5_b_Housing_Model_Selection_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNf/ZV4stHXlDocCeT8H30o",
      "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/Cintia0528/Project-6-Supervised-Machine-Learning---Classification/blob/main/5_b_Housing_Model_Selection_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1 Data Reading"
      ],
      "metadata": {
        "id": "V2iLqTjIb4cw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Hil4XLeVbWTN"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "from sklearn import set_config\n",
        "\n",
        "# reading\n",
        "url = \"https://drive.google.com/file/d/1JsZV_7u1TGiQA863-YtYxlia1VNxAJZj/view?usp=sharing\"\n",
        "path = 'https://drive.google.com/uc?export=download&id='+url.split('/')[-2]\n",
        "data = df = pd.read_csv(path)\n",
        "\n",
        "# X and y creation\n",
        "## Removing ID as it is unique to each property, and removing MSSubClass as it is a categorization\n",
        "## of property types, but there is no way to tell which one is better / worse\n",
        "X = data\n",
        "y = X.pop(\"Expensive\")\n",
        "Id = X.pop(\"Id\")\n",
        "\n",
        "# # Feature Engineering\n",
        "# X.loc[:, \"Cabin\"] = X.Cabin.str[0]\n",
        "\n",
        "# data splitting\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2 Exploratory Analysis\n"
      ],
      "metadata": {
        "id": "LMu4rDDsd5XW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X.head(20)"
      ],
      "metadata": {
        "id": "kOiNr5Xyd851"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.info()"
      ],
      "metadata": {
        "id": "DCfFaEYgeZ0U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = X.drop_duplicates()"
      ],
      "metadata": {
        "id": "vjKGq2rzhwxz"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.hist(figsize = (10,8));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "2LJ_mU8Bh0CI",
        "outputId": "9dcc0962-4b9f-4401-8b3f-56b7c0cd8f32"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 36 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3 Building a preprocessor"
      ],
      "metadata": {
        "id": "svYX-xLAiEZI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 0. Set the config so that we can view our preprocessor\n",
        "set_config(display=\"diagram\")\n",
        "\n",
        "# 1. defining categorical & ordinal columns\n",
        "X_cat = X.select_dtypes(exclude=\"number\").copy()\n",
        "X_num = X.select_dtypes(include=\"number\").copy()\n",
        "\n",
        "# 2. numerical pipeline , default strategy we search for the best parameters later\n",
        "numeric_pipe = make_pipeline(\n",
        "    SimpleImputer())\n",
        "\n",
        "# 3. categorical pipeline\n",
        "\n",
        "# # 3.1 defining ordinal & onehot columns\n",
        "# .get_indexer() get's the index to solve the problem described above about losing column names\n",
        "ordinal_cols = X_cat.columns.get_indexer([\"Street\",\n",
        "                                          \"CentralAir\",\n",
        "                                          \"ExterQual\",\n",
        "                                          \"ExterCond\",\n",
        "                                          \"BsmtQual\",\n",
        "                                          \"BsmtCond\",\n",
        "                                          \"BsmtExposure\",\n",
        "                                          \"BsmtFinType1\",\n",
        "                                          \"KitchenQual\",\n",
        "                                          \"FireplaceQu\",\n",
        "                                          \"Alley\",\n",
        "                                          \"LotShape\",\n",
        "                                          \"Utilities\",\n",
        "                                          \"LandSlope\",\n",
        "                                          \"RoofMatl\",\n",
        "                                          \"BsmtFinType2\",\n",
        "                                          \"Functional\",\n",
        "                                          \"GarageType\",\n",
        "                                          \"GarageFinish\",\n",
        "                                          \"GarageQual\",\n",
        "                                          \"GarageCond\",\n",
        "                                          \"PavedDrive\",\n",
        "                                          \"PoolQC\",\n",
        "                                          \"Fence\"])\n",
        "\n",
        "\n",
        "onehot_cols = X_cat.columns.get_indexer([\"MSZoning\",\n",
        "                                         \"Condition1\",\n",
        "                                         \"Heating\",\n",
        "                                         \"Foundation\",\n",
        "                                         \"LotShape\",\n",
        "                                         \"LotCongig\",\n",
        "                                         \"Neighborhood\",\n",
        "                                         \"Condition2\",\n",
        "                                         \"BldgType\",\n",
        "                                         \"HouseStyle\",\n",
        "                                         \"LandContour\",\n",
        "                                         \"LotConfig\",\n",
        "                                         \"Neighborhood\",\n",
        "                                         \"Condition2\",\n",
        "                                         \"BldgType\",\n",
        "                                         \"HouseStyle\",\n",
        "                                         \"RoofStyle\",\n",
        "                                         \"Exterior1st\",\n",
        "                                         \"Exterior2nd\",\n",
        "                                         \"MasVnrType\",\n",
        "                                         \"HeatingQc\",\n",
        "                                         \"Electrical\",\n",
        "                                         \"MiscFeature\",\n",
        "                                         \"SaleType\",\n",
        "                                         \"SaleCondition\"])\n",
        "\n",
        "\n",
        "# # 3.2. defining the categorical encoder\n",
        "\n",
        "Street_cats = [ \"NA\", \"Pave\", \"Grvl\"]\n",
        "CentralAir_cats = [\"NA\",\"N\", \"Y\" ]\n",
        "ExterQual_cats = [\"NA\",\"Po\",\"Fa\",\"TA\",\"Gd\",\"Ex\"]\n",
        "ExterCond_cats = [\"NA\",\"Po\",\"Fa\",\"TA\",\"Gd\",\"Ex\"]\n",
        "BsmtQual_cats = [\"NA\",\"Po\",\"Fa\",\"TA\",\"Gd\",\"Ex\"]\n",
        "BsmtCond_cats = [\"NA\",\"Po\",\"Fa\",\"TA\",\"Gd\",\"Ex\"]\n",
        "BsmtExposure_cats = [\"NA\", \"No\",\"Mn\",\"Av\",\"Gd\"]\n",
        "BsmtFinType1_cats = [\"NA\", \"Unf\",\"LwQ\",\"Rec\",\"BLQ\",\"ALQ\",\"GLQ\"]\n",
        "KitchenQual_cats = [\"NA\",\"Po\",\"Fa\",\"TA\",\"Gd\",\"Ex\"]\n",
        "FireplaceQu_cats = [\"NA\",\"Po\",\"Fa\",\"TA\",\"Gd\",\"Ex\"]\n",
        "Alley_cats = [ \"NA\", \"Pave\", \"Grvl\"]\n",
        "LotShape_cats = [\"NA\", \"IR3\",\"IR2\",\"IR1\",\"Reg\"]\n",
        "Utilities_cats = [\"NA\", \"ELO\",\"NoSeWa\",\"NoSewr\",\"AllPub\"]\n",
        "LandSlope_cats = [\"NA\", \"Sev\",\"Mod\",\"Gtl\"]\n",
        "RoofMatl_cats = [\"NA\",\"WdShngl\",\"WdShake\",\"Tar&Grv\",\"Roll\",\"Metal\",\"Membran\",\"CompShg\",\"ClyTile\"]\n",
        "BsmtFinType2_cats = [\"NA\", \"Unf\",\"LwQ\",\"Rec\",\"BLQ\",\"ALQ\",\"GLQ\"]\n",
        "Functional_cats = [\"NA\",\"Sal\",\"Sev\",\"Maj2\",\"Maj1\",\"Mod\",\"Min2\",\"Min1\",\"Typ\"]\n",
        "GarageType_cats = [\"NA\", \"Detchd\",\"CarPort\",\"BuiltIn\",\"Basment\",\"Attchd\",\"2Types\"]\n",
        "GarageFinish_cats = [\"NA\", \"Unf\",\"RFn\",\"Fin\"]\n",
        "GarageQual_cats = [\"NA\",\"Po\",\"Fa\",\"TA\",\"Gd\",\"Ex\"]\n",
        "GarageCond_cats = [\"NA\",\"Po\",\"Fa\",\"TA\",\"Gd\",\"Ex\"]\n",
        "PavedDrive_cats = [\"NA\",\"N\",\"P\",\"Y\"]\n",
        "PoolQC_cats = [\"NA\",\"Fa\",\"TA\",\"Gd\",\"Ex\"]\n",
        "Fence_cats = [\"NA\", \"MnWw\",\"GdWo\",\"MnPrv\",\"GdPrv\"]\n",
        "\n",
        "\n",
        "\n",
        "# # # 3.2.1. we manually establish the order of the categories for our ordinal feature (Cabin) from worst to best, including \"N_A\"\n",
        "\n",
        "ordinal_cats = [Street_cats,\n",
        "                CentralAir_cats,\n",
        "                ExterQual_cats,\n",
        "                ExterCond_cats,\n",
        "                BsmtQual_cats,\n",
        "                BsmtCond_cats,\n",
        "                BsmtExposure_cats,\n",
        "                BsmtFinType1_cats,\n",
        "                KitchenQual_cats,\n",
        "                FireplaceQu_cats,\n",
        "                Alley_cats,\n",
        "                LotShape_cats,\n",
        "                Utilities_cats,\n",
        "                LandSlope_cats,\n",
        "                RoofMatl_cats,\n",
        "                BsmtFinType2_cats,\n",
        "                Functional_cats,\n",
        "                GarageType_cats,\n",
        "                GarageFinish_cats,\n",
        "                GarageQual_cats,\n",
        "                GarageCond_cats,\n",
        "                PavedDrive_cats,\n",
        "                PoolQC_cats,\n",
        "                Fence_cats]\n",
        "\n",
        "ordinal_encoder = OrdinalEncoder(categories=ordinal_cats)\n",
        "\n",
        "# # # 3.2.2. defining the categorical encoder: a ColumnTransformer with 2 branches: ordinal & onehot\n",
        "categorical_encoder = ColumnTransformer(\n",
        "    transformers=[\n",
        "        (\"cat_ordinal\", ordinal_encoder, ordinal_cols),\n",
        "        (\"cat_onehot\", OneHotEncoder(sparse_output=False, handle_unknown=\"ignore\"), onehot_cols),\n",
        "    ]\n",
        ")\n",
        "\n",
        "# # 3.3. categorical pipeline = \"N_A\" imputer + categorical encoder\n",
        "categorical_pipe = make_pipeline(SimpleImputer(strategy=\"constant\", fill_value=\"NA\"),\n",
        "                                 categorical_encoder\n",
        "                                )\n",
        "\n",
        "# 4. full preprocessing: a ColumnTransformer with 2 branches: numeric & categorical\n",
        "full_preprocessing = ColumnTransformer(\n",
        "    transformers=[\n",
        "        (\"num_pipe\", numeric_pipe, X_num.columns),\n",
        "        (\"cat_pipe\", categorical_pipe, X_cat.columns),\n",
        "    ]\n",
        ")\n",
        "\n",
        "full_preprocessing"
      ],
      "metadata": {
        "id": "zK8qGNpUiGj5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "86d71e26-8913-4482-ea8e-c1afcc1ce83f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColumnTransformer(transformers=[('num_pipe',\n",
              "                                 Pipeline(steps=[('simpleimputer',\n",
              "                                                  SimpleImputer())]),\n",
              "                                 Index(['LotArea', 'LotFrontage', 'TotalBsmtSF', 'BedroomAbvGr', 'Fireplaces',\n",
