <th>ratio_benefice</th>\n",
       "      <th>ratio_perte</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.239378</td>\n",
       "      <td>-0.356521</td>\n",
       "      <td>-0.100577</td>\n",
       "      <td>0.059840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.438888</td>\n",
       "      <td>-0.701408</td>\n",
       "      <td>0.957940</td>\n",
       "      <td>0.281809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.351830</td>\n",
       "      <td>1.925187</td>\n",
       "      <td>-0.893345</td>\n",
       "      <td>-0.328591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.777463</td>\n",
       "      <td>0.400914</td>\n",
       "      <td>-2.251030</td>\n",
       "      <td>0.750194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.121917</td>\n",
       "      <td>0.688311</td>\n",
       "      <td>1.123288</td>\n",
       "      <td>-0.830244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.411805</td>\n",
       "      <td>0.574500</td>\n",
       "      <td>-0.543606</td>\n",
       "      <td>-0.683865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   taux_de_ventes  croissance_vente  ratio_benefice  ratio_perte\n",
       "7        0.000000          0.000000        0.000000     0.000000\n",
       "0       -1.239378         -0.356521       -0.100577     0.059840\n",
       "1        0.438888         -0.701408        0.957940     0.281809\n",
       "2       -0.351830          1.925187       -0.893345    -0.328591\n",
       "3        1.777463          0.400914       -2.251030     0.750194\n",
       "4       -0.121917          0.688311        1.123288    -0.830244\n",
       "5        0.411805          0.574500       -0.543606    -0.683865\n",
       "6        0.000000          0.000000        0.000000     0.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bd.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f47223f",
   "metadata": {},
   "source": [
    "### Suppression des lignes nulles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "942901d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "bd = bd.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb3228d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>taux_de_ventes</th>\n",
       "      <th>croissance_vente</th>\n",
       "      <th>ratio_benefice</th>\n",
       "      <th>ratio_perte</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.239378</td>\n",
       "      <td>-0.356521</td>\n",
       "      <td>-0.100577</td>\n",
       "      <td>0.059840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.438888</td>\n",
       "      <td>-0.701408</td>\n",
       "      <td>0.957940</td>\n",
       "      <td>0.281809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.351830</td>\n",
       "      <td>1.925187</td>\n",
       "      <td>-0.893345</td>\n",
       "      <td>-0.328591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.777463</td>\n",
       "      <td>0.400914</td>\n",
       "      <td>-2.251030</td>\n",
       "      <td>0.750194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.121917</td>\n",
       "      <td>0.688311</td>\n",
       "      <td>1.123288</td>\n",
       "      <td>-0.830244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.411805</td>\n",
       "      <td>0.574500</td>\n",
       "      <td>-0.543606</td>\n",
       "      <td>-0.683865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   taux_de_ventes  croissance_vente  ratio_benefice  ratio_perte\n",
       "0       -1.239378         -0.356521       -0.100577     0.059840\n",
       "1        0.438888         -0.701408        0.957940     0.281809\n",
       "2       -0.351830          1.925187       -0.893345    -0.328591\n",
       "3        1.777463          0.400914       -2.251030     0.750194\n",
       "4       -0.121917          0.688311        1.123288    -0.830244\n",
       "5        0.411805          0.574500       -0.543606    -0.683865"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ad7d888",
   "metadata": {},
   "source": [
    "## Traitement des données"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65c2b543",
   "metadata": {},
   "source": [
    "### Imporation des bibliothèques nécessaires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "488811d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "168bd8ca",
   "metadata": {},
   "source": [
    "### Importation du jeu de données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "987e1d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cd_immo = pd.read_csv(\"./base_donnees/credit_immo/credit_immo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a69b16ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_NOM</th>\n",
       "      <th>Niv_Etude_Bac</th>\n",
       "      <th>age</th>\n",
       "      <th>contrat_de_travail</th>\n",
       "      <th>Salaire</th>\n",
       "      <th>dette_anterieure</th>\n",
       "      <th>etat_civile</th>\n",
       "      <th>apport</th>\n",
       "      <th>enfant_a_Charge</th>\n",
       "      <th>Solvable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jean</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45</td>\n",
       "      <td>CDI</td>\n",
       "      <td>40000</td>\n",
       "      <td>4000</td>\n",
       "      <td>M</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VANESSA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28</td>\n",
       "      <td>CDI</td>\n",
       "      <td>30500</td>\n",
