Pump_It_Up_Benchmark.ipynb

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  "metadata": {
    "colab": {
      "name": "Pump It Up - Benchmark",
      "provenance": [],
      "collapsed_sections": [
        "E9kBQ8IF2xL3",
        "rAT3Nl2suv3R",
        "EgNdUPvclpeh",
        "CdeLxc1XwxJ_",
        "1-RTAKVp-iSS"
      ],
      "mount_file_id": "1yYg0lnf8D5JJ2HGyxi44QFl3JogMpo7r",
      "authorship_tag": "ABX9TyM4+Z+KTTBErf6dClW/jTaJ",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "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/Yoshani/Pump-It-Up/blob/master/Pump_It_Up_Benchmark.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfi7MUgog3zA"
      },
      "source": [
        "!pip install catboost\n",
        "!pip install category_encoders"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_fZp6pPfIOW"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pandas_profiling\n",
        "from sklearn import preprocessing\n",
        "import datetime as dt\n",
        "from numpy import mean\n",
        "from numpy import std\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# plotting\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import pyplot\n",
        "import seaborn as sns\n",
        "\n",
        "# feature selection\n",
        "from sklearn.feature_selection import mutual_info_classif\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2\n",
        "from sklearn.feature_selection import f_classif\n",
        "\n",
        "# encoders\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "import category_encoders as ce\n",
        "\n",
        "# training\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# classifiers\n",
        "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, StackingClassifier, VotingClassifier, GradientBoostingClassifier\n",
        "from xgboost.sklearn import XGBClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "import lightgbm as lgb\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from catboost import CatBoostClassifier\n",
        "\n",
        "# hyperparameter tuning\n",
        "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gTk_KRku_ibH"
      },
      "source": [
        "def impute_median(series):\n",
        "    return series.fillna(series.median())\n",
        "\n",
        "def impute_mean(series):\n",
        "    return series.fillna(series.mean())\n",
        "\n",
        "def make_mi_scores(X, y):\n",
        "    X_mi = X.copy()\n",
        "    y_mi = y.copy()\n",
        "    for colname in X_mi.select_dtypes([\"object\", \"category\"]):\n",
        "        X_mi[colname], _ = X_mi[colname].factorize()\n",
        "    # All discrete features should now have integer dtypes\n",
        "    discrete_features = [pd.api.types.is_integer_dtype(t) for t in X_mi.dtypes]\n",
        "    mi_scores = mutual_info_classif(X_mi, y_mi.values.ravel(), discrete_features=discrete_features, random_state=0)\n",
        "    mi_scores = pd.Series(mi_scores, name=\"MI Scores\", index=X_mi.columns)\n",
        "    mi_scores = mi_scores.sort_values(ascending=False)\n",
        "    return mi_scores\n",
        "\n",
        "def plot_mi_scores(scores):\n",
        "    scores = scores.sort_values(ascending=True)\n",
        "    width = np.arange(len(scores))\n",
        "    ticks = list(scores.index)\n",
        "    plt.barh(width, scores)\n",
        "    plt.yticks(width, ticks)\n",
        "    plt.title(\"Mutual Information Scores\")\n",
        "    return\n",
        "\n",
        "def apply_pca(X, standardize=True):\n",
        "    # Standardize\n",
        "    if standardize:\n",
        "        X = (X - X.mean(axis=0)) / X.std(axis=0)\n",
        "    # Create principal components\n",
        "    pca = PCA()\n",
        "    X_pca = pca.fit_transform(X)\n",
        "    # Convert to dataframe\n",
        "    component_names = [f\"PC{i+1}\" for i in range(X_pca.shape[1])]\n",
        "    X_pca = pd.DataFrame(X_pca, columns=component_names)\n",
        "    # Create loadings\n",
        "    loadings = pd.DataFrame(\n",
        "        pca.components_.T,  # transpose the matrix of loadings\n",
        "        columns=component_names,  # so the columns are the principal components\n",
        "        index=X.columns,  # and the rows are the original features\n",
        "    )\n",
        "    return pca, X_pca, loadings\n",
        "\n",
        "def plot_variance(pca, width=8, dpi=100):\n",
        "    # Create figure\n",
        "    fig, axs = plt.subplots(1, 2)\n",
        "    n = pca.n_components_\n",
        "    grid = np.arange(1, n + 1)\n",
        "    # Explained variance\n",
        "    evr = pca.explained_variance_ratio_\n",
        "    axs[0].bar(grid, evr)\n",
        "    axs[0].set(\n",
        "        xlabel=\"Component\", title=\"% Explained Variance\", ylim=(0.0, 1.0)\n",
        "    )\n",
        "    # Cumulative Variance\n",
        "    cv = np.cumsum(evr)\n",
        "    axs[1].plot(np.r_[0, grid], np.r_[0, cv], \"o-\")\n",
        "    axs[1].set(\n",
        "        xlabel=\"Component\", title=\"% Cumulative Variance\", ylim=(0.0, 1.0)\n",
        "    )\n",
        "    # Set up figure\n",
        "    fig.set(figwidth=8, dpi=100)\n",
        "    return axs"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6JFdBsQDWkc"
      },
      "source": [
        "# Get datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0qjcDzpe8Bt"
      },
      "source": [
        "features_df = pd.read_csv(\n",
        "    \"/content/drive/MyDrive/PumpItUp/TrainingSet.csv\", \n",
        "    index_col=\"id\",\n",
        "    low_memory=False\n",
        ")\n",
        "labels_df = pd.read_csv(\n",
        "    \"/content/drive/MyDrive/PumpItUp/TrainingLabels.csv\", \n",
        "    index_col=\"id\"\n",
        ")\n",
        "test_df = pd.read_csv(\n",
        "    \"/content/drive/MyDrive/PumpItUp/TestSet.csv\", \n",
        "    index_col=\"id\",\n",
        "    low_memory=False\n",
        ")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "eYFFu_ZBfkh8",
        "outputId": "a2dff9f6-ba4b-4b10-f11b-c1e4622612fa"
      },
      "source": [
        "features_df.tail()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>amount_tsh</th>\n",
              "      <th>date_recorded</th>\n",
              "      <th>funder</th>\n",
              "      <th>gps_height</th>\n",
              "      <th>installer</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>wpt_name</th>\n",
              "      <th>num_private</th>\n",
