home_loan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_credit = pd.read_csv(\"./train.csv\")\n",
    "\n",
    "df_credit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_credit.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             601 non-null    object \n",
      " 2   Married            611 non-null    object \n",
      " 3   Dependents         599 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      582 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         592 non-null    float64\n",
      " 9   Loan_Amount_Term   600 non-null    float64\n",
      " 10  Credit_History     564 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "verifier si on a des valeurs manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour supprimer les valeurs manquantes :\n",
    "- Pour les variables catégoriquesn on va les remplacer par le mode\n",
    "- Pour les variables nuémriques , on va les remplacer par la moyenne ou la mediane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Les variables categoriques sont : ['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'Credit_History', 'Property_Area', 'Loan_Status']\n",
      "Les variables numeriques sont : ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term']\n"
     ]
    }
   ],
   "source": [
    "var_cat = ['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'Credit_History', 'Property_Area', 'Loan_Status']\n",
    "var_num = ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount','Loan_Amount_Term']\n",
    "\n",
    "print('Les variables categoriques sont :', var_cat)\n",
    "print('Les variables numeriques sont :', var_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Supprimer les valeurs manquantes coté variables categoriques\n",
    "df['Gender'].fillna(df['Gender'].mode()[0],inplace=True) \n",
    "df['Married'].fillna(df['Married'].mode()[0],inplace=True)  \n",
    "df['Dependents'].fillna(df['Dependents'].mode()[0],inplace=True) \n",
    "df['Self_Employed'].fillna(df['Self_Employed'].mode()[0],inplace=True)  \n",
    "df['Credit_History'].fillna(df['Credit_History'].mode()[0],inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Supprimer les valeurs manquantes coté variables numeriques\n",
    "df['LoanAmount'].fillna(df['LoanAmount'].median(),inplace=True) \n",
    "df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fonction pour remplacer les valeurs manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trans(data):\n",
    "    for c in data.columns:\n",
    "        if data[c].dtype== 'int64' or data[c].dtype=='float64':\n",
    "            data[c].fillna(data[c].median(), inplace=True)\n",
    "        else:\n",
    "            data[c].fillna(data[c].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse univariée"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>LP002978</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>LP002979</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>LP002983</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>LP002984</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>LP002990</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
       "0    LP001002    Male      No          0      Graduate            No   \n",
       "1    LP001003    Male     Yes          1      Graduate            No   \n",
       "2    LP001005    Male     Yes          0      Graduate           Yes   \n",
       "3    LP001006    Male     Yes          0  Not Graduate            No   \n",
       "4    LP001008    Male      No          0      Graduate            No   \n",
       "..        ...     ...     ...        ...           ...           ...   \n",
       "609  LP002978  Female      No          0      Graduate            No   \n",
       "610  LP002979    Male     Yes         3+      Graduate            No   \n",
       "611  LP002983    Male     Yes          1      Graduate            No   \n",
       "612  LP002984    Male     Yes          2      Graduate            No   \n",
       "613  LP002990  Female      No          0      Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5849                0.0       128.0             360.0   \n",
       "1               4583             1508.0       128.0             360.0   \n",
       "2               3000                0.0        66.0             360.0   \n",
       "3               2583             2358.0       120.0             360.0   \n",
       "4               6000                0.0       141.0             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "0               1.0         Urban           Y  \n",
       "1               1.0         Rural           N  \n",
       "2               1.0         Urban           Y  \n",
       "3               1.0         Urban           Y  \n",
       "4               1.0         Urban           Y  \n",
       "..              ...           ...         ...  \n",
       "609             1.0         Rural           Y  \n",
       "610             1.0         Rural           Y  \n",
       "611             1.0         Urban           Y  \n",
       "612             1.0         Urban           Y  \n",
       "613             0.0     Semiurban           N  \n",
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    "\n",
    "\n",
    "fig = px.histogram(df, x=\"Loan_Status\", title=\"Credit accordé ou pas\", color=\"Loan_Status\", template=\"plotly_dark\")\n",
    "fig.show(font = dict(size=17, family='Franklin Gothic'))"
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   "source": [
    "les Variables catégoriques"
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   "cell_type": "code",
   "execution_count": 17,
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       "Gender\n",
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       "Female    18.241042\n",
