모델훈련캐글필사.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOy+wm3dSJlKJ+pV8fbHKkb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/wlsisl/ESAA_/blob/main/%EB%AA%A8%EB%8D%B8%ED%9B%88%EB%A0%A8%EC%BA%90%EA%B8%80%ED%95%84%EC%82%AC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Stacked Regressions to predict House Prices"
      ],
      "metadata": {
        "id": "UHzLPGyJUCqQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import some necessary librairies\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt  # Matlab-style plotting\n",
        "import seaborn as sns\n",
        "color = sns.color_palette()\n",
        "sns.set_style('darkgrid')\n",
        "import warnings\n",
        "def ignore_warn(*args, **kwargs):\n",
        "    pass\n",
        "warnings.warn = ignore_warn #ignore annoying warning (from sklearn and seaborn)\n",
        "\n",
        "\n",
        "from scipy import stats\n",
        "from scipy.stats import norm, skew #for some statistics\n",
        "\n",
        "\n",
        "pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x)) #Limiting floats output to 3 decimal points\n",
        "\n",
        "\n",
        "from subprocess import check_output\n",
        "print(check_output([\"ls\", \"/content\"]).decode(\"utf8\")) #check the files available in the directory"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6X61Rs6vUhbo",
        "outputId": "3a265d8f-a34b-4060-fe5b-97e868999974"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample_data\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5XEJql-VVUqi",
        "outputId": "0dcafee7-01f0-4e0b-ba5b-2bb04fd50f5d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.read_csv('/content/gdrive/MyDrive/train.csv')\n",
        "test = pd.read_csv('/content/gdrive/MyDrive/test.csv')"
      ],
      "metadata": {
        "id": "Z1GBRXWxUmYy"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "-r_zQbWIZLIn",
        "outputId": "cee3faef-309c-4d50-9187-f80172f2a54c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0   1          60       RL       65.000     8450   Pave   NaN      Reg   \n",
              "1   2          20       RL       80.000     9600   Pave   NaN      Reg   \n",
              "2   3          60       RL       68.000    11250   Pave   NaN      IR1   \n",
              "3   4          70       RL       60.000     9550   Pave   NaN      IR1   \n",
              "4   5          60       RL       84.000    14260   Pave   NaN      IR1   \n",
              "\n",
              "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
              "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
              "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
              "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
              "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
              "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
              "\n",
              "  YrSold  SaleType  SaleCondition  SalePrice  \n",
              "0   2008        WD         Normal     208500  \n",
              "1   2007        WD         Normal     181500  \n",
              "2   2008        WD         Normal     223500  \n",
              "3   2006        WD        Abnorml     140000  \n",
              "4   2008        WD         Normal     250000  \n",
              "\n",
              "[5 rows x 81 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-50aeac7d-d55b-4654-9e54-d1c2c3f15877\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>...</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>65.000</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>...</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>80.000</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>...</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>68.000</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>...</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>60.000</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>...</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>84.000</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>...</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",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 81 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50aeac7d-d55b-4654-9e54-d1c2c3f15877')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-50aeac7d-d55b-4654-9e54-d1c2c3f15877 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-50aeac7d-d55b-4654-9e54-d1c2c3f15877');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "Cbo39a88ZMPj",
        "outputId": "8ea5f177-37d6-45a3-d482-a032d7f17937"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0  1461          20       RH       80.000    11622   Pave   NaN      Reg   \n",
              "1  1462          20       RL       81.000    14267   Pave   NaN      IR1   \n",
              "2  1463          60       RL       74.000    13830   Pave   NaN      IR1   \n",
              "3  1464          60       RL       78.000     9978   Pave   NaN      IR1   \n",
              "4  1465         120       RL       43.000     5005   Pave   NaN      IR1   \n",
              "\n",
              "  LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence MiscFeature  \\\n",
              "0         Lvl    AllPub  ...         120        0    NaN  MnPrv         NaN   \n",
              "1         Lvl    AllPub  ...           0        0    NaN    NaN        Gar2   \n",
              "2         Lvl    AllPub  ...           0        0    NaN  MnPrv         NaN   \n",
              "3         Lvl    AllPub  ...           0        0    NaN    NaN         NaN   \n",
              "4         HLS    AllPub  ...         144        0    NaN    NaN         NaN   \n",
              "\n",
              "  MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
              "0       0      6    2010        WD         Normal  \n",
              "1   12500      6    2010        WD         Normal  \n",
              "2       0      3    2010        WD         Normal  \n",
              "3       0      6    2010        WD         Normal  \n",
              "4       0      1    2010        WD         Normal  \n",
              "\n",
              "[5 rows x 80 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9b560b0c-e715-4084-a2e6-7a1fb361797d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>...</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1461</td>\n",
              "      <td>20</td>\n",
              "      <td>RH</td>\n",
              "      <td>80.000</td>\n",
              "      <td>11622</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1462</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>81.000</td>\n",
              "      <td>14267</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gar2</td>\n",
              "      <td>12500</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1463</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>74.000</td>\n",
