TP_Regularization.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "TP_Regularization.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "kScgTUtU8z_H",
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        "_srveVgE8mNO",
        "TbJn72rM-Glh",
        "EB712b53F6MF",
        "crF9YXe7IBrz"
      ],
      "authorship_tag": "ABX9TyN+7PwOglrCRLNKvDPIFU7B",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BDouchet/SDATA/blob/main/TP_Regularization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SLz952qO6psF"
      },
      "source": [
        "# Import & Load \r\n",
        "\r\n",
        "Two datasets are used :\r\n",
        "\r\n",
        "*  **house_rent** : made of two features (surface and monthly rent). The aim is to estimate the monthly rent based on the surface. It is composed of 545 flats localized in Paris.\r\n",
        "*  **house_price** : which stores house prices in USA according to many other features (80). The data descripton is available [here](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data).\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_URjAxtx5UFy"
      },
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "from pydrive.auth import GoogleAuth\r\n",
        "from pydrive.drive import GoogleDrive\r\n",
        "from google.colab import auth\r\n",
        "from oauth2client.client import GoogleCredentials\r\n",
        "\r\n",
        "auth.authenticate_user()\r\n",
        "gauth = GoogleAuth()\r\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\r\n",
        "drive = GoogleDrive(gauth)\r\n",
        "\r\n",
        "id='1O7J3Zw34gyaA6XpLqkdtLv7JIn_gISct'\r\n",
        "\r\n",
        "downloaded = drive.CreateFile({'id':id})\r\n",
        "downloaded.GetContentFile('house_rent.csv')\r\n",
        "\r\n",
        "id='1bw0xOuSlAt9mT66q7NudC6LqkLVHnTjx'\r\n",
        "\r\n",
        "downloaded = drive.CreateFile({'id':id})\r\n",
        "downloaded.GetContentFile('house_price.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "pqVlcMC-61xV",
        "outputId": "0ed2b788-d7e3-441e-b1b8-d933bc585db5"
      },
      "source": [
        "df=pd.read_csv('house_rent.csv')\r\n",
        "df"
      ],
      "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>loyer</th>\n",
              "      <th>surface</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1330</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1400</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>904</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>955</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2545</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>540</th>\n",
              "      <td>1490</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541</th>\n",
              "      <td>2020</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>542</th>\n",
              "      <td>2050</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>543</th>\n",
              "      <td>1220</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>544</th>\n",
              "      <td>1610</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>545 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     loyer  surface\n",
              "0     1330       37\n",
              "1     1400       32\n",
              "2      904       26\n",
              "3      955       30\n",
              "4     2545       70\n",
              "..     ...      ...\n",
              "540   1490       48\n",
              "541   2020       58\n",
              "542   2050       70\n",
              "543   1220       42\n",
              "544   1610       44\n",
              "\n",
              "[545 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "poi-54Un7Uf1",
        "outputId": "f853ac6a-d806-4ff3-a141-8103597b354f"
      },
      "source": [
        "plt.scatter(df['loyer'],df['surface'],s=2)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "kScgTUtU8z_H"
      },
      "source": [
        "# Single Linear Regression\r\n",
        "\r\n",
        "Linear Regression performed on **house_rent** dataset. Estimation of the price according to the surface"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SsUmWhJ27oos"
      },
      "source": [
        "## Remove outliers\r\n",
        "\r\n",
        "Remove of the outliers :\r\n",
        "\r\n",
        "*  price > 8000 €/month\r\n",
        "*  surface > 200 m²"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uEKr_ApD7rcL",
        "outputId": "de802c22-0ddf-4d61-ec4d-9e719c4d07d1"
      },
      "source": [
        "df.drop(df[df.loyer>8000].index,inplace=True)\r\n",
        "df.drop(df[df.surface>200].index,inplace=True)\r\n",
        "\r\n",
        "plt.scatter(df['loyer'],df['surface'],s=2)\r\n",
        "plt.xlabel('Rent (€/month)')\r\n",
