Régression prix de vente biens immobiliers.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Projet 2 : Régression : SalePrice.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XeQG-S7OvufO"
      },
      "source": [
        "Objectif : prédire le prix de vente d'un bien immobiliers à partir de données"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DFdKCY1f5C8"
      },
      "source": [
        "# Import des bibliothèques\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tc6ki39UupIi"
      },
      "source": [
        "# Import des données (via Kaggle : https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)\n",
        "\n",
        "data_train = pd.read_csv('/content/train.csv')\n",
        "# data_test = pd.read_csv('/content/test.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wuPUILruxyVq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff6720f2-5897-41d7-9720-0d472eed9eb9"
      },
      "source": [
        "print(f'Le dataset contient {data_train.shape[0]} lignes et {data_train.shape[1]} colonnes')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Le dataset contient 1460 lignes et 81 colonnes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jd7mlxOqvDtt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "2fe4240e-cd5c-4631-ce0b-cabe43e378d2"
      },
      "source": [
        "data_train.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>...</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>196.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>706</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>856</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>8</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>548</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Veenker</td>\n",
              "      <td>Feedr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>978</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>284</td>\n",
              "      <td>1262</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>460</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>162.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Mn</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>486</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>434</td>\n",
              "      <td>920</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>608</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Crawfor</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>Wd Shng</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>BrkTil</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>No</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>216</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>540</td>\n",
              "      <td>756</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>7</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Detchd</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>3</td>\n",
              "      <td>642</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NoRidge</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>350.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Av</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>655</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>490</td>\n",
              "      <td>1145</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>9</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>3</td>\n",
              "      <td>836</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>50</td>\n",
              "      <td>RL</td>\n",
              "      <td>85.0</td>\n",
              "      <td>14115</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Mitchel</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1.5Fin</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1993</td>\n",
              "      <td>1995</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Wood</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>732</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>64</td>\n",
              "      <td>796</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>796</td>\n",
              "      <td>566</td>\n",
              "      <td>0</td>\n",
              "      <td>1362</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>5</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1993.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>2</td>\n",
              "      <td>480</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>40</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>320</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>Shed</td>\n",
              "      <td>700</td>\n",
              "      <td>10</td>\n",
              "      <td>2009</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>143000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>75.0</td>\n",
              "      <td>10084</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Somerst</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2004</td>\n",
              "      <td>2005</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>Stone</td>\n",
              "      <td>186.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Ex</td>\n",
              "      <td>TA</td>\n",
              "      <td>Av</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>1369</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>317</td>\n",
              "      <td>1686</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1694</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1694</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>7</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2004.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>636</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>255</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>307000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10382</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NWAmes</td>\n",
              "      <td>PosN</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>1973</td>\n",
              "      <td>1973</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>HdBoard</td>\n",
              "      <td>HdBoard</td>\n",
              "      <td>Stone</td>\n",
              "      <td>240.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Mn</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>859</td>\n",
              "      <td>BLQ</td>\n",
              "      <td>32</td>\n",
              "      <td>216</td>\n",
              "      <td>1107</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1107</td>\n",
              "      <td>983</td>\n",
              "      <td>0</td>\n",
              "      <td>2090</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>7</td>\n",
              "      <td>Typ</td>\n",
              "      <td>2</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1973.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>484</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>235</td>\n",
              "      <td>204</td>\n",
              "      <td>228</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Shed</td>\n",
              "      <td>350</td>\n",
              "      <td>11</td>\n",
              "      <td>2009</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>50</td>\n",
              "      <td>RM</td>\n",
              "      <td>51.0</td>\n",
              "      <td>6120</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>OldTown</td>\n",