              "      <th>basin</th>\n",
              "      <th>subvillage</th>\n",
              "      <th>region</th>\n",
              "      <th>region_code</th>\n",
              "      <th>district_code</th>\n",
              "      <th>lga</th>\n",
              "      <th>ward</th>\n",
              "      <th>population</th>\n",
              "      <th>public_meeting</th>\n",
              "      <th>recorded_by</th>\n",
              "      <th>scheme_management</th>\n",
              "      <th>scheme_name</th>\n",
              "      <th>permit</th>\n",
              "      <th>construction_year</th>\n",
              "      <th>extraction_type</th>\n",
              "      <th>extraction_type_group</th>\n",
              "      <th>extraction_type_class</th>\n",
              "      <th>management</th>\n",
              "      <th>management_group</th>\n",
              "      <th>payment</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>water_quality</th>\n",
              "      <th>quality_group</th>\n",
              "      <th>quantity</th>\n",
              "      <th>quantity_group</th>\n",
              "      <th>source</th>\n",
              "      <th>source_type</th>\n",
              "      <th>source_class</th>\n",
              "      <th>waterpoint_type</th>\n",
              "      <th>waterpoint_type_group</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>60739</th>\n",
              "      <td>10.0</td>\n",
              "      <td>2013-05-03</td>\n",
              "      <td>Germany Republi</td>\n",
              "      <td>1210</td>\n",
              "      <td>CES</td>\n",
              "      <td>37.169807</td>\n",
              "      <td>-3.253847</td>\n",
              "      <td>Area Three Namba 27</td>\n",
              "      <td>0</td>\n",
              "      <td>Pangani</td>\n",
              "      <td>Kiduruni</td>\n",
              "      <td>Kilimanjaro</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>Hai</td>\n",
              "      <td>Masama Magharibi</td>\n",
              "      <td>125</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>Water Board</td>\n",
              "      <td>Losaa Kia water supply</td>\n",
              "      <td>True</td>\n",
              "      <td>1999</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>water board</td>\n",
              "      <td>user-group</td>\n",
              "      <td>pay per bucket</td>\n",
              "      <td>per bucket</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>enough</td>\n",
              "      <td>enough</td>\n",
              "      <td>spring</td>\n",
              "      <td>spring</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>communal standpipe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27263</th>\n",
              "      <td>4700.0</td>\n",
              "      <td>2011-05-07</td>\n",
              "      <td>Cefa-njombe</td>\n",
              "      <td>1212</td>\n",
              "      <td>Cefa</td>\n",
              "      <td>35.249991</td>\n",
              "      <td>-9.070629</td>\n",
              "      <td>Kwa Yahona Kuvala</td>\n",
              "      <td>0</td>\n",
              "      <td>Rufiji</td>\n",
              "      <td>Igumbilo</td>\n",
              "      <td>Iringa</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>Njombe</td>\n",
              "      <td>Ikondo</td>\n",
              "      <td>56</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>Ikondo electrical water sch</td>\n",
              "      <td>True</td>\n",
              "      <td>1996</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>pay annually</td>\n",
              "      <td>annually</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>enough</td>\n",
              "      <td>enough</td>\n",
              "      <td>river</td>\n",
              "      <td>river/lake</td>\n",
              "      <td>surface</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>communal standpipe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37057</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2011-04-11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.017087</td>\n",
              "      <td>-8.750434</td>\n",
              "      <td>Mashine</td>\n",
              "      <td>0</td>\n",
              "      <td>Rufiji</td>\n",
              "      <td>Madungulu</td>\n",
              "      <td>Mbeya</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>Mbarali</td>\n",
              "      <td>Chimala</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>swn 80</td>\n",
              "      <td>swn 80</td>\n",
              "      <td>handpump</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>pay monthly</td>\n",
              "      <td>monthly</td>\n",
              "      <td>fluoride</td>\n",
              "      <td>fluoride</td>\n",
              "      <td>enough</td>\n",
              "      <td>enough</td>\n",
              "      <td>machine dbh</td>\n",
              "      <td>borehole</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>hand pump</td>\n",
              "      <td>hand pump</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31282</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2011-03-08</td>\n",
              "      <td>Malec</td>\n",
              "      <td>0</td>\n",
              "      <td>Musa</td>\n",
              "      <td>35.861315</td>\n",
              "      <td>-6.378573</td>\n",
              "      <td>Mshoro</td>\n",
              "      <td>0</td>\n",
              "      <td>Rufiji</td>\n",
              "      <td>Mwinyi</td>\n",
              "      <td>Dodoma</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Chamwino</td>\n",
              "      <td>Mvumi Makulu</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>nira/tanira</td>\n",
              "      <td>nira/tanira</td>\n",
              "      <td>handpump</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>never pay</td>\n",
              "      <td>never pay</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>insufficient</td>\n",
              "      <td>insufficient</td>\n",
              "      <td>shallow well</td>\n",
              "      <td>shallow well</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>hand pump</td>\n",
              "      <td>hand pump</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26348</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2011-03-23</td>\n",
              "      <td>World Bank</td>\n",