        "plt.ylabel('Surface (m²)')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tvr0eKOu94BW",
        "outputId": "44ade7f7-d641-4ac2-9a08-03444e6aaa40"
      },
      "source": [
        "x=df.surface.to_numpy()\r\n",
        "y=df.loyer.to_numpy()\r\n",
        "\r\n",
        "print('Average Rent : '+ str(x.mean()))\r\n",
        "print('Std Rent : '+ str(x.std()))\r\n",
        "print()\r\n",
        "print('Average Surface : '+ str(y.mean()))\r\n",
        "print('Std Surface : '+ str(y.std()))\r\n",
        "print()\r\n",
        "print('covariance : '+str(np.cov(x,y)[0,1]))\r\n",
        "print('correlation : '+str(np.corrcoef(x,y)[0,1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average Rent : 58.6123595505618\n",
            "Std Rent : 34.8957451670273\n",
            "\n",
            "Average Surface : 2057.750936329588\n",
            "Std Surface : 1157.8079878994151\n",
            "\n",
            "covariance : 37115.6105887809\n",
            "correlation : 0.9169244285569261\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_srveVgE8mNO"
      },
      "source": [
        "## Split\r\n",
        "\r\n",
        "Split into two datasets : Training (0.8) and Testing (0.2)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kw_LvIws8llK",
        "outputId": "e0344fc1-4702-4c1c-e42f-60e53ae37db6"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\r\n",
        "\r\n",
        "X_train,X_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=1000)\r\n",
        "print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(427,) (107,) (427,) (107,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TbJn72rM-Glh"
      },
      "source": [
        "## Linear Regression (Manual)\r\n",
        "\r\n",
        "Calculation of the equation : $f(x) =\\hat{\\beta_0}*x + \\hat{\\beta_1}$ \r\n",
        "\r\n",
        "with $\\hat{\\beta_0} = \\bar{y} − \\hat{\\beta_1}*\\bar{x}$ \r\n",
        "\r\n",
        "and $\\hat{\\beta_1}=\\frac{Cov(x,y)}{Var(x)}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dd9eC2jE-IOA",
        "outputId": "54e797ce-4cae-4a6b-f72c-911a1278d40f"
      },
      "source": [
        "# Training\r\n",
        "cov_mat=np.cov(X_train,y_train)\r\n",
        "b1hat=cov_mat[0,1]/cov_mat[0,0]\r\n",
        "b0hat=y_train.mean()-b1hat*X_train.mean()\r\n",
        "\r\n",
        "# Prediction\r\n",
        "yhat_train=np.array([b0hat+b1hat*x_i for x_i in X_train])\r\n",
        "yhat_test=np.array([b0hat+b1hat*x_i for x_i in X_test])\r\n",
        "\r\n",
        "# Metrics\r\n",
        "from sklearn.metrics import mean_squared_error,r2_score\r\n",
        "mse_train,r2_train=mean_squared_error(y_train,yhat_train),r2_score(y_train,yhat_train)\r\n",
        "mse_test,r2_test=mean_squared_error(y_test,yhat_test),r2_score(y_test,yhat_test)\r\n",
        "\r\n",
        "# Display\r\n",
        "_,[ax1,ax2]=plt.subplots(ncols=2,nrows=1,figsize=(14,5))\r\n",
        "\r\n",
        "ax1.scatter(X_train,y_train,s=1)\r\n",
        "ax1.plot(X_train,yhat_train)\r\n",
        "ax1.set_title('Train dataset')\r\n",
        "ax1.set_xlabel('Surface')\r\n",
        "ax1.set_ylabel('Monthly Rent')\r\n",
        "\r\n",
        "ax2.scatter(X_test,y_test,s=1)\r\n",
        "ax2.plot(X_test,yhat_test)\r\n",
        "ax2.set_title('Test dataset')\r\n",
        "ax2.set_xlabel('Surface')\r\n",
        "ax2.set_ylabel('Monthly Rent')\r\n",
        "\r\n",
        "plt.show()\r\n",
        "\r\n",
        "print(\"MSE Train : \"+ '%.3f' % mse_train)\r\n",
        "print(\"R2 Train : \"+ '%.3f' % r2_train)\r\n",
        "\r\n",
        "print(\"MSE Test : \"+ '%.3f' % mse_test)\r\n",
        "print(\"R2 Test : \"+ '%.3f' % r2_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "MSE Train : 229486.551\n",
            "R2 Train : 0.831\n",
            "MSE Test : 150316.932\n",
            "R2 Test : 0.881\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EB712b53F6MF"
      },
      "source": [
        "## Linear Regression (sklearn)\r\n",
        "\r\n",
        "With `sklearn.linear_model.LinearRegression`, the results are the same."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HD2gykVPGtmb",
        "outputId": "14592bbd-d0f6-4a9e-f8e1-ae49d9de5ff9"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\r\n",
        "\r\n",
        "# Training\r\n",
        "lr=LinearRegression()\r\n",
        "lr.fit(np.expand_dims(X_train,1),y_train)\r\n",
        "\r\n",
        "# Prediction\r\n",
        "yhat_train=lr.predict(np.expand_dims(X_train,1))\r\n",
        "yhat_test=lr.predict(np.expand_dims(X_test,1))\r\n",
        "\r\n",
        "# Metrics\r\n",
        "from sklearn.metrics import mean_squared_error,r2_score\r\n",
        "mse_train,r2_train=mean_squared_error(y_train,yhat_train),r2_score(y_train,yhat_train)\r\n",
        "mse_test,r2_test=mean_squared_error(y_test,yhat_test),r2_score(y_test,yhat_test)\r\n",