              "       'PoolArea', 'GarageCars', 'WoodDeckSF', 'ScreenPorch', 'MSSubClass',\n",
              "       'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea',\n",
              "       'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', '1stFlrSF',...\n",
              "       'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope',\n",
              "       'Neighborhood', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle',\n",
              "       'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'BsmtFinType2',\n",
              "       'HeatingQC', 'Electrical', 'Functional', 'GarageType', 'GarageFinish',\n",
              "       'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence',\n",
              "       'MiscFeature', 'SaleType', 'SaleCondition'],\n",
              "      dtype='object'))])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                  SimpleImputer())]),\n",
              "                                 Index([&#x27;LotArea&#x27;, &#x27;LotFrontage&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;Fireplaces&#x27;,\n",
              "       &#x27;PoolArea&#x27;, &#x27;GarageCars&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;ScreenPorch&#x27;, &#x27;MSSubClass&#x27;,\n",
              "       &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;, &#x27;MasVnrArea&#x27;,\n",
              "       &#x27;BsmtFinSF1&#x27;, &#x27;BsmtFinSF2&#x27;, &#x27;BsmtUnfSF&#x27;, &#x27;1stFlrSF&#x27;,...\n",
              "       &#x27;LotShape&#x27;, &#x27;LandContour&#x27;, &#x27;Utilities&#x27;, &#x27;LotConfig&#x27;, &#x27;LandSlope&#x27;,\n",
              "       &#x27;Neighborhood&#x27;, &#x27;Condition2&#x27;, &#x27;BldgType&#x27;, &#x27;HouseStyle&#x27;, &#x27;RoofStyle&#x27;,\n",
              "       &#x27;RoofMatl&#x27;, &#x27;Exterior1st&#x27;, &#x27;Exterior2nd&#x27;, &#x27;MasVnrType&#x27;, &#x27;BsmtFinType2&#x27;,\n",
              "       &#x27;HeatingQC&#x27;, &#x27;Electrical&#x27;, &#x27;Functional&#x27;, &#x27;GarageType&#x27;, &#x27;GarageFinish&#x27;,\n",
              "       &#x27;GarageQual&#x27;, &#x27;GarageCond&#x27;, &#x27;PavedDrive&#x27;, &#x27;PoolQC&#x27;, &#x27;Fence&#x27;,\n",
              "       &#x27;MiscFeature&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;],\n",
              "      dtype=&#x27;object&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                  SimpleImputer())]),\n",
              "                                 Index([&#x27;LotArea&#x27;, &#x27;LotFrontage&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;Fireplaces&#x27;,\n",
              "       &#x27;PoolArea&#x27;, &#x27;GarageCars&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;ScreenPorch&#x27;, &#x27;MSSubClass&#x27;,\n",
              "       &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;, &#x27;MasVnrArea&#x27;,\n",
              "       &#x27;BsmtFinSF1&#x27;, &#x27;BsmtFinSF2&#x27;, &#x27;BsmtUnfSF&#x27;, &#x27;1stFlrSF&#x27;,...\n",
              "       &#x27;LotShape&#x27;, &#x27;LandContour&#x27;, &#x27;Utilities&#x27;, &#x27;LotConfig&#x27;, &#x27;LandSlope&#x27;,\n",
              "       &#x27;Neighborhood&#x27;, &#x27;Condition2&#x27;, &#x27;BldgType&#x27;, &#x27;HouseStyle&#x27;, &#x27;RoofStyle&#x27;,\n",
              "       &#x27;RoofMatl&#x27;, &#x27;Exterior1st&#x27;, &#x27;Exterior2nd&#x27;, &#x27;MasVnrType&#x27;, &#x27;BsmtFinType2&#x27;,\n",
              "       &#x27;HeatingQC&#x27;, &#x27;Electrical&#x27;, &#x27;Functional&#x27;, &#x27;GarageType&#x27;, &#x27;GarageFinish&#x27;,\n",
              "       &#x27;GarageQual&#x27;, &#x27;GarageCond&#x27;, &#x27;PavedDrive&#x27;, &#x27;PoolQC&#x27;, &#x27;Fence&#x27;,\n",
              "       &#x27;MiscFeature&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;],\n",
              "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;LotArea&#x27;, &#x27;LotFrontage&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;Fireplaces&#x27;,\n",
              "       &#x27;PoolArea&#x27;, &#x27;GarageCars&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;ScreenPorch&#x27;, &#x27;MSSubClass&#x27;,\n",
              "       &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;, &#x27;MasVnrArea&#x27;,\n",
              "       &#x27;BsmtFinSF1&#x27;, &#x27;BsmtFinSF2&#x27;, &#x27;BsmtUnfSF&#x27;, &#x27;1stFlrSF&#x27;, &#x27;2ndFlrSF&#x27;,\n",
              "       &#x27;LowQualFinSF&#x27;, &#x27;GrLivArea&#x27;, &#x27;BsmtFullBath&#x27;, &#x27;BsmtHalfBath&#x27;, &#x27;FullBath&#x27;,\n",
              "       &#x27;HalfBath&#x27;, &#x27;KitchenAbvGr&#x27;, &#x27;TotRmsAbvGrd&#x27;, &#x27;GarageYrBlt&#x27;, &#x27;GarageArea&#x27;,\n",
              "       &#x27;OpenPorchSF&#x27;, &#x27;EnclosedPorch&#x27;, &#x27;3SsnPorch&#x27;, &#x27;MiscVal&#x27;, &#x27;MoSold&#x27;,\n",
              "       &#x27;YrSold&#x27;],\n",
              "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;MSZoning&#x27;, &#x27;Condition1&#x27;, &#x27;Heating&#x27;, &#x27;Street&#x27;, &#x27;CentralAir&#x27;,\n",
              "       &#x27;Foundation&#x27;, &#x27;ExterQual&#x27;, &#x27;ExterCond&#x27;, &#x27;BsmtQual&#x27;, &#x27;BsmtCond&#x27;,\n",
              "       &#x27;BsmtExposure&#x27;, &#x27;BsmtFinType1&#x27;, &#x27;KitchenQual&#x27;, &#x27;FireplaceQu&#x27;, &#x27;Alley&#x27;,\n",
              "       &#x27;LotShape&#x27;, &#x27;LandContour&#x27;, &#x27;Utilities&#x27;, &#x27;LotConfig&#x27;, &#x27;LandSlope&#x27;,\n",
              "       &#x27;Neighborhood&#x27;, &#x27;Condition2&#x27;, &#x27;BldgType&#x27;, &#x27;HouseStyle&#x27;, &#x27;RoofStyle&#x27;,\n",
              "       &#x27;RoofMatl&#x27;, &#x27;Exterior1st&#x27;, &#x27;Exterior2nd&#x27;, &#x27;MasVnrType&#x27;, &#x27;BsmtFinType2&#x27;,\n",
              "       &#x27;HeatingQC&#x27;, &#x27;Electrical&#x27;, &#x27;Functional&#x27;, &#x27;GarageType&#x27;, &#x27;GarageFinish&#x27;,\n",
              "       &#x27;GarageQual&#x27;, &#x27;GarageCond&#x27;, &#x27;PavedDrive&#x27;, &#x27;PoolQC&#x27;, &#x27;Fence&#x27;,\n",
              "       &#x27;MiscFeature&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;],\n",
              "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=&#x27;NA&#x27;, strategy=&#x27;constant&#x27;)</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;cat_ordinal&#x27;,\n",
              "                                 OrdinalEncoder(categories=[[&#x27;NA&#x27;, &#x27;Pave&#x27;,\n",
              "                                                             &#x27;Grvl&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;N&#x27;, &#x27;Y&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;No&#x27;, &#x27;Mn&#x27;,\n",
              "                                                             &#x27;Av&#x27;, &#x27;Gd&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;LwQ&#x27;,\n",
              "                                                             &#x27;Rec&#x27;, &#x27;BLQ&#x27;,\n",
              "                                                             &#x27;ALQ&#x27;, &#x27;GLQ&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;...\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;N&#x27;, &#x27;P&#x27;,\n",
              "                                                             &#x27;Y&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;,\n",
              "                                                             &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;MnWw&#x27;,\n",
              "                                                             &#x27;GdWo&#x27;, &#x27;MnPrv&#x27;,\n",
              "                                                             &#x27;GdPrv&#x27;]]),\n",
              "                                 array([ 3,  4,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 17, 19, 25, 29, 32,\n",
              "       33, 34, 35, 36, 37, 38, 39])),\n",
              "                                (&#x27;cat_onehot&#x27;,\n",
              "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
              "                                               sparse_output=False),\n",
              "                                 array([ 0,  1,  2,  5, 15, -1, 20, 21, 22, 23, 16, 18, 20, 21, 22, 23, 24,\n",
              "       26, 27, 28, -1, 31, 40, 41, 42]))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat_ordinal</label><div class=\"sk-toggleable__content\"><pre>[ 3  4  6  7  8  9 10 11 12 13 14 15 17 19 25 29 32 33 34 35 36 37 38 39]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;NA&#x27;, &#x27;Pave&#x27;, &#x27;Grvl&#x27;], [&#x27;NA&#x27;, &#x27;N&#x27;, &#x27;Y&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;No&#x27;, &#x27;Mn&#x27;, &#x27;Av&#x27;, &#x27;Gd&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;LwQ&#x27;, &#x27;Rec&#x27;, &#x27;BLQ&#x27;, &#x27;ALQ&#x27;, &#x27;GLQ&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Pave&#x27;, &#x27;Grvl&#x27;],\n",
              "                           [&#x27;NA...\n",
              "                            &#x27;Metal&#x27;, &#x27;Membran&#x27;, &#x27;CompShg&#x27;, &#x27;ClyTile&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;LwQ&#x27;, &#x27;Rec&#x27;, &#x27;BLQ&#x27;, &#x27;ALQ&#x27;, &#x27;GLQ&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Sal&#x27;, &#x27;Sev&#x27;, &#x27;Maj2&#x27;, &#x27;Maj1&#x27;, &#x27;Mod&#x27;, &#x27;Min2&#x27;,\n",
              "                            &#x27;Min1&#x27;, &#x27;Typ&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Detchd&#x27;, &#x27;CarPort&#x27;, &#x27;BuiltIn&#x27;, &#x27;Basment&#x27;,\n",
              "                            &#x27;Attchd&#x27;, &#x27;2Types&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;RFn&#x27;, &#x27;Fin&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;N&#x27;, &#x27;P&#x27;, &#x27;Y&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;MnWw&#x27;, &#x27;GdWo&#x27;, &#x27;MnPrv&#x27;, &#x27;GdPrv&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat_onehot</label><div class=\"sk-toggleable__content\"><pre>[ 0  1  2  5 15 -1 20 21 22 23 16 18 20 21 22 23 24 26 27 28 -1 31 40 41\n",
              " 42]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4 Modelling\n"
      ],
      "metadata": {
        "id": "4rIzgwsQHVPQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.1 Decision Tree"
      ],
      "metadata": {
        "id": "hsDgPEIPHb2f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# full pipeline: preprocessor + model\n",
        "full_pipeline = make_pipeline(full_preprocessing,\n",
        "                              DecisionTreeClassifier())\n",
        "\n",
        "# define parameter grid\n",
        "param_grid = {\n",
        "    \"columntransformer__num_pipe__simpleimputer__strategy\":[\"mean\", \"median\", \"most_frequent\", \"constant\"],\n",
        "    \"decisiontreeclassifier__max_depth\": range(1, 16, 2),\n",
        "    \"decisiontreeclassifier__min_samples_leaf\": range(2, 16, 2)\n",
        "}\n",
        "\n",
        "# define GridSearchCV\n",
        "search = GridSearchCV(full_pipeline,\n",
        "                      param_grid,\n",
        "                      cv=5,\n",
        "                      verbose=1)\n",
        "\n",
        "search\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "BBX3Jk_2HegT",