              "      <td>191</td>\n",
              "      <td>World</td>\n",
              "      <td>38.104048</td>\n",
              "      <td>-6.747464</td>\n",
              "      <td>Kwa Mzee Lugawa</td>\n",
              "      <td>0</td>\n",
              "      <td>Wami / Ruvu</td>\n",
              "      <td>Kikatanyemba</td>\n",
              "      <td>Morogoro</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>Morogoro Rural</td>\n",
              "      <td>Ngerengere</td>\n",
              "      <td>150</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>2002</td>\n",
              "      <td>nira/tanira</td>\n",
              "      <td>nira/tanira</td>\n",
              "      <td>handpump</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>pay when scheme fails</td>\n",
              "      <td>on failure</td>\n",
              "      <td>salty</td>\n",
              "      <td>salty</td>\n",
              "      <td>enough</td>\n",
              "      <td>enough</td>\n",
              "      <td>shallow well</td>\n",
              "      <td>shallow well</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>hand pump</td>\n",
              "      <td>hand pump</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       amount_tsh date_recorded  ...     waterpoint_type  waterpoint_type_group\n",
              "id                               ...                                           \n",
              "60739        10.0    2013-05-03  ...  communal standpipe     communal standpipe\n",
              "27263      4700.0    2011-05-07  ...  communal standpipe     communal standpipe\n",
              "37057         0.0    2011-04-11  ...           hand pump              hand pump\n",
              "31282         0.0    2011-03-08  ...           hand pump              hand pump\n",
              "26348         0.0    2011-03-23  ...           hand pump              hand pump\n",
              "\n",
              "[5 rows x 39 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LLqkGqgFqnlF"
      },
      "source": [
        "X = features_df.copy()\n",
        "y = labels_df.copy()\n",
        "X_test = test_df.copy()"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9kBQ8IF2xL3"
      },
      "source": [
        "# **Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D_QRHQV8chIA"
      },
      "source": [
        "Apply feature selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhi_Lz1g3pNB"
      },
      "source": [
        "# Combine train data with test data\n",
        "df=X.append(X_test)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6rMjLDEo21A"
      },
      "source": [
        "# Combined training dataset with labels\n",
        "combined = X.copy()\n",
        "combined['status_group'] = y['status_group']"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sUO8bPNi3GwR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7240ae6d-ecda-465d-e49d-c9ff4cfd65cc"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "amount_tsh               float64\n",
              "date_recorded             object\n",
              "funder                    object\n",
              "gps_height                 int64\n",
              "installer                 object\n",
              "longitude                float64\n",
              "latitude                 float64\n",
              "wpt_name                  object\n",
              "num_private                int64\n",
              "basin                     object\n",
              "subvillage                object\n",
              "region                    object\n",
              "region_code                int64\n",
              "district_code              int64\n",
              "lga                       object\n",
              "ward                      object\n",
              "population                 int64\n",
              "public_meeting            object\n",
              "recorded_by               object\n",
              "scheme_management         object\n",
              "scheme_name               object\n",
              "permit                    object\n",
              "construction_year          int64\n",
              "extraction_type           object\n",
              "extraction_type_group     object\n",
              "extraction_type_class     object\n",
              "management                object\n",
              "management_group          object\n",
              "payment                   object\n",
              "payment_type              object\n",
              "water_quality             object\n",
              "quality_group             object\n",
              "quantity                  object\n",
              "quantity_group            object\n",
              "source                    object\n",
              "source_type               object\n",
              "source_class              object\n",
              "waterpoint_type           object\n",
              "waterpoint_type_group     object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAqGIAFYp7uW"
      },
      "source": [
        "# Recorded_by column contains a constant value --> Drop it\n",
        "df.drop(['recorded_by'], axis=1, inplace=True)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvIV5XSPw8Qn"
      },
      "source": [
        "# Remove columns with high zero count\n",
        "df.drop(columns=['amount_tsh', 'num_private', 'scheme_name'], axis=1, inplace=True)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5vT31I7cZgv"
      },
      "source": [
        "# Some columns have another column expressing the same thing --> Drop them\n",
        "df.drop(columns=['extraction_type','extraction_type_group', 'payment_type', 'quantity_group', 'source_type', 'waterpoint_type_group', 'region'], axis=1, inplace=True)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vfuT5zl-fuH"
      },
      "source": [
        "# Drop some high cardinality columns\n",
        "df.drop(columns=['wpt_name', 'subvillage', 'ward'], axis=1, inplace=True)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7pYKf0h44jDx"
      },
      "source": [
        "# Handling missing values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PFpCqSQfdTKO"
      },
      "source": [
        "amount_tsh,\n",
        "gps_height,\n",
        "longitude,\n",
        "population,\n",
        "num_private,\n",
        "construction_year columns have zeros"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hEIZMGUJd7xL"
      },
      "source": [