        "\r\n",
        "# Display\r\n",
        "_,[ax1,ax2]=plt.subplots(ncols=2,nrows=1,figsize=(14,5))\r\n",
        "\r\n",
        "ax1.scatter(X_train,y_train,s=1)\r\n",
        "ax1.plot(X_train,yhat_train)\r\n",
        "ax1.set_title('Train dataset')\r\n",
        "ax1.set_xlabel('Surface')\r\n",
        "ax1.set_ylabel('Monthly Rent')\r\n",
        "\r\n",
        "ax2.scatter(X_test,y_test,s=1)\r\n",
        "ax2.plot(X_test,yhat_test)\r\n",
        "ax2.set_title('Test dataset')\r\n",
        "ax2.set_xlabel('Surface')\r\n",
        "ax2.set_ylabel('Monthly Rent')\r\n",
        "\r\n",
        "plt.show()\r\n",
        "\r\n",
        "print(\"MSE Train : \"+ '%.3f' % mse_train)\r\n",
        "print(\"R2 Train : \"+ '%.3f' % r2_train)\r\n",
        "\r\n",
        "print(\"MSE Test : \"+ '%.3f' % mse_test)\r\n",
        "print(\"R2 Test : \"+ '%.3f' % r2_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "MSE Train : 229486.551\n",
            "R2 Train : 0.831\n",
            "MSE Test : 150316.932\n",
            "R2 Test : 0.881\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MUQFwNVrG-4A"
      },
      "source": [
        "# Multiple Linear Regression\r\n",
        "\r\n",
        "MLR performed on **house_price** to estimate the price of an house according to many features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "gpDmifbQHMqd",
        "outputId": "3320fa5c-c319-427e-cfd9-b5ed0c927a32"
      },
      "source": [
        "data=pd.read_csv('house_price.csv')\r\n",
        "data"
      ],
      "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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1455</th>\n",
              "      <td>1456</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>62.0</td>\n",
              "      <td>7917</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>Gilbert</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1999</td>\n",
              "      <td>2000</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>PConc</td>\n",
              "      <td>Gd</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>953</td>\n",
              "      <td>953</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>953</td>\n",
              "      <td>694</td>\n",
              "      <td>0</td>\n",
              "      <td>1647</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1999.0</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>0</td>\n",
              "      <td>40</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>175000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>1457</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>85.0</td>\n",
              "      <td>13175</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>NWAmes</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1978</td>\n",
              "      <td>1988</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>Plywood</td>\n",
              "      <td>Plywood</td>\n",
              "      <td>Stone</td>\n",
              "      <td>119.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>790</td>\n",
              "      <td>Rec</td>\n",
              "      <td>163</td>\n",
              "      <td>589</td>\n",
              "      <td>1542</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>2073</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2073</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>TA</td>\n",
              "      <td>7</td>\n",
              "      <td>Min1</td>\n",
              "      <td>2</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1978.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>2</td>\n",
              "      <td>500</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>349</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>MnPrv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1457</th>\n",
              "      <td>1458</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>66.0</td>\n",
              "      <td>9042</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>Crawfor</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>1941</td>\n",
              "      <td>2006</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>CemntBd</td>\n",
              "      <td>CmentBd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Stone</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>275</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>877</td>\n",
              "      <td>1152</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1188</td>\n",
              "      <td>1152</td>\n",
              "      <td>0</td>\n",
              "      <td>2340</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>9</td>\n",
              "      <td>Typ</td>\n",