       "Name: proportion, dtype: float64"
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     "execution_count": 17,
     "metadata": {},
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    "df[\"Gender\"].value_counts(normalize=True)*100"
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   "execution_count": 18,
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    "fig = px.histogram(df, x='Gender', title='Genre', color='Gender', template=\"plotly_dark\")\n",
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       "Married\n",
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           "zerolinewidth": 2
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            "standoff": 15
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          "No",
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   ],
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    "\n",
    "fig = px.histogram(df, x='Married',title=\"Nombres de gens marriés\", color='Married', template=\"plotly_dark\")\n",
    "fig.show(font= dict(size=17, family='Franklin Gothic'))"
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   "metadata": {},
   "source": [
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       "1     102\n",
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       "3+     51\n",
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            "ticks": ""
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           "title": {
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        },
        "title": {
         "text": "Le nombre d enfants"
        },
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         "anchor": "y",
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          "0",
          "1",
          "2",
          "3+"
         ],
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        },
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   ],
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    "#df['Dependents'].value_counts().plot.bar(title=\"Le nombre d enfants\")\n",
    "\n",
    "\n",
    "fig = px.histogram(df, x='Dependents',title=\"Le nombre d enfants\", color='Dependents', template=\"plotly_dark\")\n",
    "fig.show(font= dict(size=17, family='Franklin Gothic'))"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Les variables numériques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>145.752443</td>\n",
       "      <td>342.410423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>84.107233</td>\n",
       "      <td>64.428629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.250000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>164.750000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term\n",
       "count       614.000000         614.000000  614.000000        614.000000\n",
       "mean       5403.459283        1621.245798  145.752443        342.410423\n",
       "std        6109.041673        2926.248369   84.107233         64.428629\n",
       "min         150.000000           0.000000    9.000000         12.000000\n",
       "25%        2877.500000           0.000000  100.250000        360.000000\n",
       "50%        3812.500000        1188.500000  128.000000        360.000000\n",
       "75%        5795.000000        2297.250000  164.750000        360.000000\n",
       "max       81000.000000       41667.000000  700.000000        480.000000"
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[var_num].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             614 non-null    object \n",
      " 2   Married            614 non-null    object \n",
      " 3   Dependents         614 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      614 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         614 non-null    float64\n",
      " 9   Loan_Amount_Term   614 non-null    float64\n",
      " 10  Credit_History     614 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          0.49
         ],
         "title": {
          "text": "LoanAmount"
         }
        },
        "xaxis2": {
         "anchor": "y2",
         "domain": [
          0.51,
          1
         ],
         "matches": "x",
         "title": {
          "text": "LoanAmount"
         }
        },
        "xaxis3": {
         "anchor": "y3",
         "domain": [
          0,
          0.49
         ],
         "matches": "x",
         "showgrid": true,
         "showticklabels": false
        },
        "xaxis4": {
         "anchor": "y4",
         "domain": [
          0.51,
          1
         ],
         "matches": "x",
         "showgrid": true,
         "showticklabels": false
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          0.7326
         ],
         "title": {
          "text": "count"
         }
        },
        "yaxis2": {
         "anchor": "x2",
         "domain": [
          0,
          0.7326
         ],
         "matches": "y",
         "showticklabels": false
        },
        "yaxis3": {
         "anchor": "x3",
         "domain": [
          0.7426,
          1
         ],
         "matches": "y3",
         "showgrid": false,
         "showline": false,
         "showticklabels": false,
         "ticks": ""
        },
        "yaxis4": {
         "anchor": "x4",
         "domain": [
          0.7426,
          1
         ],
         "matches": "y3",
         "showgrid": false,
         "showline": false,
         "showticklabels": false,
         "ticks": ""
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#LoanAmount\n",
    "\n",
    "fig = px.histogram(df, x=\"LoanAmount\", facet_col=\"Loan_Status\", marginal=\"box\", color='Loan_Status')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applicantincome\n",
    "\n",
    "#plt.figure(1)\n",
    "#plt.subplot(121)\n",
    "#sns.distplot(df['ApplicantIncome'])\n",