        "We already dropped amount_tsh, num_private"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wfW82UyadjTN"
      },
      "source": [
        "Convert missing values with 0 to NaN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5iSTYB8dnmi"
      },
      "source": [
        "# Construction year\n",
        "df.loc[df['construction_year'] == 0,'construction_year'] = np.nan"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHK6T3K14lU3",
        "outputId": "42670240-0b8a-4a70-9e73-1909e6f979f4"
      },
      "source": [
        "# Number of missing values in each column\n",
        "missing_val_count_by_column = (df.isnull().sum())\n",
        "print(missing_val_count_by_column[missing_val_count_by_column > 0])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "funder                4504\n",
            "installer             4532\n",
            "public_meeting        4155\n",
            "scheme_management     4846\n",
            "permit                3793\n",
            "construction_year    25969\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CnLD-Y2DNX8K"
      },
      "source": [
        "1. Funder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWSNapyzcUG-"
      },
      "source": [
        "# Fill missing funder values with 'Unknown'\n",
        "df['funder'] = df['funder'].fillna(\"Unknown\")"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ou5b3e_zh6-5"
      },
      "source": [
        "2. Installer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "myj4RU1djnf7",
        "outputId": "e43f22f6-26ff-4867-ab17-cfbaa3c3ed52"
      },
      "source": [
        "item_counts = df['installer'].value_counts()\n",
        "item_counts = item_counts[:10,]\n",
        "plt.figure(figsize=(14,6))\n",
        "sns.barplot(x=item_counts.index, y=item_counts.values, alpha=0.8)\n",
        "plt.title('Installer counts comparison')\n",
        "plt.ylabel('Count', fontsize=13)\n",
        "plt.xlabel('Installer', fontsize=13)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uM-UUTy1uqpi"
      },
      "source": [
        "DWE is significantly frequent. Therefore let's fill the missing values with this value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ElNBMJ7iEKT",
        "outputId": "16c56477-de6b-4743-f16c-60d32d772730"
      },
      "source": [
        "# Replace NaN with most frequent value\n",
        "most_frequent_category=X['installer'].mode()[0]  # 'DWE' is the most frequent value\n",
        "print(most_frequent_category)\n",
        "\n",
        "df['installer'] = df['installer'].fillna(most_frequent_category)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DWE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_7v4RQsZ451O"
      },
      "source": [
        "3. Population"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1psDAT-k69BJ"
      },
      "source": [
        "Population values are related to region"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xzzz06depuI"
      },
      "source": [
        "# Fill missing values with median grouped by region code\n",
        "df['population'] = df['population'].fillna(df.groupby('region_code')['population'].transform('median'))"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uuZPJrufey1v"
      },
      "source": [
        "4. Public meeting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "lYaYot2ffGjJ",
        "outputId": "48308afa-2cb9-4ff3-a23d-a727c495155e"
      },
      "source": [
        "sns.countplot(x=\"public_meeting\", data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5158fce1d0>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vyf_srI2fq3j"
      },
      "source": [
        "Majority are public meeetings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQKvmm7FfuT-"
      },
      "source": [
        "# Fill missing values with true\n",
        "df['public_meeting'] = df['public_meeting'].fillna(True)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98Hl_dmKf79w"
      },
      "source": [
        "6. Scheme Management"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFYjjj0Rf7ak",
        "outputId": "7eed12ae-d43d-48f3-929c-d8f5ea0a2f4d"
      },
      "source": [
        "df['scheme_management'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['VWC', 'Other', nan, 'Private operator', 'WUG', 'Water Board',\n",
              "       'WUA', 'Water authority', 'Company', 'Parastatal', 'Trust', 'SWC',\n",
              "       'None'], dtype=object)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "tU3MI9SwgsXw",
        "outputId": "035759ee-7909-4f17-a9cb-3d42e7e2a462"
      },
      "source": [
        "plt.subplots(figsize=(15, 5))\n",
        "sns.countplot(x='scheme_management', data=df, palette=\"Set3\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f57c4b2f610>"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pcN578kBiYn9"
      },
      "source": [
        "VWC has a significant frequency"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbQEjpSTwDPF",
        "outputId": "31b9f3fc-4b75-4814-ff27-5c776037d518"
      },
      "source": [
        "# Fill missing scheme management values with mode\n",
        "most_frequent_category=X['scheme_management'].mode()[0]  # 'VWC' is the most frequent value\n",
        "print(most_frequent_category)\n",
        "\n",
        "df['scheme_management'] = df['scheme_management'].fillna(most_frequent_category)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VWC\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "--qPghb_jDla"
      },
      "source": [
        "7. Permit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "zYPBOrr4jHu5",
        "outputId": "a87d3a39-1590-497c-e465-4d4f92736630"
      },
      "source": [
        "sns.countplot(x='permit', data=df, palette=\"Set3\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5158ed44d0>"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLxdwXujjWwW",
        "outputId": "656bee33-f789-411f-b8d6-3806acde7445"
      },
      "source": [
        "df['permit'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True     48606\n",
              "False    21851\n",
              "Name: permit, dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qPE2VhP4jgVN"
      },
      "source": [
        "# Fill missing permit values with 'Unknown'\n",
        "df['permit'] = df['permit'].fillna(\"Unknown\")"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VlLvUdfvjFLw"
      },
      "source": [
        "8. Construction year"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W3dRyLjJuhUj",
        "outputId": "50158246-a0e6-4cff-e117-c6e40fdf3b24"
      },