       "      <td>1320</td>\n",
       "      <td>M</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TARCISSE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55</td>\n",
       "      <td>CDI</td>\n",
       "      <td>28000</td>\n",
       "      <td>40000</td>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TIBAULT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23</td>\n",
       "      <td>CDD</td>\n",
       "      <td>15000</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GILES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33</td>\n",
       "      <td>CDD</td>\n",
       "      <td>27000</td>\n",
       "      <td>3000</td>\n",
       "      <td>C</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID_NOM  Niv_Etude_Bac  age contrat_de_travail  Salaire  dette_anterieure  \\\n",
       "0      jean            3.0   45                CDI    40000              4000   \n",
       "1   VANESSA            5.0   28                CDI    30500              1320   \n",
       "2  TARCISSE            0.0   55                CDI    28000             40000   \n",
       "3   TIBAULT            4.0   23                CDD    15000                 0   \n",
       "4     GILES            0.0   33                CDD    27000              3000   \n",
       "\n",
       "  etat_civile  apport  enfant_a_Charge Solvable  \n",
       "0           M     0.3              3.0      OUI  \n",
       "1           M     0.1              0.0      OUI  \n",
       "2           C     0.0              0.0      NON  \n",
       "3           M     0.1              NaN      OUI  \n",
       "4           C     0.1              2.0      NON  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cd_immo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5abad214",
   "metadata": {},
   "source": [
    "### Transformation des valeurs manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "084f6233",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "# imputer = imputer.fit(cd_immo.select_dtypes(include='number'))\n",
    "\n",
    "cd_immo[cd_immo.select_dtypes(include='number').columns] = imputer.fit_transform(cd_immo.select_dtypes(include='number'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "00308c6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_NOM</th>\n",
       "      <th>Niv_Etude_Bac</th>\n",
       "      <th>age</th>\n",
       "      <th>contrat_de_travail</th>\n",
       "      <th>Salaire</th>\n",
       "      <th>dette_anterieure</th>\n",
       "      <th>etat_civile</th>\n",
       "      <th>apport</th>\n",
       "      <th>enfant_a_Charge</th>\n",
       "      <th>Solvable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jean</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>CDI</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VANESSA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>CDI</td>\n",
       "      <td>30500.0</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TARCISSE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>CDI</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TIBAULT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>CDD</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GILES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>CDD</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID_NOM  Niv_Etude_Bac   age contrat_de_travail  Salaire  \\\n",
       "0      jean            3.0  45.0                CDI  40000.0   \n",
       "1   VANESSA            5.0  28.0                CDI  30500.0   \n",
       "2  TARCISSE            0.0  55.0                CDI  28000.0   \n",
       "3   TIBAULT            4.0  23.0                CDD  15000.0   \n",
       "4     GILES            0.0  33.0                CDD  27000.0   \n",
       "\n",
       "   dette_anterieure etat_civile  apport  enfant_a_Charge Solvable  \n",
       "0            4000.0           M     0.3         3.000000      OUI  \n",
       "1            1320.0           M     0.1         0.000000      OUI  \n",
       "2           40000.0           C     0.0         0.000000      NON  \n",
       "3               0.0           M     0.1         1.333333      OUI  \n",
       "4            3000.0           C     0.1         2.000000      NON  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cd_immo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0448baa5",
   "metadata": {},
   "source": [
    "### Encoder les valeurs catégoriques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d3ba1b02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_NOM</th>\n",
       "      <th>contrat_de_travail</th>\n",
       "      <th>etat_civile</th>\n",
       "      <th>Solvable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jean</td>\n",
       "      <td>CDI</td>\n",
       "      <td>M</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VANESSA</td>\n",
       "      <td>CDI</td>\n",
       "      <td>M</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TARCISSE</td>\n",
       "      <td>CDI</td>\n",
       "      <td>C</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TIBAULT</td>\n",
       "      <td>CDD</td>\n",
       "      <td>M</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GILES</td>\n",
       "      <td>CDD</td>\n",
       "      <td>C</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ETHAN</td>\n",
       "      <td>INTERIM</td>\n",
       "      <td>M</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LILIANE</td>\n",
       "      <td>CDI</td>\n",