              "      <td>2</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1941.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>1</td>\n",
              "      <td>252</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GdPrv</td>\n",
              "      <td>Shed</td>\n",
              "      <td>2500</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>266500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>1459</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>9717</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>NAmes</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1950</td>\n",
              "      <td>1996</td>\n",
              "      <td>Hip</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>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Mn</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>49</td>\n",
              "      <td>Rec</td>\n",
              "      <td>1029</td>\n",
              "      <td>0</td>\n",
              "      <td>1078</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>FuseA</td>\n",
              "      <td>1078</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1078</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>5</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>1</td>\n",
              "      <td>240</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>366</td>\n",
              "      <td>0</td>\n",
              "      <td>112</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>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>142125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1459</th>\n",
              "      <td>1460</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>75.0</td>\n",
              "      <td>9937</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>Edwards</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1965</td>\n",
              "      <td>1965</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>HdBoard</td>\n",
              "      <td>HdBoard</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>BLQ</td>\n",
              "      <td>830</td>\n",
              "      <td>LwQ</td>\n",
              "      <td>290</td>\n",
              "      <td>136</td>\n",
              "      <td>1256</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1256</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1256</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1965.0</td>\n",
              "      <td>Fin</td>\n",
              "      <td>1</td>\n",
              "      <td>276</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>736</td>\n",
              "      <td>68</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>6</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>147500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1460 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",
              "...    ...         ...      ...  ...       ...            ...       ...\n",
              "1455  1456          60       RL  ...        WD         Normal    175000\n",
              "1456  1457          20       RL  ...        WD         Normal    210000\n",
              "1457  1458          70       RL  ...        WD         Normal    266500\n",
              "1458  1459          20       RL  ...        WD         Normal    142125\n",
              "1459  1460          20       RL  ...        WD         Normal    147500\n",
              "\n",
              "[1460 rows x 81 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iyc1h-kdIiFV",
        "outputId": "b0c8e319-476d-4d96-b017-bea5b543714c"
      },
      "source": [
        "data.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": "markdown",
      "metadata": {
        "id": "crF9YXe7IBrz"
      },
      "source": [
        "## Preprocess\r\n",
        "\r\n",
        "*  Split of dataset in x_data and y_data\r\n",
        "*  Drop of columns where there is a lot of NaN Values : `['MiscFeature', 'PoolQC', 'Fence', 'FireplaceQu', 'Alley']`\r\n",
        "*  Fill the others columns with :\r\n",
        "  *  most frequent category for discrete variable\r\n",
        "  *  mean of the other available values\r\n",
        "*  Conversion of discrete variables with `LabelEncoder()`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6rImUAHID2F",
        "outputId": "78efb747-bec7-41a4-9cb1-d8aa7ddb01d1"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\r\n",
        "\r\n",
        "data=pd.read_csv('house_price.csv')\r\n",
        "\r\n",
        "# drop the id column and save the y_data column\r\n",
        "y_data = data['SalePrice']\r\n",
        "data.drop(['Id', 'SalePrice'], axis = 1, inplace = True)\r\n",
        "\r\n",
        "# drop columns that have a lot of missing or NaN values\r\n",
        "cols_to_drop = ['MiscFeature', 'PoolQC', 'Fence', 'FireplaceQu', 'Alley']\r\n",