              "      <td>Artery</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1.5Fin</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1931</td>\n",
              "      <td>1950</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>Wd Shng</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>BrkTil</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>952</td>\n",
              "      <td>952</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>FuseF</td>\n",
              "      <td>1022</td>\n",
              "      <td>752</td>\n",
              "      <td>0</td>\n",
              "      <td>1774</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>TA</td>\n",
              "      <td>8</td>\n",
              "      <td>Min1</td>\n",
              "      <td>2</td>\n",
              "      <td>TA</td>\n",
              "      <td>Detchd</td>\n",
              "      <td>1931.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>2</td>\n",
              "      <td>468</td>\n",
              "      <td>Fa</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>205</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>129900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>190</td>\n",
              "      <td>RL</td>\n",
              "      <td>50.0</td>\n",
              "      <td>7420</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>BrkSide</td>\n",
              "      <td>Artery</td>\n",
              "      <td>Artery</td>\n",
              "      <td>2fmCon</td>\n",
              "      <td>1.5Unf</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1939</td>\n",
              "      <td>1950</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>BrkTil</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>851</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>991</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1077</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1077</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>TA</td>\n",
              "      <td>5</td>\n",
              "      <td>Typ</td>\n",
              "      <td>2</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1939.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>1</td>\n",
              "      <td>205</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>118000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 81 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  MSSubClass MSZoning  ...  SaleType  SaleCondition SalePrice\n",
              "0   1          60       RL  ...        WD         Normal    208500\n",
              "1   2          20       RL  ...        WD         Normal    181500\n",
              "2   3          60       RL  ...        WD         Normal    223500\n",
              "3   4          70       RL  ...        WD        Abnorml    140000\n",
              "4   5          60       RL  ...        WD         Normal    250000\n",
              "5   6          50       RL  ...        WD         Normal    143000\n",
              "6   7          20       RL  ...        WD         Normal    307000\n",
              "7   8          60       RL  ...        WD         Normal    200000\n",
              "8   9          50       RM  ...        WD        Abnorml    129900\n",
              "9  10         190       RL  ...        WD         Normal    118000\n",
              "\n",
              "[10 rows x 81 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDfhMIVNu6Vq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66100c15-1b34-4f24-8ae0-29f82dfd64f7"
      },
      "source": [
        "# Répartition des types de chaque variable\n",
        "data_train.dtypes.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "object     43\n",
              "int64      35\n",
              "float64     3\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8EaolDQxmRk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a90f701a-0928-4dbe-9399-5b4105ff78ca"
      },
      "source": [
        "data_train.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1460 entries, 0 to 1459\n",
            "Data columns (total 81 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             1460 non-null   int64  \n",
            " 1   MSSubClass     1460 non-null   int64  \n",
            " 2   MSZoning       1460 non-null   object \n",
            " 3   LotFrontage    1201 non-null   float64\n",
            " 4   LotArea        1460 non-null   int64  \n",
            " 5   Street         1460 non-null   object \n",
            " 6   Alley          91 non-null     object \n",
            " 7   LotShape       1460 non-null   object \n",
            " 8   LandContour    1460 non-null   object \n",
            " 9   Utilities      1460 non-null   object \n",
            " 10  LotConfig      1460 non-null   object \n",
            " 11  LandSlope      1460 non-null   object \n",
            " 12  Neighborhood   1460 non-null   object \n",
            " 13  Condition1     1460 non-null   object \n",
            " 14  Condition2     1460 non-null   object \n",
            " 15  BldgType       1460 non-null   object \n",
            " 16  HouseStyle     1460 non-null   object \n",
            " 17  OverallQual    1460 non-null   int64  \n",
            " 18  OverallCond    1460 non-null   int64  \n",
            " 19  YearBuilt      1460 non-null   int64  \n",
            " 20  YearRemodAdd   1460 non-null   int64  \n",
            " 21  RoofStyle      1460 non-null   object \n",
            " 22  RoofMatl       1460 non-null   object \n",
            " 23  Exterior1st    1460 non-null   object \n",
            " 24  Exterior2nd    1460 non-null   object \n",
            " 25  MasVnrType     1452 non-null   object \n",
            " 26  MasVnrArea     1452 non-null   float64\n",
            " 27  ExterQual      1460 non-null   object \n",
            " 28  ExterCond      1460 non-null   object \n",
            " 29  Foundation     1460 non-null   object \n",
            " 30  BsmtQual       1423 non-null   object \n",
            " 31  BsmtCond       1423 non-null   object \n",
            " 32  BsmtExposure   1422 non-null   object \n",
            " 33  BsmtFinType1   1423 non-null   object \n",
            " 34  BsmtFinSF1     1460 non-null   int64  \n",
            " 35  BsmtFinType2   1422 non-null   object \n",
            " 36  BsmtFinSF2     1460 non-null   int64  \n",
            " 37  BsmtUnfSF      1460 non-null   int64  \n",
            " 38  TotalBsmtSF    1460 non-null   int64  \n",
            " 39  Heating        1460 non-null   object \n",
            " 40  HeatingQC      1460 non-null   object \n",
            " 41  CentralAir     1460 non-null   object \n",
            " 42  Electrical     1459 non-null   object \n",
            " 43  1stFlrSF       1460 non-null   int64  \n",
            " 44  2ndFlrSF       1460 non-null   int64  \n",
            " 45  LowQualFinSF   1460 non-null   int64  \n",
            " 46  GrLivArea      1460 non-null   int64  \n",
            " 47  BsmtFullBath   1460 non-null   int64  \n",
            " 48  BsmtHalfBath   1460 non-null   int64  \n",
            " 49  FullBath       1460 non-null   int64  \n",
            " 50  HalfBath       1460 non-null   int64  \n",
            " 51  BedroomAbvGr   1460 non-null   int64  \n",
            " 52  KitchenAbvGr   1460 non-null   int64  \n",
            " 53  KitchenQual    1460 non-null   object \n",
            " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