        "outputId": "94902f7a-0cbb-4548-d67e-726ebc577594"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[('columntransformer',\n",
              "                                        ColumnTransformer(transformers=[('num_pipe',\n",
              "                                                                         Pipeline(steps=[('simpleimputer',\n",
              "                                                                                          SimpleImputer())]),\n",
              "                                                                         Index(['LotArea', 'LotFrontage', 'TotalBsmtSF', 'BedroomAbvGr', 'Fireplaces',\n",
              "       'PoolArea', 'GarageCars', 'WoodDeckSF', 'ScreenPorch', 'MSSubClass',\n",
              "       'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAd...\n",
              "       'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence',\n",
              "       'MiscFeature', 'SaleType', 'SaleCondition'],\n",
              "      dtype='object'))])),\n",
              "                                       ('decisiontreeclassifier',\n",
              "                                        DecisionTreeClassifier())]),\n",
              "             param_grid={'columntransformer__num_pipe__simpleimputer__strategy': ['mean',\n",
              "                                                                                  'median',\n",
              "                                                                                  'most_frequent',\n",
              "                                                                                  'constant'],\n",
              "                         'decisiontreeclassifier__max_depth': range(1, 16, 2),\n",
              "                         'decisiontreeclassifier__min_samples_leaf': range(2, 16, 2)},\n",
              "             verbose=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                                        ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                                                          SimpleImputer())]),\n",
              "                                                                         Index([&#x27;LotArea&#x27;, &#x27;LotFrontage&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;Fireplaces&#x27;,\n",
              "       &#x27;PoolArea&#x27;, &#x27;GarageCars&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;ScreenPorch&#x27;, &#x27;MSSubClass&#x27;,\n",
              "       &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAd...\n",
              "       &#x27;GarageQual&#x27;, &#x27;GarageCond&#x27;, &#x27;PavedDrive&#x27;, &#x27;PoolQC&#x27;, &#x27;Fence&#x27;,\n",
              "       &#x27;MiscFeature&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;],\n",
              "      dtype=&#x27;object&#x27;))])),\n",
              "                                       (&#x27;decisiontreeclassifier&#x27;,\n",
              "                                        DecisionTreeClassifier())]),\n",
              "             param_grid={&#x27;columntransformer__num_pipe__simpleimputer__strategy&#x27;: [&#x27;mean&#x27;,\n",
              "                                                                                  &#x27;median&#x27;,\n",
              "                                                                                  &#x27;most_frequent&#x27;,\n",
              "                                                                                  &#x27;constant&#x27;],\n",
              "                         &#x27;decisiontreeclassifier__max_depth&#x27;: range(1, 16, 2),\n",
              "                         &#x27;decisiontreeclassifier__min_samples_leaf&#x27;: range(2, 16, 2)},\n",
              "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                                        ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                                                          SimpleImputer())]),\n",
              "                                                                         Index([&#x27;LotArea&#x27;, &#x27;LotFrontage&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;Fireplaces&#x27;,\n",
              "       &#x27;PoolArea&#x27;, &#x27;GarageCars&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;ScreenPorch&#x27;, &#x27;MSSubClass&#x27;,\n",
              "       &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAd...\n",
              "       &#x27;GarageQual&#x27;, &#x27;GarageCond&#x27;, &#x27;PavedDrive&#x27;, &#x27;PoolQC&#x27;, &#x27;Fence&#x27;,\n",
              "       &#x27;MiscFeature&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;],\n",
              "      dtype=&#x27;object&#x27;))])),\n",
              "                                       (&#x27;decisiontreeclassifier&#x27;,\n",
              "                                        DecisionTreeClassifier())]),\n",
              "             param_grid={&#x27;columntransformer__num_pipe__simpleimputer__strategy&#x27;: [&#x27;mean&#x27;,\n",
              "                                                                                  &#x27;median&#x27;,\n",
              "                                                                                  &#x27;most_frequent&#x27;,\n",
              "                                                                                  &#x27;constant&#x27;],\n",
              "                         &#x27;decisiontreeclassifier__max_depth&#x27;: range(1, 16, 2),\n",
              "                         &#x27;decisiontreeclassifier__min_samples_leaf&#x27;: range(2, 16, 2)},\n",
              "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                                   SimpleImputer())]),\n",
              "                                                  Index([&#x27;LotArea&#x27;, &#x27;LotFrontage&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;Fireplaces&#x27;,\n",
              "       &#x27;PoolArea&#x27;, &#x27;GarageCars&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;ScreenPorch&#x27;, &#x27;MSSubClass&#x27;,\n",
              "       &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;, &#x27;MasVnrArea&#x27;,\n",
              "       &#x27;BsmtFinSF1&#x27;...\n",
              "       &#x27;Neighborhood&#x27;, &#x27;Condition2&#x27;, &#x27;BldgType&#x27;, &#x27;HouseStyle&#x27;, &#x27;RoofStyle&#x27;,\n",
              "       &#x27;RoofMatl&#x27;, &#x27;Exterior1st&#x27;, &#x27;Exterior2nd&#x27;, &#x27;MasVnrType&#x27;, &#x27;BsmtFinType2&#x27;,\n",
              "       &#x27;HeatingQC&#x27;, &#x27;Electrical&#x27;, &#x27;Functional&#x27;, &#x27;GarageType&#x27;, &#x27;GarageFinish&#x27;,\n",
              "       &#x27;GarageQual&#x27;, &#x27;GarageCond&#x27;, &#x27;PavedDrive&#x27;, &#x27;PoolQC&#x27;, &#x27;Fence&#x27;,\n",
              "       &#x27;MiscFeature&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;],\n",
              "      dtype=&#x27;object&#x27;))])),\n",
              "                (&#x27;decisiontreeclassifier&#x27;, DecisionTreeClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                  SimpleImputer())]),\n",
              "                                 Index([&#x27;LotArea&#x27;, &#x27;LotFrontage&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;Fireplaces&#x27;,\n",
              "       &#x27;PoolArea&#x27;, &#x27;GarageCars&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;ScreenPorch&#x27;, &#x27;MSSubClass&#x27;,\n",
              "       &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;, &#x27;MasVnrArea&#x27;,\n",
              "       &#x27;BsmtFinSF1&#x27;, &#x27;BsmtFinSF2&#x27;, &#x27;BsmtUnfSF&#x27;, &#x27;1stFlrSF&#x27;,...\n",
              "       &#x27;LotShape&#x27;, &#x27;LandContour&#x27;, &#x27;Utilities&#x27;, &#x27;LotConfig&#x27;, &#x27;LandSlope&#x27;,\n",
              "       &#x27;Neighborhood&#x27;, &#x27;Condition2&#x27;, &#x27;BldgType&#x27;, &#x27;HouseStyle&#x27;, &#x27;RoofStyle&#x27;,\n",
              "       &#x27;RoofMatl&#x27;, &#x27;Exterior1st&#x27;, &#x27;Exterior2nd&#x27;, &#x27;MasVnrType&#x27;, &#x27;BsmtFinType2&#x27;,\n",
              "       &#x27;HeatingQC&#x27;, &#x27;Electrical&#x27;, &#x27;Functional&#x27;, &#x27;GarageType&#x27;, &#x27;GarageFinish&#x27;,\n",
              "       &#x27;GarageQual&#x27;, &#x27;GarageCond&#x27;, &#x27;PavedDrive&#x27;, &#x27;PoolQC&#x27;, &#x27;Fence&#x27;,\n",
              "       &#x27;MiscFeature&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;],\n",
              "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;LotArea&#x27;, &#x27;LotFrontage&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;Fireplaces&#x27;,\n",
              "       &#x27;PoolArea&#x27;, &#x27;GarageCars&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;ScreenPorch&#x27;, &#x27;MSSubClass&#x27;,\n",
              "       &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;, &#x27;MasVnrArea&#x27;,\n",
              "       &#x27;BsmtFinSF1&#x27;, &#x27;BsmtFinSF2&#x27;, &#x27;BsmtUnfSF&#x27;, &#x27;1stFlrSF&#x27;, &#x27;2ndFlrSF&#x27;,\n",
              "       &#x27;LowQualFinSF&#x27;, &#x27;GrLivArea&#x27;, &#x27;BsmtFullBath&#x27;, &#x27;BsmtHalfBath&#x27;, &#x27;FullBath&#x27;,\n",
              "       &#x27;HalfBath&#x27;, &#x27;KitchenAbvGr&#x27;, &#x27;TotRmsAbvGrd&#x27;, &#x27;GarageYrBlt&#x27;, &#x27;GarageArea&#x27;,\n",
              "       &#x27;OpenPorchSF&#x27;, &#x27;EnclosedPorch&#x27;, &#x27;3SsnPorch&#x27;, &#x27;MiscVal&#x27;, &#x27;MoSold&#x27;,\n",
              "       &#x27;YrSold&#x27;],\n",
              "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;MSZoning&#x27;, &#x27;Condition1&#x27;, &#x27;Heating&#x27;, &#x27;Street&#x27;, &#x27;CentralAir&#x27;,\n",
              "       &#x27;Foundation&#x27;, &#x27;ExterQual&#x27;, &#x27;ExterCond&#x27;, &#x27;BsmtQual&#x27;, &#x27;BsmtCond&#x27;,\n",
              "       &#x27;BsmtExposure&#x27;, &#x27;BsmtFinType1&#x27;, &#x27;KitchenQual&#x27;, &#x27;FireplaceQu&#x27;, &#x27;Alley&#x27;,\n",
              "       &#x27;LotShape&#x27;, &#x27;LandContour&#x27;, &#x27;Utilities&#x27;, &#x27;LotConfig&#x27;, &#x27;LandSlope&#x27;,\n",
              "       &#x27;Neighborhood&#x27;, &#x27;Condition2&#x27;, &#x27;BldgType&#x27;, &#x27;HouseStyle&#x27;, &#x27;RoofStyle&#x27;,\n",
              "       &#x27;RoofMatl&#x27;, &#x27;Exterior1st&#x27;, &#x27;Exterior2nd&#x27;, &#x27;MasVnrType&#x27;, &#x27;BsmtFinType2&#x27;,\n",
              "       &#x27;HeatingQC&#x27;, &#x27;Electrical&#x27;, &#x27;Functional&#x27;, &#x27;GarageType&#x27;, &#x27;GarageFinish&#x27;,\n",
              "       &#x27;GarageQual&#x27;, &#x27;GarageCond&#x27;, &#x27;PavedDrive&#x27;, &#x27;PoolQC&#x27;, &#x27;Fence&#x27;,\n",
              "       &#x27;MiscFeature&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;],\n",
              "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=&#x27;NA&#x27;, strategy=&#x27;constant&#x27;)</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;cat_ordinal&#x27;,\n",
              "                                 OrdinalEncoder(categories=[[&#x27;NA&#x27;, &#x27;Pave&#x27;,\n",
              "                                                             &#x27;Grvl&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;N&#x27;, &#x27;Y&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;No&#x27;, &#x27;Mn&#x27;,\n",