      "source": [
        "df['construction_year'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1996.8709430210642"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKDHyaV-uz68"
      },
      "source": [
        "df['construction_year'].value_counts()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vT6ti0fvrTs"
      },
      "source": [
        "Majority of construction year values are missing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7VhoavO9-V-H"
      },
      "source": [
        "# Fill construction year with dummy value\n",
        "df['construction_year'] = df['construction_year'].fillna(1950)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YVDt73d4Kbpe"
      },
      "source": [
        "9. Longitude/latitude"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xxOs0qYiKf2j"
      },
      "source": [
        "df.loc[df['longitude'] == 0, ['longitude', 'latitude']] = np.nan\n",
        "df[\"longitude\"] = df.groupby(\"region_code\").transform(lambda x: x.fillna(x.mean())).longitude\n",
        "df[\"latitude\"] = df.groupby(\"region_code\").transform(lambda x: x.fillna(x.mean())).latitude\n",
        "\n",
        "df.loc[df['gps_height'] == 0, 'gps_height'] = np.nan\n",
        "df[\"gps_height\"] = df.groupby(\"region_code\").transform(lambda x: x.fillna(x.mean())).gps_height\n",
        "\n",
        "df=df.fillna(df.mean())"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_qFAfD318asg"
      },
      "source": [
        "# Feature Engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnER1pm6bXd5"
      },
      "source": [
        "1. New features with mathematical transforms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oje8xCJmc5OR"
      },
      "source": [
        "Calculate operation time using date recorded and construction year"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfY-7AFkbly4"
      },
      "source": [
        "df['operation_time']=df.date_recorded.apply(pd.to_datetime)-df.construction_year.apply(lambda x: pd.to_datetime(x,format='%Y'))\n",
        "df['operation_time']=df.operation_time.apply(lambda x: float(x.days)/ (365.25*24*60*60*1e9))\n",
        "df.loc[df['operation_time'] < 0,df.columns=='operation_time'] = 63.92060232717317"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JXPsFwmdC3x"
      },
      "source": [
        "Take log population to normalize the population"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCJoCcJVdCSg"
      },
      "source": [
        "df['logPopulation'] = df.population.apply(np.log1p)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Ujo2ynp1M0yp",
        "outputId": "96fe901f-8dc4-4f43-cdc7-b3d64ffce5fe"
      },
      "source": [
        "# Plot a comparison\n",
        "fig, axs = plt.subplots(1, 2, figsize=(8, 4))\n",
        "sns.kdeplot(df.population, shade=True, ax=axs[0])\n",
        "sns.kdeplot(df.logPopulation, shade=True, ax=axs[1]);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ib1qCRvfdJfb"
      },
      "source": [
        "df.drop(['population'], axis=1, inplace=True)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jCyoKPucGIPs"
      },
      "source": [
        "2. Feature splitting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c-VPKFNndP94"
      },
      "source": [
        "Extract year, month, day separately from date recorded"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "43elM2Z7Qps9"
      },
      "source": [
        "df['year']=pd.to_datetime(df.date_recorded).dt.year\n",
        "df['month']=pd.to_datetime(df.date_recorded).dt.month\n",
        "df['day']=pd.to_datetime(df.date_recorded).dt.day"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VncONVFCQra2"
      },
      "source": [
        "# Drop date recorded\n",
        "df.drop(['date_recorded'], axis=1, inplace=True)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MRmrs2Gbewp"
      },
      "source": [
        "3. Principal Component Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "ZgG3u8LvCSDP",
        "outputId": "85eb066b-1550-40d7-9a29-5eaa7714d4e1"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>funder</th>\n",
              "      <th>gps_height</th>\n",
              "      <th>installer</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>basin</th>\n",
              "      <th>region_code</th>\n",
              "      <th>district_code</th>\n",
              "      <th>lga</th>\n",
              "      <th>public_meeting</th>\n",
              "      <th>scheme_management</th>\n",
              "      <th>permit</th>\n",
              "      <th>construction_year</th>\n",
              "      <th>extraction_type_class</th>\n",
              "      <th>management</th>\n",
              "      <th>management_group</th>\n",
              "      <th>payment</th>\n",
              "      <th>water_quality</th>\n",
              "      <th>quality_group</th>\n",
              "      <th>quantity</th>\n",
              "      <th>source</th>\n",
              "      <th>source_class</th>\n",
              "      <th>waterpoint_type</th>\n",
              "      <th>operation_time</th>\n",
              "      <th>logPopulation</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>69572</th>\n",
              "      <td>Roman</td>\n",
              "      <td>1390.0</td>\n",
              "      <td>Roman</td>\n",
              "      <td>34.938093</td>\n",
              "      <td>-9.856322</td>\n",
              "      <td>Lake Nyasa</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>Ludewa</td>\n",
              "      <td>True</td>\n",
              "      <td>VWC</td>\n",
              "      <td>False</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>gravity</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>pay annually</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>enough</td>\n",
              "      <td>spring</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>1.411704e-13</td>\n",
              "      <td>4.700480</td>\n",
              "      <td>2011</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8776</th>\n",
              "      <td>Grumeti</td>\n",
              "      <td>1399.0</td>\n",
              "      <td>GRUMETI</td>\n",
              "      <td>34.698766</td>\n",
              "      <td>-2.147466</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "      <td>Serengeti</td>\n",
              "      <td>True</td>\n",
              "      <td>Other</td>\n",
              "      <td>True</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>gravity</td>\n",