            " 55  Functional     1460 non-null   object \n",
            " 56  Fireplaces     1460 non-null   int64  \n",
            " 57  FireplaceQu    770 non-null    object \n",
            " 58  GarageType     1379 non-null   object \n",
            " 59  GarageYrBlt    1379 non-null   float64\n",
            " 60  GarageFinish   1379 non-null   object \n",
            " 61  GarageCars     1460 non-null   int64  \n",
            " 62  GarageArea     1460 non-null   int64  \n",
            " 63  GarageQual     1379 non-null   object \n",
            " 64  GarageCond     1379 non-null   object \n",
            " 65  PavedDrive     1460 non-null   object \n",
            " 66  WoodDeckSF     1460 non-null   int64  \n",
            " 67  OpenPorchSF    1460 non-null   int64  \n",
            " 68  EnclosedPorch  1460 non-null   int64  \n",
            " 69  3SsnPorch      1460 non-null   int64  \n",
            " 70  ScreenPorch    1460 non-null   int64  \n",
            " 71  PoolArea       1460 non-null   int64  \n",
            " 72  PoolQC         7 non-null      object \n",
            " 73  Fence          281 non-null    object \n",
            " 74  MiscFeature    54 non-null     object \n",
            " 75  MiscVal        1460 non-null   int64  \n",
            " 76  MoSold         1460 non-null   int64  \n",
            " 77  YrSold         1460 non-null   int64  \n",
            " 78  SaleType       1460 non-null   object \n",
            " 79  SaleCondition  1460 non-null   object \n",
            " 80  SalePrice      1460 non-null   int64  \n",
            "dtypes: float64(3), int64(35), object(43)\n",
            "memory usage: 924.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrwqnN0qxr5h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "a6817d9c-dbb1-41e7-a480-159af62d0777"
      },
      "source": [
        "data_train.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1201.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1452.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1379.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>730.500000</td>\n",
              "      <td>56.897260</td>\n",
              "      <td>70.049958</td>\n",
              "      <td>10516.828082</td>\n",
              "      <td>6.099315</td>\n",
              "      <td>5.575342</td>\n",
              "      <td>1971.267808</td>\n",
              "      <td>1984.865753</td>\n",
              "      <td>103.685262</td>\n",
              "      <td>443.639726</td>\n",
              "      <td>46.549315</td>\n",
              "      <td>567.240411</td>\n",
              "      <td>1057.429452</td>\n",
              "      <td>1162.626712</td>\n",
              "      <td>346.992466</td>\n",
              "      <td>5.844521</td>\n",
              "      <td>1515.463699</td>\n",
              "      <td>0.425342</td>\n",
              "      <td>0.057534</td>\n",
              "      <td>1.565068</td>\n",
              "      <td>0.382877</td>\n",
              "      <td>2.866438</td>\n",
              "      <td>1.046575</td>\n",
              "      <td>6.517808</td>\n",
              "      <td>0.613014</td>\n",
              "      <td>1978.506164</td>\n",
              "      <td>1.767123</td>\n",
              "      <td>472.980137</td>\n",
              "      <td>94.244521</td>\n",
              "      <td>46.660274</td>\n",
              "      <td>21.954110</td>\n",
              "      <td>3.409589</td>\n",
              "      <td>15.060959</td>\n",
              "      <td>2.758904</td>\n",
              "      <td>43.489041</td>\n",
              "      <td>6.321918</td>\n",
              "      <td>2007.815753</td>\n",
              "      <td>180921.195890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>421.610009</td>\n",
              "      <td>42.300571</td>\n",
              "      <td>24.284752</td>\n",
              "      <td>9981.264932</td>\n",
              "      <td>1.382997</td>\n",
              "      <td>1.112799</td>\n",
              "      <td>30.202904</td>\n",
              "      <td>20.645407</td>\n",
              "      <td>181.066207</td>\n",
              "      <td>456.098091</td>\n",
              "      <td>161.319273</td>\n",
              "      <td>441.866955</td>\n",
              "      <td>438.705324</td>\n",
              "      <td>386.587738</td>\n",
              "      <td>436.528436</td>\n",
              "      <td>48.623081</td>\n",
              "      <td>525.480383</td>\n",
              "      <td>0.518911</td>\n",
              "      <td>0.238753</td>\n",
              "      <td>0.550916</td>\n",
              "      <td>0.502885</td>\n",
              "      <td>0.815778</td>\n",
              "      <td>0.220338</td>\n",
              "      <td>1.625393</td>\n",
              "      <td>0.644666</td>\n",
              "      <td>24.689725</td>\n",
              "      <td>0.747315</td>\n",
              "      <td>213.804841</td>\n",
              "      <td>125.338794</td>\n",
              "      <td>66.256028</td>\n",
              "      <td>61.119149</td>\n",
              "      <td>29.317331</td>\n",
              "      <td>55.757415</td>\n",
              "      <td>40.177307</td>\n",
              "      <td>496.123024</td>\n",
              "      <td>2.703626</td>\n",
              "      <td>1.328095</td>\n",
              "      <td>79442.502883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>1300.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1872.000000</td>\n",
              "      <td>1950.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>334.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>334.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1900.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2006.000000</td>\n",
              "      <td>34900.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>365.750000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>7553.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1954.000000</td>\n",
              "      <td>1967.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>795.750000</td>\n",
              "      <td>882.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1129.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1961.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>334.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2007.000000</td>\n",
              "      <td>129975.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>730.500000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>9478.500000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1973.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>383.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>477.500000</td>\n",
              "      <td>991.500000</td>\n",
              "      <td>1087.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1464.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1980.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2008.000000</td>\n",
              "      <td>163000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1095.250000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>11601.500000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>712.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>808.000000</td>\n",
              "      <td>1298.250000</td>\n",
              "      <td>1391.250000</td>\n",