              "                                                             &#x27;Av&#x27;, &#x27;Gd&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;LwQ&#x27;,\n",
              "                                                             &#x27;Rec&#x27;, &#x27;BLQ&#x27;,\n",
              "                                                             &#x27;ALQ&#x27;, &#x27;GLQ&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;...\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;N&#x27;, &#x27;P&#x27;,\n",
              "                                                             &#x27;Y&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;,\n",
              "                                                             &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;MnWw&#x27;,\n",
              "                                                             &#x27;GdWo&#x27;, &#x27;MnPrv&#x27;,\n",
              "                                                             &#x27;GdPrv&#x27;]]),\n",
              "                                 array([ 3,  4,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 17, 19, 25, 29, 32,\n",
              "       33, 34, 35, 36, 37, 38, 39])),\n",
              "                                (&#x27;cat_onehot&#x27;,\n",
              "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
              "                                               sparse_output=False),\n",
              "                                 array([ 0,  1,  2,  5, 15, -1, 20, 21, 22, 23, 16, 18, 20, 21, 22, 23, 24,\n",
              "       26, 27, 28, -1, 31, 40, 41, 42]))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat_ordinal</label><div class=\"sk-toggleable__content\"><pre>[ 3  4  6  7  8  9 10 11 12 13 14 15 17 19 25 29 32 33 34 35 36 37 38 39]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;NA&#x27;, &#x27;Pave&#x27;, &#x27;Grvl&#x27;], [&#x27;NA&#x27;, &#x27;N&#x27;, &#x27;Y&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;No&#x27;, &#x27;Mn&#x27;, &#x27;Av&#x27;, &#x27;Gd&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;LwQ&#x27;, &#x27;Rec&#x27;, &#x27;BLQ&#x27;, &#x27;ALQ&#x27;, &#x27;GLQ&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Pave&#x27;, &#x27;Grvl&#x27;],\n",
              "                           [&#x27;NA...\n",
              "                            &#x27;Metal&#x27;, &#x27;Membran&#x27;, &#x27;CompShg&#x27;, &#x27;ClyTile&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;LwQ&#x27;, &#x27;Rec&#x27;, &#x27;BLQ&#x27;, &#x27;ALQ&#x27;, &#x27;GLQ&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Sal&#x27;, &#x27;Sev&#x27;, &#x27;Maj2&#x27;, &#x27;Maj1&#x27;, &#x27;Mod&#x27;, &#x27;Min2&#x27;,\n",
              "                            &#x27;Min1&#x27;, &#x27;Typ&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Detchd&#x27;, &#x27;CarPort&#x27;, &#x27;BuiltIn&#x27;, &#x27;Basment&#x27;,\n",
              "                            &#x27;Attchd&#x27;, &#x27;2Types&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;RFn&#x27;, &#x27;Fin&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;N&#x27;, &#x27;P&#x27;, &#x27;Y&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;MnWw&#x27;, &#x27;GdWo&#x27;, &#x27;MnPrv&#x27;, &#x27;GdPrv&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat_onehot</label><div class=\"sk-toggleable__content\"><pre>[ 0  1  2  5 15 -1 20 21 22 23 16 18 20 21 22 23 24 26 27 28 -1 31 40 41\n",
              " 42]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "search.fit(X_train, y_train)\n",
        "\n",
        "print(f\"The best average score in cross validation was {search.best_score_}\")\n",
        "print(f\"The best parameters are {search.best_params_}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yYdvpQEjIHv3",
        "outputId": "66a3f41e-f440-4fa3-c2bf-ec9919e89141"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 224 candidates, totalling 1120 fits\n",
            "The best average score in cross validation was 0.9349473606984336\n",
            "The best parameters are {'columntransformer__num_pipe__simpleimputer__strategy': 'most_frequent', 'decisiontreeclassifier__max_depth': 13, 'decisiontreeclassifier__min_samples_leaf': 4}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(search.predict(X_test), y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aSJAF4roKRND",
        "outputId": "39edebc4-f558-4149-e4f2-579a04a5b57c"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.934931506849315"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###  4.2 KNN"
      ],
      "metadata": {
        "id": "VPAX8ZpdKa5Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "knn_full_pipeline = make_pipeline(full_preprocessing,\n",
        "                                StandardScaler(),\n",
        "                              KNeighborsClassifier())\n",
        "\n",
        "knn_param_grid = {\n",
        "    \"columntransformer__num_pipe__simpleimputer__strategy\":[\"mean\", \"median\"],\n",
        "    \"kneighborsclassifier__n_neighbors\": range(2,50),\n",
        "    \"kneighborsclassifier__weights\": [\"uniform\", \"distance\"]\n",
        "}\n",
        "\n",
        "knn_search = GridSearchCV(knn_full_pipeline,\n",
        "                      knn_param_grid,\n",
        "                      cv=5,\n",
        "                      verbose=1)\n",
        "\n"
      ],
      "metadata": {
        "id": "EJAr6djBKe3S"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_search.fit(X_train, y_train)\n",
        "\n",
        "print(f\"The best average score in cross validation was {knn_search.best_score_}\")\n",
        "print(f\"The best parameters are {knn_search.best_params_}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGFCjWxrLRhU",
        "outputId": "d7873341-f0b3-4795-e760-e5cb0cd7015b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 192 candidates, totalling 960 fits\n",
            "The best average score in cross validation was 0.9203844319724148\n",
            "The best parameters are {'columntransformer__num_pipe__simpleimputer__strategy': 'mean', 'kneighborsclassifier__n_neighbors': 6, 'kneighborsclassifier__weights': 'distance'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(knn_search.predict(X_test), y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCQwy7SOMghY",
        "outputId": "13c1d9d2-a272-4acb-8edc-0064841fcad5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9212328767123288"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.3 Random Forest"
      ],
      "metadata": {
        "id": "4hk-qXmiMuqb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "\n",
        "rnf_full_pipeline = make_pipeline(full_preprocessing,\n",
        "                                StandardScaler(),\n",
        "                             RandomForestClassifier())\n",
        "\n",
        "rnf_param_grid = {\n",
        "    \"columntransformer__num_pipe__simpleimputer__strategy\":[\"mean\", \"median\"],\n",
        "    \"randomforestclassifier__n_estimators\": range(100,200, 50),\n",
        "    \"randomforestclassifier__criterion\": [\"gini\", \"entropy\"],\n",
        "    \"randomforestclassifier__max_depth\": range(1,12,2),\n",
        "    \"randomforestclassifier__min_samples_leaf\": range(1,5)\n",
        "}\n",
        "\n",
        "rnf_search = GridSearchCV(rnf_full_pipeline,\n",
        "                      rnf_param_grid,\n",
        "                      cv=5,\n",
        "                      verbose=1)\n"
      ],
      "metadata": {
        "id": "JyPMHzwcMuGo"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rnf_search.fit(X_train, y_train)\n",
        "\n",
        "print(f\"The best average score in cross validation was {rnf_search.best_score_}\")\n",
        "print(f\"The best parameters are {rnf_search.best_params_}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rT46mSB0M7wT",
        "outputId": "beaa62ee-bcdd-4523-9911-91a3532ec6ba"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 192 candidates, totalling 960 fits\n",
            "The best average score in cross validation was 0.952048714280474\n",
            "The best parameters are {'columntransformer__num_pipe__simpleimputer__strategy': 'median', 'randomforestclassifier__criterion': 'gini', 'randomforestclassifier__max_depth': 11, 'randomforestclassifier__min_samples_leaf': 2, 'randomforestclassifier__n_estimators': 150}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(rnf_search.predict(X_test), y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VoN3fA4AOmf1",
        "outputId": "3384c50f-7db4-4cb8-edd8-2f9bc53573cb"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9486301369863014"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a3p3yt0H0AOA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.4 XGBoost"
      ],
      "metadata": {
        "id": "nIMMWW_nPfWm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "\n",
        "# XGBoost pipeline\n",
        "xgb_full_pipeline = make_pipeline(full_preprocessing,\n",
        "                                  StandardScaler(),\n",
        "                                  XGBClassifier())\n",
        "\n",
        "# Parameter grid for XGBoost\n",
        "xgb_param_grid = {\n",
        "    \"columntransformer__num_pipe__simpleimputer__strategy\": [\"mean\", \"median\"],\n",
        "    \"xgbclassifier__n_estimators\": [100, 150, 200],\n",
        "    \"xgbclassifier__learning_rate\": [0.01, 0.1, 0.2],\n",
        "    \"xgbclassifier__max_depth\": [3, 5, 7],\n",
        "    \"xgbclassifier__min_child_weight\": [1, 3, 5]\n",
        "}\n",
        "\n",
        "# GridSearchCV for XGBoost\n",
        "xgb_search = GridSearchCV(xgb_full_pipeline,\n",
        "                          xgb_param_grid,\n",
        "                          cv=5,\n",
        "                          verbose=1)\n"
      ],
      "metadata": {
        "id": "fBJAmazoPhZZ"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_search.fit(X_train, y_train)\n",
        "\n",
        "print(f\"The best average score in cross validation was {xgb_search.best_score_}\")\n",
        "print(f\"The best parameters are {xgb_search.best_params_}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqhBzfI_SZde",
        "outputId": "5b0684d8-d845-469e-e639-c42b600eba72"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 162 candidates, totalling 810 fits\n",
            "The best average score in cross validation was 0.9503539855471186\n",
            "The best parameters are {'columntransformer__num_pipe__simpleimputer__strategy': 'median', 'xgbclassifier__learning_rate': 0.1, 'xgbclassifier__max_depth': 5, 'xgbclassifier__min_child_weight': 1, 'xgbclassifier__n_estimators': 100}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(xgb_search.predict(X_test), y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qFOu2ckgUE-b",
        "outputId": "1ce1a4df-0e07-4f6d-a309-dc770a76939c"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9623287671232876"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Train = [0.933 , 0.920 , 0.955, 0.950]\n",
        "Test = [0.935 , 0.921 , 0.949, 0.962]\n",
        "Scores = pd.DataFrame([Train, Test],\n",
        "                                  columns = [\"Decision Tree\" ,\"KNN\" , \"Random Forest\" , \"XGBoost\" ],\n",