              "      <td>wug</td>\n",
              "      <td>user-group</td>\n",
              "      <td>never pay</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>insufficient</td>\n",
              "      <td>rainwater harvesting</td>\n",
              "      <td>surface</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>3.675818e-14</td>\n",
              "      <td>5.638355</td>\n",
              "      <td>2013</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34310</th>\n",
              "      <td>Lottery Club</td>\n",
              "      <td>686.0</td>\n",
              "      <td>World vision</td>\n",
              "      <td>37.460664</td>\n",
              "      <td>-3.821329</td>\n",
              "      <td>Pangani</td>\n",
              "      <td>21</td>\n",
              "      <td>4</td>\n",
              "      <td>Simanjiro</td>\n",
              "      <td>True</td>\n",
              "      <td>VWC</td>\n",
              "      <td>True</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>gravity</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>pay per bucket</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>enough</td>\n",
              "      <td>dam</td>\n",
              "      <td>surface</td>\n",
              "      <td>communal standpipe multiple</td>\n",
              "      <td>4.803914e-14</td>\n",
              "      <td>5.525453</td>\n",
              "      <td>2013</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67743</th>\n",
              "      <td>Unicef</td>\n",
              "      <td>263.0</td>\n",
              "      <td>UNICEF</td>\n",
              "      <td>38.486161</td>\n",
              "      <td>-11.155298</td>\n",
              "      <td>Ruvuma / Southern Coast</td>\n",
              "      <td>90</td>\n",
              "      <td>63</td>\n",
              "      <td>Nanyumbu</td>\n",
              "      <td>True</td>\n",
              "      <td>VWC</td>\n",
              "      <td>True</td>\n",
              "      <td>1986.0</td>\n",
              "      <td>submersible</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>never pay</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>dry</td>\n",
              "      <td>machine dbh</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>communal standpipe multiple</td>\n",
              "      <td>3.133635e-13</td>\n",
              "      <td>4.077537</td>\n",
              "      <td>2013</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19728</th>\n",
              "      <td>Action In A</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Artisan</td>\n",
              "      <td>31.130847</td>\n",
              "      <td>-1.825359</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>Karagwe</td>\n",
              "      <td>True</td>\n",
              "      <td>VWC</td>\n",
              "      <td>True</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>gravity</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>never pay</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>seasonal</td>\n",
              "      <td>rainwater harvesting</td>\n",
              "      <td>surface</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>7.121264e-13</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             funder  gps_height     installer  ...  year  month day\n",
              "id                                             ...                 \n",
              "69572         Roman      1390.0         Roman  ...  2011      3  14\n",
              "8776        Grumeti      1399.0       GRUMETI  ...  2013      3   6\n",
              "34310  Lottery Club       686.0  World vision  ...  2013      2  25\n",
              "67743        Unicef       263.0        UNICEF  ...  2013      1  28\n",
              "19728   Action In A        26.0       Artisan  ...  2011      7  13\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "99aExvkAM8gr",
        "outputId": "b7566e95-67d6-4f42-ac1e-982cb2b7d895"
      },
      "source": [
        "pca_df = df[:len(features_df)]\n",
        "X_pca = pca_df.loc[:, ['longitude', 'latitude']]\n",
        "pca, X_pca, loadings = apply_pca(X_pca)\n",
        "loadings"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PC1</th>\n",
              "      <th>PC2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>longitude</th>\n",
              "      <td>0.707107</td>\n",
              "      <td>-0.707107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latitude</th>\n",
              "      <td>-0.707107</td>\n",
              "      <td>-0.707107</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                PC1       PC2\n",
              "longitude  0.707107 -0.707107\n",
              "latitude  -0.707107 -0.707107"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
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        "id": "jfvxNKV0RcCj",
        "outputId": "cc87b0d0-38c7-46ce-b8d1-d7da042233e5"
      },
      "source": [
        "plot_variance(pca)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f868e386f90>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f868d5d4f10>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x400 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ovzeFUKaUe1R",
        "outputId": "c98b174b-bd3c-4f22-c1cc-bbe8b7d22c23"
      },
      "source": [
        "mi_scores = make_mi_scores(X_pca, y)\n",
        "mi_scores"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PC1    0.079115\n",
              "PC2    0.052694\n",
              "Name: MI Scores, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WdkrD8wuY4Uq"
      },
      "source": [
        "PCA components for longitude and latitude are not very significant"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dpsc5PDp8oNG"
      },
      "source": [
        "# Encoding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yj-PPMWWRO_5"
      },
      "source": [
        "1. Binary encoding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FnTIDklCYwlQ"
      },
      "source": [
        "Suitable for high cardinality attributes. funder, installer and lga have high cardinality"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "IeQz3FuSeU9z",
        "outputId": "17cc42c6-7216-4c75-e841-7db59dd9d90b"
      },
      "source": [
        "enc = ce.BinaryEncoder(cols=['funder', 'installer', 'lga']).fit(df)\n",
        "df = enc.transform(df)\n",
        "df.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/category_encoders/utils.py:21: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