              "      <td>13830</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</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>3</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1464</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>78.000</td>\n",
              "      <td>9978</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</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>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1465</td>\n",
              "      <td>120</td>\n",
              "      <td>RL</td>\n",
              "      <td>43.000</td>\n",
              "      <td>5005</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>HLS</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>144</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>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 80 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b560b0c-e715-4084-a2e6-7a1fb361797d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9b560b0c-e715-4084-a2e6-7a1fb361797d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9b560b0c-e715-4084-a2e6-7a1fb361797d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check the numbers of samples and features\n",
        "print(\"The train data size before dropping Id feature is : {} \".format(train.shape))\n",
        "print(\"The test data size before dropping Id feature is : {} \".format(test.shape))\n",
        "\n",
        "#Save the 'Id' column\n",
        "train_ID = train['Id']\n",
        "test_ID = test['Id']\n",
        "\n",
        "#drop the  'Id' colum since it's unnecessary for  the prediction process.\n",
        "train.drop(\"Id\", axis = 1, inplace = True)\n",
        "test.drop(\"Id\", axis = 1, inplace = True)\n",
        "\n",
        "#check again the data size after dropping the 'Id' variable\n",
        "print(\"\\nThe train data size after dropping Id feature is : {} \".format(train.shape)) \n",
        "print(\"The test data size after dropping Id feature is : {} \".format(test.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RzK9Ix48ZOIH",
        "outputId": "0ccc95c3-df24-4761-e632-640fa0791602"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The train data size before dropping Id feature is : (1460, 81) \n",
            "The test data size before dropping Id feature is : (1459, 80) \n",
            "\n",
            "The train data size after dropping Id feature is : (1460, 80) \n",
            "The test data size after dropping Id feature is : (1459, 79) \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Processing\n",
        "\n",
        "##Outliers"
      ],
      "metadata": {
        "id": "n3tcOCD6UhDX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.scatter(x = train['GrLivArea'], y = train['SalePrice'])\n",
        "plt.ylabel('SalePrice', fontsize=13)\n",
        "plt.xlabel('GrLivArea', fontsize=13)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "5l58qULlZpVM",
        "outputId": "fac4248e-1630-4191-d8e9-d648f0d34b91"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "오른쪽 두개의 이상치 발견.\n",
        "매우 큰 griv area가 저렴한 가격으로 표시된다.\n",
        "\n",
        "즉 삭제할 필요가 있음."
      ],
      "metadata": {
        "id": "rXsX6_JcZrR8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#이상치 삭제\n",
        "train = train.drop(train[(train['GrLivArea']>4000) & (train['SalePrice']<300000)].index)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(train['GrLivArea'], train['SalePrice'])\n",
        "plt.ylabel('SalePrice', fontsize=13)\n",
        "plt.xlabel('GrLivArea', fontsize=13)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "09n6RDntZ96h",
        "outputId": "42a20607-3c85-40fd-eda5-16f106cb7dd9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAEJCAYAAACt9OGOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAABBrklEQVR4nO3dfXRU1bn48e+ZGUh4STLEJjNCs1AocJHyYq1gDIQaSCKEYBBYvbbXVm5dlGK1KOIqIBgJpLaVlrZcLVneem172wpIghgpgaCEUASVRoRGFDU/QZgZb8wbYBIyOb8/4gzJ5MxbMm+ZPJ+1XEtOZs7ZswnnmbP3s5+tqKqqIoQQQgSJLtwNEEIIEd0k0AghhAgqCTRCCCGCSgKNEEKIoJJAI4QQIqgM4W5AJGpvb8du77/JeHq90q8/vy+kj3wj/eRdNPXRgAF6zeMSaDTY7Sr19VfC3YywMRoH9+vP7wvpI99IP3kXTX2UlBSneVyGzoQQQgSVBBohhBBBJYFGCCFEUEmgEUIIEVQSaIQQQgSVZJ0JIUQU2ltt5ZnDNVibWjDFxbB8xg3MGW8KS1sk0AghRJTZW22lsOwDmtvaAbA0tVBY9gFAWIKNDJ0JIUSUeeZwjTPIODS3tfPM4ZqwtEeeaIQQIspYm1r8Oh7sYTZ5ohFCiChjiovx+bhjmM3S1ILKtWG2vdXWgLVHAo0QQkSZ5TNuINbQ9fYea9CxfMYN3V4bimE2GToTQogo4xj28mU4zN9htp6QQCOEEFFozniTT/MsprgYLBpBxd3wW0/I0JkQQvRj/gyz9ZQ80QghRD/mzzBbT0mgEUKIfs7XYbaekqEzIYQQQSWBRgghRFBJoBFCCBFUEmiEEEIEVUgCzUcffcRdd93l/O8b3/gG//M//0N9fT1LliwhKyuLJUuW0NDQAICqqmzcuJHMzExyc3M5ffq081zFxcVkZWWRlZVFcXGx8/ipU6fIzc0lMzOTjRs3oqoqgNtrCCGECI2QBJpRo0axe/dudu/eza5duxg0aBCZmZkUFRWRmppKWVkZqampFBUVAVBRUUFNTQ1lZWUUFBSQn58PdASNrVu3sn37dnbs2MHWrVudgSM/P5+CggLKysqoqamhoqICwO01hBBChEbIh86OHj1KSkoKI0aMoLy8nLy8PADy8vI4cOAAgPO4oihMmTKFxsZGbDYblZWVpKWlYTQaSUhIIC0tjcOHD2Oz2bh06RJTpkxBURTy8vIoLy/vci7XawghhAiNkK+jKS0tZd68eQDU1taSnJwMQFJSErW1tQBYrVbMZrPzPWazGavV2u24yWTSPO54vadreKLXKxiNg3v5SfsuvV7Xrz+/L6SPfCP95F1/6KOQBprW1lYOHjzIypUru/1MURQURQnq9X29ht2uUl9/JahtiWRG4+B+/fl9IX3kG+kn76Kpj5KS4jSPh3TorKKiggkTJvCVr3wFgOuuuw6bzQaAzWYjMTER6HhSsVgszvdZLBZMJlO341arVfO44/WeriGEECI0QhpoSktLycnJcf45IyODkpISAEpKSpg1a1aX46qqUlVVRVxcHMnJyUyfPp3KykoaGhpoaGigsrKS6dOnk5yczNChQ6mqqkJVVc1zuV5DCCFEaCiqIw84yK5cucIdd9zBgQMHiIvreLyqq6tjxYoVXLx4keHDh7NlyxaMRiOqqrJhwwYOHz7MoEGDKCwsZOLEiQDs3LmTbdu2AbBs2TIWLlwIwLvvvsvq1atpbm4mPT2ddevWoSiK22t4cvWqPWoeZXsimh7lg0X6yDfST95FUx+5GzoLWaDpSyTQRM8vfrBIH/lG+sm7aOqjiJijEUII0f9IoBFCCBFUEmiEEEIElQQaIYQQQSWBRgghRFBJoBFCCBFUEmiEEEIElQQaIYQQQSWBRgghRFBJoBFCCBFUEmiEEEIElQQaIYQQQRXyHTaFECJS7a228szhGqxNLZjiYlg+4wbmjDeFu1l9ngQaIYSgI8gUln1Ac1s7AJamFgrLPgCQYNNLMnQmhBDAM4drnEHGobmtnWcO14SnQVFEAo0QQgDWpha/jgvfydCZEBFK5gtCyxQXg0UjqJjiYsLQmugiTzRCRCDHfIGlqQWVa/MFe6ut4W5a1Fo+4wZiDV1vibEGHctn3BCeBkURCTRCRCCZLwi9OeNNrMkagzkuBgUwx8WwJmuMPEUGQMiGzhobG3n88cd5//33URSFwsJCbrzxRh5++GE+/fRTRowYwZYtW0hISEBVVTZt2sShQ4eIjY3lqaeeYsKECQAUFxfz7LPPAvCjH/2IBQsWAHDq1ClWr15Nc3MzM2fOZO3atSiKQn19veY1hIhkMl8QHnPGmySwBEHInmg2bdrEjBkz+Pvf/87u3bsZPXo0RUVFpKamUlZWRmpqKkVFRQBUVFRQU1NDWVkZBQUF5OfnA1BfX8/WrVvZvn07O3bsYOvWrTQ0NACQn59PQUEBZWVl1NTUUFFRAeD2GkJEMnfzAjJfIPqikASapqYm3nzzTRYtWgTAwIEDiY+Pp7y8nLy8PADy8vI4cOAAgPO4oihMmTKFxsZGbDYblZWVpKWlYTQaSUhIIC0tjcOHD2Oz2bh06RJTpkxBURTy8vIoLy/vci7XawgRyWS+QESTkAydnT9/nsTERFavXs17773HhAkTWLt2LbW1tSQnJwOQlJREbW0tAFarFbPZ7Hy/2WzGarV2O24ymTSPO14PuL2GJ3q9gtE4uPcfvI/S63X9+vP7Ith9dE/qjQwZHMPm/e9zsaGZ6xNiWZk5lvmThwftmsEgv0ve9Yc+CkmgaWtr41//+hfr1q1j8uTJbNy4sdsQlqIoKIoS1Hb4eg27XaW+/kpQ2xLJjMbB/frz+yIUfZQ+0kj6/VO7HAvH30tv0qzld8m7aOqjpKQ4zeMhGTozm82YzWYmT54MwJ133sm//vUvrrvuOmw2GwA2m43ExESg40nFYrE432+xWDCZTN2OW61WzeOO1wNuryGE8E7SrEUghCTQJCUlYTab+eijjwA4evQoo0ePJiMjg5KSEgBKSkqYNWsWgPO4qqpUVVURFxdHcnIy06dPp7KykoaGBhoaGqisrGT69OkkJyczdOhQqqqqUFVV81yu1xBCeCdp1iIQQpbevG7dOh599FGuXr1KSkoKP/vZz2hvb2fFihXs3LmT4cOHs2XLFgBmzpzJoUOHyMzMZNCgQRQWFgJgNBpZvny5M6nggQcewGg0AvDEE08405vT09NJT08HYOnSpZrXEEJ4J2nWIhAUVVXVcDci0ly9ao+aMdOeiKYx42DpL32UW3RMsyyLOS6GPUuneX1/f+mn3oimPgrrHI0Qom+SNGsRCFJUUwjhliO7TIp7it6QQCOE8EjKsojekkAjRB8QKVsGREo7RN8igUaICBcpWwxHSjtE3yPJAEJEuEhZyxIp7RB9jwQaISJcpKxliZR2iL5HAo0QES5StgyIlHaIvkcCjRARLlLWsoSzHXurreQWHWPq5gpyi45JrbU+RpIBhIhwkbKWJVztkCSEvk9K0GiQEjTRUxIjWKSPfBOIfuptGZxIF02/S1KCRgjRJ0kSQt8ngUYIEdEkCaHvk0AjhIhokZIMIXpOkgGEEBEtUpIhRM9JoBFCRDwp7Nm3ydCZEEKIoJJAI4QQIqhCNnSWkZHBkCFD0Ol06PV6du3aRX19PQ8//DCffvopI0aMYMuWLSQkJKCqKps2beLQoUPExsby1FNPMWHCBACKi4t59tlnAfjRj37EggULADh16hSrV6+mubmZmTNnsnbtWhRFcXsNIcJJyu2L/iSkTzQvvPACu3fvZteuXQAUFRWRmppKWVkZqampFBUVAVBRUUFNTQ1lZWUUFBSQn58PQH19PVu3bmX79u3s2LGDrVu30tDQAEB+fj4FBQWUlZVRU1NDRUWFx2sIES6Ole6WphZUrq10l7IqIlqFdeisvLycvLw8APLy8jhw4ECX44qiMGXKFBobG7HZbFRWVpKWlobRaCQhIYG0tDQOHz6MzWbj0qVLTJkyBUVRyMvLo7y83OM1hAgXKbcv+puQZp394Ac/QFEUvv3tb/Ptb3+b2tpakpOTAUhKSqK2thYAq9WK2Wx2vs9sNmO1WrsdN5lMmscdrwfcXsMTvV7BaBzc+w/cR+n1un79+X3Rmz7ytNI92vpdfpe86w99FLJA89e//hWTyURtbS1Llixh1KhRXX6uKAqKogS1Db5ew25Xo6b2UE9EU+2lYOlNH5niYjRrd5niYqKu3731k8xVRde/t4DUOrPb7Zw4cYJXX30VgC+++ILm5maf3msydfzyXHfddWRmZnLy5Emuu+46bDYbADabjcTEROdrLRaL870WiwWTydTtuNVq1TzueL3jelrXECJcZKV7B5mr6j98DjSffPIJ8+bNY+nSpaxduxaAI0eO8Pjjj3t975UrV7h06ZLz/48cOcKYMWPIyMigpKQEgJKSEmbNmgXgPK6qKlVVVcTFxZGcnMz06dOprKykoaGBhoYGKisrmT59OsnJyQwdOpSqqipUVdU8l+s1hAiXOeNNrMkagzkuBoWOKsRrssb0u2/yMlfVf/g8dFZQUEBOTg7Lly9n2rSO0txTp05l06ZNXt9bW1vLAw88AHQ8Fc2bN4/09HQmTpzIihUr2LlzJ8OHD2fLli0AzJw5k0OHDpGZmcmgQYMoLCwEwGg0snz5chYtWgTAAw88gNFoBOCJJ55wpjenp6eTnp4OwNKlSzWvIUQ4yUr3yKvKLMN4wePzfjTTpk3jyJEjGAwGpk6dyvHjxwG45ZZbePvtt4PayFCT/WiiZ8w4WKSPfOOpnyJpnxnXzdWgYzgzFE+a0fS71Os5mqFDh9LY2NjlmNVq5Stf+UrvWiaECIlI2w45kuaqZBgvuHwONFlZWaxZs8Y56V5XV0dhYSFz584NWuOE6Csi7SbuKhIn3iNprirShvGijc9zND/5yU9Yu3Yt3/rWtwC4/fbbmTdvHsuWLQtW24ToE/rCnvaevrGHs42RMlflKeVc9J7PgSY2NpbNmzfz+OOPc/78eUaMGCGpwkIQuTfxzuQbu2fLZ9ygOUfT31LOg8XnQFNTU8OQIUNISkpi2LBhAHz22WdcuXKFkSNHBq2BQkS6QNzEg53xJN/YPZPN1YLL5zmaRx55hLq6ui7HPv/8c1auXBnwRgnRl/R2T/tQzJ9E0sR7pJoz3sSepdM4vjKdPUunSZAJIL8WbI4dO7bLsbFjx1JTUxPoNgnRp/T2Jh6KjCdPE++Rnsgg+j6fh87i4uL4/PPPu8zL1NXVMWjQoKA0TIi+orfDLqGaP9GaeO8LiQyi7/M50Nx+++3k5+fzs5/9jCFDhnD58mUKCgqYPn16MNsnRMToPI9yfUIsy9JGOm/GvcmeCuf8ibunqc0HP/Tr88iqeuGJz0NnK1euxGq1Mm3aNGbOnMm0adO4cOECq1atCmb7hIgIrvMoFxqaAzaPEs75E3dPTQ3NbT5/tkhcoyMii89PNImJifztb3/j3XffdW6LPHHixKCX9hciEgQzhTmcGU/unqYc7fGlDZ765p7UG3vcNnlKih5+7UejKAqTJk1i0qRJwWqPEBEp2PMo4Vq4uHzGDax/9Yzmz3z9bMHoG5k7ii4eA83GjRud2wCsW7fO7esKCgoC2yohIoyv8yiB+BYeym/yc8abeLr8LI0t9m4/c3w2b+0JxhxTX1gEK3zncY7m6tWrzv9va2tz+58Q0c6XeZRAzFWEY77j0Vlfc/vZfGlPMOaYpJJBdPH4RPPkk08CoKoqjz76KMOGDUOn82tTTiGigus8imvWmeNnvc3gCsc3eU9zRLlFx7y2JxhzTFLJILr4NEejqip33HEHJ06ckEAj+q3O8yhae4h4y+Dy5cYbqm/yWsNhWnvA+NqeQM8xSe2x6OJT1NDpdKSkpHTbj0YIcY2nb9u+rvLvbTkbX/gzPBeK9miZM95EzoRkdF8mteoUyJmQLPMzfZTPjydLlixh5cqVvP3221gsFqxWq/M/IQQev237+kQSijU1/pS8Cdcan73VVkpP22j/cv/fdhVKT9tkbU4f5XN6syP77OjRo861M6qqoigK1dXVwWmdEBHq5Xcu8Mt9Z7rNSXjL4PLGl/mO3mal+Toc5rhOc1s7OqXjZm8O0XoWyTqLLj4HmvLy8l5fzG63s3DhQkwmE9u2bePcuXM88sgj1NfXM2HCBH7xi18wcOBAWltbeeyxxzh9+jRGo5Ff//rXfPWrXwVg27Zt7Ny5E51Ox+OPP86MGTMAqKioYNOmTbS3t7N48WKWLl0K4PYaQvTU3morhfs/oPlq9zUej876Wq/nFjzNdwRifYkvE+2u12lXr32OUNzoJessuvg0dHbs2DH279/PxYsXGTFiRLf/fPXHP/6R0aNHO//89NNPc99997F//37i4+PZuXMnADt27CA+Pp79+/dz33338fTTTwNw9uxZSktLKS0t5bnnnuPJJ5/Ebrdjt9vZsGEDzz33HKWlpbzyyiucPXvW4zWE6EyrgvHeaiuz/+sf3Lq5ost/+XvPOIOMQ+dv28HcnjgQlZ59GQ4LRUVpT8I1NySCw2ug+dvf/sb3v/99fv/73/O9732PkpKSHl3IYrHw+uuvs2jRIqBj2O2NN94gOzsbgAULFjifmg4ePMiCBQsAyM7O5ujRo6iqSnl5OTk5OQwcOJCUlBRGjhzJyZMnOXnyJCNHjiQlJYWBAweSk5NDeXm5x2uIyBKIUvU9PYfW5PiGvWd4cu8ZGpq7rxNzzBu4cnzbDua+JoH4pu9LMAz3E4XsnxNdvA6d/fnPf2bz5s3k5OSwZ88enn/+efLy8vy+UGFhIatWreLy5ctAxxYD8fHxGAwdTTCbzc7EAqvVyvXXX9/RQIOBuLg46urqsFqtTJ482XlOk8nkfI/ZbO5y/OTJkx6v4Yler2A0Dvb7M0YLvV4X0s//8jsXug9F7f+AIYNjmD95eNDP8fsj/6/bt/c2N8HEk+sTYoPSby+/c4HN+9/nYkMzOgXsGm3z99r3pN7osQ7Z9QmxXGho7vV1evq7dE/qjQwZHOP83NcnxLIyc6zPvw99Saj/vYWD10BjsVjIyckBICcnh02bNvl9kddee43ExES+/vWvc+zYMf9bGWJ2u9ptjUR/orVGJJh+uU9jKOpqO7/cd4b0kcagn0PrhuqvWIOO1BuMzPjFawEtHeM6V6IVZGINOpaljezR35m7xIJlaSM155r8vU5vfpfSRxpJv39ql2PR+O8y1P/egikpKU7zuNdA095+7RdNp9N1+bOvTpw4wcGDB6moqKClpYVLly6xadMmGhsbaWtrw2AwYLFYMJk6/lGaTCYuXryI2Wymra2NpqYmhg0bhslkwmKxOM9rtVqd79E6PmzYMLfXEJEjEMM0PT1Hb9JldQqoase8QdqoYZSetgW8CKTWXInrtf0JaJ0DS3ysgcstbc6nN6029+XqyVL9OXJ4DTStra1dCmp+8cUX3QpseiuquXLlSlauXAl0JBb84Q9/YPPmzTz00EPs27ePnJwciouLycjIACAjI4Pi4mJuvvlm9u3bx2233YaiKGRkZLBy5UqWLFmC1WqlpqaGSZMmoaoqNTU1nDt3DpPJRGlpKZs3b0ZRFKZNm6Z5DRE5AlFuxNs53N10PE1uO1J6tRh0MGSggcYv53D2v/dZUNJx3QVKVYXjK9P9Opfr05HW/FPnNoeronQgSPXnyOI1GSA3N7dLAc158+YFrKjmqlWreP7558nMzKS+vp7FixcDsGjRIurr68nMzOT555/n0UcfBWDMmDHMmTOHuXPncv/997N+/Xr0ej0Gg4H169dz//33M3fuXObMmcOYMWM8XkNEjkBM/GqdY4BO4UprG7durmD9q2e6TPavf/UMt26ucLsXC7j/xxEfo0dRFBqa25zn01o7A72fPA9k9pW7pyNX0ZBCHO6sOdGVoqpqD6Y9o9vVq/aoGTPtiXCMGQe6vL7rsJC/3D3NmL+8wXsKUK6v16oh5ivXb+bQEYR7kjI9dXMFvnRHb9vcWbjmH9x9VgX/nwSDTeZoXNjtdt555x0sFgtz587liy++QFEUYmNjA9JI0X8FYpim8zlyi45pDg35Itagc/vN359v+4FIxw3kXImn3TQdoiWFWKo/RxafA80nn3zCD3/4Qz777DPsdjtz587lyJEj/P3vf3cuqBQiUvj6xNGZwrXJ9WcO13i8UXk7fyBLtfQ0CLs+JbomLEDH8OKgATqaWuxRNWEu1Z8ji8+BpqCggJycHJYvX860aR2P1VOnTu1RurMQweZpIl+L1nCR640KIG3UMCaPSHC7/bG7c4Wa1mR46WkbOROSOfJRXcRmYgUqUywasuaiic+B5uTJkzz77LPodDpnUc34+HjZOkBEJH+CjNY33TnjTbzzaQMvvWPpcrz0tI3JIxIYPEDHlavaw2uh+tbs6absbjL8yEd1YQ+C7gQ6U6wvZ81FG5+3CRg6dGi3oGK1WvnKV74S8EYJ0VtmH8fi42P0bifWj3xU1+2YI3Op4K6vM8CxWUonCyd3VKjwVAonUOV2PO0pE+4SMj0hmWLRy+cnmqysLNasWUN+fj7QUUKmsLCQuXPnBqttQvSY1hi9v/MRnm7W8ycP5/KVlm5PFIDHb+U9+dau9eTirYx+X5wM74vBUfjG50Dzk5/8hLVr1/Ktb30LgNtvv5158+axbNmyYLVNiB4LxBi9t5u11tBMbtExjwHAW4DwNoHvCEzesuK0Ai10zDF1Fkmr5/ticBS+8TnQxMbGsnnzZtauXcunn37KiBEjSExMDGbbhOiV3o7R+5O55Lhhu8tGcwQAT9/atZ52XOeIAI+LLjsHQU9zTD19ugomyRSLXn6towFITEyUACP6jN58Y/f1qUhrUaUrRwDw9K3d15X7nnR+YnE3x/R0+Vmfnq5CTTLFopfHQLNkyRJnhpknf/jDHwLWICECpaff2P0NTt4CRKxBR9qoYeQWHdMMMo5v7U94SJn2VfFJC7vesXhcnNnYYmdvtTUi50QkUyw6eQw0t9xyS6jaIUTAbT74od/f2HsSnDzdmM1uFko6OOqmPfHqGZQvKzL3RnunSsyePHO4RuZERMh4DDQ//vGPQ9UOIXrNtdaZuxI0roGh8/sUjYWe3oKTuxu2ozpA/t4zmut64mP0tNpVmts6CnKGsuqgpamFDXPHac6JOJ6+ZPhKBIrP62gcWlpauHjxIhcuXHD+J0S4ua4r8VTnrPM3dtf3edumWYu76tNpo4ZRWPaB23M2ttjd7jUTKq5bOudMSKb0tM3t+hwhesLnZIBz586xatUq3nnnnW4/q66uDmijhPCXPxPp/3epI8NrzngTT5ef9el9noaT3E1i93RyX1Vhw9xxHsvcDDIofNHT0tRfeuZwDXuWTuvytOItPTtUIintWvSez4Fmw4YNmM1mNmzYwHe+8x3++te/8tvf/pY77rgjmO0Twqu91Va/imi2qbBh7xne+bTB7T4yrtJGDety87s+IZZlaSOdNz/XSWxvbYo16Igx6DSfvExxMcwZb/IYaFp8CDIGBRRF4aqbR6rO7fM1PTsUIi3tWvSez0NnJ0+eZOPGjYwdOxbo2ITsySef5L//+7+D1jghvHHclPzVpqK5RsWdl96xdNk87UJDM+tfPcPs//qHZokZT23SKR1DViszRnvc8M1TGR1fnpPummRm3Z1jPQ7F7a22dhk+dCeUCQJSiib6+BxoOu87M3jwYBobGzEajTJHI8IqEGtPeqOhua3bHIa3Ni2YZHY+AbnOkXSuu6Y19+OP0tM2APLnjHP7mmcO1/iUnh3KRZORmHYtesfn3+IxY8Zw4sQJACZPnszPfvYzNm7cyFe/+tWgNU4IbyLh5uP6bdtbm7QWUmpxBKJAtUuLtanFa3p2T3bz7I1Abl8tIoPPgWbt2rUkJCQA8Nhjj2G1Wjl9+jQbNmzw+t6WlhYWLVrE/PnzycnJ4be//S3QkWCwePFiMjMzWbFiBa2trQC0trayYsUKMjMzWbx4MefPn3eea9u2bWRmZpKdnc3hw4edxysqKsjOziYzM5OioiLncXfXENEhUm4+nW/W3trkeK23CszQEWx8rUStpfP8hhZTXAxxMXrNn5k7JTX0ptK0v9xl8Ukpmr7La6Bpa2vj6tWr/Nu//RvjxnU8gr/55puMHj2a//zP/+Tmm2/2epGBAwfywgsv8PLLL1NSUsLhw4epqqri6aef5r777mP//v3Ex8ezc+dOAHbs2EF8fDz79+/nvvvuc+7gefbsWUpLSyktLeW5557jySefxG63Y7fb2bBhA8899xylpaW88sornD17FsDtNUR0WD7jBgxBSAd2nNLXVOPOwcXbkJfjtb7ORfRmCE2nuK+N5kjBvtyqnRCRYozxGgiDwduQouh7vP72Pvzww+zatcv552eeeYb169fz9ttvs2rVKnbs2OH1IoqiMGTIEKAjcLW1taEoCm+88QbZ2dkALFiwgPLycgAOHjzIggULAMjOzubo0aOoqkp5eTk5OTkMHDiQlJQURo4cycmTJzl58iQjR44kJSWFgQMHkpOTQ3l5Oaqqur2GiB6+lEnq7NaUeI837oRYA8dXpvPmynTy54zz6Sbf+du240YZ7+ZJwVGPzNe5CNcbr6/BL9ag87gB3JqsMRz5qA67m9e8fb4xbJPyc8ab2LN0GsdXpndLwe6JQOwBJHrOa3rzqVOnePzxx51//vOf/8zGjRvJy8tj3759bNu2jcWLF3u9kN1u5+677+aTTz7hO9/5DikpKcTHx2MwdDTBbDZjtX65aZPVyvXXX9/RQIOBuLg46urqsFqtTJ482XlOk8nkfI/ZbO5y/OTJk9TV1bm9hid6vYLRONjr66KVXq/rM5//90f+n9v0XXc+bWzl7m+M4C/Hz2n+vLG5zfn570m9kSGDY9i8/30uNDS7Pec9qTd2+/M9qTfyxJ7T/PX4OTq3sPRfNm4fk8z1CbGa57w+IbZb/zvOB/DyOxdYu/sUzZ12+IwdoCM+1oCt6drQ8FeHDeJKq13zGsMTYjva5yGF2tPiVV9/PyLhd+nldy5QuP8DZ39Zmloo3P8BQwbHMH/y8LC2DSKjj4LNa6BpaGjAZOr4NvHhhx/S1NTEnDlzAJg9ezbr16/36UJ6vZ7du3fT2NjIAw88wEcffdSLZgeX3a5SX38l3M0IG6NxcER/fm9rPry50NDMrhOfuv25KS6my+dPH2kk/f6pzP6vf2iue0mINbjtr4PVNlzv181X2/nlvjOkjRqmmWKdeoPReT53CxfXZI7pcnzQAIWPP+8aUM5+dpkbE2OJNei6lZlZljaS+vorHotv6jTK8QDExxr45qYDzr6Ij9Hz6KyvaT51RMLv0i/3nekSlOHa30H6SGN4GtVJJPRRoCQlxWke9zomMHjwYC5fvgzAu+++y5gxY4iJ6RhjVlWVtjb3pT60xMfHM23aNKqqqmhsbHS+32KxOAOayWTi4sWLQMdQW1NTE8OGDcNkMmGxXPuHabVaMZlMbo8PGzbM7TVE5PI0zOHLmg9feJq3cLffjBa90vHvwN2QjKfhsf3vfab5M8dxT8kCjqGlJ+d2zJu6BhmHjz9v9ppCrbUltUHpSMN2HTYcoFNoam7rEnAbW+xs2HsmYoejJF06/LwGmltuuYXf/OY3fPjhh7z44ovMmDHD+bOPP/6YpKQkrxf5/PPPaWxsBKC5uZl//OMfjB49mmnTprFv3z4AiouLycjIACAjI4Pi4mIA9u3bx2233YaiKGRkZFBaWkprayvnzp2jpqaGSZMmMXHiRGpqajh37hytra2UlpaSkZGBoihuryEik9bNdf2rZ5ix5TCz/+sfrH/1TFDXzeRMSHbehB0B79bNFax/9Uy3p5lBBgWFjhtt57Y+deB95/vdTR+Z4mLcViVwHPeWLOBr0PU03zFnvIl1d44lIfba4EZ8jJ71c8bx09ljuwWpQQN0motF21QidkGlpEuHn6KqnmvGnjt3jqVLl/Lxxx8zZswY/vSnP2E0GgH41a9+RW1tLZs2bfJ4kffee4+f/vSn2O12VFXlzjvv5Mc//jHnzp3j4YcfpqGhgfHjx/P0008zcOBAWlpaWLVqFdXV1SQkJPDrX/+alJQUAJ599lleeukl9Ho9a9asYebMmQAcOnSIwsJC7HY7Cxcu5Ec/+pGz/VrX8OTqVXvUPMr2RDgf5d3t2RIqsQYdOROSOXDm/zwW5uytQQN0fHHVfcB8c2U6UzdXdBt2g46MuOMr033uKwUCVi/MXZs6t6uzSBgW0tqYLtagi5hMtkjoo0BxN3TmNdA41NfXOwOMQ2NjIwMGDGDQoEG9bmAkkUATvl98Tzey/iIh1sCBB253G0jMcTHsWTrN774KxM3VU3BztKuzSLmJRnKRzkjpo0DodaDpTyTQ9N8nmkgQq1eIGaDXfKJyBAvA7T43nui+3Fytpzfbpw68r5nAoAPy547rdr5ouokGSzT1UY+TAYQIpd7W9+qLBg3o+nmb7apmkHFM5AMe97nxpF2lV4sv3ZXPGRqjj5gnBBF5fN4mQIhQcNysPJXIjzYtPiY3pI0a1qu0blfNbe1sPvihXwHCXaZWk4/bLYj+qX99dRR9wpzxppDuMhluvj6ZvPSOxWuQcWSIJcQafCrN09Dc5vapRivNXDK4RE/IE40IGX8mZHsyLOQPxz04NgA7VUYK18n4zv2tuFl8CWjunulu8zHHVs+uGVxS8FJ4IskAGiQZIPCTk1oppg5mjaAT7KQARyrutF9VBD2ohYJBgSExBhqb2zSD+N5qq8fhyDdd0pJnbT2iuc6nc0VnX74wRNNEd7BEUx+5SwaQJxoRVL6Ui3HdqndvtZUrrcFbwwLXhnqCGWS8baUcKPExer642u5MINDa+njOeBNPl591u0jUUW3A8f/uXmdtaum2bbUQ3sgcjQgaf8rFOFa8O97j7kYXKKEY6mlTwaC7NkwXaAYFNswdx+CBhm7BTKvK8qOzvub2XJ1f62mFv7e5GNd5nZffkR14hQQaEUT+brNsaWoJ2dbMhfveZ+rmiqBf54s2NWgLUIfEGJgz3uRTLa+nDrxP/l73Q2edX+upBpinAK1VPmjt7lMRWwNNhI4MnYmA62l1ZZ0SukKHze42YelDGr8cKnNXgdnx9OFukaXWaz2dLyHW4HHITLM229V2t8kGkbpSXwSePNGIgOpNdeV2VdJk/eHoK29bHxef9BxkXLPG3J1vZcZoj+fxtUqyL1tYi+gigUYEVG+GvnQK/b78jK86BwdvWx97ykXQ2ia5p1sp+7rGxtctrEX0kKEzEVC9GfqKhjTjUNBKB/eUCeZuAzOdQrcimL6cz53lM27oXiV5QPc1NrI/TP8jgUYElKcdG0XvGBRYP6d74UpvFkwya87RLJhk1nh1zzna1XnuZVX2uG67WHqbUxLRRwKNCCitb7Wi9zxtl+zNT2ePBTrmatrVjieZBZPMzuOB5PokpLUYUfPJR6oLRDWpDKBBKgP0bqWyr6VPhHeuK/b7Gne/S5J1do1UBhDCD643jyfnjuOJflSFOdDMUTyUJNUF+hcJNCIgXNdqOFJW42L0QV/lH41kKElEk5CkN1+8eJF7772XuXPnkpOTwwsvvAB0bA+9ZMkSsrKyWLJkCQ0NDQCoqsrGjRvJzMwkNzeX06dPO89VXFxMVlYWWVlZFBcXO4+fOnWK3NxcMjMz2bhxI44RQXfXEIGzt9qqOdnc3NYu+5R0otAxFLZh7jiPr/M1nbgzrZL+kaivtFMEVkgCjV6v56c//SmvvvoqL774In/5y184e/YsRUVFpKamUlZWRmpqKkVFRQBUVFRQU1NDWVkZBQUF5OfnAx1BY+vWrWzfvp0dO3awdetWZ+DIz8+noKCAsrIyampqqKjoKC/i7hoicDytf5DpmWscWVVzxptYOFk742vhZDN7lk7zO8j4sgAy3Df5vdVWCv7+fpd2Fvz9/YC1I9yfT7gXkkCTnJzMhAkTABg6dCijRo3CarVSXl5OXl4eAHl5eRw4cADAeVxRFKZMmUJjYyM2m43KykrS0tIwGo0kJCSQlpbG4cOHsdlsXLp0iSlTpqAoCnl5eZSXl3c5l+s1RODI+gffpI0a5vz/n84eqxlsSk/b/L5B+rIAMhJW428++GG34p9X21U2H/yw1+eOhM8n3At5ZYDz589TXV3N5MmTqa2tJTk5GYCkpCRqa2sBsFqtmM3X/hGazWasVmu34yaTSfO44/WA22uI3nN8g5SnFt+4BpEjH9V1e01PVsi7C/SWphbnN/vNBz8M+2p8xzYGvh73h1QbiGwhTQa4fPkyDz30EGvWrGHo0KFdfqYoCooS3P17fb2GXq9gNA4OalsimV6v8/r5n9hzmr8ePydBxg/Nbe38/sj/457UGwHPK+T9+f27PiGWCw3Nmj+zNLXw5N/fx+4mx9zfa/nLl98loNdtCFRfhoOvfdSXhSzQXL16lYceeojc3FyysrIAuO6667DZbCQnJ2Oz2UhMTAQ6nlQslk4ZTBYLJpMJk8nE8ePHncetVitTp051+3pP1/DEblejJq+9J7zl9e+ttvKX4+dC2KLocbGh2dm3nlbI+/P7tyxtpMdFsu6CTE+u5a/Ov0vxbjIQ42P0vW5DoPoyHPrDOpqQDJ2pqsratWsZNWoUS5YscR7PyMigpKQEgJKSEmbNmtXluKqqVFVVERcXR3JyMtOnT6eyspKGhgYaGhqorKxk+vTpJCcnM3ToUKqqqlBVVfNcrtcQXXWeSJ359Osex7ZlOKJ3bt1cwa2bK6i/0soAXdcn7J6kNXcugumvUKZQPzrraxhcBhQMiucN2XzlrYK1CK+QVAZ46623+O53v8vYsWPR6Tp+GR555BEmTZrEihUruHjxIsOHD2fLli0YjUZUVWXDhg0cPnyYQYMGUVhYyMSJEwHYuXMn27ZtA2DZsmUsXLgQgHfffZfVq1fT3NxMeno669atQ1EU6urqNK/hSX+rDOCYSHX9RrxwcvcyJd72nhf+UYD4WAONzW0BWSGfW3TM51pz8TF6yn+c1uNr+cL123owKwL01WoD/eGJRkrQaOhvgcbTzWnD3HFd9pLPf/UMUsUssMxxMW6rKPvL3ZcGLZ3/boMlmm6iwRJNfSQlaIRbntKT8/ee4Z1PGzjyUZ1UZXbDHBdD2qhhlJ629aiYaOf+9+VbuafXuFZQjo81cLmljTaXr5MLJ5v7xLd9ER0k0AiPpf3bVbxuAyxg8ogEJo9I6NEW1o6FnK5PI461IECXp0pvr3GtI9ZXh5RE9JBA08/trbZypbX36xj6M8fNfk3WGOcQmOvNPW3UMF5+19ptwaJBuTYh72ktSOcnFW+vcSUFLEW4SaDpx/wZzxeedb7Zu3uCmDwigc0HP3QuUHTdY8bbzpN7q61un5b8HX4TIpQk0EQ5rZsO0KMhHuGZtanF69CW1g3f8XfkLivHFBfjPK87/gy/CRFqknWmIVqyzuSJJbQc61i0Ari7zDJvf0exBh1rssZ4/GLgeA10JG9orc8MZGabP6IpoypYoqmPwrpgU4SH1ni+CA7H4kBPw19a1YU9/R113i7AU2agI8gUln3gdjdTKXwqwkkCTRSTobHgUVz+P2dCMnPGm5xDWK7iYvTdqguvf/WM278jBbpsF+DuvOa4GOaMN3n9UuHu/UKEggQaITS4lkpxpbr8/+6TFvZWW92WQlEUxa+nS9fA4K3EiqcnFinFIsJNkgGikGNIRvSc3c+Zyza1Y35EVTtKyqCqNH95khiDzq9S+FqBwXUhpms2mbu1UDoFv3frFCLQJNBEieXbq3jzXGO4mxE1epIh45gfcQ0q/u63MlCv/TjlaT1M2qhhmgtrF0ySCgAi/CTQRAEJMtGlscXeJZXZlzUxWpuoeTouRChJoIkCEmT6DrOHcj+dNbe183T5WVrtqk9rYrwt9hQinCQZoI+TPdH7DsdaljdXprNh7jjMcTF4yjlobLH7vD2xu6wyyTYTkUCeaPqgzqv9g7z7tQgQxwT/3mprtzI0rW3tzsQBX2g9pSyfcUO3hZ+SbSYihQSaPsT1JgUgdR3Cy91Q2KABOhJiB3Qr/VPw9/e7FNbU2trYG62nFG9ZaUKEkwSaPkLKyQTPrSnxnKtv8XuBa3yM3u2TxOrM7inFuUXHulVv7gl3TylSpVlEKgk0fYSUkwme9z+7wsqM0R63qFbomvJs0NGl8rLWk4RrQdNAVGoYNEAnwUT0ORJo+ggpJxM8Dc1tzBlv4p1PG9xu8hYfa2DQAL0zaKzKHkf6SCOg/SShVUXZHwN0CvZ2tdu22Vfb2tlbbZVgI/qUkGSdrV69mtTUVObNm+c8Vl9fz5IlS8jKymLJkiU0NDQAoKoqGzduJDMzk9zcXE6fPu18T3FxMVlZWWRlZVFcXOw8furUKXJzc8nMzGTjxo04ClK7u0ZfpJNJf78p4DGry9VPZ491+7PG5jb2LJ3G8ZXp7Fk6jfmTh3s8l79PoLemxDuz0MxxMay7cyxDY/TdXtemIlUfRJ8TkkBz991389xzz3U5VlRURGpqKmVlZaSmplJUVARARUUFNTU1lJWVUVBQQH5+PtARNLZu3cr27dvZsWMHW7dudQaO/Px8CgoKKCsro6amhoqKCo/X6IsCMLTf76j4tsI/vtMN3RygNGF/16+cq2/pEsjmjDfR5CZRQNbGiL4mJIHm1ltvJSEhocux8vJy8vLyAMjLy+PAgQNdjiuKwpQpU2hsbMRms1FZWUlaWhpGo5GEhATS0tI4fPgwNpuNS5cuMWXKFBRFIS8vj/Lyco/X6EscpeVFcBiUjrkWB2/FK30ViMAka2NEtAjbHE1tbS3JyckAJCUlUVtbC4DVasVsNjtfZzabsVqt3Y6bTCbN447Xe7qGN3q9gtE4uHcfMACe2HOavx4/16O6W8K74QmxrMwc22UY7J7UGxkyOIbN+9/nYkMz12u8BkCv13n8HVmVPY61u0/RfLVTNtoAHbEGHfVfdK99dn1CrPN8L79zgc3738fS1NItCSF2gI5V2eMi4vfTF976SfSPPoqIZABFUVCCvPLQn2vY7WrYd7x76sD7biemXQ0yKHzR1j/DkTkuxjlB/8VVu18FLNvbVS5faen2d50+0kj6/VO7HHN9jbddEdNHGlmTOUZzG22tdOhlaSOpr7/SLYmg89+q+ctzpI80hv3301fRtHtksERTH7nbYTNsgea6667DZrORnJyMzWYjMTER6HhSsViu3WAtFgsmkwmTycTx48edx61WK1OnTnX7ek/XiHR7q60+Bxmg3waZhFhDl+2J/V1r5Kl2WCB4WtfibmGluySCcG3FLEQghK3WWUZGBiUlJQCUlJQwa9asLsdVVaWqqoq4uDiSk5OZPn06lZWVNDQ00NDQQGVlJdOnTyc5OZmhQ4dSVVWFqqqa53K9RqSTrCLfqC5lEeaMN7Ema0yXyX2HWINO87i72mHBNGe8qdvEv4MUxxTRKCRPNI888gjHjx+nrq6O9PR0HnzwQZYuXcqKFSvYuXMnw4cPZ8uWLQDMnDmTQ4cOkZmZyaBBgygsLATAaDSyfPlyFi1aBMADDzyA0WgE4IknnmD16tU0NzeTnp5Oeno6gNtrRDq5qfhGKyvL8RThulhy+YwbeMLNgsxI6m93CzslAUD0ZYrq+rVQcPWqPaxjprlFx6J+gWZCrMHranzoSD0ePNCg2R/+Die569eeDEsFa1xda/gv1qDrs7tkRtP8Q7BEUx+5m6ORbQLCzJG+PHVzBblFx9zuOx9tGr9cje9u3YpDU4s9YCnHgTpPMDmG/zov3uyrQUYIh4jIOuuvtMqUFJZ9wJqsMazJGuP1234ouKbX+kunaC82dQwFaRWldH1doCoT95UKx1IcU0QbCTQhojVnoJVh5Jic3rN0Gk+Xn+1RGflAiTXoGKhXetSGATqF+RNNHDjzf91Sjjs/RThuqK7bH2i9LhA3X7mJCxF60T0+EyEcTy6WphZUrj25uJuHcUxOPzrraxhclv4YlI75jWBToMdBJiHWwPyJJkpP27oFj/gYfbehoDnjTRx44PYuu07KkJEQ0UOeaELA3ZOLt2ElraGetFHDOHDm/4LeZhX/NuVynVDPLTqmORw2eKDBbfCQpw0hopMEmhBwlz7brnYMD3nafrfzzTdSNz8zKN0345L1IEIIBxk6CwF3ayAcw0O+DhdF4uZn8TF61s8Z163NUhBSCOEgTzQh4G67X0fGk6/DRcF4Gog16MiZkMyRj+qcw3PNbXbNwo+uPK0/8fSZhRD9izzRBIjWehiHQK2N6MnTwACdwsLJZue1E2INxMfou7Rj8oiuWzjMnXi913U83oKG62dOiDUwUK/wxKtnuvWPECK6SWUADf5WBvB3NbdWqrMvQcfbHI3W04m3c2u2fYCOnJu6nidt1DC/zuv1Gn14tTtE12ruYJJ+8i6a+ijiqjdHE0/rYXzZS97XCsKe1pwA5ExI9rgdsc9tv9rOkY/qAlYt2J/+EUJEHxk6CwB/Mqw83XR9MWe8iUEDulchBjjyUZ1P5/DWRk/He0Iy0ITo3yTQBIA/GVaBuOkG8sYdiuwwyUATon+TQBMA/hRrDMRNN5A3bs22DwhsdlhfKGYphAgeCTQB4E9WWSBuuoG8cWu1fdNdXw/o3IlUJBaif5OsMw3B3o+mp1lngT6HO9GUBRMs0ke+kX7yLpr6yF3WmQQaDeHe+CzcoukXP1ikj3wj/eRdNPWRbHwmhBAiLPpFoKmoqCA7O5vMzEyKiorC3RwhhOhXoj7Q2O12NmzYwHPPPUdpaSmvvPIKZ8+eDXezhBCi34j6QHPy5ElGjhxJSkoKAwcOJCcnh/Ly8nA3Swgh+o2oL0FjtVoxm83OP5tMJk6ePOnxPXq9gtE4ONhNi1h6va5ff35fSB/5RvrJu/7QR1EfaHpCp9Ohi/pnPc90Ou0yN+Ia6SPfSD95F+19FPW3U5PJhMVicf7ZarViMslCQSGECJWoDzQTJ06kpqaGc+fO0draSmlpKRkZGeFulhBC9BtRP3RmMBhYv349999/P3a7nYULFzJmzJhwN0sIIfoNqQwghBAiqKJ+6EwIIUR4SaARQggRVBJohBBCBJUEmn5i9erVpKamMm/ePOex+vp6lixZQlZWFkuWLKGhoQEAVVXZuHEjmZmZ5Obmcvr0aed7iouLycrKIisri+Li4pB/jmC6ePEi9957L3PnziUnJ4cXXngBkH7qrKWlhUWLFjF//nxycnL47W9/C8C5c+dYvHgxmZmZrFixgtbWVgBaW1tZsWIFmZmZLF68mPPnzzvPtW3bNjIzM8nOzubw4cNh+TzBZLfbycvL44c//CHQz/tIFf3C8ePH1VOnTqk5OTnOYz//+c/Vbdu2qaqqqtu2bVN/8YtfqKqqqq+//rr6gx/8QG1vb1f/+c9/qosWLVJVVVXr6urUjIwMta6uTq2vr1czMjLU+vr60H+YILFareqpU6dUVVXVpqYmNSsrS/3ggw+knzppb29XL126pKqqqra2tqqLFi1S//nPf6oPPfSQ+sorr6iqqqrr1q1T//d//1dVVVX985//rK5bt05VVVV95ZVX1J/85CeqqqrqBx98oObm5qotLS3qJ598os6aNUtta2sL/QcKoj/84Q/qI488oi5dulRVVbVf95E80fQTt956KwkJCV2OlZeXk5eXB0BeXh4HDhzoclxRFKZMmUJjYyM2m43KykrS0tIwGo0kJCSQlpbWt79luUhOTmbChAkADB06lFGjRmG1WqWfOlEUhSFDhgDQ1tZGW1sbiqLwxhtvkJ2dDcCCBQuc9QQPHjzIggULAMjOzubo0aOoqkp5eTk5OTkMHDiQlJQURo4c6bU0VF9isVh4/fXXWbRoEdDx9Nuf+0gCTT9WW1tLcnIyAElJSdTW1gLd68OZzWasVqtm3Tir1RraRofI+fPnqa6uZvLkydJPLux2O3fddRe33347t99+OykpKcTHx2MwdCzLc/QDdPTR9ddfD3SsaYuLi6Ouri7q+6iwsJBVq1ah+7KWVV1dXb/uIwk0Auj4pqooSribEREuX77MQw89xJo1axg6dGiXn0k/gV6vZ/fu3Rw6dIiTJ0/y0UcfhbtJEeW1114jMTGRr3/96+FuSsSQQNOPXXfdddhsNgBsNhuJiYlA9/pwFosFk8nUL+rGXb16lYceeojc3FyysrIA6Sd34uPjmTZtGlVVVTQ2NtLW1gZc6wfo6KOLFy8CHUNtTU1NDBs2LKr76MSJExw8eJCMjAweeeQR3njjDTZt2tSv+0gCTT+WkZFBSUkJACUlJcyaNavLcVVVqaqqIi4ujuTkZKZPn05lZSUNDQ00NDRQWVnJ9OnTw/gJAktVVdauXcuoUaNYsmSJ87j00zWff/45jY2NADQ3N/OPf/yD0aNHM23aNPbt2wd0ZNw56glmZGQ4s+727dvHbbfdhqIoZGRkUFpaSmtrK+fOnaOmpoZJkyaF50MF2MqVK6moqODgwYP86le/4rbbbmPz5s39u4/CmYkgQufhhx9W09LS1JtuukmdMWOGun37dvXzzz9Xv/e976mZmZnq97//fbWurk5V1Y7Movz8fHXWrFnqvHnz1JMnTzrPs2PHDnX27Nnq7Nmz1Z07d4bp0wTHm2++qY4dO1adN2+eOn/+fHX+/Pnq66+/Lv3USXV1tXrXXXep8+bNU3NyctTf/e53qqqq6ieffKIuXLhQnT17tvrggw+qLS0tqqqqanNzs/rggw+qs2fPVhcuXKh+8sknznM988wz6qxZs9SsrCz19ddfD8vnCbY33njDmXXWn/tIap0JIYQIKhk6E0IIEVQSaIQQQgSVBBohhBBBJYFGCCFEUEmgEUIIEVQSaISIEBcuXODmm2/us2VGhHBHAo0QPXTq1CkefPBBUlNTufnmm8nIyODBBx/k6NGjbt9z7NgxbrrpJs2fDR8+nH/+859+r/5++eWXGTduHFu3bvXrfUKEigQaIXrgyJEj3HPPPaSkpPDSSy9x4sQJXn75ZebNm+es7uzq6tWrQWnLiy++iNFoZOfOndjtdo+vDVYbhPBEAo0QPZCfn8/8+fN57LHHGD58OIqiMHToULKzs1m3bh0A9957L5s2bWL58uV84xvf4Pnnn/d4zvPnzzNu3DgsFgv19fVMnDiR6urqLq+59957uzy5fPjhh7z11ls89dRTfPbZZ1RUVHR5fUZGBlu3buXee+/l5ptvpqysjLa2Nn7/+9+TnZ3NN7/5Tf793/+dd9991/meo0ePsnjxYm699VZuu+02Hn74YWfFaiF6QgKNEH76+OOP+eSTT7rsVurOSy+9xL333svbb7/Nvffe6/M1jEYjGRkZ7Nq1y3ns3LlzvP322869S6DjaWbcuHHccccdpKen8+KLL3Y7144dO1i9ejUnTpxg1qxZ/O53v6O8vJznnnuOY8eOsXDhQu6//37nzqEDBgxg3bp1HD16lD179mCz2di0aZPPbRfClQQaIfz0+eefAzj3qIGOTdC++c1vcssttzBx4kTn8ezsbFJTU1EUhUGDBvl1nbvvvps9e/Y4h7t27drFtGnTGDFiBNCxrfLu3bu5++67AVi0aBEVFRVdKv4CLF68mJtuuglFUYiJieGPf/wjjz32GCkpKej1ehYvXkxycjKvv/46AN/85jeZNGkSBoOBpKQk7r//fo/zTkJ4Ywh3A4Toa4YNGwZ0lG0fPXo0ALNmzeKtt97irbfe4rvf/a7ztY6g0BPTp09nwIABvPbaa2RmZlJSUsLKlSudP9+7dy+XL19m/vz5AMycOZPExER27NjBgw8+6HzdV7/6Vef/19XVceXKFZYtW9ZlX522tjZnttupU6f49a9/zXvvvccXX3yBqqpcuXKlx59DCAk0QvjpxhtvJCUlhdLSUm6//XaPr3XssNgTer2evLw8du3aRVxcHJcuXSIzM9P58+3bt9Pe3k5ubq7zWGNjIzt37mT58uXo9XqALgFl2LBhDB48mOeff95tyflHHnmE7OxsfvOb3zB06FBee+01li1b1uPPIYQEGiH8pCgK69evZ/ny5RiNRv7jP/4Ds9lMc3Ozz3u6t7S0dPmzIyi4WrBgAbm5ubS2tpKTk0NMTAwAZ8+e5e233+bZZ5/tMlRXW1vLwoULOXTokHO/E9e2f+973+MXv/gFGzdu5IYbbuDy5cucOHGCsWPHYjKZuHTpEnFxcQwZMoQLFy5QVFTka9cIoUkCjRA9kJ6ezl/+8he2bdvGggULaG5uJjExkZtuuokXXnjB43vtdnu3p4lvf/vbLF26tNtrR40axcSJEzly5AgPP/yw8/jf/vY3JkyY0C2YJCUlceedd/Liiy9qBhqABx98kD/96U8sX74ci8XC4MGDmTx5sjNbbsOGDfz85z/n2Wef5cYbb+Suu+7ixIkTPvWLEFpkPxohhBBBJVlnQgghgkoCjRBCiKCSQCOEECKoJNAIIYQIKgk0QgghgkoCjRBCiKCSQCOEECKoJNAIIYQIqv8PYRACBAYU1ZQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*주의\n",
        "이상치 제거가 항상 안전하지는 않다. 하지만 이 두 개의 이상치가 매우 나쁘기 때문에 삭제한 것임.\n",
        "\n",
        "훈련데이터에 다른 특이치가 있을 수 있다. 그러나 검정 데이터에 특이치가 있는 경우 이를 모두 제거하면 모형에 나쁜 영향을 줄 수 있다. \n",
        "따라서 모델을 모두 제거하는 대신 일부 모델을 견고하게 만들 수 있다. "
      ],
      "metadata": {
        "id": "cOAao2TaaEu1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Target Variable\n",
        "\n",
        "SalePrice는 예측해야하는 변수이다."
      ],
      "metadata": {
        "id": "P7DezabFaZJB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(train['SalePrice'] , fit=norm);\n",
        "\n",
        "# Get the fitted parameters used by the function\n",
        "(mu, sigma) = norm.fit(train['SalePrice'])\n",
        "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
        "\n",
        "#Now plot the distribution\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
        "            loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('SalePrice distribution')\n",
        "\n",
        "#Get also the QQ-plot\n",
        "fig = plt.figure()\n",
        "res = stats.probplot(train['SalePrice'], plot=plt)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "3C1NQFhvajyX",
        "outputId": "b7824680-0f59-43c3-c33a-b18ced17337f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " mu = 180932.92 and sigma = 79467.79\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SalePrice는 오른쪽으로 치우쳐 있다.\n",
        "\n",
        "선형모델은 정규분포이면 좋기 때문에 이 변수를 변환하여 정규분포를 따르도록 만들어야함.\n",
        "\n",
        "-> 로그변환 사용\n",
        "\n",
        "##Log-transformation of the target variable"
      ],
      "metadata": {
        "id": "hYDlaucnarUe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#We use the numpy fuction log1p which  applies log(1+x) to all elements of the column\n",
        "train[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\n",
        "\n",
        "#Check the new distribution \n",
        "sns.distplot(train['SalePrice'] , fit=norm);\n",
        "\n",
        "# Get the fitted parameters used by the function\n",