        "data.drop(cols_to_drop, axis = 1, inplace = True)\r\n",
        "\r\n",
        "# fill columns that have a few NaN values\r\n",
        "cols_with_nan = ['LotFrontage', 'MasVnrType', 'MasVnrArea', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1',\r\n",
        " 'BsmtFinType2', 'Electrical', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond']\r\n",
        "\r\n",
        "for col in cols_with_nan:\r\n",
        "    if data[col].dtype == 'object':\r\n",
        "        data.fillna(data[col].value_counts().index[0], inplace = True)\r\n",
        "    else:\r\n",
        "        data.fillna(data[col].mean(), inplace = True)\r\n",
        "        \r\n",
        "# convert columns with object datatypes into numbers by using Label Encoding\r\n",
        "object_cols = [col for col in data.columns if data[col].dtype == 'object']\r\n",
        "\r\n",
        "for col in object_cols:\r\n",
        "    data[col] = data[col].astype('str')\r\n",
        "    le = LabelEncoder()\r\n",
        "    data[col] = le.fit_transform(data[col])\r\n",
        "    \r\n",
        "X_data=data[:]\r\n",
        "\r\n",
        "\r\n",
        "#Data information after transformation\r\n",
        "print('----------')\r\n",
        "print(\"Data after transformation\")\r\n",
        "print('----------')\r\n",
        "print(\"Data information\")\r\n",
        "print('----------')\r\n",
        "data.info()\r\n",
        "print('----------')\r\n",
        "print(\"First data of the file\")\r\n",
        "print('----------')\r\n",
        "print(data.head(10))\r\n",
        "\r\n",
        "\r\n",
        "#Data analysis after transformation\r\n",
        "print('----------')\r\n",
        "print(\"Statistical properties\")\r\n",
        "print('----------')\r\n",
        "print(data.describe())\r\n",
        "print('----------')\r\n",
        "print(\"Table size\")\r\n",
        "print('----------')\r\n",
        "nb_val, nb_col = data.shape\r\n",
        "print(data.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------\n",
            "Data after transformation\n",
            "----------\n",
            "Data information\n",
            "----------\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1460 entries, 0 to 1459\n",
            "Data columns (total 74 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   MSSubClass     1460 non-null   int64  \n",
            " 1   MSZoning       1460 non-null   int64  \n",
            " 2   LotFrontage    1460 non-null   float64\n",
            " 3   LotArea        1460 non-null   int64  \n",
            " 4   Street         1460 non-null   int64  \n",
            " 5   LotShape       1460 non-null   int64  \n",
            " 6   LandContour    1460 non-null   int64  \n",
            " 7   Utilities      1460 non-null   int64  \n",
            " 8   LotConfig      1460 non-null   int64  \n",
            " 9   LandSlope      1460 non-null   int64  \n",
            " 10  Neighborhood   1460 non-null   int64  \n",
            " 11  Condition1     1460 non-null   int64  \n",
            " 12  Condition2     1460 non-null   int64  \n",
            " 13  BldgType       1460 non-null   int64  \n",
            " 14  HouseStyle     1460 non-null   int64  \n",
            " 15  OverallQual    1460 non-null   int64  \n",
            " 16  OverallCond    1460 non-null   int64  \n",
            " 17  YearBuilt      1460 non-null   int64  \n",
            " 18  YearRemodAdd   1460 non-null   int64  \n",
            " 19  RoofStyle      1460 non-null   int64  \n",
            " 20  RoofMatl       1460 non-null   int64  \n",
            " 21  Exterior1st    1460 non-null   int64  \n",
            " 22  Exterior2nd    1460 non-null   int64  \n",
            " 23  MasVnrType     1460 non-null   int64  \n",
            " 24  MasVnrArea     1460 non-null   float64\n",
            " 25  ExterQual      1460 non-null   int64  \n",
            " 26  ExterCond      1460 non-null   int64  \n",
            " 27  Foundation     1460 non-null   int64  \n",
            " 28  BsmtQual       1460 non-null   int64  \n",
            " 29  BsmtCond       1460 non-null   int64  \n",
            " 30  BsmtExposure   1460 non-null   int64  \n",
            " 31  BsmtFinType1   1460 non-null   int64  \n",
            " 32  BsmtFinSF1     1460 non-null   int64  \n",
            " 33  BsmtFinType2   1460 non-null   int64  \n",
            " 34  BsmtFinSF2     1460 non-null   int64  \n",
            " 35  BsmtUnfSF      1460 non-null   int64  \n",
            " 36  TotalBsmtSF    1460 non-null   int64  \n",
            " 37  Heating        1460 non-null   int64  \n",
            " 38  HeatingQC      1460 non-null   int64  \n",
            " 39  CentralAir     1460 non-null   int64  \n",
            " 40  Electrical     1460 non-null   int64  \n",