       "      <td>C</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GLODI</td>\n",
       "      <td>INTERIM</td>\n",
       "      <td>C</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FLORIANE</td>\n",
       "      <td>CDI</td>\n",
       "      <td>M</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MARIE</td>\n",
       "      <td>CDI</td>\n",
       "      <td>M</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BAKAMBU</td>\n",
       "      <td>CDI</td>\n",
       "      <td>M</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FATOU</td>\n",
       "      <td>CDD</td>\n",
       "      <td>C</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>WUAN</td>\n",
       "      <td>INTERIM</td>\n",
       "      <td>M</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HUGO</td>\n",
       "      <td>CDI</td>\n",
       "      <td>M</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>JOSEPH</td>\n",
       "      <td>CDD</td>\n",
       "      <td>C</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DIANE</td>\n",
       "      <td>ENTRPRENEUR</td>\n",
       "      <td>M</td>\n",
       "      <td>NON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FENG</td>\n",
       "      <td>CDI</td>\n",
       "      <td>M</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FRICHET</td>\n",
       "      <td>CDI</td>\n",
       "      <td>M</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HOLLANDE</td>\n",
       "      <td>CDI</td>\n",
       "      <td>M</td>\n",
       "      <td>OUI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID_NOM contrat_de_travail etat_civile Solvable\n",
       "0       jean                CDI           M      OUI\n",
       "1    VANESSA                CDI           M      OUI\n",
       "2   TARCISSE                CDI           C      NON\n",
       "3    TIBAULT                CDD           M      OUI\n",
       "4      GILES                CDD           C      NON\n",
       "5      ETHAN            INTERIM           M      NON\n",
       "6    LILIANE                CDI           C      NON\n",
       "7      GLODI            INTERIM           C      NON\n",
       "8   FLORIANE                CDI           M      OUI\n",
       "9      MARIE                CDI           M      OUI\n",
       "10   BAKAMBU                CDI           M      OUI\n",
       "11     FATOU                CDD           C      NON\n",
       "12      WUAN            INTERIM           M      NON\n",
       "13      HUGO                CDI           M      NON\n",
       "14    JOSEPH                CDD           C      NON\n",
       "15     DIANE        ENTRPRENEUR           M      NON\n",
       "16      FENG                CDI           M      OUI\n",
       "17   FRICHET                CDI           M      OUI\n",
       "18  HOLLANDE                CDI           M      OUI"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cd_immo.select_dtypes(include='object') # sélectionner les colonnes objet "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8db3c6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ID_NOM', 'contrat_de_travail', 'etat_civile', 'Solvable'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "label_categ = cd_immo.select_dtypes(include='object').columns # récupération des noms des colonnes objet\n",
    "print(label_categ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c8dda989",
   "metadata": {},
   "outputs": [],
   "source": [
    "labelE = LabelEncoder() # attribution fonction LabelEncoder() à la variable labelE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "212aa4ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in label_categ[1:]: # Boucle pour traiter les différentes colonne, car la fonction prend uniquement les arrya 1D\n",
    "    cd_immo[i] = labelE.fit_transform(cd_immo[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "34dfa466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_NOM</th>\n",
       "      <th>Niv_Etude_Bac</th>\n",
       "      <th>age</th>\n",
       "      <th>contrat_de_travail</th>\n",
       "      <th>Salaire</th>\n",
       "      <th>dette_anterieure</th>\n",
       "      <th>etat_civile</th>\n",
       "      <th>apport</th>\n",
       "      <th>enfant_a_Charge</th>\n",
       "      <th>Solvable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jean</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VANESSA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>30500.0</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TARCISSE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TIBAULT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GILES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID_NOM  Niv_Etude_Bac   age  contrat_de_travail  Salaire  \\\n",
       "0      jean            3.0  45.0                   1  40000.0   \n",
       "1   VANESSA            5.0  28.0                   1  30500.0   \n",
       "2  TARCISSE            0.0  55.0                   1  28000.0   \n",
       "3   TIBAULT            4.0  23.0                   0  15000.0   \n",
       "4     GILES            0.0  33.0                   0  27000.0   \n",
       "\n",
       "   dette_anterieure  etat_civile  apport  enfant_a_Charge  Solvable  \n",
       "0            4000.0            1     0.3         3.000000         1  \n",
       "1            1320.0            1     0.1         0.000000         1  \n",
       "2           40000.0            0     0.0         0.000000         0  \n",