        "(mu, sigma) = norm.fit(train['SalePrice'])\n",
        "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
        "\n",
        "#Now plot the distribution\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
        "            loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('SalePrice distribution')\n",
        "\n",
        "#Get also the QQ-plot\n",
        "fig = plt.figure()\n",
        "res = stats.probplot(train['SalePrice'], plot=plt)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "X_aoKyDKaoXu",
        "outputId": "6d226c47-a1a2-4116-9915-318ac58c3a6e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " mu = 12.02 and sigma = 0.40\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "데이터가 정규분포를 따르는 것으로 보임\n",
        "\n",
        "##Features engineering\n"
      ],
      "metadata": {
        "id": "0Jh_q9iMa8pL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ntrain = train.shape[0]\n",
        "ntest = test.shape[0]\n",
        "y_train = train.SalePrice.values\n",
        "all_data = pd.concat((train, test)).reset_index(drop=True)\n",
        "all_data.drop(['SalePrice'], axis=1, inplace=True)\n",
        "print(\"all_data size is : {}\".format(all_data.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a81K9zxSbHx2",
        "outputId": "ce821efd-7029-42af-a575-864e7638203f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "all_data size is : (2917, 79)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aeD7BdPij24g",
        "outputId": "b8085719-78eb-4b20-a87b-3cb8fa236263"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1458, 80)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Missing Data"
      ],
      "metadata": {
        "id": "cagIKXkybg1s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_data_na = (all_data.isnull().sum() / len(all_data)) * 100\n",
        "all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)[:30]\n",
        "missing_data = pd.DataFrame({'Missing Ratio' :all_data_na})\n",
        "missing_data.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "-E9LeUZzbwze",
        "outputId": "c19b10a3-39a3-4cd2-99e6-49964dfc6ca7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Missing Ratio\n",
              "PoolQC               99.691\n",
              "MiscFeature          96.400\n",
              "Alley                93.212\n",
              "Fence                80.425\n",
              "FireplaceQu          48.680\n",
              "LotFrontage          16.661\n",
              "GarageFinish          5.451\n",
              "GarageQual            5.451\n",
              "GarageCond            5.451\n",
              "GarageYrBlt           5.451\n",
              "GarageType            5.382\n",
              "BsmtExposure          2.811\n",
              "BsmtCond              2.811\n",
              "BsmtQual              2.777\n",
              "BsmtFinType2          2.743\n",
              "BsmtFinType1          2.708\n",
              "MasVnrType            0.823\n",
              "MasVnrArea            0.788\n",
              "MSZoning              0.137\n",
              "BsmtFullBath          0.069"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bd93eff8-75c3-4941-a7bd-f501c8f82eee\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Missing Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PoolQC</th>\n",
              "      <td>99.691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MiscFeature</th>\n",
              "      <td>96.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alley</th>\n",
              "      <td>93.212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fence</th>\n",
              "      <td>80.425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FireplaceQu</th>\n",
              "      <td>48.680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LotFrontage</th>\n",
              "      <td>16.661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageFinish</th>\n",
              "      <td>5.451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageQual</th>\n",
              "      <td>5.451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageCond</th>\n",
              "      <td>5.451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <td>5.451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageType</th>\n",
              "      <td>5.382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtExposure</th>\n",
              "      <td>2.811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtCond</th>\n",
              "      <td>2.811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtQual</th>\n",
              "      <td>2.777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <td>2.743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <td>2.708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MasVnrType</th>\n",
              "      <td>0.823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MasVnrArea</th>\n",
              "      <td>0.788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSZoning</th>\n",
              "      <td>0.137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <td>0.069</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd93eff8-75c3-4941-a7bd-f501c8f82eee')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bd93eff8-75c3-4941-a7bd-f501c8f82eee button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bd93eff8-75c3-4941-a7bd-f501c8f82eee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "합친 데이터에서 null 의 비율을 확인한다."
      ],
      "metadata": {
        "id": "NmxAMfaNq_oT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f, ax = plt.subplots(figsize=(15, 12))\n",
        "plt.xticks(rotation='vertical')\n",
        "sns.barplot(x=all_data_na.index, y=all_data_na)\n",
        "plt.xlabel('Features', fontsize=15)\n",
        "plt.ylabel('Percent of missing values', fontsize=15)\n",
        "plt.title('Percent missing data by feature', fontsize=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "qpZxOcv4bxu_",
        "outputId": "abab9c0e-e12a-46e4-c03e-3b6719acce28"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Percent missing data by feature')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x864 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Data Correlation"
      ],
      "metadata": {
        "id": "AC-CgBjXbvhJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation map to see how features are correlated with SalePrice\n",
        "corrmat = train.corr()\n",
        "plt.subplots(figsize=(12,9))\n",
        "sns.heatmap(corrmat, vmax=0.9, square=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "L7WELyG8b0Mw",
        "outputId": "a343dac7-1796-46d8-da84-b9d4d9df5b9e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "train데이터의 상관관계를 확인한다. "
      ],
      "metadata": {
        "id": "yxpxV9IprN7o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Imputing missing values"
      ],
      "metadata": {
        "id": "hxdVQ6ixb15d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "모든 null 데이터를 대체하여 넣어준다!"