              "      <td>728.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1776.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2002.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>576.000000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2009.000000</td>\n",
              "      <td>214000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1460.000000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>313.000000</td>\n",
              "      <td>215245.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>1600.000000</td>\n",
              "      <td>5644.000000</td>\n",
              "      <td>1474.000000</td>\n",
              "      <td>2336.000000</td>\n",
              "      <td>6110.000000</td>\n",
              "      <td>4692.000000</td>\n",
              "      <td>2065.000000</td>\n",
              "      <td>572.000000</td>\n",
              "      <td>5642.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1418.000000</td>\n",
              "      <td>857.000000</td>\n",
              "      <td>547.000000</td>\n",
              "      <td>552.000000</td>\n",
              "      <td>508.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>738.000000</td>\n",
              "      <td>15500.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>755000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Id   MSSubClass  ...       YrSold      SalePrice\n",
              "count  1460.000000  1460.000000  ...  1460.000000    1460.000000\n",
              "mean    730.500000    56.897260  ...  2007.815753  180921.195890\n",
              "std     421.610009    42.300571  ...     1.328095   79442.502883\n",
              "min       1.000000    20.000000  ...  2006.000000   34900.000000\n",
              "25%     365.750000    20.000000  ...  2007.000000  129975.000000\n",
              "50%     730.500000    50.000000  ...  2008.000000  163000.000000\n",
              "75%    1095.250000    70.000000  ...  2009.000000  214000.000000\n",
              "max    1460.000000   190.000000  ...  2010.000000  755000.000000\n",
              "\n",
              "[8 rows x 38 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVcJcB901H_R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "67333bed-b1bb-4e42-ae3d-721d6f1996bd"
      },
      "source": [
        "# Distribution des ventes des bien immobiliers\n",
        " dessns.distplot(data_train['SalePrice'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f744886b828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZfmE4_lHisB"
      },
      "source": [
        "# Création de nouvelles variables pour améliorer les performances\n",
        "\n",
        "# 1* Simplifications of existing features\n",
        "data_train[\"SimplOverallQual\"] = data_train.OverallQual.replace({1 : 1, 2 : 1, 3 : 1, # bad\n",
        "                                                       4 : 2, 5 : 2, 6 : 2, # average\n",
        "                                                       7 : 3, 8 : 3, 9 : 3, 10 : 3 # good\n",
        "                                                      })\n",
        "data_train[\"SimplOverallCond\"] = data_train.OverallCond.replace({1 : 1, 2 : 1, 3 : 1, # bad\n",
        "                                                       4 : 2, 5 : 2, 6 : 2, # average\n",
        "                                                       7 : 3, 8 : 3, 9 : 3, 10 : 3 # good\n",
        "                                                      })\n",
        "data_train[\"SimplPoolQC\"] = data_train.PoolQC.replace({1 : 1, 2 : 1, # average\n",
        "                                             3 : 2, 4 : 2 # good\n",
        "                                            })\n",
        "data_train[\"SimplGarageCond\"] = data_train.GarageCond.replace({1 : 1, # bad\n",
        "                                                     2 : 1, 3 : 1, # average\n",
        "                                                     4 : 2, 5 : 2 # good\n",
        "                                                    })\n",
        "data_train[\"SimplGarageQual\"] = data_train.GarageQual.replace({1 : 1, # bad\n",
        "                                                     2 : 1, 3 : 1, # average\n",
        "                                                     4 : 2, 5 : 2 # good\n",
        "                                                    })\n",
        "data_train[\"SimplFireplaceQu\"] = data_train.FireplaceQu.replace({1 : 1, # bad\n",
        "                                                       2 : 1, 3 : 1, # average\n",
        "                                                       4 : 2, 5 : 2 # good\n",
        "                                                      })\n",
        "data_train[\"SimplFireplaceQu\"] = data_train.FireplaceQu.replace({1 : 1, # bad\n",
        "                                                       2 : 1, 3 : 1, # average\n",
        "                                                       4 : 2, 5 : 2 # good\n",
        "                                                      })\n",
        "data_train[\"SimplFunctional\"] = data_train.Functional.replace({1 : 1, 2 : 1, # bad\n",
        "                                                     3 : 2, 4 : 2, # major\n",
        "                                                     5 : 3, 6 : 3, 7 : 3, # minor\n",
        "                                                     8 : 4 # typical\n",
        "                                                    })\n",
        "data_train[\"SimplKitchenQual\"] = data_train.KitchenQual.replace({1 : 1, # bad\n",
        "                                                       2 : 1, 3 : 1, # average\n",
        "                                                       4 : 2, 5 : 2 # good\n",
        "                                                      })\n",
        "data_train[\"SimplHeatingQC\"] = data_train.HeatingQC.replace({1 : 1, # bad\n",
        "                                                   2 : 1, 3 : 1, # average\n",
        "                                                   4 : 2, 5 : 2 # good\n",
        "                                                  })\n",
        "data_train[\"SimplBsmtFinType1\"] = data_train.BsmtFinType1.replace({1 : 1, # unfinished\n",
        "                                                         2 : 1, 3 : 1, # rec room\n",
        "                                                         4 : 2, 5 : 2, 6 : 2 # living quarters\n",
        "                                                        })\n",
        "data_train[\"SimplBsmtFinType2\"] = data_train.BsmtFinType2.replace({1 : 1, # unfinished\n",
        "                                                         2 : 1, 3 : 1, # rec room\n",
        "                                                         4 : 2, 5 : 2, 6 : 2 # living quarters\n",
        "                                                        })\n",
        "data_train[\"SimplBsmtCond\"] = data_train.BsmtCond.replace({1 : 1, # bad\n",
        "                                                 2 : 1, 3 : 1, # average\n",
        "                                                 4 : 2, 5 : 2 # good\n",
        "                                                })\n",
        "data_train[\"SimplBsmtQual\"] = data_train.BsmtQual.replace({1 : 1, # bad\n",
        "                                                 2 : 1, 3 : 1, # average\n",
        "                                                 4 : 2, 5 : 2 # good\n",