        "                                  index = [\"Train\", \"Test\"])\n",
        "Scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "QkInFLTDJxKq",
        "outputId": "6ab60542-dada-469b-9b7d-67650a4a76c3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Decision Tree    KNN  Random Forest  XGBoost\n",
              "Train          0.933  0.920          0.955    0.950\n",
              "Test           0.935  0.921          0.949    0.962"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-71762864-22f0-4dbd-9a25-f698e4ada9dd\" 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>Decision Tree</th>\n",
              "      <th>KNN</th>\n",
              "      <th>Random Forest</th>\n",
              "      <th>XGBoost</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Train</th>\n",
              "      <td>0.933</td>\n",
              "      <td>0.920</td>\n",
              "      <td>0.955</td>\n",
              "      <td>0.950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Test</th>\n",
              "      <td>0.935</td>\n",
              "      <td>0.921</td>\n",
              "      <td>0.949</td>\n",
              "      <td>0.962</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71762864-22f0-4dbd-9a25-f698e4ada9dd')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-71762864-22f0-4dbd-9a25-f698e4ada9dd 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-71762864-22f0-4dbd-9a25-f698e4ada9dd');\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",
              "\n",
              "\n",
              "<div id=\"df-9cc71e9d-f99f-486b-9f35-b80459148d17\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9cc71e9d-f99f-486b-9f35-b80459148d17')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9cc71e9d-f99f-486b-9f35-b80459148d17 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Error Analysis"
      ],
      "metadata": {
        "id": "z9YTqHsTfngg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.1 Decision Tree"
      ],
      "metadata": {
        "id": "ZZ7So2dv8XUz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "ConfusionMatrixDisplay.from_estimator(search,\n",
        "                                      X_test,\n",
        "                                      y_test,\n",
        "                                      display_labels=[\"Not Expensive\", \"Expensive\"]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "A5cWRhGUfqNQ",
        "outputId": "6df98314-109f-4875-b343-78299a0adc66"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAGwCAYAAACAZ5AeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABHxUlEQVR4nO3dd3iUZdbH8d8kkEIqoaQIhA5BaaJCVjrBBFFBcBGNShQQWSIIgohKCyoWEAURV1gTWIOgUlxZF2XBAFJEQESlSC/SFIQQhJSZ+/0jL7OMAUyYJ4Qx38913dfFPG3OjBNzcs793GMzxhgBAADALV4lHQAAAMCfAUkVAACABUiqAAAALEBSBQAAYAGSKgAAAAuQVAEAAFiApAoAAMACZUo6AHgGh8OhQ4cOKSgoSDabraTDAQAUkTFGp0+fVlRUlLy8iqemcu7cOeXk5FhyLR8fH/n5+VlyrauFpAqFcujQIVWtWrWkwwAAuOnAgQOqUqWK5dc9d+6cakQH6sgxuyXXi4iI0J49ezwqsSKpQqEEBQVJkvZtrK7gQLrG+HO6u27Dkg4BKDZ5ytWX+tT5/3Or5eTk6Mgxu/ZtqK7gIPd+T2Sedii62V7l5OSQVOHP53zLLzjQy+0fFuBaVcZWtqRDAIrP/38pXXFP4QgMsikwyL3ncMgzp5mQVAEAAMvYjUN2N79V2G4c1gRzlZFUAQAAyzhk5JB7WZW755cU+jgAAAAWoFIFAAAs45BD7jbv3L9CySCpAgAAlrEbI7txr33n7vklhfYfAACABahUAQAAy5TmieokVQAAwDIOGdlLaVJF+w8AAMACVKoAAIBlaP8BAABYgLv/AAAA4BYqVQAAwDKO/x/uXsMTkVQBAADL2C24+8/d80sKSRUAALCM3eQPd6/hiZhTBQAAYAEqVQAAwDLMqQIAALCAQzbZZXP7Gp6I9h8AAIAFqFQBAADLOEz+cPcanoikCgAAWMZuQfvP3fNLCu0/AAAAC1CpAgAAlinNlSqSKgAAYBmHsclh3Lz7z83zSwrtPwAAAAtQqQIAAJah/QcAAGABu7xkd7MRZrcolquNpAoAAFjGWDCnyjCnCgAAoPSiUgUAACzDnCoAAAAL2I2X7MbNOVUe+jU1tP8AAAAsQKUKAABYxiGbHG7WbBzyzFIVSRUAALBMaZ5TRfsPAADAAlSqAACAZayZqE77DwAAlHL5c6rc/EJl2n8AAAClF5UqAABgGYcF3/3H3X8AAKDUY04VAACABRzyKrXrVDGnCgAAwAJUqgAAgGXsxia7cXPxTzfPLykkVQAAwDJ2Cyaq22n/AQAAlF5UqgAAgGUcxksON+/+c3D3HwAAKO1o/wEAAMAtVKoAAIBlHHL/7j2HNaFcdSRVAADAMtYs/umZjTTPjBoAAOAaQ6UKAABYxprv/vPMmg9JFQAAsIxDNjnk7pwqVlQHAAClXGmuVHlm1AAAANcYkioAAGCZ84t/ujuKYvz48br55psVFBSkypUrq2vXrtq+fbvLMefOndOAAQNUoUIFBQYGqnv37jp69KjLMfv371fnzp1Vrlw5Va5cWcOGDVNeXl6h4yCpAgAAlnEYmyWjKJYvX64BAwZo7dq1WrJkiXJzc3XbbbfpzJkzzmMGDx6sTz75RB9++KGWL1+uQ4cOqVu3bs79drtdnTt3Vk5OjlavXq2ZM2cqLS1No0aNKnQcNmM89At2cFVlZmYqJCREv/5YU8FB5OL4c4qPalLSIQDFJs/kKkMf69SpUwoODrb8+ud/T7zydSv5B7o3ZftsVp6eunnlFcf6888/q3Llylq+fLlat26tU6dOqVKlSpo9e7buueceSdK2bdsUExOjNWvWqEWLFvrPf/6jO+64Q4cOHVJ4eLgk6e2339bw4cP1888/y8fH5w+fl9+OAADAMg4LWn/nF//MzMx0GdnZ2YWK4dSpU5KksLAwSdKGDRuUm5uruLg45zH169dXtWrVtGbNGknSmjVr1LBhQ2dCJUnx8fHKzMzUDz/8UKjnJakCAACWcRgvS4YkVa1aVSEhIc4xfvz4P35+h0NPPPGEbr31Vt1www2SpCNHjsjHx0ehoaEux4aHh+vIkSPOYy5MqM7vP7+vMFhSAQAAXJMOHDjg0v7z9fX9w3MGDBig77//Xl9++WVxhnZRJFUAAMAydtlkd3PxzvPnBwcHF2lOVXJyshYtWqQVK1aoSpUqzu0RERHKycnRyZMnXapVR48eVUREhPOYdevWuVzv/N2B54/5I7T/AACAZaxs/xWWMUbJyclasGCBli1bpho1arjsb9asmcqWLaulS5c6t23fvl379+9XbGysJCk2Nlbfffedjh075jxmyZIlCg4OVoMGDQoVB5UqAADg0QYMGKDZs2fr448/VlBQkHMOVEhIiPz9/RUSEqLevXtryJAhCgsLU3BwsB5//HHFxsaqRYsWkqTbbrtNDRo00IMPPqhXXnlFR44c0XPPPacBAwYUqu0okVQBAAAL2SUL2n9FM23aNElS27ZtXbanpqYqKSlJkjRp0iR5eXmpe/fuys7OVnx8vN566y3nsd7e3lq0aJH69++v2NhYBQQEqFevXkpJSSl0HCRVAADAMlfSvrvYNYqiMEtu+vn5aerUqZo6deolj4mOjtann35apOe+EEkVAACwDF+oDAAAALdQqQIAAJYxssnh5pwq4+b5JYWkCgAAWIb2HwAAANxCpQoAAFjGYWxyGPfad+6eX1JIqgAAgGXs8pLdzUaYu+eXFM+MGgAA4BpDpQoAAFiG9h8AAIAFHPKSw81GmLvnlxTPjBoAAOAaQ6UKAABYxm5ssrvZvnP3/JJCUgUAACzDnCoAAAALGOMlh5srohtWVAcAACi9qFQBAADL2GWT3c0vRHb3/JJCUgUAACzjMO7PiXIYi4K5ymj/AQAAWIBKFXCVzJlSWas+DdWBnb7y8XOowU2/qfezh1S1drbzmDeeqqJvVgbp+NGy8i/nUMxNZ9T72UOqVud/x3yzMlAzX4nU3m1+8ivnUNxfT+jhpw/Lm59meIAHnjyiB5886rLtwE5f9Wldv4QigtUcFkxUd/f8ksL/hj1U27Zt1aRJE73++uslHQoKafOaQN2Z9IvqNvlN9jwp7aVIPXNfLU1fvk1+5RySpDqNzqp9t19V6bpcnf7VW+9NjNAz99XSzK+2yNtb2vWDn0Y+WFM9Bx7VsMn7dPxIWU0eXlUOu02Pjj5Uwq8QKJy92/z09L01nY/tds+cP4OLc8gmh5tzotw9v6SUaCqYlJQkm82ml156yWX7woULZbMV7Q2tXr16oRKM6tWry2azFRi/j+FaN3/+fI0bN66kw0ARvDh7t26794Sq1zunWtef05Ov79exn3y0Y7O/85jbHziuhi3OKKJqjuo0Oqteww/r50M+OnrAR5K0/F/lVSPmnB4YclTX1chRo9gz6vPcIX0ys6J+y/LMv+xQ+tjt0q8/l3WOzBP8fY8/hxL/JPv5+enll19Wv379VL58+avynCkpKerbt6/LtqCgoKvy3FYJCwsr6RDgpjOZ3pKkoFD7Rfef+81Ln88NU0S1bFWKypUk5ebYVNbX4XKcj59DOee8tGNzOTX+S1bxBg1Y4LoaOZq98QflZHtp64Zyend8pH7+yaekw4JFSvOK6iX+p21cXJwiIiI0fvz4yx43b948XX/99fL19VX16tU1ceJE5762bdtq3759Gjx4sLPydDlBQUGKiIhwGQEBAZLyE66oqCgdP37ceXznzp3Vrl07ORz5v8xsNpumTZumTp06yd/fXzVr1tRHH33k8hwHDhxQjx49FBoaqrCwMHXp0kV79+517k9KSlLXrl01YcIERUZGqkKFChowYIByc3Odx7z11luqU6eO/Pz8FB4ernvuucflNT/xxBOSpGeeeUbNmzcv8DobN26slJQU5+MZM2YoJiZGfn5+ql+/vt56663Lvk8oPg6H9Pbo63T9zVmqXv+cy75P0iqoS+2G6lK7kb5eFqzxc3aprE/+rTA3tTmtresD9MWCUNnt0i+Hyyp9UoQk6cTREv8bCfhD2zaW04QnqurZxJqa8vR1iqiWo4kLdso/4OJ/XMDznJ9T5e7wRCUetbe3t1588UVNmTJFBw8evOgxGzZsUI8ePdSzZ0999913GjNmjEaOHKm0tDRJ+a2wKlWqKCUlRYcPH9bhw4evOJ5nn31W1atXV58+fSRJU6dO1erVqzVz5kx5ef3v7Ro5cqS6d++ub7/9VomJierZs6e2bt0qScrNzVV8fLyCgoK0cuVKrVq1SoGBgUpISFBOTo7zGl988YV27dqlL774QjNnzlRaWprzNa1fv14DBw5USkqKtm/frsWLF6t169YXjTkxMVHr1q3Trl27nNt++OEHbd68Wffff78kKT09XaNGjdILL7ygrVu36sUXX9TIkSM1c+bMi14zOztbmZmZLgPWefOZKtq3zV8jpu0rsK99t1/11ufbNWH+DlWpma0X+lVXzrn8PxSatT2tPiMPafLTVXVH9cZ6pGV93dI+/7+NrcR/moE/tv6LYK1cFKo9W/21YXmwnnugpgKD7Wp918mSDg1w2zXxv+G7775bTZo00ejRoy+6/7XXXlOHDh00cuRI1a1bV0lJSUpOTtarr74qKb8V5u3t7VKBupzhw4crMDDQZaxcuVJSfpL33nvvaenSpXr66ac1bNgwTZ06VdWqVXO5xl//+lf16dNHdevW1bhx43TTTTdpypQpkqS5c+fK4XBoxowZatiwoWJiYpSamqr9+/crIyPDeY3y5cvrzTffVP369XXHHXeoc+fOWrp0qSRp//79CggI0B133KHo6Gg1bdpUAwcOvOjruf7669W4cWPNnj3buS09PV3NmzdX7dq1JUmjR4/WxIkT1a1bN9WoUUPdunXT4MGD9fe///2i1xw/frxCQkKco2rVqpd9T1F4bz5znb5aEqxXPtrpbOtdKCDYoetq5qhhizN6bvpeHdjpq1X/CXHu797vZ83f9p3e+/oHffj994pNOCVJiozOLnAt4Fp3JtNbB3f7Kqp6zh8fDI/gkM35/X9XPJio7p6XX35ZM2fOdFZ7LrR161bdeuutLttuvfVW7dixQ3Z70UvGw4YN06ZNm1zGTTfd5Nxfs2ZNTZgwQS+//LLuuusuZ7XnQrGxsQUen4/922+/1c6dOxUUFORM2sLCwnTu3DmXatL1118vb29v5+PIyEgdO3ZMktSxY0dFR0erZs2aevDBB5Wenq7ffvvtkq8pMTHRmVQZY/T+++8rMTFRknTmzBnt2rVLvXv3dkkkn3/+eZd4LjRixAidOnXKOQ4cOHDZ9xR/zJj8hGr14hC98uFORVT7418ixkgyNuXmuP6o2mxShYg8+fobfbGgvCpF5ah2w7PFFDlQfPzK2RUVnaMTx2hf/1mY/7/7z51hPDSpumY+xa1bt1Z8fLxGjBihpKSkYn2uihUrOis4l7JixQp5e3tr7969ysvLU5kyhX+rsrKy1KxZM6WnpxfYV6lSJee/y5Yt67LPZrM5520FBQVp48aNysjI0Oeff65Ro0ZpzJgx+vrrrxUaGlrguvfdd5+GDx+ujRs36uzZszpw4IDuvfdeZzySNH369AJzry5M6i7k6+srX1/fQr9m/LE3n6miLxaU15jU3fIPdDh/iQQE2eXrb3R4n4+W/ytUzdqcVkhYnn4+XFYfvBkuH3+Hbunwv/brh29V0k3tTsvmJa36NEQfTK2sZ9/ep0v8pwSuKX1HHdLaz4N17KCPKkTk6sGhR2R3SBkLrs6NSih+56tN7l7DE10zSZUkvfTSS2rSpInq1avnsj0mJkarVq1y2bZq1SrVrVvXmRT4+PhcUdXqYubOnav58+crIyNDPXr00Lhx4zR27FiXY9auXauHHnrI5XHTpk0lSTfeeKPmzp2rypUrKzg4+IrjKFOmjOLi4hQXF6fRo0crNDRUy5YtU7du3QocW6VKFbVp00bp6ek6e/asOnbsqMqVK0uSwsPDFRUVpd27dzurV7j6Fs2sKEka1r2Oy/YnJ+3XbfeekI+vQ99/FagF0ysp65S3QivmqWGLLE36eIdCK+Y5j//6i2C9PzlCuTk21WxwVmNS9+jm9qev6msBrlTFyFyNeGufgsrbdep4Gf3wdYCeuKOOTrGsAv4ErqlPccOGDZWYmKjJkye7bH/yySd18803a9y4cbr33nu1Zs0avfnmmy53r1WvXl0rVqxQz5495evrq4oVK17yeU6fPq0jR464bCtXrpyCg4N18OBB9e/fXy+//LJatmyp1NRU3XHHHerUqZNatGjhPP7DDz/UTTfdpJYtWyo9PV3r1q3TP/7xD0n5rbhXX31VXbp0UUpKiqpUqaJ9+/Zp/vz5euqpp1SlSpU/fC8WLVqk3bt3q3Xr1ipfvrw+/fRTORyOAgnnhRITEzV69Gjl5ORo0qRJLvvGjh2rgQMHKiQkRAkJCcrOztb69ev166+/asiQIX8YD9z32aFNl91fISJPz7+3+w+v88qHF2/ZAp5gfP/okg4Bxaw0r6h+zUWdkpLibIGdd+ONN+qDDz7QnDlzdMMNN2jUqFFKSUlxaROmpKRo7969qlWrlkuL7WJGjRqlyMhIl/HUU0/JGKOkpCTdcsstSk5OliTFx8erf//+euCBB5xtNCk/SZkzZ44aNWqkWbNm6f3331eDBg0k5SdoK1asULVq1dStWzfFxMSod+/eOnfuXKErV6GhoZo/f77at2+vmJgYvf3223r//fd1/fXXX/Kce+65R8ePH9dvv/2mrl27uuzr06ePZsyYodTUVDVs2FBt2rRRWlqaatSoUah4AAAoDLcnqVvQPiwpNmOMh34XdMmx2WxasGBBgcTlzywzM1MhISH69ceaCg665nJxwBLxUU1KOgSg2OSZXGXoY506dcqtqSmXcv73RJfPH1HZAPcWc809k6OPb3u32GItLtdU+w8AAHi20vzdfyRVAADAMtz9hyKhYwoAAH6PpAoAAFiGShUAAIAFSnNSxW1cAAAAFqBSBQAALFOaK1UkVQAAwDJG7i+J4Km3g5FUAQAAy5TmShVzqgAAACxApQoAAFimNFeqSKoAAIBlSnNSRfsPAADAAlSqAACAZUpzpYqkCgAAWMYYm4ybSZG755cU2n8AAAAWoFIFAAAs45DN7cU/3T2/pJBUAQAAy5TmOVW0/wAAACxApQoAAFimNE9UJ6kCAACWKc3tP5IqAABgmdJcqWJOFQAAgAWoVAEAAMsYC9p/nlqpIqkCAACWMZKMcf8anoj2HwAAgAWoVAEAAMs4ZJONFdUBAADcw91/AAAAcAuVKgAAYBmHscnG4p8AAADuMcaCu/889PY/2n8AAAAWoFIFAAAsU5onqpNUAQAAy5BUAQAAWKA0T1RnThUAAIAFqFQBAADLcPcfAACABfKTKpubo2jPuWLFCt15552KioqSzWbTwoULXfYnJSXJZrO5jISEBJdjTpw4ocTERAUHBys0NFS9e/dWVlZWkeIgqQIAAB7tzJkzaty4saZOnXrJYxISEnT48GHneP/99132JyYm6ocfftCSJUu0aNEirVixQo8++miR4qD9BwAALFMSd/916tRJnTp1uuwxvr6+ioiIuOi+rVu3avHixfr666910003SZKmTJmi22+/XRMmTFBUVFSh4qBSBQAALGMsGpKUmZnpMrKzs684royMDFWuXFn16tVT//79dfz4cee+NWvWKDQ01JlQSVJcXJy8vLz01VdfFfo5SKoAAMA1qWrVqgoJCXGO8ePHX9F1EhISNGvWLC1dulQvv/yyli9frk6dOslut0uSjhw5osqVK7ucU6ZMGYWFhenIkSOFfh7afwAAwDJWtv8OHDig4OBg53ZfX98rul7Pnj2d/27YsKEaNWqkWrVqKSMjQx06dHAr1gtRqQIAANaxsP8XHBzsMq40qfq9mjVrqmLFitq5c6ckKSIiQseOHXM5Ji8vTydOnLjkPKyLIakCAADWcXs5BZtUzCuqHzx4UMePH1dkZKQkKTY2VidPntSGDRucxyxbtkwOh0PNmzcv9HVp/wEAAI+WlZXlrDpJ0p49e7Rp0yaFhYUpLCxMY8eOVffu3RUREaFdu3bpqaeeUu3atRUfHy9JiomJUUJCgvr27au3335bubm5Sk5OVs+ePQt9559EpQoAAFjo/Irq7o6iWL9+vZo2baqmTZtKkoYMGaKmTZtq1KhR8vb21ubNm3XXXXepbt266t27t5o1a6aVK1e6tBPT09NVv359dejQQbfffrtatmypd955p0hxUKkCAACWKYl1qtq2bStzmUzss88++8NrhIWFafbs2UV63t+jUgUAAGABKlUAAMA6Vkw0L+aJ6sWFpAoAAFjmSuZEXewanoj2HwAAgAWoVAEAAOtc+OV97lzDA5FUAQAAy5TE3X/XikIlVf/6178KfcG77rrrioMBAADwVIVKqrp27Vqoi9lsNuc3PgMAgFLKQ9t37ipUUuVwOIo7DgAA8CdQmtt/bt39d+7cOaviAAAAfwbGouGBipxU2e12jRs3Ttddd50CAwO1e/duSdLIkSP1j3/8w/IAAQAAPEGRk6oXXnhBaWlpeuWVV+Tj4+PcfsMNN2jGjBmWBgcAADyNzaLheYqcVM2aNUvvvPOOEhMT5e3t7dzeuHFjbdu2zdLgAACAh6H9V3g//fSTateuXWC7w+FQbm6uJUEBAAB4miInVQ0aNNDKlSsLbP/oo4/UtGlTS4ICAAAeqhRXqoq8ovqoUaPUq1cv/fTTT3I4HJo/f762b9+uWbNmadGiRcURIwAA8BTGlj/cvYYHKnKlqkuXLvrkk0/03//+VwEBARo1apS2bt2qTz75RB07diyOGAEAAK55V/Tdf61atdKSJUusjgUAAHg4Y/KHu9fwRFf8hcrr16/X1q1bJeXPs2rWrJllQQEAAA9lxZyo0pJUHTx4UPfdd59WrVql0NBQSdLJkyf1l7/8RXPmzFGVKlWsjhEAAOCaV+Q5VX369FFubq62bt2qEydO6MSJE9q6dascDof69OlTHDECAABPcX6iurvDAxW5UrV8+XKtXr1a9erVc26rV6+epkyZolatWlkaHAAA8Cw2kz/cvYYnKnJSVbVq1Ysu8mm32xUVFWVJUAAAwEOV4jlVRW7/vfrqq3r88ce1fv1657b169dr0KBBmjBhgqXBAQAAeIpCVarKly8vm+1//c0zZ86oefPmKlMm//S8vDyVKVNGjzzyiLp27VosgQIAAA9Qihf/LFRS9frrrxdzGAAA4E+hFLf/CpVU9erVq7jjAAAA8GhXvPinJJ07d045OTku24KDg90KCAAAeLBSXKkq8kT1M2fOKDk5WZUrV1ZAQIDKly/vMgAAQClmLBoeqMhJ1VNPPaVly5Zp2rRp8vX11YwZMzR27FhFRUVp1qxZxREjAADANa/I7b9PPvlEs2bNUtu2bfXwww+rVatWql27tqKjo5Wenq7ExMTiiBMAAHiCUnz3X5ErVSdOnFDNmjUl5c+fOnHihCSpZcuWWrFihbXRAQAAj3J+RXV3hycqclJVs2ZN7dmzR5JUv359ffDBB5LyK1jnv2AZAACgtClyUvXwww/r22+/lSQ9/fTTmjp1qvz8/DR48GANGzbM8gABAIAHKcUT1Ys8p2rw4MHOf8fFxWnbtm3asGGDateurUaNGlkaHAAAgKdwa50qSYqOjlZ0dLQVsQAAAA9nk/tzojxzmnohk6rJkycX+oIDBw684mAAAAA8VaGSqkmTJhXqYjabjaTqT6570+YqY/Mp6TCAYuF9fZWSDgEoNsaeLW29Gk9UepdUKFRSdf5uPwAAgMvia2oAAADgDrcnqgMAADiV4koVSRUAALCMFSuil5oV1QEAAFAQlSoAAGCdUtz+u6JK1cqVK/XAAw8oNjZWP/30kyTpn//8p7788ktLgwMAAB6mFH9NTZGTqnnz5ik+Pl7+/v765ptvlJ2dLUk6deqUXnzxRcsDBAAA8ARFTqqef/55vf3225o+fbrKli3r3H7rrbdq48aNlgYHAAA8y/mJ6u4OT1TkOVXbt29X69atC2wPCQnRyZMnrYgJAAB4qlK8onqRK1URERHauXNnge1ffvmlatasaUlQAADAQzGnqvD69u2rQYMG6auvvpLNZtOhQ4eUnp6uoUOHqn///sURIwAAwDWvyO2/p59+Wg6HQx06dNBvv/2m1q1by9fXV0OHDtXjjz9eHDECAAAPUZoX/yxyUmWz2fTss89q2LBh2rlzp7KystSgQQMFBgYWR3wAAMCTlOJ1qq548U8fHx81aNDAylgAAAA8VpGTqnbt2slmu/Ss/GXLlrkVEAAA8GBWLIlQWipVTZo0cXmcm5urTZs26fvvv1evXr2sigsAAHgi2n+FN2nSpItuHzNmjLKystwOCAAAwBNd0Xf/XcwDDzygd99916rLAQAAT1SK16m64onqv7dmzRr5+flZdTkAAOCBWFKhCLp16+by2Bijw4cPa/369Ro5cqRlgQEAAHiSIidVISEhLo+9vLxUr149paSk6LbbbrMsMAAAAE9SpKTKbrfr4YcfVsOGDVW+fPniigkAAHiqUnz3X5Emqnt7e+u2227TyZMniykcAADgyc7PqXJ3eKIi3/13ww03aPfu3cURCwAAgMcqclL1/PPPa+jQoVq0aJEOHz6szMxMlwEAAEq5UricglSEOVUpKSl68skndfvtt0uS7rrrLpevqzHGyGazyW63Wx8lAADwDKV4TlWhk6qxY8fqscce0xdffFGc8QAAAHikQidVxuSnjW3atCm2YAAAgGdj8c9CurDdBwAAUEApbv8VaaJ63bp1FRYWdtkBAABwNa1YsUJ33nmnoqKiZLPZtHDhQpf9xhiNGjVKkZGR8vf3V1xcnHbs2OFyzIkTJ5SYmKjg4GCFhoaqd+/eysrKKlIcRapUjR07tsCK6gAAAOeVRPvvzJkzaty4sR555JECX6cnSa+88oomT56smTNnqkaNGho5cqTi4+O1ZcsW5/cWJyYm6vDhw1qyZIlyc3P18MMP69FHH9Xs2bMLHUeRkqqePXuqcuXKRTkFAACUJiXQ/uvUqZM6dep08UsZo9dff13PPfecunTpIkmaNWuWwsPDtXDhQvXs2VNbt27V4sWL9fXXX+umm26SJE2ZMkW33367JkyYoKioqELFUej2H/OpAADA1fT7tTCzs7OLfI09e/boyJEjiouLc24LCQlR8+bNtWbNGknSmjVrFBoa6kyoJCkuLk5eXl766quvCv1chU6qzt/9BwAAcEnuLvx5QaWratWqCgkJcY7x48cXOZwjR45IksLDw122h4eHO/cdOXKkQCeuTJkyCgsLcx5TGIVu/zkcjkJfFAAAlE5Wzqk6cOCAgoODndt9fX3du3AxK/LX1AAAAFyShZWq4OBgl3ElSVVERIQk6ejRoy7bjx496twXERGhY8eOuezPy8vTiRMnnMcUBkkVAAD406pRo4YiIiK0dOlS57bMzEx99dVXio2NlSTFxsbq5MmT2rBhg/OYZcuWyeFwqHnz5oV+riLd/QcAAHBZJXD3X1ZWlnbu3Ol8vGfPHm3atElhYWGqVq2annjiCT3//POqU6eOc0mFqKgode3aVZIUExOjhIQE9e3bV2+//bZyc3OVnJysnj17FvrOP4mkCgAAWKgk1qlav3692rVr53w8ZMgQSVKvXr2Ulpamp556SmfOnNGjjz6qkydPqmXLllq8eLFzjSpJSk9PV3Jysjp06CAvLy91795dkydPLlIcJFUAAMCjtW3b9rKrFNhsNqWkpCglJeWSx4SFhRVpoc+LIakCAADWKcXf/UdSBQAALFMS7b9rBXf/AQAAWIBKFQAAsA7tPwAAAAuU4qSK9h8AAIAFqFQBAADL2P5/uHsNT0RSBQAArFOK238kVQAAwDIsqQAAAAC3UKkCAADWof0HAABgEQ9NitxF+w8AAMACVKoAAIBlSvNEdZIqAABgnVI8p4r2HwAAgAWoVAEAAMvQ/gMAALAC7T8AAAC4g0oVAACwDO0/AAAAK5Ti9h9JFQAAsE4pTqqYUwUAAGABKlUAAMAyzKkCAACwAu0/AAAAuINKFQAAsIzNGNmMe6Umd88vKSRVAADAOrT/AAAA4A4qVQAAwDLc/QcAAGAF2n8AAABwB5UqAABgGdp/AAAAVijF7T+SKgAAYJnSXKliThUAAIAFqFQBAADr0P4DAACwhqe279xF+w8AAMACVKoAAIB1jMkf7l7DA5FUAQAAy3D3HwAAANxCpQoAAFiHu/8AAADcZ3PkD3ev4Ylo/wEAAFiAShVQgm64+ZTu6XNIta/PUoXwXKX0r6c1/63g3J/4+H616XxclSKzlZtr087vAzVzUjVt/zaoBKMGCuf2O3aq8527FB5+RpK0b1+I3n+vgdZ/HSlJemnCF2rU+GeXcz5dVFNvvnHTVY8VFqL9B08yZswYLVy4UJs2bSrpUOAmP3+Hdm8L0OcfVdbIt7YX2P/TXn+9lVJDRw74ycfXobsfPqQXUreod9yNOnWibAlEDBTeL7+UU+o/GunQT4GySepw216NHLtKj/fvqP37QiRJ//l3Tb0383rnOeey+bXk6bj7rxRJSkqSzWYrMBISEko6tEIbOnSoli5dWtJhwALrV5TXrEnVtHpJhYvuz/ikkjatDtWRA37av7Ocpo+vroAgu2rUO3OVIwWKbt3aKK1fF6lDPwXpp5+CNCu1oc6dLaP6Mcedx2Rne+vXX/2d4+xv/LHg8c6vU+Xu8ECl8k+ChIQEpaamumzz9fUtoWiKLjAwUIGBgSUdBq6yMmUd6nTvUWVlemv3toCSDgcoEi8vh1q2Pig/vzxt3fK/PyLatd+vdh326dcTflq3NkrvpzdQNtUqeKhSV6mS8hOoiIgIl1G+fHllZGTIx8dHK1eudB77yiuvqHLlyjp69KgkqW3btkpOTlZycrJCQkJUsWJFjRw5UuaCrDo7O1tDhw7Vddddp4CAADVv3lwZGRnO/WlpaQoNDdVnn32mmJgYBQYGKiEhQYcPH3Yek5GRoVtuuUUBAQEKDQ3Vrbfeqn379knKb/81adJEkvT555/Lz89PJ0+edHmNgwYNUvv27Z2Pv/zyS7Vq1Ur+/v6qWrWqBg4cqDNnLl3tyM7OVmZmpstAybil3QnN37RWH3+/Vl2TDuvZpAbK/JW/5uEZqlc/qXn/mq+PP52n5EEbNG7srTqwP7/1l7Gsml59ublGDG2rD+bEqH3cPg19+qsSjhjuOt/+c3d4olKZVF1K27Zt9cQTT+jBBx/UqVOn9M0332jkyJGaMWOGwsPDncfNnDlTZcqU0bp16/TGG2/otdde04wZM5z7k5OTtWbNGs2ZM0ebN2/WX//6VyUkJGjHjh3OY3777TdNmDBB//znP7VixQrt379fQ4cOlSTl5eWpa9euatOmjTZv3qw1a9bo0Ucflc1mKxBzhw4dFBoaqnnz5jm32e12zZ07V4mJiZKkXbt2KSEhQd27d9fmzZs1d+5cffnll0pOTr7kezF+/HiFhIQ4R9WqVa/8jYVbvl0bogF3NdaT9zbUhpWhGvHGjwoJyynpsIBCOXgwSMmPddTgxzvo009q6clh61S12ilJ0uJPa2nj+gjt3RuqjGXRmvjKLbq15U+KiMwq4ajhFmPR8EClMqlatGiRs4V2frz44ouSpOeff17ly5fXo48+qgceeEC9evXSXXfd5XJ+1apVNWnSJNWrV0+JiYl6/PHHNWnSJEnS/v37lZqaqg8//FCtWrVSrVq1NHToULVs2dKl5Zibm6u3335bN910k2688UYlJyc750llZmbq1KlTuuOOO1SrVi3FxMSoV69eqlatWoHX4u3trZ49e2r27NnObUuXLtXJkyfVvXt3SfkJUmJiop544gnVqVNHf/nLXzR58mTNmjVL586du+h7NGLECJ06dco5Dhw44MY7Dndkn/XW4f3+2rYpSK8/U1t2u03xfz1W0mEBhZKX563Dh4K0c0eY0t5tpN27Q9Tl7h0XPXbbtvy2YNR1JFXwTKWycd2uXTtNmzbNZVtYWJgkycfHR+np6WrUqJGio6OdydKFWrRo4VI1io2N1cSJE2W32/Xdd9/Jbrerbt26LudkZ2erQoX/zSMoV66catWq5XwcGRmpY8eOOWNJSkpSfHy8OnbsqLi4OPXo0UORkZEXfT2JiYlq0aKFDh06pKioKKWnp6tz584KDQ2VJH377bfavHmz0tPTnecYY+RwOLRnzx7FxMQUuKavr69HzTMrTby8jMr6eOjKeCj1vGy65Oe3Vq2TkqQTx/2uYkSwWmm++69UJlUBAQGqXbv2JfevXr1aknTixAmdOHFCAQGFnxSclZUlb29vbdiwQd7e3i77LpxcXras65wYm83mMi8rNTVVAwcO1OLFizV37lw999xzWrJkiVq0aFHgOW+++WbVqlVLc+bMUf/+/bVgwQKlpaW5xNSvXz8NHDiwwLkXq37h6vErZ1dU9P+qheFVslUz5oxOnyyjzJNl1LP/QX21LEwnjpVVcPk83fnAEVUIz9HK/1QswaiBwkl6ZLPWfx2pY8fKqZx/rtq236+GjY9p5IjWiojMUrv2+/T1ukhlZvqqRs2TevSxTfpucyXt3RNa0qHDHVbcvcfdf38Ou3bt0uDBgzV9+nTNnTtXvXr10n//+195ef2vU/rVV64TKdeuXas6derI29tbTZs2ld1u17Fjx9SqVSu3YmnatKmaNm2qESNGKDY2VrNnz75oUiXlV6vS09NVpUoVeXl5qXPnzs59N954o7Zs2XLZRBIlo84NWXol/Qfn437P7pUkLZlfSVNG1lLVWmcVd/d2hYTlKvPXMvrxu0ANu+8G7d9ZroQiBgovJDRbTz71lcLCzunMmbLasydEI0e01jcbI1Sx0m9qcuMxdem2Q35+efr553JatbKK3p/doKTDBq5YqUyqsrOzdeTIEZdtZcqUUfny5fXAAw8oPj5eDz/8sBISEtSwYUNNnDhRw4YNcx67f/9+DRkyRP369dPGjRs1ZcoUTZw4UZJUt25dJSYm6qGHHtLEiRPVtGlT/fzzz1q6dKkaNWrkkuxcyp49e/TOO+/orrvuUlRUlLZv364dO3booYceuuQ5iYmJGjNmjF544QXdc889Lq274cOHq0WLFkpOTlafPn0UEBCgLVu2aMmSJXrzzTeL+vbBQt+tC1GnOn+55P7nB9S/itEA1nrjtZsvue+Xn8tp+JPtrmI0uFpo/5UyixcvLjA/qV69err//vu1b98+LVq0SFL+PKd33nlH9913n2677TY1btxYkvTQQw/p7NmzuuWWW+Tt7a1Bgwbp0UcfdV4rNTVVzz//vJ588kn99NNPqlixolq0aKE77rijUPGVK1dO27Zt08yZM3X8+HFFRkZqwIAB6tev3yXPqV27tm655RatW7dOr7/+usu+Ro0aafny5Xr22WfVqlUrGWNUq1Yt3XvvvYWKBwCAQivFX1NjM8ZDG5clpG3btmrSpEmBxOXPLjMzUyEhIWofcJ/K2HxKOhygWNiqVynpEIBik2fP1tKtE3Tq1CkFBwdbfv3zvydiE1JUpqx7Nxvk5Z7TmsWjii3W4lIqK1UAAKB40P4DAACwgsPkD3ev4YFIqorowq+bAQAAv1OK51SVyhXVAQAArEalCgAAWMYmC+ZUWRLJ1UdSBQAArFOKV1Sn/QcAAGABKlUAAMAyLKkAAABgBe7+AwAAgDtIqgAAgGVsxlgyimLMmDGy2Wwuo379/30h/blz5zRgwABVqFBBgYGB6t69u44ePWr1SyepAgAAFnJYNIro+uuv1+HDh53jyy+/dO4bPHiwPvnkE3344Ydavny5Dh06pG7dul35a7wE5lQBAACPV6ZMGUVERBTYfurUKf3jH//Q7Nmz1b59e0lSamqqYmJitHbtWrVo0cKyGKhUAQAAy1jZ/svMzHQZ2dnZl3zeHTt2KCoqSjVr1lRiYqL2798vSdqwYYNyc3MVFxfnPLZ+/fqqVq2a1qxZY+lrJ6kCAADWMRYNSVWrVlVISIhzjB8//qJP2bx5c6WlpWnx4sWaNm2a9uzZo1atWun06dM6cuSIfHx8FBoa6nJOeHi4jhw5YulLp/0HAACsY+GK6gcOHFBwcLBzs6+v70UP79Spk/PfjRo1UvPmzRUdHa0PPvhA/v7+7sVSBFSqAADANSk4ONhlXCqp+r3Q0FDVrVtXO3fuVEREhHJycnTy5EmXY44ePXrROVjuIKkCAACWOb+iurvDHVlZWdq1a5ciIyPVrFkzlS1bVkuXLnXu3759u/bv36/Y2Fg3X60r2n8AAMA6JfCFykOHDtWdd96p6OhoHTp0SKNHj5a3t7fuu+8+hYSEqHfv3hoyZIjCwsIUHBysxx9/XLGxsZbe+SeRVAEAAA938OBB3XfffTp+/LgqVaqkli1bau3atapUqZIkadKkSfLy8lL37t2VnZ2t+Ph4vfXWW5bHQVIFAAAsY3PkD3evURRz5sy57H4/Pz9NnTpVU6dOdSOqP0ZSBQAArFMC7b9rBRPVAQAALEClCgAAWOeCxTvduoYHIqkCAACWufBrZty5hiei/QcAAGABKlUAAMA6pXiiOkkVAACwjpHk5pIKzKkCAAClHnOqAAAA4BYqVQAAwDpGFsypsiSSq46kCgAAWKcUT1Sn/QcAAGABKlUAAMA6Dkk2C67hgUiqAACAZbj7DwAAAG6hUgUAAKxTiieqk1QBAADrlOKkivYfAACABahUAQAA65TiShVJFQAAsA5LKgAAALiPJRUAAADgFipVAADAOsypAgAAsIDDSDY3kyKHZyZVtP8AAAAsQKUKAABYh/YfAACAFSxIquSZSRXtPwAAAAtQqQIAANah/QcAAGABh5Hb7Tvu/gMAACi9qFQBAADrGEf+cPcaHoikCgAAWIc5VQAAABZgThUAAADcQaUKAABYh/YfAACABYwsSKosieSqo/0HAABgASpVAADAOrT/AAAALOBwSHJznSmHZ65TRfsPAADAAlSqAACAdWj/AQAAWKAUJ1W0/wAAACxApQoAAFinFH9NDUkVAACwjDEOGePe3Xvunl9SSKoAAIB1jHG/0sScKgAAgNKLShUAALCOsWBOlYdWqkiqAACAdRwOyebmnCgPnVNF+w8AAMACVKoAAIB1aP8BAAC4zzgcMm62/zx1SQXafwAAABagUgUAAKxD+w8AAMACDiPZSmdSRfsPAADAAlSqAACAdYyR5O46VZ5ZqSKpAgAAljEOI+Nm+8+QVAEAgFLPOOR+pYolFQAAAEotKlUAAMAytP8AAACsUIrbfyRVKJTzfzXkmdwSjgQoPjZ7dkmHABSbvP//fBd3FShPuW6v/Zknz/xdQ1KFQjl9+rQkacVvH5VwJEAx2lrSAQDF7/Tp0woJCbH8uj4+PoqIiNCXRz615HoRERHy8fGx5FpXi814auMSV5XD4dChQ4cUFBQkm81W0uH86WVmZqpq1ao6cOCAgoODSzocwHJ8xq8+Y4xOnz6tqKgoeXkVz31q586dU05OjiXX8vHxkZ+fnyXXulqoVKFQvLy8VKVKlZIOo9QJDg7mFw7+1PiMX13FUaG6kJ+fn8clQlZiSQUAAAALkFQBAABYgKQKuAb5+vpq9OjR8vX1LelQgGLBZxx/RkxUBwAAsACVKgAAAAuQVAEAAFiApAoAAMACJFUAXLRt21ZPPPFESYcBXNaYMWPUpEmTkg4DcEFShT+lpKQk2Ww2vfTSSy7bFy5cWOQV4atXr67XX3+9UMfZbLYC4/cxXOvmz5+vcePGlXQYKEbnfz5+PxISEko6tEIbOnSoli5dWtJhAC5YUR1/Wn5+fnr55ZfVr18/lS9f/qo8Z0pKivr27euyLSgo6Ko8t1XCwsJKOgRcBQkJCUpNTXXZ5knLGwQGBiowMLCkwwBcUKnCn1ZcXJwiIiI0fvz4yx43b948XX/99fL19VX16tU1ceJE5762bdtq3759Gjx4sPOv+csJCgpSRESEywgICJCUn3BFRUXp+PHjzuM7d+6sdu3ayeFwSJJsNpumTZumTp06yd/fXzVr1tRHH7l+ifWBAwfUo0cPhYaGKiwsTF26dNHevXud+5OSktS1a1dNmDBBkZGRqlChggYMGKDc3P996/tbb72lOnXqyM/PT+Hh4brnnntcXvP59t8zzzyj5s2bF3idjRs3VkpKivPxjBkzFBMTIz8/P9WvX19vvfXWZd8nlDxfX98Cn9Xy5csrIyNDPj4+WrlypfPYV155RZUrV9bRo0cl5X9GkpOTlZycrJCQEFWsWFEjR47UhSv0ZGdna+jQobruuusUEBCg5s2bKyMjw7k/LS1NoaGh+uyzzxQTE6PAwEAlJCTo8OHDzmMyMjJ0yy23KCAgQKGhobr11lu1b98+Sa7tv88//1x+fn46efKky2scNGiQ2rdv73z85ZdfqlWrVvL391fVqlU1cOBAnTlzxqq3FJAM8CfUq1cv06VLFzN//nzj5+dnDhw4YIwxZsGCBebCj/369euNl5eXSUlJMdu3bzepqanG39/fpKamGmOMOX78uKlSpYpJSUkxhw8fNocPH77kc0ZHR5tJkyZdcn9eXp6JjY01Xbt2NcYY8+abb5rQ0FCzb98+5zGSTIUKFcz06dPN9u3bzXPPPWe8vb3Nli1bjDHG5OTkmJiYGPPII4+YzZs3my1btpj777/f1KtXz2RnZztfe3BwsHnsscfM1q1bzSeffGLKlStn3nnnHWOMMV9//bXx9vY2s2fPNnv37jUbN240b7zxhjOGNm3amEGDBhljjPn++++NJLNz507n/vPbduzYYYwx5r333jORkZFm3rx5Zvfu3WbevHkmLCzMpKWlXfa/EUrO+Z+PSxk2bJiJjo42J0+eNBs3bjQ+Pj7m448/du5v06aNCQwMNIMGDTLbtm0z7733nstnzBhj+vTpY/7yl7+YFStWmJ07d5pXX33V+Pr6mh9//NEYY0xqaqopW7asiYuLM19//bXZsGGDiYmJMffff78xxpjc3FwTEhJihg4danbu3Gm2bNli0tLSnD8vo0ePNo0bNzbG5P9shYeHmxkzZjif//fbdu7caQICAsykSZPMjz/+aFatWmWaNm1qkpKSLHlPAWOMIanCn9KFvzRatGhhHnnkEWNMwaTq/vvvNx07dnQ5d9iwYaZBgwbOx3+ULF14nI+PjwkICHAZK1ascB6za9cuExQUZIYPH278/f1Nenq6yzUkmccee8xlW/PmzU3//v2NMcb885//NPXq1TMOh8O5Pzs72/j7+5vPPvvM+dqjo6NNXl6e85i//vWv5t577zXGGDNv3jwTHBxsMjMzL/o6LkyqjDGmcePGJiUlxfl4xIgRpnnz5s7HtWrVMrNnz3a5xrhx40xsbOyl3yyUqF69ehlvb+8Cn9UXXnjBGJP/mWrSpInp0aOHadCggenbt6/L+W3atDExMTEun8Phw4ebmJgYY4wx+/btM97e3uann35yOa9Dhw5mxIgRxpj8pOr3CfvUqVNNeHi4MSb/DxpJJiMj46Kv4cKkyhhjBg0aZNq3b+98/NlnnxlfX1/z66+/GmOM6d27t3n00UddrrFy5Urj5eVlzp49+4fvGVAYzKnCn97LL7+s9u3ba+jQoQX2bd26VV26dHHZduutt+r111+X3W6Xt7d3kZ5r2LBhSkpKctl23XXXOf9ds2ZNTZgwQf369dO9996r+++/v8A1YmNjCzzetGmTJOnbb7/Vzp07C8zTOnfunHbt2uV8fP3117vEHhkZqe+++06S1LFjR0VHR6tmzZpKSEhQQkKC7r77bpUrV+6irykxMVHvvvuus73z/vvva8iQIZKkM2fOaNeuXerdu7fLXLK8vDyFhIRc6m3CNaBdu3aaNm2ay7bz8+l8fHyUnp6uRo0aKTo6WpMmTSpwfosWLVza4bGxsZo4caLsdru+++472e121a1b1+Wc7OxsVahQwfm4XLlyqlWrlvNxZGSkjh075owlKSlJ8fHx6tixo+Li4tSjRw9FRkZe9PUkJiaqRYsWOnTokKKiopSenq7OnTsrNDRUUv7PzubNm5Wenu48xxgjh8OhPXv2KCYmpjBvG3BZJFX402vdurXi4+M1YsSIAgmP1SpWrKjatWtf9pgVK1bI29tbe/fuVV5ensqUKfyPYVZWlpo1a+byi+G8SpUqOf9dtmxZl302m805bysoKEgbN25URkaGPv/8c40aNUpjxozR119/7fwFdKH77rtPw4cP18aNG3X27FkdOHBA9957rzMeSZo+fXqBuVdFTUhxdQUEBFz2s7p69WpJ0okTJ3TixAnn3MDCyMrKkre3tzZs2FDgc3Dh5PKLfU7NBfOyUlNTNXDgQC1evFhz587Vc889pyVLlqhFixYFnvPmm29WrVq1NGfOHPXv318LFixQWlqaS0z9+vXTwIEDC5xbrVq1Qr824HJIqlAqvPTSS2rSpInq1avnsj0mJkarVq1