            " 41  1stFlrSF       1460 non-null   int64  \n",
            " 42  2ndFlrSF       1460 non-null   int64  \n",
            " 43  LowQualFinSF   1460 non-null   int64  \n",
            " 44  GrLivArea      1460 non-null   int64  \n",
            " 45  BsmtFullBath   1460 non-null   int64  \n",
            " 46  BsmtHalfBath   1460 non-null   int64  \n",
            " 47  FullBath       1460 non-null   int64  \n",
            " 48  HalfBath       1460 non-null   int64  \n",
            " 49  BedroomAbvGr   1460 non-null   int64  \n",
            " 50  KitchenAbvGr   1460 non-null   int64  \n",
            " 51  KitchenQual    1460 non-null   int64  \n",
            " 52  TotRmsAbvGrd   1460 non-null   int64  \n",
            " 53  Functional     1460 non-null   int64  \n",
            " 54  Fireplaces     1460 non-null   int64  \n",
            " 55  GarageType     1460 non-null   int64  \n",
            " 56  GarageYrBlt    1460 non-null   float64\n",
            " 57  GarageFinish   1460 non-null   int64  \n",
            " 58  GarageCars     1460 non-null   int64  \n",
            " 59  GarageArea     1460 non-null   int64  \n",
            " 60  GarageQual     1460 non-null   int64  \n",
            " 61  GarageCond     1460 non-null   int64  \n",
            " 62  PavedDrive     1460 non-null   int64  \n",
            " 63  WoodDeckSF     1460 non-null   int64  \n",
            " 64  OpenPorchSF    1460 non-null   int64  \n",
            " 65  EnclosedPorch  1460 non-null   int64  \n",
            " 66  3SsnPorch      1460 non-null   int64  \n",
            " 67  ScreenPorch    1460 non-null   int64  \n",
            " 68  PoolArea       1460 non-null   int64  \n",
            " 69  MiscVal        1460 non-null   int64  \n",
            " 70  MoSold         1460 non-null   int64  \n",
            " 71  YrSold         1460 non-null   int64  \n",
            " 72  SaleType       1460 non-null   int64  \n",
            " 73  SaleCondition  1460 non-null   int64  \n",
            "dtypes: float64(3), int64(71)\n",
            "memory usage: 844.2 KB\n",
            "----------\n",
            "First data of the file\n",
            "----------\n",
            "   MSSubClass  MSZoning  LotFrontage  ...  YrSold  SaleType  SaleCondition\n",
            "0          60         3    65.000000  ...    2008         8              4\n",
            "1          20         3    80.000000  ...    2007         8              4\n",
            "2          60         3    68.000000  ...    2008         8              4\n",
            "3          70         3    60.000000  ...    2006         8              0\n",
            "4          60         3    84.000000  ...    2008         8              4\n",
            "5          50         3    85.000000  ...    2009         8              4\n",
            "6          20         3    75.000000  ...    2007         8              4\n",
            "7          60         3    70.049958  ...    2009         8              4\n",
            "8          50         4    51.000000  ...    2008         8              0\n",
            "9         190         3    50.000000  ...    2008         8              4\n",
            "\n",
            "[10 rows x 74 columns]\n",
            "----------\n",
            "Statistical properties\n",
            "----------\n",
            "        MSSubClass     MSZoning  ...     SaleType  SaleCondition\n",
            "count  1460.000000  1460.000000  ...  1460.000000    1460.000000\n",
            "mean     56.897260     3.028767  ...     7.513014       3.770548\n",
            "std      42.300571     0.632017  ...     1.552100       1.100854\n",
            "min      20.000000     0.000000  ...     0.000000       0.000000\n",
            "25%      20.000000     3.000000  ...     8.000000       4.000000\n",
            "50%      50.000000     3.000000  ...     8.000000       4.000000\n",
            "75%      70.000000     3.000000  ...     8.000000       4.000000\n",
            "max     190.000000     4.000000  ...     8.000000       5.000000\n",
            "\n",
            "[8 rows x 74 columns]\n",
            "----------\n",
            "Table size\n",
            "----------\n",
            "(1460, 74)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zxctpt38Ky4s"
      },
      "source": [
        "Display of the correlation matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ReNZKuumJt1P",
        "outputId": "451c4327-baf5-4d8d-82c5-5ee034359459"
      },
      "source": [
        "df_corr=data.corr()\r\n",
        "import seaborn as sn\r\n",
        "plt.figure(figsize=(25,20))\r\n",
        "sn.heatmap(df_corr)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OvozalVHK1rf"
      },
      "source": [
        "Split into two datasets : Training (0.8) and Testing (0.2)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INV1jmQNKZOF",