    "\n",
    "#plt.subplot(122)\n",
    "#df['ApplicantIncome'].plot.box(figsize=(16,5))\n",
    "#plt.suptitle('')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse bivariée"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Gender',\n",
       " 'Married',\n",
       " 'Dependents',\n",
       " 'Education',\n",
       " 'Self_Employed',\n",
       " 'Credit_History',\n",
       " 'Property_Area',\n",
       " 'Loan_Status']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Credit_History'] = df[\"Credit_History\"].replace(1.0, 'Yes')\n",
    "df['Credit_History'] = df[\"Credit_History\"].replace(0.0, 'No')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# les variables catégoriques\n",
    "fig,axes= plt.subplots(4,2,figsize=(12,15))\n",
    "for idx, cat_col in enumerate(var_cat):\n",
    "    row,col =idx//2, idx%2\n",
    "    sns.countplot(x=cat_col,data=df, hue='Loan_Status',ax=axes[row,col])\n",
    "plt.subplots_adjust(hspace=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matrice de correlation des variables numeriques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "appliquer la matrice de correlation uniquement sur les valeur numeriques du dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[var_num].corr(),cmap = 'Wistia', annot= True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creation du modele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>Yes</td>\n",
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       "      <th>610</th>\n",
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       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>Male</td>\n",
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       "      <td>2</td>\n",
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       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender Married Dependents     Education Self_Employed Credit_History  \\\n",
       "0      Male      No          0      Graduate            No            Yes   \n",
       "1      Male     Yes          1      Graduate            No            Yes   \n",
       "2      Male     Yes          0      Graduate           Yes            Yes   \n",
       "3      Male     Yes          0  Not Graduate            No            Yes   \n",
       "4      Male      No          0      Graduate            No            Yes   \n",
       "..      ...     ...        ...           ...           ...            ...   \n",
       "609  Female      No          0      Graduate            No            Yes   \n",
       "610    Male     Yes         3+      Graduate            No            Yes   \n",
       "611    Male     Yes          1      Graduate            No            Yes   \n",
       "612    Male     Yes          2      Graduate            No            Yes   \n",
       "613  Female      No          0      Graduate           Yes             No   \n",
       "\n",
       "    Property_Area Loan_Status  \n",
       "0           Urban           Y  \n",
       "1           Rural           N  \n",
       "2           Urban           Y  \n",
       "3           Urban           Y  \n",
       "4           Urban           Y  \n",
       "..            ...         ...  \n",
       "609         Rural           Y  \n",
       "610         Rural           Y  \n",
       "611         Urban           Y  \n",
       "612         Urban           Y  \n",
       "613     Semiurban           N  \n",
       "\n",
       "[614 rows x 8 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num = df[var_num]\n",
    "df_cat =  df[var_cat]\n",
    "df_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "     Gender_Male  Married_Yes  Dependents_1  Dependents_2  Dependents_3+  \\\n",
       "0           True        False         False         False          False   \n",
       "1           True         True          True         False          False   \n",
       "2           True         True         False         False          False   \n",
       "3           True         True         False         False          False   \n",
       "4           True        False         False         False          False   \n",
       "..           ...          ...           ...           ...            ...   \n",
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       "\n",
       "     Education_Not Graduate  Self_Employed_Yes  Credit_History_Yes  \\\n",
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       "1                     False              False                True   \n",
       "2                     False               True                True   \n",
       "3                      True              False                True   \n",
       "4                     False              False                True   \n",
       "..                      ...                ...                 ...   \n",
       "609                   False              False                True   \n",
       "610                   False              False                True   \n",
       "611                   False              False                True   \n",
       "612                   False              False                True   \n",
       "613                   False               True               False   \n",
       "\n",
       "     Property_Area_Semiurban  Property_Area_Urban  Loan_Status_Y  \n",
       "0                      False                 True           True  \n",
       "1                      False                False          False  \n",
       "2                      False                 True           True  \n",
       "3                      False                 True           True  \n",
       "4                      False                 True           True  \n",
       "..                       ...                  ...            ...  \n",