       "3               0.0            1     0.1         1.333333         1  \n",
       "4            3000.0            0     0.1         2.000000         0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cd_immo.head() # afficher les 5 premières lignes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5418de74",
   "metadata": {},
   "source": [
    "### Mise à l’échelle des features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f76938ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = StandardScaler().fit_transform(cd_immo.iloc[:,1:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ba8448",
   "metadata": {},
   "source": [
    "### Fractionner le jeu de données pour l’entrainement et le test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "905d0811",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, cd_immo[\"Solvable\"], test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "63efa605",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taille de X_train : 15\n",
      "Taille de X_test : 4\n"
     ]
    }
   ],
   "source": [
    "print(\"Taille de X_train :\", len(X_train))\n",
    "print(\"Taille de X_test :\", len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f56a9328",
   "metadata": {},
   "source": [
    "## Visualisation de données"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ef28b8b",
   "metadata": {},
   "source": [
    "#### Chargement des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7627fcac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Montant_du_capitale</th>\n",
       "      <th>Temps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Montant_du_capitale  Temps\n",
       "0                    1      5\n",
       "1                   12      6\n",
       "2                   13      7\n",
       "3                    2      8\n",
       "4                    3      9"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_montant = pd.read_csv(\"./base_donnees/Montant_Temps.csv\")\n",
    "data_montant.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8146285",
   "metadata": {},
   "source": [
    "#### Séparation des données en abscisses et ordonnées"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8b956b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data_montant.iloc[:,1] # Valeurs d'abscisses\n",
    "y = data_montant.iloc[:,0] # Valeurs d'ordonnées"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbc4a13a",
   "metadata": {},
   "source": [
    "#### Tracer les données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "abe59a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.plot(x,y, linewidth=2) # réalisation du plot (graphique) de y en fonction de x\n",
    "plt.savefig(\"./Figures/ligne.png\", bbox_inches='tight') # Sauvegarde du graphique\n",
    "plt.show() # affichage du plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f0feeb2",
   "metadata": {},
   "source": [
    "###  Rajoutez du style à vos graphiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "52c418d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y, linewidth=2, color=\"red\", linestyle = \"dashed\", marker=\"^\", markersize=10, markerfacecolor=\"blue\")\n",
    "plt.title(\"Montant du capital en fonction du temps\", fontsize=15) # Ajout d'un titre\n",
    "plt.xlabel(\"Temps\") # Ajout du nom de l'axe des x\n",
    "plt.ylabel(\"Montant du capital\") # Ajout du nom de l'axe des y\n",
    "plt.grid(visible=True) # Activation de la grille\n",
    "plt.savefig(\"./Figures/ligne_personnalise.png\", bbox_inches='tight') # Sauvegarde du graphique\n",
    "plt.show() # Affichage de la représentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3b93de6",
   "metadata": {},
   "source": [
    "### Visualisation de données sous forme de nuage de points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d03b65b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y) # Création d'un nuage depoint, de y en fonction de x\n",
    "plt.title(\"Montant du capital en fonction du temps\", fontsize=15) # Ajout d'un titre\n",
    "plt.xlabel(\"Temps\") # Ajout de nom de l'axe X\n",
    "plt.ylabel(\"Montant du capital\")# Ajout de l'axe Y\n",
    "plt.grid(visible=True) # Activation de la grille\n",
    "plt.savefig(\"./Figures/nuage_point.png\", bbox_inches='tight') # Sauvegarde du graphique\n",
    "plt.show() # Affichage du plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e732946",
   "metadata": {},
   "source": [
    "## Analyse de données"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bb17d9d",
   "metadata": {},
   "source": [
    "### Mesure de tendance centrale"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "651d3207",
   "metadata": {},
   "source": [
    "#### Chargement des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "51ba6240",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_tc = pd.read_csv(\"./base_donnees/tendance_centrale.csv\") # Chargement des données tendance_centrale.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e9b15229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nom</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANATOLE</td>\n",
       "      <td>55</td>\n",
       "      <td>4.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vincent</td>\n",
       "      <td>59</td>\n",
       "      <td>3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brenda</td>\n",
       "      <td>55</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Philippe</td>\n",
       "      <td>53</td>\n",