            "  elif pd.api.types.is_categorical(cols):\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>funder_0</th>\n",
              "      <th>funder_1</th>\n",
              "      <th>funder_2</th>\n",
              "      <th>funder_3</th>\n",
              "      <th>funder_4</th>\n",
              "      <th>funder_5</th>\n",
              "      <th>funder_6</th>\n",
              "      <th>funder_7</th>\n",
              "      <th>funder_8</th>\n",
              "      <th>funder_9</th>\n",
              "      <th>funder_10</th>\n",
              "      <th>funder_11</th>\n",
              "      <th>funder_12</th>\n",
              "      <th>gps_height</th>\n",
              "      <th>installer_0</th>\n",
              "      <th>installer_1</th>\n",
              "      <th>installer_2</th>\n",
              "      <th>installer_3</th>\n",
              "      <th>installer_4</th>\n",
              "      <th>installer_5</th>\n",
              "      <th>installer_6</th>\n",
              "      <th>installer_7</th>\n",
              "      <th>installer_8</th>\n",
              "      <th>installer_9</th>\n",
              "      <th>installer_10</th>\n",
              "      <th>installer_11</th>\n",
              "      <th>installer_12</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>basin</th>\n",
              "      <th>region_code</th>\n",
              "      <th>district_code</th>\n",
              "      <th>lga_0</th>\n",
              "      <th>lga_1</th>\n",
              "      <th>lga_2</th>\n",
              "      <th>lga_3</th>\n",
              "      <th>lga_4</th>\n",
              "      <th>lga_5</th>\n",
              "      <th>lga_6</th>\n",
              "      <th>lga_7</th>\n",
              "      <th>public_meeting</th>\n",
              "      <th>scheme_management</th>\n",
              "      <th>permit</th>\n",
              "      <th>construction_year</th>\n",
              "      <th>extraction_type_class</th>\n",
              "      <th>management</th>\n",
              "      <th>management_group</th>\n",
              "      <th>payment</th>\n",
              "      <th>water_quality</th>\n",
              "      <th>quality_group</th>\n",
              "      <th>quantity</th>\n",
              "      <th>source</th>\n",
              "      <th>source_class</th>\n",
              "      <th>waterpoint_type</th>\n",
              "      <th>operation_time</th>\n",
              "      <th>logPopulation</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>69572</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1390.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>34.938093</td>\n",
              "      <td>-9.856322</td>\n",
              "      <td>Lake Nyasa</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>VWC</td>\n",
              "      <td>False</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>gravity</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>pay annually</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>enough</td>\n",
              "      <td>spring</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>1.411704e-13</td>\n",
              "      <td>4.700480</td>\n",
              "      <td>2011</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8776</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1399.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>34.698766</td>\n",
              "      <td>-2.147466</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>Other</td>\n",
              "      <td>True</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>gravity</td>\n",
              "      <td>wug</td>\n",
              "      <td>user-group</td>\n",
              "      <td>never pay</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>insufficient</td>\n",
              "      <td>rainwater harvesting</td>\n",
              "      <td>surface</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>3.675818e-14</td>\n",
              "      <td>5.638355</td>\n",
              "      <td>2013</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34310</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>686.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>37.460664</td>\n",
              "      <td>-3.821329</td>\n",
              "      <td>Pangani</td>\n",
              "      <td>21</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>VWC</td>\n",
              "      <td>True</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>gravity</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>pay per bucket</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>enough</td>\n",
              "      <td>dam</td>\n",
              "      <td>surface</td>\n",
              "      <td>communal standpipe multiple</td>\n",
              "      <td>4.803914e-14</td>\n",
              "      <td>5.525453</td>\n",
              "      <td>2013</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67743</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38.486161</td>\n",
              "      <td>-11.155298</td>\n",
              "      <td>Ruvuma / Southern Coast</td>\n",
              "      <td>90</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>VWC</td>\n",
              "      <td>True</td>\n",
              "      <td>1986.0</td>\n",
              "      <td>submersible</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>never pay</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>dry</td>\n",
              "      <td>machine dbh</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>communal standpipe multiple</td>\n",
              "      <td>3.133635e-13</td>\n",
              "      <td>4.077537</td>\n",
              "      <td>2013</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19728</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>31.130847</td>\n",
              "      <td>-1.825359</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>VWC</td>\n",
              "      <td>True</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>gravity</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>never pay</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>seasonal</td>\n",
              "      <td>rainwater harvesting</td>\n",
              "      <td>surface</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>7.121264e-13</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       funder_0  funder_1  funder_2  funder_3  ...  logPopulation  year  month  day\n",