       "609                    False                False           True  \n",
       "610                    False                False           True  \n",
       "611                    False                 True           True  \n",
       "612                    False                 True           True  \n",
       "613                     True                False          False  \n",
       "\n",
       "[614 rows x 11 columns]"
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     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat = pd.get_dummies(df_cat, drop_first=True)\n",
    "df_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender_Male  Married_Yes  Dependents_1  Dependents_2  Dependents_3+  \\\n",
       "0           True        False         False         False          False   \n",
       "1           True         True          True         False          False   \n",
       "2           True         True         False         False          False   \n",
       "3           True         True         False         False          False   \n",
       "4           True        False         False         False          False   \n",
       "..           ...          ...           ...           ...            ...   \n",
       "609        False        False         False         False          False   \n",
       "610         True         True         False         False           True   \n",
       "611         True         True          True         False          False   \n",
       "612         True         True         False          True          False   \n",
       "613        False        False         False         False          False   \n",
       "\n",
       "     Education_Not Graduate  Self_Employed_Yes  Credit_History_Yes  \\\n",
       "0                     False              False                True   \n",
       "1                     False              False                True   \n",
       "2                     False               True                True   \n",
       "3                      True              False                True   \n",
       "4                     False              False                True   \n",
       "..                      ...                ...                 ...   \n",
       "609                   False              False                True   \n",
       "610                   False              False                True   \n",
       "611                   False              False                True   \n",
       "612                   False              False                True   \n",
       "613                   False               True               False   \n",
       "\n",
       "     Property_Area_Semiurban  Property_Area_Urban  Loan_Status_Y  \\\n",
       "0                      False                 True           True   \n",
       "1                      False                False          False   \n",
       "2                      False                 True           True   \n",
       "3                      False                 True           True   \n",
       "4                      False                 True           True   \n",
       "..                       ...                  ...            ...   \n",
       "609                    False                False           True   \n",
       "610                    False                False           True   \n",
       "611                    False                 True           True   \n",
       "612                    False                 True           True   \n",
       "613                     True                False          False   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \n",
       "0               5849                0.0       128.0             360.0  \n",
       "1               4583             1508.0       128.0             360.0  \n",
       "2               3000                0.0        66.0             360.0  \n",
       "3               2583             2358.0       120.0             360.0  \n",
       "4               6000                0.0       141.0             360.0  \n",
       "..               ...                ...         ...               ...  \n",
       "609             2900                0.0        71.0             360.0  \n",
       "610             4106                0.0        40.0             180.0  \n",
       "611             8072              240.0       253.0             360.0  \n",
       "612             7583                0.0       187.0             360.0  \n",
       "613             4583                0.0       133.0             360.0  \n",
       "\n",
       "[614 rows x 15 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded = pd.concat([df_cat, df_num], axis=1)\n",
    "df_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       True\n",
       "1      False\n",
       "2       True\n",
       "3       True\n",
       "4       True\n",
       "       ...  \n",
       "609     True\n",
       "610     True\n",
       "611     True\n",
       "612     True\n",
       "613    False\n",
       "Name: Loan_Status_Y, Length: 614, dtype: bool"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_encoded[\"Loan_Status_Y\"]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Gender_Male</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Dependents_3+</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Credit_History_Yes</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender_Male  Married_Yes  Dependents_1  Dependents_2  Dependents_3+  \\\n",
       "0           True        False         False         False          False   \n",
       "1           True         True          True         False          False   \n",
       "2           True         True         False         False          False   \n",