       "      <td>5.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Michel</td>\n",
       "      <td>30</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Nom  Age  Rating\n",
       "0    ANATOLE   55    4.53\n",
       "1   Vincent    59    3.54\n",
       "2    Brenda    55    3.98\n",
       "3  Philippe    53    5.59\n",
       "4    Michel    30    3.50"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tc.head() # afficher les 5 premières lignes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d551552",
   "metadata": {},
   "source": [
    "#### Analyse descriptive et des modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7a72a095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>45.647059</td>\n",
       "      <td>4.438824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.723462</td>\n",
       "      <td>0.800390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>3.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>4.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>4.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>5.980000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age     Rating\n",
       "count  17.000000  17.000000\n",
       "mean   45.647059   4.438824\n",
       "std    10.723462   0.800390\n",
       "min    30.000000   3.500000\n",
       "25%    34.000000   3.800000\n",
       "50%    49.000000   4.300000\n",
       "75%    53.000000   4.800000\n",
       "max    59.000000   5.980000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tc.describe() # Analyse descriptive du jeu de données tendance centrale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "342925ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nom</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>perez</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Nom   Age  Rating\n",
       "0  perez  53.0    3.78\n",
       "1    NaN   NaN    3.95\n",
       "2    NaN   NaN    4.30\n",
       "3    NaN   NaN    4.80\n",
       "4    NaN   NaN    5.98"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tc.mode() # Calcul des modes (les valeurs les plus fréquente) du jeu de données"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed9af399",
   "metadata": {},
   "source": [
    "Ici la valeur la plus fréquente parmi le **Nom** est *Perez*, l'**Age** le plus fréquent est *53*, et plusieurs valeurs **Rating** (5) sont représenté 2 fois."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "960494f7",
   "metadata": {},
   "source": [
    "### Analyse de la variance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf0c8aed",
   "metadata": {},
   "source": [
    "On cherche ici a vérifier si l'age des personnes a une relation particulière avec le rating.\n",
    "On emet donc les deux hypothèses suivantes :  \n",
    "    - **H0** : Hypothèse nulle, on suppose que les échantillons sont issus d'une même population.  \n",
    "    - **H1** : On suppose que chaque échantillon analysé est indépendant des autres échantillons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "93ef586a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nom</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANATOLE</td>\n",
       "      <td>55</td>\n",
       "      <td>4.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vincent</td>\n",
       "      <td>59</td>\n",
       "      <td>3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brenda</td>\n",
       "      <td>55</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Philippe</td>\n",
       "      <td>53</td>\n",
       "      <td>5.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Michel</td>\n",
       "      <td>30</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tillier</td>\n",
       "      <td>59</td>\n",
       "      <td>4.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Blanchard</td>\n",
       "      <td>53</td>\n",
       "      <td>3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>perez</td>\n",
       "      <td>34</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>théo</td>\n",
       "      <td>40</td>\n",
       "      <td>5.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>cyprien</td>\n",
       "      <td>30</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>manuella</td>\n",
       "      <td>53</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LEGGERET</td>\n",
       "      <td>49</td>\n",
       "      <td>3.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>perez</td>\n",
       "      <td>34</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sestre</td>\n",
       "      <td>40</td>\n",
       "      <td>5.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Fazal</td>\n",
       "      <td>30</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Donald</td>\n",
       "      <td>53</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Estelle</td>\n",
       "      <td>49</td>\n",
       "      <td>3.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Nom  Age  Rating\n",
       "0      ANATOLE   55    4.53\n",
       "1     Vincent    59    3.54\n",
       "2      Brenda    55    3.98\n",
       "3    Philippe    53    5.59\n",
       "4      Michel    30    3.50\n",
       "5     Tillier    59    4.90\n",
       "6   Blanchard    53    3.80\n",
       "7        perez   34    3.78\n",
       "8        théo    40    5.98\n",
       "9     cyprien    30    4.80\n",
       "10   manuella    53    4.30\n",
       "11   LEGGERET    49    3.95\n",