      ],
      "metadata": {
        "id": "mmwtLVv3rW0i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_data[\"PoolQC\"] = all_data[\"PoolQC\"].fillna(\"None\")\n",
        "all_data[\"MiscFeature\"] = all_data[\"MiscFeature\"].fillna(\"None\")\n",
        "all_data[\"Alley\"] = all_data[\"Alley\"].fillna(\"None\")\n",
        "all_data[\"Fence\"] = all_data[\"Fence\"].fillna(\"None\")\n",
        "all_data[\"FireplaceQu\"] = all_data[\"FireplaceQu\"].fillna(\"None\")"
      ],
      "metadata": {
        "id": "phZ8TUQ1b8bv"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Group by neighborhood and fill in missing value by the median LotFrontage of all the neighborhood\n",
        "all_data[\"LotFrontage\"] = all_data.groupby(\"Neighborhood\")[\"LotFrontage\"].transform(\n",
        "    lambda x: x.fillna(x.median()))"
      ],
      "metadata": {
        "id": "VQYA7myzcDVj"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in ('GarageType', 'GarageFinish', 'GarageQual', 'GarageCond'):\n",
        "    all_data[col] = all_data[col].fillna('None')"
      ],
      "metadata": {
        "id": "8yXN59NScHT3"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'):\n",
        "    all_data[col] = all_data[col].fillna(0)"
      ],
      "metadata": {
        "id": "Y2e0vM8IcIS_"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath'):\n",
        "    all_data[col] = all_data[col].fillna(0)"
      ],
      "metadata": {
        "id": "tLVxWe79cJu3"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):\n",
        "    all_data[col] = all_data[col].fillna('None')"
      ],
      "metadata": {
        "id": "0dFJqg1dcLFj"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data[\"MasVnrType\"] = all_data[\"MasVnrType\"].fillna(\"None\")\n",
        "all_data[\"MasVnrArea\"] = all_data[\"MasVnrArea\"].fillna(0)"
      ],
      "metadata": {
        "id": "DARgkoR3cL5D"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data['MSZoning'] = all_data['MSZoning'].fillna(all_data['MSZoning'].mode()[0])"
      ],
      "metadata": {
        "id": "DhGwExRccM9d"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data = all_data.drop(['Utilities'], axis=1)"
      ],
      "metadata": {
        "id": "FRHlxMxccN2T"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data[\"Functional\"] = all_data[\"Functional\"].fillna(\"Typ\")"
      ],
      "metadata": {
        "id": "FgvUCM9xcOrF"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0])"
      ],
      "metadata": {
        "id": "F0OvDakYcPfd"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0])"
      ],
      "metadata": {
        "id": "ytBAE5-scQR1"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data['Exterior1st'] = all_data['Exterior1st'].fillna(all_data['Exterior1st'].mode()[0])\n",
        "all_data['Exterior2nd'] = all_data['Exterior2nd'].fillna(all_data['Exterior2nd'].mode()[0])"
      ],
      "metadata": {
        "id": "lbjyUDQVcRCs"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0])"
      ],
      "metadata": {
        "id": "k0BEGvBBcR4f"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data['MSSubClass'] = all_data['MSSubClass'].fillna(\"None\")"
      ],
      "metadata": {
        "id": "3VacNz9scSrY"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Check remaining missing values if any \n",
        "all_data_na = (all_data.isnull().sum() / len(all_data)) * 100\n",
        "all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)\n",
        "missing_data = pd.DataFrame({'Missing Ratio' :all_data_na})\n",
        "missing_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "qIK1NzGlcTpt",
        "outputId": "ed4e292e-a33a-4683-af3b-0bc10c31c694"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Missing Ratio]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-df3daa76-0a43-4420-bfec-49720d0c7cb6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Missing Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df3daa76-0a43-4420-bfec-49720d0c7cb6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-df3daa76-0a43-4420-bfec-49720d0c7cb6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-df3daa76-0a43-4420-bfec-49720d0c7cb6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#MSSubClass=The building class\n",
        "all_data['MSSubClass'] = all_data['MSSubClass'].apply(str)\n",
        "\n",
        "\n",
        "#Changing OverallCond into a categorical variable\n",
        "all_data['OverallCond'] = all_data['OverallCond'].astype(str)\n",
        "\n",
        "\n",
        "#Year and month sold are transformed into categorical features.\n",
        "all_data['YrSold'] = all_data['YrSold'].astype(str)\n",
        "all_data['MoSold'] = all_data['MoSold'].astype(str)"
      ],
      "metadata": {
        "id": "DKC_PvS9cZb7"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###label encoding"
      ],
      "metadata": {
        "id": "2lY16jlRceeC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "cols = ('FireplaceQu', 'BsmtQual', 'BsmtCond', 'GarageQual', 'GarageCond', \n",
        "        'ExterQual', 'ExterCond','HeatingQC', 'PoolQC', 'KitchenQual', 'BsmtFinType1', \n",
        "        'BsmtFinType2', 'Functional', 'Fence', 'BsmtExposure', 'GarageFinish', 'LandSlope',\n",
        "        'LotShape', 'PavedDrive', 'Street', 'Alley', 'CentralAir', 'MSSubClass', 'OverallCond', \n",
        "        'YrSold', 'MoSold')\n",
        "# process columns, apply LabelEncoder to categorical features\n",
        "for c in cols:\n",
        "    lbl = LabelEncoder() \n",
        "    lbl.fit(list(all_data[c].values)) \n",
        "    all_data[c] = lbl.transform(list(all_data[c].values))\n",
        "\n",
        "# shape        \n",
        "print('Shape all_data: {}'.format(all_data.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9vXlMfvrcd-m",
        "outputId": "b58fc9f1-d298-4de4-cd88-3762f6ecfff8"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape all_data: (2917, 78)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding total sqfootage feature \n",
        "all_data['TotalSF'] = all_data['TotalBsmtSF'] + all_data['1stFlrSF'] + all_data['2ndFlrSF']"
      ],
      "metadata": {
        "id": "VrRYvHCfcj_i"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#skewed features\n",
        "numeric_feats = all_data.dtypes[all_data.dtypes != \"object\"].index\n",
        "\n",
        "# Check the skew of all numerical features\n",
        "skewed_feats = all_data[numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
        "print(\"\\nSkew in numerical features: \\n\")\n",
        "skewness = pd.DataFrame({'Skew' :skewed_feats})\n",
        "skewness.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "I-J1RJDzclXQ",
        "outputId": "f0edd059-73b2-43f6-b3e0-c1863eaf4791"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Skew in numerical features: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Skew\n",
              "MiscVal       21.940\n",
              "PoolArea      17.689\n",
              "LotArea       13.109\n",
              "LowQualFinSF  12.085\n",
              "3SsnPorch     11.372\n",
              "LandSlope      4.973\n",
              "KitchenAbvGr   4.301\n",
              "BsmtFinSF2     4.145\n",
              "EnclosedPorch  4.002\n",
              "ScreenPorch    3.945"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d3fbcdf8-07b7-42e5-b2a1-e0f96341cd14\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Skew</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MiscVal</th>\n",
              "      <td>21.940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PoolArea</th>\n",
              "      <td>17.689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LotArea</th>\n",
              "      <td>13.109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <td>12.085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3SsnPorch</th>\n",
              "      <td>11.372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LandSlope</th>\n",
              "      <td>4.973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <td>4.301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <td>4.145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <td>4.002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ScreenPorch</th>\n",
              "      <td>3.945</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3fbcdf8-07b7-42e5-b2a1-e0f96341cd14')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d3fbcdf8-07b7-42e5-b2a1-e0f96341cd14 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d3fbcdf8-07b7-42e5-b2a1-e0f96341cd14');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Box Cox Transformation of (highly) skewed features"
      ],
      "metadata": {
        "id": "b4TBkOnxcwLl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "skewness = skewness[abs(skewness) > 0.75]\n",
        "print(\"There are {} skewed numerical features to Box Cox transform\".format(skewness.shape[0]))\n",
        "\n",
        "from scipy.special import boxcox1p\n",
        "skewed_features = skewness.index\n",
        "lam = 0.15\n",
        "for feat in skewed_features:\n",
        "    #all_data[feat] += 1\n",
        "    all_data[feat] = boxcox1p(all_data[feat], lam)\n",
        "    \n",
        "#all_data[skewed_features] = np.log1p(all_data[skewed_features])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XIpr-phfcsIe",
        "outputId": "5a4b51f0-4254-4c1d-baac-ffb65c2446ed"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 59 skewed numerical features to Box Cox transform\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Getting dummy categorical features"
      ],
      "metadata": {
        "id": "rFLTjH27ct8H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_data = pd.get_dummies(all_data)\n",
        "print(all_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uwWI0j90ctH2",
        "outputId": "5e27ba57-b15b-4632-9d0f-56cf19d88873"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2917, 220)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train=all_data[:ntrain]\n",
        "test=all_data[ntrain:]"
      ],
      "metadata": {
        "id": "aHE_i0mjsPRu"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Modelling"
      ],
      "metadata": {
        "id": "FDSb4D5rcxf7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import ElasticNet, Lasso, BayesianRidge, LassoLarsIC\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
        "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb"
      ],
      "metadata": {
        "id": "V9dCZ5TnczHL"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Define a cross validation strategy \n",
        "> 교차 검증 정의"
      ],
      "metadata": {
        "id": "UPYH2tZMc2S5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Validation function\n",
        "n_folds=5\n",
        "\n",
        "def rmsle_cv(model):\n",
        "  kf=KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train.values)\n",
        "  rmse=np.sqrt(-cross_val_score(model, train.values, y_train, scoring=\"neg_mean_squared_error\", cv=kf))\n",
        "  return(rmse)\n",
        "     "
      ],
      "metadata": {
        "id": "q-6RgNtSc6C7"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "bE8H6D7DlzKx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Base models\n",
        "\n",
        "* LASSO Regression"
      ],
      "metadata": {
        "id": "yOyP9UHlc66w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lasso=make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=1))"
      ],
      "metadata": {
        "id": "Wh_ki6Pyc_9t"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Elastic Net Regression"
      ],
      "metadata": {
        "id": "OEiy6dUwdA_l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))"
      ],
      "metadata": {
        "id": "HAPtLbQUdDRa"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Kernel Ridge Regression"
      ],
      "metadata": {
        "id": "tCDb1CRDdEos"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)"
      ],
      "metadata": {
        "id": "DUT6X0f8dFUt"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "* Gradient Boosting Regression "
      ],
      "metadata": {
        "id": "PyLnZmagdFaG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n",
        "                                   max_depth=4, max_features='sqrt',\n",
        "                                   min_samples_leaf=15, min_samples_split=10, \n",
        "                                   loss='huber', random_state =5)"
      ],
      "metadata": {
        "id": "o7yghzRwdFeV"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* XGBoost"
      ],
      "metadata": {
        "id": "DfRbK44cdIxI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, \n",
        "                             learning_rate=0.05, max_depth=3, \n",
        "                             min_child_weight=1.7817, n_estimators=2200,\n",
        "                             reg_alpha=0.4640, reg_lambda=0.8571,\n",
        "                             subsample=0.5213, silent=1,\n",
        "                             random_state =7, nthread = -1)"
      ],
      "metadata": {
        "id": "a1W7INxbdK6a"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* LightGBM"
      ],
      "metadata": {
        "id": "COSmtnAVdLP_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,\n",
        "                              learning_rate=0.05, n_estimators=720,\n",
        "                              max_bin = 55, bagging_fraction = 0.8,\n",
        "                              bagging_freq = 5, feature_fraction = 0.2319,\n",
        "                              feature_fraction_seed=9, bagging_seed=9,\n",
        "                              min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)"