        "                                                })\n",
        "data_train[\"SimplExterCond\"] = data_train.ExterCond.replace({1 : 1, # bad\n",
        "                                                   2 : 1, 3 : 1, # average\n",
        "                                                   4 : 2, 5 : 2 # good\n",
        "                                                  })\n",
        "data_train[\"SimplExterQual\"] = data_train.ExterQual.replace({1 : 1, # bad\n",
        "                                                   2 : 1, 3 : 1, # average\n",
        "                                                   4 : 2, 5 : 2 # good\n",
        "                                                  })\n",
        "\n",
        "data_train[\"HasMasVnr\"] = data_train.MasVnrType.replace({\"BrkCmn\" : 1, \"BrkFace\" : 1, \"CBlock\" : 1, \n",
        "                                               \"Stone\" : 1, \"None\" : 0})\n",
        "\n",
        "data_train[\"BoughtOffPlan\"] = data_train.SaleCondition.replace({\"Abnorml\" : 0, \"Alloca\" : 0, \"AdjLand\" : 0, \n",
        "                                                      \"Family\" : 0, \"Normal\" : 0, \"Partial\" : 1})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Tgy6k863beH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "outputId": "1cf243c7-8cf5-45db-d869-47278971ce29"
      },
      "source": [
        "# Visualisation pour détecter la présence de données manquantes dans le jeu de données\n",
        "plt.figure(figsize=(25,10))\n",
        "sns.heatmap(data_train.isna(),cbar=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74487ad8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1800x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NBzv9NZMR86h"
      },
      "source": [
        "# Suppression de variables inutiles pour le modèle car trop de données manquantes\n",
        "data_train.drop(['Id','Alley','FireplaceQu','PoolQC','Fence','MiscFeature','SimplPoolQC','SimplFireplaceQu'], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yCQagkUEzion",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "outputId": "f7b554e9-3cf1-4058-c63d-a11b3d3392dd"
      },
      "source": [
        "# Corrélation entre chaque variable du jeu de données\n",
        "plt.figure(figsize=(15,10))\n",
        "sns.heatmap(data_train.corr())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7448815908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DcezK-Dd1IIf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a95b6fd-3d74-459e-b45d-6879f8859d7c"
      },
      "source": [
        "# Visualisation des catégories de chaque variable catégorielle\n",
        "for col in data_train.select_dtypes('object'):\n",
        "  print(f'{col :-<20}{data_train[col].unique()}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSZoning------------['RL' 'RM' 'C (all)' 'FV' 'RH']\n",
            "Street--------------['Pave' 'Grvl']\n",
            "LotShape------------['Reg' 'IR1' 'IR2' 'IR3']\n",
            "LandContour---------['Lvl' 'Bnk' 'Low' 'HLS']\n",
            "Utilities-----------['AllPub' 'NoSeWa']\n",
            "LotConfig-----------['Inside' 'FR2' 'Corner' 'CulDSac' 'FR3']\n",
            "LandSlope-----------['Gtl' 'Mod' 'Sev']\n",
            "Neighborhood--------['CollgCr' 'Veenker' 'Crawfor' 'NoRidge' 'Mitchel' 'Somerst' 'NWAmes'\n",
            " 'OldTown' 'BrkSide' 'Sawyer' 'NridgHt' 'NAmes' 'SawyerW' 'IDOTRR'\n",
            " 'MeadowV' 'Edwards' 'Timber' 'Gilbert' 'StoneBr' 'ClearCr' 'NPkVill'\n",
            " 'Blmngtn' 'BrDale' 'SWISU' 'Blueste']\n",
            "Condition1----------['Norm' 'Feedr' 'PosN' 'Artery' 'RRAe' 'RRNn' 'RRAn' 'PosA' 'RRNe']\n",
            "Condition2----------['Norm' 'Artery' 'RRNn' 'Feedr' 'PosN' 'PosA' 'RRAn' 'RRAe']\n",
            "BldgType------------['1Fam' '2fmCon' 'Duplex' 'TwnhsE' 'Twnhs']\n",
            "HouseStyle----------['2Story' '1Story' '1.5Fin' '1.5Unf' 'SFoyer' 'SLvl' '2.5Unf' '2.5Fin']\n",
            "RoofStyle-----------['Gable' 'Hip' 'Gambrel' 'Mansard' 'Flat' 'Shed']\n",
            "RoofMatl------------['CompShg' 'WdShngl' 'Metal' 'WdShake' 'Membran' 'Tar&Grv' 'Roll'\n",
            " 'ClyTile']\n",
            "Exterior1st---------['VinylSd' 'MetalSd' 'Wd Sdng' 'HdBoard' 'BrkFace' 'WdShing' 'CemntBd'\n",
            " 'Plywood' 'AsbShng' 'Stucco' 'BrkComm' 'AsphShn' 'Stone' 'ImStucc'\n",
            " 'CBlock']\n",
            "Exterior2nd---------['VinylSd' 'MetalSd' 'Wd Shng' 'HdBoard' 'Plywood' 'Wd Sdng' 'CmentBd'\n",
            " 'BrkFace' 'Stucco' 'AsbShng' 'Brk Cmn' 'ImStucc' 'AsphShn' 'Stone'\n",
            " 'Other' 'CBlock']\n",
            "MasVnrType----------['BrkFace' 'None' 'Stone' 'BrkCmn' nan]\n",
            "ExterQual-----------['Gd' 'TA' 'Ex' 'Fa']\n",
            "ExterCond-----------['TA' 'Gd' 'Fa' 'Po' 'Ex']\n",
            "Foundation----------['PConc' 'CBlock' 'BrkTil' 'Wood' 'Slab' 'Stone']\n",
            "BsmtQual------------['Gd' 'TA' 'Ex' nan 'Fa']\n",
            "BsmtCond------------['TA' 'Gd' nan 'Fa' 'Po']\n",
            "BsmtExposure--------['No' 'Gd' 'Mn' 'Av' nan]\n",
            "BsmtFinType1--------['GLQ' 'ALQ' 'Unf' 'Rec' 'BLQ' nan 'LwQ']\n",
            "BsmtFinType2--------['Unf' 'BLQ' nan 'ALQ' 'Rec' 'LwQ' 'GLQ']\n",
            "Heating-------------['GasA' 'GasW' 'Grav' 'Wall' 'OthW' 'Floor']\n",
            "HeatingQC-----------['Ex' 'Gd' 'TA' 'Fa' 'Po']\n",
            "CentralAir----------['Y' 'N']\n",
            "Electrical----------['SBrkr' 'FuseF' 'FuseA' 'FuseP' 'Mix' nan]\n",
            "KitchenQual---------['Gd' 'TA' 'Ex' 'Fa']\n",
            "Functional----------['Typ' 'Min1' 'Maj1' 'Min2' 'Mod' 'Maj2' 'Sev']\n",
            "GarageType----------['Attchd' 'Detchd' 'BuiltIn' 'CarPort' nan 'Basment' '2Types']\n",
            "GarageFinish--------['RFn' 'Unf' 'Fin' nan]\n",
            "GarageQual----------['TA' 'Fa' 'Gd' nan 'Ex' 'Po']\n",
            "GarageCond----------['TA' 'Fa' nan 'Gd' 'Po' 'Ex']\n",
            "PavedDrive----------['Y' 'N' 'P']\n",
            "SaleType------------['WD' 'New' 'COD' 'ConLD' 'ConLI' 'CWD' 'ConLw' 'Con' 'Oth']\n",
            "SaleCondition-------['Normal' 'Abnorml' 'Partial' 'AdjLand' 'Alloca' 'Family']\n",
            "SimplGarageCond-----['TA' 'Fa' nan 'Gd' 'Po' 'Ex']\n",
            "SimplGarageQual-----['TA' 'Fa' 'Gd' nan 'Ex' 'Po']\n",
            "SimplFunctional-----['Typ' 'Min1' 'Maj1' 'Min2' 'Mod' 'Maj2' 'Sev']\n",
            "SimplKitchenQual----['Gd' 'TA' 'Ex' 'Fa']\n",
            "SimplHeatingQC------['Ex' 'Gd' 'TA' 'Fa' 'Po']\n",
            "SimplBsmtFinType1---['GLQ' 'ALQ' 'Unf' 'Rec' 'BLQ' nan 'LwQ']\n",
            "SimplBsmtFinType2---['Unf' 'BLQ' nan 'ALQ' 'Rec' 'LwQ' 'GLQ']\n",
            "SimplBsmtCond-------['TA' 'Gd' nan 'Fa' 'Po']\n",
            "SimplBsmtQual-------['Gd' 'TA' 'Ex' nan 'Fa']\n",
            "SimplExterCond------['TA' 'Gd' 'Fa' 'Po' 'Ex']\n",
            "SimplExterQual------['Gd' 'TA' 'Ex' 'Fa']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RIfxuqo3j0Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "398e3be4-ac51-43ea-9532-ff7b66770576"
      },
      "source": [
        "# Suppression des valeurs manquantes\n",
        "data_train.dropna(axis=0, inplace=True)\n",
        "print(f'Le dataset contient {data_train.shape[0]} lignes et {data_train.shape[1]} colonnes')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Le dataset contient 1094 lignes et 90 colonnes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsnlDBiJjU-c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d9a250a-1e22-4e53-b423-414aadbe6468"