y2bZq1SrVrVvX+cvAx8dHdrvdkjjmzp2r+fPnKyMjQz169NC4ceM0duxYl2PWrl2rhx56yOVx06ZNJUk33nij5s6dq8qVKys4OPiK4yhTpozi4uIUFxen0aNHKzQ0VMuWLVO3bt0KHFulShW1adNG6enpOnv2rDp27KjKlStLksLDwxUVFaXdu3crMTHxiuPBtWXXrl0aPHiwpk+frrlz56pXr17673//Ky+v/93b9NVXX7mcs3btWtWpU0fe3t5q2rSp7Ha7jh07platWrkVS9OmTdW0aVONGDFCsbGxmj179kWTKim/WpWenq4qVarIy8tLnTt3du678cYbtWXLlj/8owdwB3f/oVRo2LChEhMTNXnyZJftTz75pJYuXapx48bpxx9/1MyZM/Xmm2+6tAqrV6+uFStW6KefftIvv/xy2ec5ffq0jhw54jIyMzMlSQcPHlT//v318ssvq2XLlkpNTdWLL76otWvXulzjww8/1Lvvvqsff/xRo0eP1rp165ScnCwp/5dGxYoV1aVLF61cuVJ79uxRRkaGBg4cqIMHDxbqvVi0aJEmT56sTZs2ad++fZo1a5YcDkeBhPNCiYmJmjNnjj788MMCydPYsWM1fvx4TZ48WT/++KO+++47paam6rXXXitUPCgZ2dnZBT6rv/zyi+x2ux544AHFx8fr4YcfVmpqqjZv3uxyV6wk7d+/X0OGDNH27dv1/vvva8qUKRo0aJAkqW7dukpMTNRDDz2k+fPna8+ePVq3bp3Gjx+vf//734WKb8+ePRoxYoTWrFmjffv26fPPP9eOHTsu26ZLTEzUxo0b9cILL+iee+5xWSJi+PDhWr16tZKTk7Vp0ybt2LFDH3/8sfNnC7BECc/pAorFxe5u2rNnj/Hx8TG//9h/9NFHpkGDBqZs2bKmWrVq5tVXX3XZv2bNGtOoUSPj6+tb4NwLRUdHG0kFRr9+/YzD4TAdOnQw8fHxLpN7H3/8cVOrVi1z+vRpY0z+RPWpU6eajh07Gl9fX1O9enUzd+5cl+c5fPiweeihh0zFihWNr6+vqVmzpunbt685derUJV/7oEGDTJs2bYwx+ZNz27RpY8qXL2/8/f1No0aNXJ7j9xPVjTHm119/Nb6+vqZcuXLOWC+Unp5umjRpYnx8fEz58uVN69atzfz58y/5XqFk9erV66Kf1Xr16pmxY8eayMhI88svvziPnzdvnvHx8TGbNm0yxuR/Rv72t7+Zxx57zAQHB5vy5cubZ555xuWznZOTY0aNGmWqV69uypYtayIjI83dd99tNm/ebIzJn6geEhLiEteFN5IcOXLEdO3a1URGRhofHx8THR1tRo0aZex2uzGm4ET182655RYjySxbtqzAvnXr1pmOHTuawMBAExAQYBo1auScnA9YwWbMBQ1sACXKZrNpwYIF6tq1a0mHAlxS27Zt1aRJk0J90wBQmtD+AwAAsABJFQAAgAVo/wEAAFiAShUAAIAFSKoAAAAsQFIFAABgAZIqAAAAC5BUAQAAWICkCoDHSEpKclkYtW3btnriiSeuehwZGRmy2Ww6efLkJY+x2WxauHBhoa85ZswYNWnSxK249u7dK5vNpk2bNrl1HQBXhqQKgFuSkpJks9lks9nk4+Oj2rVrKyUlRXl5ecX+3PPnz9e4ceMKdWxhEiEAcEeZkg4AgOdLSEhQamqqsrOz9emnn2rAgAEqW7asRowYUeDYnJwc+fj4WPK8YWFhllwHAKxApQqA23x9fRUREaHo6Gj1799fcXFx+te//iXpfy27F154QVFRUapXr54k6cCBA+rRo4dCQ0MVFhamLl26aO/evc5r2u12DRkyRKGhoapQoYKeeuop/X6t4t+3/7KzszV8+HBVrVpVvr6+ql27tv7xj39o7969ateunSSpfPnystlsSkpKkiQ5HA6NHz9eNWrUkL+/vxo3bqyPPvrI5Xk+/fRT1a1bV/7+/mrXrp1LnIU1fPhw1a1bV+XKlVPNmjU1cuRI5ebmFjju73//u6pWrapy5cqpR48eOnXqlMv+GTNmKCYmRn5+fqpfv77eeuutIscCoHiQVAGwnL+/v3JycpyPly5dqu3bt2vJkiVatGiRcnNzFR8fr6CgIK1cuVKrVq1SYGCgEhISnOdNnDhRaWlpevfdd/Xll1/qxIkTWrBgwWWf96GHHtL777+vyZMna+vWrfr73/+uwMBAVa1aVfPmzZMkbd++XYcPH9Ybb7whSRo/frxmzZqlt99+Wz/88IMGDx6sBx54QMuXL5eUn/x169ZNd955pzZt2qQ+ffro6aefLvJ7EhQUpLS0NG3ZskVvvPGGpk+frkmTJrkcs3PnTn3wwQf65JNPtHjxYn3zzTf629/+5tyfnp6uUaNG6YUXXtDWrVv14osvauTIkZo5c2aR4wFQDAwAuKFXr16mS5cuxhhjHA6HWbJkifH19TVDhw517g8PDzfZ2dnOc/75z3+aevXqGYfD4dyWnZ1t/P39zWeffWaMMSYyMtK88sorzv25ubmmSpUqzucyxpg2bdqYQYMGGWOM2b59u5FklixZctE4v/jiCyPJ/Prrr85t586dM+XKlTOrV692ObZ3797mvvvuM8YYM2LECNOgQQOX/cOHDy9wrd+TZBYsWHDJ/a+++qpp1qyZ8/Ho0aONt7e3OXjwoHPbf/7zH+Pl5WUOHz5sjDGmVq1aZvbs2S7XGTdunImNjTXGGLNnzx4jyXzzzTeXfF4AxYc5VQDctmjRIgUGBio3N1cOh0P333+/xowZ49zfsGFDl3lU3377rXbu3KmgoCCX65w7d067du3SqVOndPjwYTVv3ty5r0yZMrrpppsKtADP27Rpk7y9vdWmTZtCx71z50799ttv6tixo8v2nJwcNW3aVJK0detWlzgkKTY2ttDPcd7cuXM1efJk7dq1S1lZWcrLy1NwcLDLMdWqVdN1113n8jwOh0Pbt29XUFCQdu3apd69e6tv377OY/Ly8hQSElLkeABYj6QKgNvatWunadOmycfHR1FRUSpTxvV/LQEBAS6Ps7Ky1KxZM6Wnpxe4VqVKla4oBn9//yKfk5WVJUn697//7ZLMSPnzxKyyZs0aJSYmauzYsYqPj1dISIjmzJmjiRMnFjnW6dOnF0jyvL29LYsVwJUjqQLgtoCAANWuXbvQx994442aO3euKleuXKBac15kZKS++uortW7dWlJ+RWbDhg268cYbL3p8w4YN5XA4tHz5csXFxRXYf75SZrfbndsaNGggX19f7d+//5IVrpiYGOek+/PWrl37xy/yAqtXr1Z0dLSeffZZ57Z9+/YVOG7//v06dOiQoqKinM/j5eWlevXqKTw8XFFRUdq9e7cSExOL9PwArg4mqgO46hITE1WxYkV16dJFK1eu1J49e5SRkaGBAwfq4MGDkqRBgwbppZde0sKFC7Vt2zb97W9/u+waU9WrV1evXr30yCOPaOHChc5rfvDBB5Kk6Oho2Ww2LVq0SD///LOysrIUFBSkoUOHavDgwZo5c6Z27dqljRs3asqUKc7J34899ph27NihYcOGafv27Zo9e7bS0tKK9Hrr1Kmj/fv3a86cOdq1a5cmT5580Un3fn5+6tWrl7799lutXLlSAwcOVI8ePRQRESFJGjt2rMaPH6/Jkyfrxx9/1HfffafU1FS99tprRYoHQPEgqQJw1ZUrV04rVqxQtWrV1K1bN8XExKh37946d+6cs3L15JNP6sEHH1SvXr0UGxuroKAg3X333Ze97rRp03TPPffob3/7m+rXr6++ffvqzJkzkqTrrrtOY8eO1dNPP63w8HAlJydLksaNG6eRI0dq/PjxiomJUUJCgv7973+rRo0akvLnOc2bN08LFy5U48aN9fbbb+vFF18s0uu96667NHjwYCUnJ6tJkyZavXq1Ro4cWeC42rVrq1u3brr99tt12223qVGjRi5LJvTp00czZsxQamqqGjZsqDZt2igtLc0ZK4CSZTOXmvUJAACAQqNSBQAAYAGSKgAAAAuQVAEAAFiApAoAAMACJFUAAAAWIKkCAACwAEkVAACABUiqAAAALEBSBQAAYAGSKgAAAAuQVAEAAFjg/wDWzFLyH7hOMAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.2 KNN"
      ],
      "metadata": {
        "id": "EZsOSxpE8abO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "ConfusionMatrixDisplay.from_estimator(knn_search,\n",
        "                                      X_test,\n",
        "                                      y_test,\n",
        "                                      display_labels=[\"Not Expensive\", \"Expensive\"]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Pg4hpiNq8jro",
        "outputId": "97b5ee10-8c53-410a-93b0-6caf55d76d1e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.3 Random Forest"
      ],
      "metadata": {
        "id": "9Z51NM4a85sJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "ConfusionMatrixDisplay.from_estimator(rnf_search,\n",
        "                                      X_test,\n",
        "                                      y_test,\n",
        "                                      display_labels=[\"Not Expensive\", \"Expensive\"]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "jAhgnA6M88GO",
        "outputId": "c10cc0aa-0311-4e71-de68-f52a20857fef"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.4 XGBoost"
      ],
      "metadata": {
        "id": "iom4Fp8J9LSP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "ConfusionMatrixDisplay.from_estimator(xgb_search,\n",
        "                                      X_test,\n",
        "                                      y_test,\n",
        "                                      display_labels=[\"Not Expensive\", \"Expensive\"]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "6XdJ-Brq9OO8",
        "outputId": "3e2e330d-5a91-48e0-b173-1fc5b85fc6ca"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Expensive = [35, 28, 33, 39]\n",
        "Not_Expensive = [239, 241, 244, 242]\n",
        "False_Positive = [5, 3, 0, 2]\n",
        "False_Negative = [13, 20, 15, 9]\n",
        "Test_Accuracy = [0.935 , 0.921 , 0.949, 0.962]\n",
        "\n",
        "Scores = pd.DataFrame([Expensive, Not_Expensive, False_Positive, False_Negative, Test_Accuracy],\n",
        "                      columns=[\"Decision Tree\", \"KNN\", \"Random Forest\", \"XGBoost\"],\n",
        "                      index=[\"Expensive\", \"Not Expensive\", \"False Positive\", \"False Negative\", \"Test Accuracy\"])\n",
        "\n",
        "\n",
        "Scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3FllQlr97Orq",
        "outputId": "3f9cb5ee-ea48-46d7-94c1-ea999a5be7c4"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Decision Tree      KNN  Random Forest  XGBoost\n",
              "Expensive              35.000   28.000         33.000   39.000\n",
              "Not Expensive         239.000  241.000        244.000  242.000\n",
              "False Positive          5.000    3.000          0.000    2.000\n",
              "False Negative         13.000   20.000         15.000    9.000\n",
              "Test Accuracy           0.935    0.921          0.949    0.962"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b37b12d2-d0b6-4e12-a046-c220e108b763\" 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>Decision Tree</th>\n",
              "      <th>KNN</th>\n",
              "      <th>Random Forest</th>\n",
              "      <th>XGBoost</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Expensive</th>\n",
              "      <td>35.000</td>\n",
              "      <td>28.000</td>\n",
              "      <td>33.000</td>\n",
              "      <td>39.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Not Expensive</th>\n",
              "      <td>239.000</td>\n",
              "      <td>241.000</td>\n",
              "      <td>244.000</td>\n",
              "      <td>242.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>False Positive</th>\n",
              "      <td>5.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>False Negative</th>\n",
              "      <td>13.000</td>\n",
              "      <td>20.000</td>\n",
              "      <td>15.000</td>\n",
              "      <td>9.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Test Accuracy</th>\n",
              "      <td>0.935</td>\n",
              "      <td>0.921</td>\n",
              "      <td>0.949</td>\n",
              "      <td>0.962</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b37b12d2-d0b6-4e12-a046-c220e108b763')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-b37b12d2-d0b6-4e12-a046-c220e108b763 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-b37b12d2-d0b6-4e12-a046-c220e108b763');\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",
              "\n",
              "\n",
              "<div id=\"df-2e7ba44e-8bef-4aef-a264-a4c286caa623\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2e7ba44e-8bef-4aef-a264-a4c286caa623')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2e7ba44e-8bef-4aef-a264-a4c286caa623 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5 Final Submission\n"
      ],
      "metadata": {
        "id": "bt77D10_lUFj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://drive.google.com/file/d/14gPDFgjp9Up3tQBetXVMhh0MQepvaj_c/view?usp=drive_link\"\n",
        "path = 'https://drive.google.com/uc?export=download&id='+url.split('/')[-2]\n",
        "new_data = pd.read_csv(path)"
      ],
      "metadata": {
        "id": "xOrasuSFlXBY"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id_col = new_data.pop('Id')"
      ],
      "metadata": {
        "id": "QCTk_LW5lf7q"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_search.best_estimator_.fit(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "ONFKild6mQCV",
        "outputId": "c025aac6-efc4-46c4-bac6-3d1a9739086d"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('columntransformer',\n",
              "                 ColumnTransformer(transformers=[('num_pipe',\n",
              "                                                  Pipeline(steps=[('simpleimputer',\n",
              "                                                                   SimpleImputer(strategy='median'))]),\n",
              "                                                  Index(['LotArea', 'LotFrontage', 'TotalBsmtSF', 'BedroomAbvGr', 'Fireplaces',\n",
              "       'PoolArea', 'GarageCars', 'WoodDeckSF', 'ScreenPorch', 'MSSubClass',\n",
              "       'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrA...\n",
              "                               feature_types=None, gamma=None, grow_policy=None,\n",
              "                               importance_type=None,\n",
              "                               interaction_constraints=None, learning_rate=0.1,\n",
              "                               max_bin=None, max_cat_threshold=None,\n",
              "                               max_cat_to_onehot=None, max_delta_step=None,\n",
              "                               max_depth=5, max_leaves=None, min_child_weight=1,\n",
              "                               missing=nan, monotone_constraints=None,\n",
              "                               multi_strategy=None, n_estimators=100,\n",
              "                               n_jobs=None, num_parallel_tree=None,\n",
              "                               random_state=None, ...))])"