        "outputId": "e5bbdafe-75c7-42c8-8293-bc062ea4154a"
      },
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(X_data,y_data,test_size=0.2,random_state=1000)\r\n",
        "print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1168, 74) (292, 74) (1168,) (292,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ealTawhEK4Ju"
      },
      "source": [
        "## MLR sklearn\r\n",
        "\r\n",
        "Vanilla MLR with `sklearn.linear_model.LinearRegression`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X95Qir2MK3wI",
        "outputId": "f15458ff-fcf9-457f-8858-4474f29a66e8"
      },
      "source": [
        "lr=LinearRegression()\r\n",
        "lr.fit(X_train,y_train)\r\n",
        "coef_mlr=np.array(lr.coef_)\r\n",
        "print(coef_mlr)\r\n",
        "print(np.max(np.abs(lr.coef_)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-1.06599819e+02 -1.56542583e+03 -1.82966316e+02  3.31044338e-01\n",
            "  2.91076804e+04 -1.51506921e+03  5.31252253e+03 -5.73517232e+04\n",
            " -2.45624834e+01  6.82969392e+03  4.63528434e+02 -1.83074248e+02\n",
            " -9.40483113e+03 -2.99788031e+03 -1.62646952e+03  1.18403801e+04\n",
            "  5.59720955e+03  2.47959741e+02  1.98607999e+01  1.21354932e+03\n",
            "  5.75799418e+03 -6.84059944e+02  6.05098585e+01  2.69813124e+03\n",
            "  2.12513226e+01 -8.58340636e+03  8.81246399e+02 -1.02330074e+02\n",
            " -6.32811472e+03  3.32987520e+03 -3.45530931e+03 -6.19089602e+02\n",
            "  3.94850519e+00  2.68804107e+03  1.37357881e+01 -6.13457009e+00\n",
            "  1.15497232e+01 -2.67564319e+03 -8.61289368e+02  1.68590338e+02\n",
            " -1.01418310e+03  1.50885702e+01  1.84794566e+01 -7.76361243e+00\n",
            "  2.58044144e+01  7.37464570e+03  2.13748737e+03  2.47803102e+03\n",
            " -1.02032278e+03 -4.00145994e+03 -1.24735034e+04 -9.24163573e+03\n",
            "  4.07098863e+03  3.22553713e+03  5.84039620e+03  5.82696656e+02\n",
            " -1.38200705e+01 -6.64591233e+02  1.66044076e+04 -4.06162345e+00\n",
            " -1.72429891e+03  2.62425138e+03  3.37724208e+03  1.43964996e+01\n",
            " -1.78343526e+01  1.79138541e+01  1.04514691e+01  4.92537347e+01\n",
            "  1.93004775e+00 -3.86681971e+00 -1.45625828e+02 -7.15256781e+02\n",
            " -7.93443682e+02  2.46933471e+03]\n",
            "57351.72318017527\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6lmCdgeTMqQB",
        "outputId": "0e6d41c2-2d72-4807-bffa-6071fad09bf5"
      },
      "source": [
        "yhat_train=lr.predict(X_train)\r\n",
        "yhat_test=lr.predict(X_test)\r\n",
        "\r\n",
        "# Metrics\r\n",
        "from sklearn.metrics import mean_squared_error,r2_score\r\n",
        "mse_train,r2_train=mean_squared_error(y_train,yhat_train),r2_score(y_train,yhat_train)\r\n",
        "mse_test,r2_test=mean_squared_error(y_test,yhat_test),r2_score(y_test,yhat_test)\r\n",
        "\r\n",
        "\r\n",
        "print(\"MSE Train : \"+ '%.3f' % mse_train)\r\n",
        "print(\"R2 Train : \"+ '%.3f' % r2_train)\r\n",
        "\r\n",
        "print(\"MSE Test : \"+ '%.3f' % mse_test)\r\n",
        "print(\"R2 Test : \"+ '%.3f' % r2_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE Train : 991794301.340\n",
            "R2 Train : 0.841\n",
            "MSE Test : 845257758.004\n",
            "R2 Test : 0.873\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lE6ZaB7XNa3E"
      },
      "source": [
        "## Ridge Regression\r\n",
        "\r\n",
        "Appliction of Ridge Regresion (MLR with L2 Regularization). The penalty term  is quadratic : $R(\\beta)=\t\\frac{\\lambda}{2}||\\beta||_2^2$. Thus a high $\\lambda$ is more penalizing. The idea is to promote small values, get a more 'explainable' model and prevent overfitting. I try with various values of $\\lambda$ from 1 to 10000."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "XSg32I8GNedC",
        "outputId": "c6481dd5-717c-4ce1-dc3f-2f5c3dd1280a"
      },
      "source": [
        "from sklearn.linear_model import Ridge\r\n",
        "\r\n",
        "_,[ax1,ax2,ax3]=plt.subplots(ncols=3,nrows=1,figsize=(20,5))\r\n",
        "\r\n",
        "lambdas=[1,10,100,1000,10000]\r\n",
        "coefs_ridge=[]\r\n",
        "mse_ridge=[]\r\n",
        "r2_ridge=[]\r\n",
        "for lamb in lambdas:\r\n",
        "    rd=Ridge(alpha=lamb)\r\n",
        "    rd.fit(X_train,y_train)\r\n",
        "    coefs_ridge.append(np.array(rd.coef_))\r\n",
        "    mse_ridge.append(mean_squared_error(y_test,rd.predict(X_test)))\r\n",
        "    r2_ridge.append(r2_score(y_test,rd.predict(X_test)))\r\n",
        "    ax1.plot(np.arange(74),rd.coef_,label='lambda = '+str(lamb))\r\n",
        "ax1.legend(loc='lower right')\r\n",