      },
      "source": [
        "print(f'Le dataset contient maintenant {data_train.isna().sum().sum()} donnée manquante')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Le dataset contient maintenant 0 donnée manquante\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZI1fbv8jDq1O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e6b2cbe-54dc-4e80-fddb-0122fa85fc24"
      },
      "source": [
        "print(\"Find most important features relative to target\")\n",
        "corr = data_train.corr()\n",
        "corr.sort_values([\"SalePrice\"], ascending = False, inplace = True)\n",
        "print(corr.SalePrice)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Find most important features relative to target\n",
            "SalePrice           1.000000\n",
            "OverallQual         0.795437\n",
            "GrLivArea           0.707481\n",
            "SimplOverallQual    0.660308\n",
            "GarageCars          0.652103\n",
            "GarageArea          0.620772\n",
            "TotalBsmtSF         0.617741\n",
            "1stFlrSF            0.617692\n",
            "FullBath            0.578299\n",
            "TotRmsAbvGrd        0.560521\n",
            "YearBuilt           0.523434\n",
            "YearRemodAdd        0.519806\n",
            "GarageYrBlt         0.502248\n",
            "MasVnrArea          0.485409\n",
            "Fireplaces          0.458182\n",
            "HasMasVnr           0.381164\n",
            "BsmtFinSF1          0.378678\n",
            "BoughtOffPlan       0.369081\n",
            "LotFrontage         0.343978\n",
            "OpenPorchSF         0.338600\n",
            "WoodDeckSF          0.330286\n",
            "2ndFlrSF            0.302569\n",
            "LotArea             0.302268\n",
            "HalfBath            0.259469\n",
            "BsmtFullBath        0.223948\n",
            "BsmtUnfSF           0.191247\n",
            "BedroomAbvGr        0.168489\n",
            "ScreenPorch         0.106479\n",
            "PoolArea            0.092085\n",
            "MoSold              0.052584\n",
            "3SsnPorch           0.033947\n",
            "LowQualFinSF       -0.003541\n",
            "YrSold             -0.006723\n",
            "MiscVal            -0.036001\n",
            "BsmtFinSF2         -0.036923\n",
            "BsmtHalfBath       -0.041341\n",
            "MSSubClass         -0.089478\n",
            "KitchenAbvGr       -0.115382\n",
            "SimplOverallCond   -0.117633\n",
            "OverallCond        -0.138511\n",
            "EnclosedPorch      -0.161711\n",
            "Name: SalePrice, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7HchfOw2bHJ"
      },
      "source": [
        "# Encodage des variables catégorielles\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, OrdinalEncoder\n",
        "\n",
        "data_train = pd.get_dummies(data_train, prefix_sep=\"_\", columns=data_train.select_dtypes(include='object').columns)\n",
        "\n",
        "# encoder = OneHotEncoder()\n",
        "\n",
        "# for col in data_train.select_dtypes('object'):\n",
        "#   print(col)\n",
        "#   data_train[col] = encoder.fit_transform(np.array(data_train[col]).reshape(-1, 1))\n",
        "\n",
        "\n",
        "# for col in data_test.select_dtypes('object'):\n",
        "#   print(col)\n",
        "#   data_test[col] = encoder.fit_transform(np.array(data_test[col]).reshape(-1, 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JdKmiJqSwjOf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61d2004b-7a8e-4c89-ad71-8aba8306ade2"
      },
      "source": [
        "# Vérification qu'il n'y a plus que des variables numériques (discrètes ou continues)\n",
        "data_train.dtypes.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "uint8      276\n",
              "int64       37\n",
              "float64      4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JEY5-6eWoD0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cea5fdca-1cf8-4c9e-d022-827097bacbff"
      },
      "source": [
        "# Création des jeux d'entraînement et de test\n",
        "\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "\n",
        "y = data_train['SalePrice']\n",
        "X = data_train.drop('SalePrice',axis=1)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
        "\n",
        "print(f'Shape :\\nX_train : {X_train.shape}\\nX_test : {X_test.shape}\\ny_train : {y_train.shape}\\ny_test : {y_test.shape}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape :\n",
            "X_train : (984, 316)\n",
            "X_test : (110, 316)\n",
            "y_train : (984,)\n",
            "y_test : (110,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Klmh36RvVTfM"
      },
      "source": [
        "# Import de bibliothèques\n",
        "\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.svm import SVR\n",
        "\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, BaggingRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "from sklearn.preprocessing import PolynomialFeatures"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yj4QtIaMWLhv"
      },
      "source": [
        "# Création de plusieurs modèles de Machine Learning\n",
        "\n",
        "model_lr = LinearRegression()\n",
        "model_ridge = make_pipeline(StandardScaler(),Ridge())\n",
        "# svr_lin = SVR(kernel='linear')\n",
        "model_dtree = make_pipeline(StandardScaler(), DecisionTreeRegressor(random_state=0))\n",
        "model_rforest = make_pipeline(StandardScaler(), RandomForestRegressor(random_state=0))\n",
        "model_ada = make_pipeline(StandardScaler(), AdaBoostRegressor(random_state=0))\n",
        "model_gboosting = make_pipeline(StandardScaler(), GradientBoostingRegressor(random_state=0))\n",
        "model_bagging = make_pipeline(StandardScaler(), BaggingRegressor(random_state=0))\n",
        "model_knn = make_pipeline(StandardScaler(), KNeighborsRegressor())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqIqZBBq1Jyn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2b5ee61-c08d-4611-9af8-d04e6d07fe57"
      },
      "source": [
        "# Comparaison de la performance de chacun des modèles\n",
        "for model in [model_lr, model_ridge,model_dtree,model_rforest,model_ada,model_gboosting,model_bagging,model_knn]:\n",
        "  print(model)\n",
        "  model.fit(X_train, y_train)\n",
        "  MAE = round(100*((abs(cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_absolute_error').mean()))/(y_train.mean())),3)\n",
        "  print(f'MAE cross-validation : {MAE} %\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)\n",
            "Score test : 0.8585105112814282\n",
            "MAE cross-validation : 11.366 %\n",
            "\n",
            "Pipeline(memory=None,\n",
            "         steps=[('standardscaler',\n",
            "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
            "                ('ridge',\n",
            "                 Ridge(alpha=1.0, copy_X=True, fit_intercept=True,\n",
            "                       max_iter=None, normalize=False, random_state=None,\n",
            "                       solver='auto', tol=0.001))],\n",
            "         verbose=False)\n",
            "Score test : 0.8584490752539128\n",
            "MAE cross-validation : 10.946 %\n",
            "\n",