      ],
      "metadata": {
        "id": "WexszR9ndN9Z"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Base models scores"
      ],
      "metadata": {
        "id": "lY65rc6pdOM-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "score=rmsle_cv(lasso)\n",
        "print(\"\\nLasso score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jET7VU9SdRhv",
        "outputId": "a08a03fa-a523-4a48-e524-9d91b2a4ec59"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Lasso score: 0.1115 (0.0074)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = rmsle_cv(ENet)\n",
        "print(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ynIUgiDdSWk",
        "outputId": "8dcf34d8-c3fa-4429-e099-1aca71e7674f"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ElasticNet score: 0.1116 (0.0074)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = rmsle_cv(KRR)\n",
        "print(\"Kernel Ridge score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "id": "9N56YD3gdTW-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de657903-5b18-47ab-8f28-42bec54f778c"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kernel Ridge score: 0.1153 (0.0075)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = rmsle_cv(GBoost)\n",
        "print(\"Gradient Boosting score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "id": "_QV-2VEddUGy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "901830c9-809d-4a69-dad6-dc1bc2e7a802"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting score: 0.1167 (0.0083)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = rmsle_cv(model_xgb)\n",
        "print(\"Xgboost score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "id": "Q7VQ8V_TdVc1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f83e55a-e88d-45b5-d926-fa277695610c"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[11:47:50] WARNING: ../src/learner.cc:767: \n",
            "Parameters: { \"silent\" } are not used.\n",
            "\n",
            "[11:48:00] WARNING: ../src/learner.cc:767: \n",
            "Parameters: { \"silent\" } are not used.\n",
            "\n",
            "[11:48:12] WARNING: ../src/learner.cc:767: \n",
            "Parameters: { \"silent\" } are not used.\n",
            "\n",
            "[11:48:23] WARNING: ../src/learner.cc:767: \n",
            "Parameters: { \"silent\" } are not used.\n",
            "\n",
            "[11:48:34] WARNING: ../src/learner.cc:767: \n",
            "Parameters: { \"silent\" } are not used.\n",
            "\n",
            "Xgboost score: 0.1172 (0.0050)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = rmsle_cv(model_lgb)\n",
        "print(\"LGBM score: {:.4f} ({:.4f})\\n\" .format(score.mean(), score.std()))"
      ],
      "metadata": {
        "id": "-RiwkNANdVod",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e34a215-446e-453d-8af8-9c8ad4c7393f"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] feature_fraction is set=0.2319, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2319\n",
            "[LightGBM] [Warning] min_sum_hessian_in_leaf is set=11, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=11\n",
            "[LightGBM] [Warning] min_data_in_leaf is set=6, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6\n",
            "[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
            "[LightGBM] [Warning] feature_fraction is set=0.2319, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2319\n",
            "[LightGBM] [Warning] min_sum_hessian_in_leaf is set=11, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=11\n",
            "[LightGBM] [Warning] min_data_in_leaf is set=6, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6\n",
            "[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
            "[LightGBM] [Warning] feature_fraction is set=0.2319, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2319\n",
            "[LightGBM] [Warning] min_sum_hessian_in_leaf is set=11, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=11\n",
            "[LightGBM] [Warning] min_data_in_leaf is set=6, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6\n",
            "[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
            "[LightGBM] [Warning] feature_fraction is set=0.2319, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2319\n",
            "[LightGBM] [Warning] min_sum_hessian_in_leaf is set=11, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=11\n",
            "[LightGBM] [Warning] min_data_in_leaf is set=6, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6\n",
            "[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
            "[LightGBM] [Warning] feature_fraction is set=0.2319, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2319\n",
            "[LightGBM] [Warning] min_sum_hessian_in_leaf is set=11, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=11\n",
            "[LightGBM] [Warning] min_data_in_leaf is set=6, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6\n",
            "[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
            "LGBM score: 0.1167 (0.0059)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Stacking models\n",
        "\n",
        "* Averaged base models class"
      ],
      "metadata": {
        "id": "w4w0GnADdXM2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):\n",
        "    def __init__(self, models):\n",
        "        self.models = models\n",
        "        \n",
        "    # we define clones of the original models to fit the data in\n",
        "    def fit(self, X, y):\n",
        "        self.models_ = [clone(x) for x in self.models]\n",
        "        \n",
        "        # Train cloned base models\n",
        "        for model in self.models_:\n",
        "            model.fit(X, y)\n",
        "\n",
        "        return self\n",
        "    \n",
        "    #Now we do the predictions for cloned models and average them\n",
        "    def predict(self, X):\n",
        "        predictions = np.column_stack([\n",
        "            model.predict(X) for model in self.models_\n",
        "        ])\n",
        "        return np.mean(predictions, axis=1) "
      ],
      "metadata": {
        "id": "HIetP-jmdVq8"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Averaged base models score"
      ],
      "metadata": {
        "id": "4LAiPaMyddqu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "averaged_models = AveragingModels(models = (ENet, GBoost, KRR, lasso))\n",
        "\n",
        "score = rmsle_cv(averaged_models) \n",
        "print(\" Averaged base models score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNUO0NjNdgJs",
        "outputId": "dd8aec8e-fbe8-4fd4-f791-7e12dea47608"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Averaged base models score: 0.1087 (0.0077)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Less simple Stacking : Adding a Meta-model¶"
      ],
      "metadata": {
        "id": "BRRIMo8GhZpg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Stacking averaged Models Class"
      ],
      "metadata": {
        "id": "a6SdxMbShb7J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class StackingAveragedModels(BaseEstimator, RegressorMixin, TransformerMixin):\n",
        "    def __init__(self, base_models, meta_model, n_folds=5):\n",
        "        self.base_models = base_models\n",
        "        self.meta_model = meta_model\n",
        "        self.n_folds = n_folds\n",
        "   \n",
        "    # We again fit the data on clones of the original models\n",
        "    def fit(self, X, y):\n",
        "        self.base_models_ = [list() for x in self.base_models]\n",
        "        self.meta_model_ = clone(self.meta_model)\n",
        "        kfold = KFold(n_splits=self.n_folds, shuffle=True, random_state=156)\n",
        "        \n",
        "        # Train cloned base models then create out-of-fold predictions\n",
        "        # that are needed to train the cloned meta-model\n",
        "        out_of_fold_predictions = np.zeros((X.shape[0], len(self.base_models)))\n",
        "        for i, model in enumerate(self.base_models):\n",
        "            for train_index, holdout_index in kfold.split(X, y):\n",
        "                instance = clone(model)\n",
        "                self.base_models_[i].append(instance)\n",
        "                instance.fit(X[train_index], y[train_index])\n",
        "                y_pred = instance.predict(X[holdout_index])\n",
        "                out_of_fold_predictions[holdout_index, i] = y_pred\n",
        "                \n",
        "        # Now train the cloned  meta-model using the out-of-fold predictions as new feature\n",
        "        self.meta_model_.fit(out_of_fold_predictions, y)\n",
        "        return self\n",
        "   \n",
        "    #Do the predictions of all base models on the test data and use the averaged predictions as \n",
        "    #meta-features for the final prediction which is done by the meta-model\n",
        "    def predict(self, X):\n",
        "        meta_features = np.column_stack([\n",
        "            np.column_stack([model.predict(X) for model in base_models]).mean(axis=1)\n",
        "            for base_models in self.base_models_ ])\n",
        "        return self.meta_model_.predict(meta_features)"
      ],
      "metadata": {
        "id": "aIib1cGEhdyn"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Stacking Averaged models Score"
      ],
      "metadata": {
        "id": "uS4lP2vjhe5g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stacked_averaged_models = StackingAveragedModels(base_models = (ENet, GBoost, KRR),\n",
        "                                                 meta_model = lasso)\n",
        "\n",
        "score = rmsle_cv(stacked_averaged_models)\n",
        "print(\"Stacking Averaged models score: {:.4f} ({:.4f})\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "id": "e1s0R7GIhgUa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd68f08f-b901-447f-d773-fc5a262552b5"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stacking Averaged models score: 0.1081 (0.0073)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Ensembling StackedRegressor, XGBoost and LightGBM"
      ],
      "metadata": {
        "id": "ZHfCZ0Mthhwg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def rmsle(y, y_pred):\n",
        "    return np.sqrt(mean_squared_error(y, y_pred))"
      ],
      "metadata": {
        "id": "NT8s9XxuhjSr"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Final Training and Prediction\n",
        "\n",
        "* StackedRegressor"
      ],
      "metadata": {
        "id": "38PtNHA2hkmh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stacked_averaged_models.fit(train.values, y_train)\n",
        "stacked_train_pred = stacked_averaged_models.predict(train.values)\n",
        "stacked_pred = np.expm1(stacked_averaged_models.predict(test.values))\n",
        "print(rmsle(y_train, stacked_train_pred))"
      ],
      "metadata": {
        "id": "63LVY1mJhnbz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd029fa9-276c-4e06-de44-89971f4f78d2"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.07839506096665937\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* XGBoost"
      ],
      "metadata": {
        "id": "XT4V3w7uhoPp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_xgb.fit(train, y_train)\n",
        "xgb_train_pred = model_xgb.predict(train)\n",
        "xgb_pred = np.expm1(model_xgb.predict(test))\n",
        "print(rmsle(y_train, xgb_train_pred))"
      ],
      "metadata": {
        "id": "V7eleVo7hp4f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98b03a57-8bb9-4313-b52d-28f795673abf"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[11:58:15] WARNING: ../src/learner.cc:767: \n",
            "Parameters: { \"silent\" } are not used.\n",
            "\n",
            "0.0790460201201611\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* LightGBM"
      ],
      "metadata": {
        "id": "L-C5Zvtshqyf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_lgb.fit(train, y_train)\n",
        "lgb_train_pred = model_lgb.predict(train)\n",
        "lgb_pred = np.expm1(model_lgb.predict(test.values))\n",
        "print(rmsle(y_train, lgb_train_pred))"
      ],
      "metadata": {
        "id": "j_lGTKIWhruz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "194ff1c5-c68d-4502-81d4-f4476a229317"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] feature_fraction is set=0.2319, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.2319\n",
            "[LightGBM] [Warning] min_sum_hessian_in_leaf is set=11, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=11\n",
            "[LightGBM] [Warning] min_data_in_leaf is set=6, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=6\n",
            "[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
            "0.07169383068991829\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''RMSE on the entire Train data when averaging'''\n",
        "\n",
        "print('RMSLE score on train data:')\n",
        "print(rmsle(y_train,stacked_train_pred*0.70 +\n",
        "               xgb_train_pred*0.15 + lgb_train_pred*0.15 ))"
      ],
      "metadata": {
        "id": "mkAbHGGdhsgW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66b22b0d-6953-433b-97f0-8b33343222aa"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSLE score on train data:\n",
            "0.07548697935004024\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ensemble = stacked_pred*0.70 + xgb_pred*0.15 + lgb_pred*0.15"
      ],
      "metadata": {
        "id": "IolCMsUthtNN"
      },
      "execution_count": 87,
      "outputs": []
    }
  ]
}