       "12       perez   34    3.78\n",
       "13    sestre     40    5.98\n",
       "14      Fazal    30    4.80\n",
       "15      Donald   53    4.30\n",
       "16     Estelle   49    3.95"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9a1f1f35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La valeur de statistique F est : 249.65148011543107\n",
      "La valeur de p_value associée est : 1.1416831449220863e-16\n"
     ]
    }
   ],
   "source": [
    "F, p_value = stats.f_oneway(data_tc[\"Age\"], data_tc[\"Rating\"])\n",
    "print(\"La valeur de statistique F est : \" + str(F))\n",
    "print(\"La valeur de p_value associée est : \" + str(p_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa47cdf",
   "metadata": {},
   "source": [
    "La valeur de de p est inférieur au seuil de 0,05 on peut donc rejeté l'hypothèse H0. Il s'emblerait donc y avoir une influence de l'**Age** sur le **Rating**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6db2e2",
   "metadata": {},
   "source": [
    "Test ANOVA depuis la bibliothèque statsmodels :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d2b8d2b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api\n",
    "import statsmodels.api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a41bb2dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.049125</td>\n",
       "      <td>0.049125</td>\n",
       "      <td>0.072237</td>\n",
       "      <td>0.791769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>15.0</td>\n",
       "      <td>10.200851</td>\n",
       "      <td>0.680057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df     sum_sq   mean_sq         F    PR(>F)\n",
       "Age        1.0   0.049125  0.049125  0.072237  0.791769\n",
       "Residual  15.0  10.200851  0.680057       NaN       NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit = statsmodels.formula.api.ols('Rating ~ Age', data = data_tc).fit()\n",
    "table = statsmodels.api.stats.anova_lm(fit)\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "979956c3",
   "metadata": {},
   "source": [
    "La valeur de de p est supérieur au seuil de 0,05 on peut donc accepter l'hypothèse H0. Il n'y a donc aucune infuence de l'**Age** sur le **Rating**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed7659f4",
   "metadata": {},
   "source": [
    "### Analyse de corrélations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d71c318f",
   "metadata": {},
   "source": [
    "####  Charrgement des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bf633307",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_iris = pd.read_csv(\"./base_donnees/iris.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c745e892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longueur_sepal</th>\n",
       "      <th>largeur_sepal</th>\n",
       "      <th>longueur_petal</th>\n",
       "      <th>largeur_petal</th>\n",
       "      <th>espece</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longueur_sepal  largeur_sepal  longueur_petal  largeur_petal  espece\n",
       "0             5.1            3.5             1.4            0.2  setosa\n",
       "1             4.9            3.0             1.4            0.2  setosa\n",
       "2             4.7            3.2             1.3            0.2  setosa\n",
       "3             4.6            3.1             1.5            0.2  setosa\n",
       "4             5.0            3.6             1.4            0.2  setosa"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_iris.head() # Affichage des 5 premières lignes des données iris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f197aba3",
   "metadata": {},
   "source": [
    "#### Analyse graphique "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c808fb",
   "metadata": {},
   "source": [
    "Réalisation de graphique nuage de point, pour visualiser l'allure du jeu de donnée. Dans un premier temps on cherche a déterminer le nombre d'espèces différentes présente dans le jeux de données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a9da80c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_iris[\"espece\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79e608bf",
   "metadata": {},
   "source": [
    "Ce qui nous permet de créer liste contenant une couleur par catégorie, ce qui pourra nous être utile pour la suite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c5261152",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "legende_especes = data_iris[\"espece\"].unique()\n",
    "print(legende_especes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4f76efbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longueur_sepal</th>\n",
       "      <th>largeur_sepal</th>\n",
       "      <th>longueur_petal</th>\n",
       "      <th>largeur_petal</th>\n",
       "      <th>espece</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longueur_sepal  largeur_sepal  longueur_petal  largeur_petal  espece  label\n",
       "0             5.1            3.5             1.4            0.2  setosa      0\n",
       "1             4.9            3.0             1.4            0.2  setosa      0\n",
       "2             4.7            3.2             1.3            0.2  setosa      0\n",
       "3             4.6            3.1             1.5            0.2  setosa      0\n",
       "4             5.0            3.6             1.4            0.2  setosa      0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_especes = labelE.fit_transform(data_iris[\"espece\"])\n",