            "Pipeline(memory=None,\n",
            "         steps=[('standardscaler',\n",
            "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
            "                ('decisiontreeregressor',\n",
            "                 DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse',\n",
            "                                       max_depth=None, max_features=None,\n",
            "                                       max_leaf_nodes=None,\n",
            "                                       min_impurity_decrease=0.0,\n",
            "                                       min_impurity_split=None,\n",
            "                                       min_samples_leaf=1, min_samples_split=2,\n",
            "                                       min_weight_fraction_leaf=0.0,\n",
            "                                       presort='deprecated', random_state=0,\n",
            "                                       splitter='best'))],\n",
            "         verbose=False)\n",
            "Score test : 0.7537631725529715\n",
            "MAE cross-validation : 14.699 %\n",
            "\n",
            "Pipeline(memory=None,\n",
            "         steps=[('standardscaler',\n",
            "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
            "                ('randomforestregressor',\n",
            "                 RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
            "                                       criterion='mse', max_depth=None,\n",
            "                                       max_features='auto', max_leaf_nodes=None,\n",
            "                                       max_samples=None,\n",
            "                                       min_impurity_decrease=0.0,\n",
            "                                       min_impurity_split=None,\n",
            "                                       min_samples_leaf=1, min_samples_split=2,\n",
            "                                       min_weight_fraction_leaf=0.0,\n",
            "                                       n_estimators=100, n_jobs=None,\n",
            "                                       oob_score=False, random_state=0,\n",
            "                                       verbose=0, warm_start=False))],\n",
            "         verbose=False)\n",
            "Score test : 0.8702375702170404\n",
            "MAE cross-validation : 10.213 %\n",
            "\n",
            "Pipeline(memory=None,\n",
            "         steps=[('standardscaler',\n",
            "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
            "                ('adaboostregressor',\n",
            "                 AdaBoostRegressor(base_estimator=None, learning_rate=1.0,\n",
            "                                   loss='linear', n_estimators=50,\n",
            "                                   random_state=0))],\n",
            "         verbose=False)\n",
            "Score test : 0.811942804069562\n",
            "MAE cross-validation : 13.823 %\n",
            "\n",
            "Pipeline(memory=None,\n",
            "         steps=[('standardscaler',\n",
            "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
            "                ('gradientboostingregressor',\n",
            "                 GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0,\n",
            "                                           criterion='friedman_mse', init=None,\n",
            "                                           learning_rate=0.1, loss='ls',\n",
            "                                           max_depth=3, max_features=None,\n",
            "                                           max_leaf_nodes=None,\n",
            "                                           min_impurity_decrease=0.0,\n",
            "                                           min_impurity_split=None,\n",
            "                                           min_samples_leaf=1,\n",
            "                                           min_samples_split=2,\n",
            "                                           min_weight_fraction_leaf=0.0,\n",
            "                                           n_estimators=100,\n",
            "                                           n_iter_no_change=None,\n",
            "                                           presort='deprecated', random_state=0,\n",
            "                                           subsample=1.0, tol=0.0001,\n",
            "                                           validation_fraction=0.1, verbose=0,\n",
            "                                           warm_start=False))],\n",
            "         verbose=False)\n",
            "Score test : 0.9074173250301744\n",
            "MAE cross-validation : 9.461 %\n",
            "\n",
            "Pipeline(memory=None,\n",
            "         steps=[('standardscaler',\n",
            "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
            "                ('baggingregressor',\n",
            "                 BaggingRegressor(base_estimator=None, bootstrap=True,\n",
            "                                  bootstrap_features=False, max_features=1.0,\n",
            "                                  max_samples=1.0, n_estimators=10, n_jobs=None,\n",
            "                                  oob_score=False, random_state=0, verbose=0,\n",
            "                                  warm_start=False))],\n",
            "         verbose=False)\n",
            "Score test : 0.8741253437012667\n",
            "MAE cross-validation : 10.81 %\n",
            "\n",
            "Pipeline(memory=None,\n",
            "         steps=[('standardscaler',\n",
            "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
            "                ('kneighborsregressor',\n",
            "                 KNeighborsRegressor(algorithm='auto', leaf_size=30,\n",
            "                                     metric='minkowski', metric_params=None,\n",
            "                                     n_jobs=None, n_neighbors=5, p=2,\n",
            "                                     weights='uniform'))],\n",
            "         verbose=False)\n",
            "Score test : 0.6775264795548559\n",
            "MAE cross-validation : 14.686 %\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1IcQZTusKmhy"
      },
      "source": [
        "Feature importance : PermutationImportance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28FCTZaoIw-A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7de5232-e257-47d0-e204-ab1a53c726d6"
      },
      "source": [
        "from sklearn.inspection import permutation_importance\n",
        "\n",
        "perm = permutation_importance(model_gboosting, X_train, y_train, random_state=1)\n",
        "\n",
        "indice = 0\n",
        "for i in perm.importances_mean.argsort()[::-1]:\n",
        "  if perm.importances_mean[i] - 2 * perm.importances_std[i] > 0 and indice < 10:\n",
        "    indice += 1\n",
        "    print(f\"{X_train.columns[i]:<15} {perm.importances_mean[i]:.3f} +/- {perm.importances_std[i]:.3f}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OverallQual     0.188 +/- 0.008\n",
            "GrLivArea       0.119 +/- 0.006\n",
            "TotalBsmtSF     0.029 +/- 0.002\n",
            "2ndFlrSF        0.021 +/- 0.001\n",
            "BsmtFinSF1      0.019 +/- 0.001\n",