        "ax1.set_xlabel('Vectors')\r\n",
        "ax1.set_ylabel('Weights')\r\n",
        "\r\n",
        "ax2.plot([0,1,2,3,4],mse_ridge)\r\n",
        "ax2.set_xlabel('Lambda (10^x)')\r\n",
        "ax2.set_ylabel('MSE')\r\n",
        "\r\n",
        "ax3.plot([0,1,2,3,4],r2_ridge)\r\n",
        "ax3.set_xlabel('Lambda (10^x)')\r\n",
        "ax3.set_ylabel('R2')\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3fDKqE3NOHCp"
      },
      "source": [
        "I observe the best parameter for $\\lambda$ is 100 in my case. I compare with the previous results obtained with Vanilla MLR."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "2bMfacnGUBEI",
        "outputId": "89d7a7a5-5814-4631-e964-8b5befe30b0d"
      },
      "source": [
        "plt.plot(np.arange(74),coef_mlr,label='MLR')\r\n",
        "plt.plot(np.arange(74),coefs_ridge[2],label='Ridge lambda=100')\r\n",
        "plt.legend(loc='lower right')\r\n",
        "plt.show()\r\n",
        "\r\n",
        "print(\"MSE MLR  : \"+ '%.3f' % mse_test)\r\n",
        "print(\"R2 MLR : \"+ '%.3f' % r2_test)\r\n",
        "\r\n",
        "print(\"MSE Ridge 100 : \"+ '%.3f' % mse_ridge[2])\r\n",
        "print(\"R2 Ridge 100 : \"+ '%.3f' % r2_ridge[2])\r\n",
        "\r\n",
        "print()\r\n",
        "print('Most Important feature : '+data.columns[np.argmax(np.abs(coefs_ridge[2]))])\r\n",
        "print('Less Important feature : '+data.columns[np.argmin(np.abs(coefs_ridge[2]))])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "MSE MLR  : 845257758.004\n",
            "R2 MLR : 0.873\n",
            "MSE Ridge 100 : 825451576.423\n",
            "R2 Ridge 100 : 0.876\n",
            "\n",
            "Most Important feature : OverallQual\n",
            "Less Important feature : LotArea\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sePSgWs6W97X"
      },
      "source": [
        "## Lasso Regression\r\n",
        "\r\n",
        "Similarly with Ridge Regression, I apply Lasso Regression (MLR with L1 Regularization). The penalty term is sparsity/shrinkage : $R(\\beta)=\t\\lambda||\\beta||_1$. I also compare with $\\lambda$ values from 1 to 10000."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "BPU0OQXsXAxe",
        "outputId": "72163608-63cf-411a-b3c1-f20bdba372b9"
      },
      "source": [
        "from sklearn.linear_model import Lasso\r\n",
        "\r\n",
        "_,[ax1,ax2,ax3]=plt.subplots(ncols=3,nrows=1,figsize=(20,5))\r\n",
        "\r\n",
        "lambdas=[1,10,100,1000,10000]\r\n",
        "coefs_lasso=[]\r\n",
        "mse_lasso=[]\r\n",
        "r2_lasso=[]\r\n",
        "for lamb in lambdas:\r\n",
        "    lss=Lasso(alpha=lamb)\r\n",
        "    lss.fit(X_train,y_train)\r\n",
        "    coefs_lasso.append(np.array(lss.coef_))\r\n",
        "    mse_lasso.append(mean_squared_error(y_test,lss.predict(X_test)))\r\n",
        "    r2_lasso.append(r2_score(y_test,lss.predict(X_test)))\r\n",
        "    ax1.plot(np.arange(74),lss.coef_,label='lambda = '+str(lamb))\r\n",
        "ax1.legend(loc='lower right')\r\n",
        "ax1.set_xlabel('Vectors')\r\n",
        "ax1.set_ylabel('Weights')\r\n",
        "\r\n",
        "ax2.plot([0,1,2,3,4],mse_lasso)\r\n",
        "ax2.set_xlabel('Lambda (10^x)')\r\n",
        "ax2.set_ylabel('MSE')\r\n",
        "\r\n",
        "ax3.plot([0,1,2,3,4],r2_lasso)\r\n",
        "ax3.set_xlabel('Lambda (10^x)')\r\n",
        "ax3.set_ylabel('R2')\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XpSrhoJPRFo"
      },
      "source": [
        "The best model is obtained with $\\lambda=1000$. I compare it with results obtained with Vanilla MLR."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "s37igjNGYLQD",
        "outputId": "b917fc2f-ad0c-42df-8dd2-d11b579a3dee"
      },
      "source": [
        "plt.plot(np.arange(74),coef_mlr,label='MLR')\r\n",
        "plt.plot(np.arange(74),coefs_lasso[3],label='Ridge lambda=100')\r\n",
        "plt.legend(loc='lower right')\r\n",
        "plt.show()\r\n",
        "\r\n",
        "print(\"MSE MLR  : \"+ '%.3f' % mse_test)\r\n",
        "print(\"R2 MLR : \"+ '%.3f' % r2_test)\r\n",
        "\r\n",
        "print(\"MSE Ridge 100 : \"+ '%.3f' % mse_lasso[3])\r\n",
        "print(\"R2 Ridge 100 : \"+ '%.3f' % r2_lasso[3])\r\n",
        "\r\n",
        "print()\r\n",
        "print('Most Important feature : '+data.columns[np.argmax(np.abs(coefs_lasso[3]))])\r\n",
        "print('Less Important feature : '+data.columns[np.argmin(np.abs(coefs_lasso[3]))])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "MSE MLR  : 845257758.004\n",
            "R2 MLR : 0.873\n",
            "MSE Ridge 100 : 839556110.489\n",
            "R2 Ridge 100 : 0.873\n",
            "\n",
            "Most Important feature : OverallQual\n",
            "Less Important feature : MSZoning\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}