    "data_iris[\"label\"] = label_especes\n",
    "data_iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f733d237",
   "metadata": {},
   "outputs": [],
   "source": [
    "couleur_cat = np.array(['r','g','b'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05d8efb",
   "metadata": {},
   "source": [
    "On peut alors tracer la visualisation des donées avec 1 couleur pour chaque espèce. Naturellement on a tendance à associer la *longueur_sepal* à le *largeur_sepal*, ainsi que la *longueur_petal* à la *largeur_petal*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "889aa5cf",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "# ax1 = plt.subplot(121)\n",
    "ax1.scatter(data_iris[\"longueur_sepal\"], data_iris[\"largeur_sepal\"], c=couleur_cat[label_especes])\n",
    "ax1.set_title(\"Visualisation de la taille des sepals\", fontsize = 15)\n",
    "ax1.set_xlabel(\"Largeur des sepales\")\n",
    "ax1.set_ylabel(\"Longueur des sepales\")\n",
    "ax1.grid(True)\n",
    "\n",
    "# ax2 = plt.subplot(122)\n",
    "ax2.scatter(data_iris[\"longueur_petal\"], data_iris[\"largeur_petal\"], c=couleur_cat[label_especes])\n",
    "ax2.set_title(\"Visualisation de la taille des petales\", fontsize = 15)\n",
    "ax2.set_xlabel(\"Largeur des petales\")\n",
    "ax2.set_ylabel(\"Longueur des petales\")\n",
    "ax2.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left = 0,\n",
    "           bottom = 0,\n",
    "           right = 2, # Largeur du graphique\n",
    "           top = 1, # Hauteur du graphique\n",
    "           wspace = 0.2,\n",
    "           hspace = 0)\n",
    "\n",
    "plt.savefig(\"./Figures/nuage_point_especes.png\", bbox_inches='tight') # Sauvegarde du graphique\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "799c40f5",
   "metadata": {},
   "source": [
    "*Remarque : Il manque les légendes, pour les ajouter facilement on aurait pu isoler les valeurs de chaque catégories et réaliser ensuite trois nuages de points superposés. Ici nous avons uniquement cherché à visualiser rapidement les données.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e61f9bb",
   "metadata": {},
   "source": [
    "Au vu des deux graphiques obtenu il semblerait que la taille des sépales d'une des trois espèces se distingue des deux autres. Cette hypothèse est également visible avec la taille des petales. La taille des petales semble également distinguer les deux autres espèces."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec6a1325",
   "metadata": {},
   "source": [
    "On peut également représenter les autres paramètres entre eux, pour se faire on peut réaliser une boucle pour représenter tous les subplots :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "35543939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xy = len(data_iris.columns[:-1])\n",
    "fig, axs = plt.subplots(xy-1, xy-1)\n",
    "\n",
    "for y, p2 in zip(data_iris.columns[:-2], range(xy)):\n",
    "    for x, p1 in zip(data_iris.columns[:-2], range(xy)):\n",
    "        if y != x :\n",
    "            axs[p1, p2].scatter(data_iris[x], data_iris[y], c=couleur_cat[label_especes])\n",
    "        else:\n",
    "            axs[p1, p2].text(0.5, 0.5, y, ha=\"center\", va=\"center\", fontsize=\"large\", fontweight=\"bold\")\n",
    "\n",
    "plt.subplots_adjust(left = 0,\n",
    "           bottom = 0,\n",
    "           right = 2, # Largeur du graphique\n",
    "           top = 2, # Hauteur du graphique\n",
    "           wspace = 0.4,\n",
    "           hspace = 0.4)\n",
    "\n",
    "plt.savefig(\"./Figures/representation_donnees_iris.png\", bbox_inches='tight') # Sauvegarde du graphique\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0f31380",
   "metadata": {},
   "source": [
    "Ce graphique peut également être obtenus par la fonction sns.pairplot de la bibliothèque seaborn :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1fdeff72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 804.75x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "fig_sns = sns.pairplot(data_iris, hue='espece',vars=data_iris.columns[:-2])\n",
    "fig_sns.savefig(\"./Figures/representation_donnees_iris_pairplot.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46e5e5a7",
   "metadata": {},
   "source": [
    "#### Analyse mathématique"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "153d4921",
   "metadata": {},
   "source": [
    "Correlation des données par fonction corr de la bibliothèque pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "147527e1",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "heat_map = sns.heatmap(data_iris.corr(method=\"pearson\"), annot=True,cmap=\"YlGnBu\")\n",
    "fig_heatmap = heat_map.get_figure()\n",
    "fig_heatmap.savefig(\"./Figures/correlation_heatmap.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60dc1128",
   "metadata": {},
   "source": [
    "Pour calculer la valeur r et la p_value associé on utilisation de scipy, avec la fonction pearsonr :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3b49b43a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La valeur r est de : -0.11756978413300204\n",
      "La valeur p est de : 0.15189826071144918\n"
     ]
    }
   ],
   "source": [
    "# Calcul de la valeur r et p entre les variable largeur_sepal et longueur_sepal\n",
    "r_sepal, p_sepal = stats.pearsonr(data_iris[\"largeur_sepal\"],data_iris[\"longueur_sepal\"])\n",
    "print(\"La valeur r est de : \" + str(r_sepal))\n",