            "GarageCars      0.015 +/- 0.002\n",
            "LotArea         0.015 +/- 0.001\n",
            "YearRemodAdd    0.011 +/- 0.001\n",
            "1stFlrSF        0.009 +/- 0.001\n",
            "YearBuilt       0.006 +/- 0.001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDkaQF0L66Ch",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "4c0cdb74-dfcc-4997-b157-767bcb05bed4"
      },
      "source": [
        "# Optimisation des paramètres du meilleur modèle : GradientBoostingRegressor\n",
        "\n",
        "print('Model : GradientBoostingRegressor')\n",
        "perf = []\n",
        "min = 50\n",
        "for k in np.linspace(0.1,0.4,4):\n",
        "  for n in range(10,201,10):\n",
        "    model_gboosting = make_pipeline(StandardScaler(), GradientBoostingRegressor(alpha=k, n_estimators=n))\n",
        "    pourcentage = round(100*((abs(cross_val_score(model_gboosting, X_train, y_train, cv=5, scoring='neg_mean_absolute_error').mean()))/(y_train.mean())),3)\n",
        "    if pourcentage < min:\n",
        "      perf = [pourcentage,k,n]\n",
        "      min = pourcentage\n",
        "      print(f'Nouveau minimumum MAE avec alpha={k} et n_estimators={n} --> {min} %')\n",
        "\n",
        "print(f'\\nMinimum : {perf[0]}% pour alpha={perf[1]} et n_estimators={perf[2]}')\n",
        "model_gboosting = make_pipeline(StandardScaler(), GradientBoostingRegressor(alpha=perf[1], n_estimators=perf[2]))\n",
        "MAE = round(100*((abs(cross_val_score(model_gboosting, X_train, y_train, cv=5, scoring='neg_mean_absolute_error').mean()))/(y_train.mean())),3)\n",
        "print(f'MAE en Cross-validation : {MAE} %')\n",
        "model_gboosting.fit(X_train, y_train)\n",
        "print(f'R² sur Test-Set : {model_gboosting.score(X_test, y_test)}')\n",
        "print(f'Précision en moyenne absolue des prédictions : {round((abs(model_gboosting.predict(X_test)-y_test)).mean(),2)}')\n",
        "print(f'Précision en % de l\\'erreur absolue des prédictions : {round(100*((abs(model_gboosting.predict(X_test)-y_test)).mean())/(y_test.mean()),3)}%\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model : GradientBoostingRegressor\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=10 --> 17.126 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=20 --> 12.281 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=30 --> 10.852 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=40 --> 10.283 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=50 --> 10.044 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=60 --> 9.893 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=70 --> 9.758 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=80 --> 9.621 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=90 --> 9.554 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=100 --> 9.484 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=110 --> 9.443 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=120 --> 9.366 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=130 --> 9.344 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=140 --> 9.312 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=150 --> 9.283 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=170 --> 9.258 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=190 --> 9.228 %\n",
            "Nouveau minimumum avec alpha=0.1 et n_estimators=200 --> 9.221 %\n",
            "Nouveau minimumum avec alpha=0.2 et n_estimators=180 --> 9.211 %\n",
            "\n",
            "Minimum : 9.211% pour alpha=0.2 et n_estimators=180\n",
            "MAE en Cross-validation : 9.258 %\n",
            "R² sur Test-Set : 0.9091868029458836\n",
            "Précision en moyenne absolue des prédictions : 17077.81\n",
            "Précision en % de l'erreur absolue des prédictions : 8.931%\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C16t9lQzUZIP"
      },
      "source": [
        "# Vérification que le modèle ne sur-apprend pas\n",
        "\n",
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "def courbe_apprentissage(model):    \n",
        "    N, train_score, val_score = learning_curve(model, X_train, y_train,\n",
        "                                              cv=4, train_sizes=np.linspace(0.1, 1, 10))\n",
        "    \n",
        "    \n",
        "    plt.figure(figsize=(12, 8))\n",
        "    plt.plot(N, train_score.mean(axis=1), label='train score')\n",
        "    plt.plot(N, val_score.mean(axis=1), label='validation score')\n",
        "    plt.legend()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzgjS5FfReo-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "outputId": "16299088-7ba0-403a-f713-b29c998b37a4"
      },
      "source": [
        "courbe_apprentissage(model_gboosting)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcgWM4ruOI-r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "17a0fecd-fac3-4cdd-9916-8c1837202a19"
      },
      "source": [
        "# Importance de chaque variable du modèle\n",
        "pd.DataFrame(model_gboosting.named_steps.gradientboostingregressor.feature_importances_, index=X_train.columns)[model_gboosting.named_steps.gradientboostingregressor.feature_importances_ > 0.001]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>GrLivArea</th>\n",
              "      <td>0.261820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageCars</th>\n",
              "      <td>0.095552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OverallQual-s2</th>\n",
              "      <td>0.199710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OverallQual-s3</th>\n",
              "      <td>0.179969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OverallQual-Sq</th>\n",
              "      <td>0.262949</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       0\n",
              "GrLivArea       0.261820\n",
              "GarageCars      0.095552\n",
              "OverallQual-s2  0.199710\n",
              "OverallQual-s3  0.179969\n",
              "OverallQual-Sq  0.262949"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYowfNr9V-pi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "outputId": "6a847ee3-c71c-43ae-f595-46c9d4577ab3"
      },
      "source": [
        "# Importance de chaque variable du modèle\n",
        "pd.DataFrame(model_gboosting.named_steps.gradientboostingregressor.feature_importances_, index=X_train.columns).plot.bar(figsize=(20,8))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f420a30ab70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LDE6ZzbSpxM6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "c13d9706-043f-4aa2-d5ec-07929a4ae187"
      },
      "source": [
        "# Performance du modèle\n",
        "\n",
        "model_gboosting = make_pipeline(StandardScaler(), GradientBoostingRegressor(random_state=0, alpha=0.1))\n",
        "model.fit(X_train, y_train)\n",
        "print(f'Score : {model.score(X_test, y_test)}')\n",
        "print(f'Précision en moyenne absolue des prédictions : {round((abs(model.predict(X_test)-y_test)).mean(),2)}')\n",
        "print(f'Précision en % de l\\'erreur absolue des prédictions : {round(100*((abs(model.predict(X_test)-y_test)).mean())/(y_test.mean()),3)}%\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Score : 0.6704664035855139\n",
            "Précision en moyenne absolue des prédictions : 27251.29\n",
            "Précision en % de l'erreur absolue des prédictions : 14.251%\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}