       "3           True         True         False         False          False   \n",
       "4           True        False         False         False          False   \n",
       "..           ...          ...           ...           ...            ...   \n",
       "609        False        False         False         False          False   \n",
       "610         True         True         False         False           True   \n",
       "611         True         True          True         False          False   \n",
       "612         True         True         False          True          False   \n",
       "613        False        False         False         False          False   \n",
       "\n",
       "     Education_Not Graduate  Self_Employed_Yes  Credit_History_Yes  \\\n",
       "0                     False              False                True   \n",
       "1                     False              False                True   \n",
       "2                     False               True                True   \n",
       "3                      True              False                True   \n",
       "4                     False              False                True   \n",
       "..                      ...                ...                 ...   \n",
       "609                   False              False                True   \n",
       "610                   False              False                True   \n",
       "611                   False              False                True   \n",
       "612                   False              False                True   \n",
       "613                   False               True               False   \n",
       "\n",
       "     Property_Area_Semiurban  Property_Area_Urban  ApplicantIncome  \\\n",
       "0                      False                 True             5849   \n",
       "1                      False                False             4583   \n",
       "2                      False                 True             3000   \n",
       "3                      False                 True             2583   \n",
       "4                      False                 True             6000   \n",
       "..                       ...                  ...              ...   \n",
       "609                    False                False             2900   \n",
       "610                    False                False             4106   \n",
       "611                    False                 True             8072   \n",
       "612                    False                 True             7583   \n",
       "613                     True                False             4583   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  \n",
       "0                  0.0       128.0             360.0  \n",
       "1               1508.0       128.0             360.0  \n",
       "2                  0.0        66.0             360.0  \n",
       "3               2358.0       120.0             360.0  \n",
       "4                  0.0       141.0             360.0  \n",
       "..                 ...         ...               ...  \n",
       "609                0.0        71.0             360.0  \n",
       "610                0.0        40.0             180.0  \n",
       "611              240.0       253.0             360.0  \n",
       "612                0.0       187.0             360.0  \n",
       "613                0.0       133.0             360.0  \n",
       "\n",
       "[614 rows x 14 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_encoded.drop(\"Loan_Status_Y\", axis=1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORTATION DES packages pour la realisation de la regression logistique\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# division notre base en train et test\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Votre model est fiable à: 83.73983739837398 %\n"
     ]
    }
   ],
   "source": [
    "# instancier le modele\n",
    "model= LogisticRegression()\n",
    "\n",
    "#entrainer le modele\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "fiabilite = model.score(X_test, y_test)*100\n",
    "print(\"Votre model est fiable à:\",fiabilite,'%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True,  True,  True,  True, False,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True, False,  True,  True,  True, False, False,\n",
       "        True,  True, False,  True,  True,  True,  True,  True,  True,\n",
       "       False,  True, False,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True, False,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "       False,  True,  True, False,  True,  True,  True,  True, False,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True, False,\n",
       "        True,  True, False, False,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tester le model\n",
    "prediction = model.predict(X_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "387    False\n",
       "266     True\n",
       "292     True\n",
       "392     True\n",
       "332     True\n",
       "       ...  \n",
       "309     True\n",
       "511     True\n",
       "98      True\n",
       "238     True\n",
       "462     True\n",
       "Name: Loan_Status_Y, Length: 123, dtype: bool"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8373983739837398"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender_Male', 'Married_Yes', 'Dependents_1', 'Dependents_2',\n",
       "       'Dependents_3+', 'Education_Not Graduate', 'Self_Employed_Yes',\n",
       "       'Credit_History_Yes', 'Property_Area_Semiurban', 'Property_Area_Urban',\n",
       "       'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# faire une prevision\n"
   ]
  }
 ],
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