            ],
            "text/html": [
              "<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;))]),\n",
              "                                                  Index([&#x27;LotArea&#x27;, &#x27;LotFrontage&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;Fireplaces&#x27;,\n",
              "       &#x27;PoolArea&#x27;, &#x27;GarageCars&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;ScreenPorch&#x27;, &#x27;MSSubClass&#x27;,\n",
              "       &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;, &#x27;MasVnrA...\n",
              "                               feature_types=None, gamma=None, grow_policy=None,\n",
              "                               importance_type=None,\n",
              "                               interaction_constraints=None, learning_rate=0.1,\n",
              "                               max_bin=None, max_cat_threshold=None,\n",
              "                               max_cat_to_onehot=None, max_delta_step=None,\n",
              "                               max_depth=5, max_leaves=None, min_child_weight=1,\n",
              "                               missing=nan, monotone_constraints=None,\n",
              "                               multi_strategy=None, n_estimators=100,\n",
              "                               n_jobs=None, num_parallel_tree=None,\n",
              "                               random_state=None, ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-189\" type=\"checkbox\" ><label for=\"sk-estimator-id-189\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;))]),\n",
              "                                                  Index([&#x27;LotArea&#x27;, &#x27;LotFrontage&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;Fireplaces&#x27;,\n",
              "       &#x27;PoolArea&#x27;, &#x27;GarageCars&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;ScreenPorch&#x27;, &#x27;MSSubClass&#x27;,\n",
              "       &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;, &#x27;MasVnrA...\n",
              "                               feature_types=None, gamma=None, grow_policy=None,\n",
              "                               importance_type=None,\n",
              "                               interaction_constraints=None, learning_rate=0.1,\n",
              "                               max_bin=None, max_cat_threshold=None,\n",
              "                               max_cat_to_onehot=None, max_delta_step=None,\n",
              "                               max_depth=5, max_leaves=None, min_child_weight=1,\n",
              "                               missing=nan, monotone_constraints=None,\n",
              "                               multi_strategy=None, n_estimators=100,\n",
              "                               n_jobs=None, num_parallel_tree=None,\n",
              "                               random_state=None, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-190\" type=\"checkbox\" ><label for=\"sk-estimator-id-190\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;median&#x27;))]),\n",
              "                                 Index([&#x27;LotArea&#x27;, &#x27;LotFrontage&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;Fireplaces&#x27;,\n",
              "       &#x27;PoolArea&#x27;, &#x27;GarageCars&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;ScreenPorch&#x27;, &#x27;MSSubClass&#x27;,\n",
              "       &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;, &#x27;MasVnrArea&#x27;,\n",
              "       &#x27;BsmtFinSF1&#x27;, &#x27;BsmtFinSF2&#x27;, &#x27;BsmtU...\n",
              "       &#x27;LotShape&#x27;, &#x27;LandContour&#x27;, &#x27;Utilities&#x27;, &#x27;LotConfig&#x27;, &#x27;LandSlope&#x27;,\n",
              "       &#x27;Neighborhood&#x27;, &#x27;Condition2&#x27;, &#x27;BldgType&#x27;, &#x27;HouseStyle&#x27;, &#x27;RoofStyle&#x27;,\n",
              "       &#x27;RoofMatl&#x27;, &#x27;Exterior1st&#x27;, &#x27;Exterior2nd&#x27;, &#x27;MasVnrType&#x27;, &#x27;BsmtFinType2&#x27;,\n",
              "       &#x27;HeatingQC&#x27;, &#x27;Electrical&#x27;, &#x27;Functional&#x27;, &#x27;GarageType&#x27;, &#x27;GarageFinish&#x27;,\n",
              "       &#x27;GarageQual&#x27;, &#x27;GarageCond&#x27;, &#x27;PavedDrive&#x27;, &#x27;PoolQC&#x27;, &#x27;Fence&#x27;,\n",
              "       &#x27;MiscFeature&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;],\n",
              "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-191\" type=\"checkbox\" ><label for=\"sk-estimator-id-191\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;LotArea&#x27;, &#x27;LotFrontage&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;Fireplaces&#x27;,\n",
              "       &#x27;PoolArea&#x27;, &#x27;GarageCars&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;ScreenPorch&#x27;, &#x27;MSSubClass&#x27;,\n",
              "       &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;, &#x27;MasVnrArea&#x27;,\n",
              "       &#x27;BsmtFinSF1&#x27;, &#x27;BsmtFinSF2&#x27;, &#x27;BsmtUnfSF&#x27;, &#x27;1stFlrSF&#x27;, &#x27;2ndFlrSF&#x27;,\n",
              "       &#x27;LowQualFinSF&#x27;, &#x27;GrLivArea&#x27;, &#x27;BsmtFullBath&#x27;, &#x27;BsmtHalfBath&#x27;, &#x27;FullBath&#x27;,\n",
              "       &#x27;HalfBath&#x27;, &#x27;KitchenAbvGr&#x27;, &#x27;TotRmsAbvGrd&#x27;, &#x27;GarageYrBlt&#x27;, &#x27;GarageArea&#x27;,\n",
              "       &#x27;OpenPorchSF&#x27;, &#x27;EnclosedPorch&#x27;, &#x27;3SsnPorch&#x27;, &#x27;MiscVal&#x27;, &#x27;MoSold&#x27;,\n",
              "       &#x27;YrSold&#x27;],\n",
              "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-192\" type=\"checkbox\" ><label for=\"sk-estimator-id-192\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-193\" type=\"checkbox\" ><label for=\"sk-estimator-id-193\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;MSZoning&#x27;, &#x27;Condition1&#x27;, &#x27;Heating&#x27;, &#x27;Street&#x27;, &#x27;CentralAir&#x27;,\n",
              "       &#x27;Foundation&#x27;, &#x27;ExterQual&#x27;, &#x27;ExterCond&#x27;, &#x27;BsmtQual&#x27;, &#x27;BsmtCond&#x27;,\n",
              "       &#x27;BsmtExposure&#x27;, &#x27;BsmtFinType1&#x27;, &#x27;KitchenQual&#x27;, &#x27;FireplaceQu&#x27;, &#x27;Alley&#x27;,\n",
              "       &#x27;LotShape&#x27;, &#x27;LandContour&#x27;, &#x27;Utilities&#x27;, &#x27;LotConfig&#x27;, &#x27;LandSlope&#x27;,\n",
              "       &#x27;Neighborhood&#x27;, &#x27;Condition2&#x27;, &#x27;BldgType&#x27;, &#x27;HouseStyle&#x27;, &#x27;RoofStyle&#x27;,\n",
              "       &#x27;RoofMatl&#x27;, &#x27;Exterior1st&#x27;, &#x27;Exterior2nd&#x27;, &#x27;MasVnrType&#x27;, &#x27;BsmtFinType2&#x27;,\n",
              "       &#x27;HeatingQC&#x27;, &#x27;Electrical&#x27;, &#x27;Functional&#x27;, &#x27;GarageType&#x27;, &#x27;GarageFinish&#x27;,\n",
              "       &#x27;GarageQual&#x27;, &#x27;GarageCond&#x27;, &#x27;PavedDrive&#x27;, &#x27;PoolQC&#x27;, &#x27;Fence&#x27;,\n",
              "       &#x27;MiscFeature&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;],\n",
              "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-194\" type=\"checkbox\" ><label for=\"sk-estimator-id-194\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=&#x27;NA&#x27;, strategy=&#x27;constant&#x27;)</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-195\" type=\"checkbox\" ><label for=\"sk-estimator-id-195\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;cat_ordinal&#x27;,\n",
              "                                 OrdinalEncoder(categories=[[&#x27;NA&#x27;, &#x27;Pave&#x27;,\n",
              "                                                             &#x27;Grvl&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;N&#x27;, &#x27;Y&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;No&#x27;, &#x27;Mn&#x27;,\n",
              "                                                             &#x27;Av&#x27;, &#x27;Gd&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;LwQ&#x27;,\n",
              "                                                             &#x27;Rec&#x27;, &#x27;BLQ&#x27;,\n",
              "                                                             &#x27;ALQ&#x27;, &#x27;GLQ&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;...\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;,\n",
              "                                                             &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;N&#x27;, &#x27;P&#x27;,\n",
              "                                                             &#x27;Y&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;,\n",
              "                                                             &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                                                            [&#x27;NA&#x27;, &#x27;MnWw&#x27;,\n",
              "                                                             &#x27;GdWo&#x27;, &#x27;MnPrv&#x27;,\n",
              "                                                             &#x27;GdPrv&#x27;]]),\n",
              "                                 array([ 3,  4,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 17, 19, 25, 29, 32,\n",
              "       33, 34, 35, 36, 37, 38, 39])),\n",
              "                                (&#x27;cat_onehot&#x27;,\n",
              "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
              "                                               sparse_output=False),\n",
              "                                 array([ 0,  1,  2,  5, 15, -1, 20, 21, 22, 23, 16, 18, 20, 21, 22, 23, 24,\n",
              "       26, 27, 28, -1, 31, 40, 41, 42]))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-196\" type=\"checkbox\" ><label for=\"sk-estimator-id-196\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat_ordinal</label><div class=\"sk-toggleable__content\"><pre>[ 3  4  6  7  8  9 10 11 12 13 14 15 17 19 25 29 32 33 34 35 36 37 38 39]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-197\" type=\"checkbox\" ><label for=\"sk-estimator-id-197\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;NA&#x27;, &#x27;Pave&#x27;, &#x27;Grvl&#x27;], [&#x27;NA&#x27;, &#x27;N&#x27;, &#x27;Y&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;No&#x27;, &#x27;Mn&#x27;, &#x27;Av&#x27;, &#x27;Gd&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;LwQ&#x27;, &#x27;Rec&#x27;, &#x27;BLQ&#x27;, &#x27;ALQ&#x27;, &#x27;GLQ&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Pave&#x27;, &#x27;Grvl&#x27;],\n",
              "                           [&#x27;NA...\n",
              "                            &#x27;Metal&#x27;, &#x27;Membran&#x27;, &#x27;CompShg&#x27;, &#x27;ClyTile&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;LwQ&#x27;, &#x27;Rec&#x27;, &#x27;BLQ&#x27;, &#x27;ALQ&#x27;, &#x27;GLQ&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Sal&#x27;, &#x27;Sev&#x27;, &#x27;Maj2&#x27;, &#x27;Maj1&#x27;, &#x27;Mod&#x27;, &#x27;Min2&#x27;,\n",
              "                            &#x27;Min1&#x27;, &#x27;Typ&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Detchd&#x27;, &#x27;CarPort&#x27;, &#x27;BuiltIn&#x27;, &#x27;Basment&#x27;,\n",
              "                            &#x27;Attchd&#x27;, &#x27;2Types&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;RFn&#x27;, &#x27;Fin&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;N&#x27;, &#x27;P&#x27;, &#x27;Y&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
              "                           [&#x27;NA&#x27;, &#x27;MnWw&#x27;, &#x27;GdWo&#x27;, &#x27;MnPrv&#x27;, &#x27;GdPrv&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-198\" type=\"checkbox\" ><label for=\"sk-estimator-id-198\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat_onehot</label><div class=\"sk-toggleable__content\"><pre>[ 0  1  2  5 15 -1 20 21 22 23 16 18 20 21 22 23 24 26 27 28 -1 31 40 41\n",
              " 42]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-199\" type=\"checkbox\" ><label for=\"sk-estimator-id-199\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-200\" type=\"checkbox\" ><label for=\"sk-estimator-id-200\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-201\" type=\"checkbox\" ><label for=\"sk-estimator-id-201\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "expensive = xgb_search.best_estimator_.predict(new_data)\n",
        "expensive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HDIBSOkxm6RD",
        "outputId": "980c9935-4374-416f-d980-8896eedb5ecf"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission_file = pd.DataFrame({\n",
        "    'Id':id_col,\n",
        "    'Expensive':expensive\n",
        "})"
      ],
      "metadata": {
        "id": "gMpz22A6nARV"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import files\n",
        "# submission_file.to_csv('submission.csv',index=False)\n",
        "# files.download('submission.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "IwGJxew1nDvu",
        "outputId": "33aba7eb-3b10-493d-b958-cd6afe117513"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_f72c8b08-cd90-4968-9711-42d0f4b9c2fe\", \"submission.csv\", 10226)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}