    "print(\"La valeur p est de : \" + str(p_sepal))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf539d9f",
   "metadata": {},
   "source": [
    "D'après les résultats obtenus, la correlation de Pearson nous renvoie une valeur *r* de -0.11, soit une valeur négative et se rapprochant de 0. On a également *p_value* de 0.15. Cette dernière étant supérieur au seuil de 0.05, on peut accepter l'hypothèse nulle, disant qu'il n'y a pas de correlation linéaire entre la largeur et la longueur des sépales. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3794ccb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La valeur r est de : 0.962865431402796\n",
      "La valeur p est de : 4.6750039073285846e-86\n"
     ]
    }
   ],
   "source": [
    "# Calcul de la valeur r et p entre les variable largeur_petal et longueur_petal\n",
    "r_petal, p_petal = stats.pearsonr(data_iris[\"largeur_petal\"],data_iris[\"longueur_petal\"])\n",
    "print(\"La valeur r est de : \" + str(r_petal))\n",
    "print(\"La valeur p est de : \" + str(p_petal))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1246e69",
   "metadata": {},
   "source": [
    "D'après les résultats obtenus, la correlation de Pearson nous renvoie une valeur *r* de 0.96, soit une valeur très de 1. On a également *p_value* de 4.68e-86. Cette dernière étant très inférieur au seuil de 0.05, on peut rejeter l'hypothèse nulle. Il semblerait donc qu'il y a une correlation linéaire positive entre la largeur et la longueur des petales. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fb975113",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La valeur r est de : -0.4284401043305397\n",
      "La valeur p est de : 4.5133142672730875e-08\n"
     ]
    }
   ],
   "source": [
    "# Calcul de la valeur r et p entre les variable largeur_petal et longueur_petal\n",
    "r_petal, p_petal = stats.pearsonr(data_iris[\"largeur_sepal\"],data_iris[\"longueur_petal\"])\n",
    "print(\"La valeur r est de : \" + str(r_petal))\n",
    "print(\"La valeur p est de : \" + str(p_petal))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9343041a",
   "metadata": {},
   "source": [
    "D'après les résultats obtenus, la correlation de Pearson nous renvoie une valeur r de -0.43. On a également p_value de 4.51e-8. Cette dernière étant très inférieur au seuil de 0.05, on peut rejeter l'hypothèse nulle. Il semblerait donc qu'il y a une légère correlation linéaire negative entre la largeur des sepales et la longueur des petales. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "894c28e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La valeur r est de : 0.8179411262715757\n",
      "La valeur p est de : 2.325498079793167e-37\n"
     ]
    }
   ],
   "source": [
    "# Calcul de la valeur r et p entre les variable largeur_petal et longueur_petal\n",
    "r_petal, p_petal = stats.pearsonr(data_iris[\"longueur_sepal\"],data_iris[\"largeur_petal\"])\n",
    "print(\"La valeur r est de : \" + str(r_petal))\n",
    "print(\"La valeur p est de : \" + str(p_petal))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76fa0343",
   "metadata": {},
   "source": [
    "D'après les résultats obtenus, la correlation de Pearson nous renvoie une valeur r de 0.82. On a également p_value de 2.33e-37. Cette dernière étant très inférieur au seuil de 0.05, on peut rejeter l'hypothèse nulle. Il semblerait donc qu'il y a une correlation linéaire positive entre la longueur des sepales et la largeur des petales. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1a7027c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La valeur r est de : 0.8717537758865832\n",
      "La valeur p est de : 1.0386674194497525e-47\n"
     ]
    }
   ],
   "source": [
    "# Calcul de la valeur r et p entre les variable largeur_petal et longueur_petal\n",
    "r_petal, p_petal = stats.pearsonr(data_iris[\"longueur_sepal\"],data_iris[\"longueur_petal\"])\n",
    "print(\"La valeur r est de : \" + str(r_petal))\n",
    "print(\"La valeur p est de : \" + str(p_petal))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f58db3",
   "metadata": {},
   "source": [
    "D'après les résultats obtenus, la correlation de Pearson nous renvoie une valeur r de 0.87. On a également p_value de 1.04e-47. Cette dernière étant très inférieur au seuil de 0.05, on peut rejeter l'hypothèse nulle. Il semblerait donc qu'il y a une correlation linéaire positive entre la longueur des sepales et la longueur des petales. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6420b44a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La valeur r est de : -0.3661259325364391\n",
      "La valeur p est de : 4.0732285132461925e-06\n"
     ]
    }
   ],
   "source": [
    "# Calcul de la valeur r et p entre les variable largeur_petal et longueur_petal\n",
    "r_petal, p_petal = stats.pearsonr(data_iris[\"largeur_sepal\"],data_iris[\"largeur_petal\"])\n",
    "print(\"La valeur r est de : \" + str(r_petal))\n",
    "print(\"La valeur p est de : \" + str(p_petal))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c8b723",
   "metadata": {},
   "source": [
    "D'après les résultats obtenus, la correlation de Pearson nous renvoie une valeur r de -0.37. On a également p_value de 4.07e-06. Cette dernière étant très inférieur au seuil de 0.05, on peut rejeter l'hypothèse nulle. Il semblerait donc qu'il y a une légère correlation linéaire negative entre la largeur des sepales et la largeur des petales. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}