              "id                                             ...                                 \n",
              "69572         0         0         0         0  ...       4.700480  2011      3   14\n",
              "8776          0         0         0         0  ...       5.638355  2013      3    6\n",
              "34310         0         0         0         0  ...       5.525453  2013      2   25\n",
              "67743         0         0         0         0  ...       4.077537  2013      1   28\n",
              "19728         0         0         0         0  ...       0.000000  2011      7   13\n",
              "\n",
              "[5 rows x 59 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MsfrjL_DySoh"
      },
      "source": [
        "# binary encoding introduced some all zero columns\n",
        "df.drop(['funder_0', 'lga_0', 'installer_0'], axis=1, inplace=True)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xYL3HWrYuBne"
      },
      "source": [
        "2. Ordinal encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CIjeEUWzuE-E"
      },
      "source": [
        "permit_map = {\n",
        "    \"Unknown\": 0,\n",
        "    False: 1,\n",
        "    True: 2\n",
        "  }\n",
        "\n",
        "df['permit'] = df['permit'].map(permit_map)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UoXsdDPtvmy3"
      },
      "source": [
        "3. Label encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yMQxoEPM56S8"
      },
      "source": [
        "df['public_meeting'] = df['public_meeting'].astype(int)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RmpCQS_Dv7ie",
        "outputId": "4254bcb7-37bc-4cab-e89f-ea86ed13377d"
      },
      "source": [
        "df.select_dtypes([\"object\", \"category\"]).nunique()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "basin                     9\n",
              "scheme_management        12\n",
              "extraction_type_class     7\n",
              "management               12\n",
              "management_group          5\n",
              "payment                   7\n",
              "water_quality             8\n",
              "quality_group             6\n",
              "quantity                  5\n",
              "source                   10\n",
              "source_class              3\n",
              "waterpoint_type           7\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eBsynwb4v9wI"
      },
      "source": [
        "Label encode columns having >10 cardinality"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JVrTZk_GwJ8A"
      },
      "source": [
        "le_df = df.select_dtypes([\"object\", \"category\"])\n",
        "le_cols = le_df.loc[:, [col for col in le_df.columns if (le_df[col].nunique() > 10)]].columns.tolist()\n",
        "\n",
        "for col in le_cols:\n",
        "  df[col] = LabelEncoder().fit_transform(df[col])"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dqWDOWq7rhY_"
      },
      "source": [
        "4. One-Hot encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Av8yiBNE0Sg"
      },
      "source": [
        "one_hot_cols = df.select_dtypes([\"object\", \"category\"]).columns.tolist()\n",
        "\n",
        "OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
        "OH_cols = pd.DataFrame(OH_encoder.fit_transform(df[one_hot_cols]))\n",
        "OH_cols.index = df.index\n",
        "\n",
        "# give intuitive column names\n",
        "OH_cols.columns = OH_encoder.get_feature_names(one_hot_cols)\n",
        "\n",
        "num_df = df.drop(one_hot_cols, axis=1)\n",
        "df = pd.concat([num_df, OH_cols], axis=1)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "nR-W0ZG9ySng",
        "outputId": "7036509f-0c00-4558-bbbb-4c47d98438db"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\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>funder_1</th>\n",
              "      <th>funder_2</th>\n",
              "      <th>funder_3</th>\n",
              "      <th>funder_4</th>\n",
              "      <th>funder_5</th>\n",
              "      <th>funder_6</th>\n",
              "      <th>funder_7</th>\n",
              "      <th>funder_8</th>\n",
              "      <th>funder_9</th>\n",
              "      <th>funder_10</th>\n",
              "      <th>funder_11</th>\n",
              "      <th>funder_12</th>\n",
              "      <th>gps_height</th>\n",
              "      <th>installer_1</th>\n",
              "      <th>installer_2</th>\n",
              "      <th>installer_3</th>\n",
              "      <th>installer_4</th>\n",
              "      <th>installer_5</th>\n",
              "      <th>installer_6</th>\n",
              "      <th>installer_7</th>\n",
              "      <th>installer_8</th>\n",
              "      <th>installer_9</th>\n",
              "      <th>installer_10</th>\n",
              "      <th>installer_11</th>\n",
              "      <th>installer_12</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>region_code</th>\n",
              "      <th>district_code</th>\n",
              "      <th>lga_1</th>\n",
              "      <th>lga_2</th>\n",
              "      <th>lga_3</th>\n",
              "      <th>lga_4</th>\n",
              "      <th>lga_5</th>\n",
              "      <th>lga_6</th>\n",
              "      <th>lga_7</th>\n",
              "      <th>public_meeting</th>\n",
              "      <th>scheme_management</th>\n",
              "      <th>permit</th>\n",
              "      <th>construction_year</th>\n",
              "      <th>...</th>\n",
              "      <th>payment_unknown</th>\n",
              "      <th>water_quality_coloured</th>\n",
              "      <th>water_quality_fluoride</th>\n",
              "      <th>water_quality_fluoride abandoned</th>\n",
              "      <th>water_quality_milky</th>\n",
              "      <th>water_quality_salty</th>\n",
              "      <th>water_quality_salty abandoned</th>\n",
              "      <th>water_quality_soft</th>\n",
              "      <th>water_quality_unknown</th>\n",
              "      <th>quality_group_colored</th>\n",
              "      <th>quality_group_fluoride</th>\n",
              "      <th>quality_group_good</th>\n",
              "      <th>quality_group_milky</th>\n",
              "      <th>quality_group_salty</th>\n",
              "      <th>quality_group_unknown</th>\n",
              "      <th>quantity_dry</th>\n",
              "      <th>quantity_enough</th>\n",
              "      <th>quantity_insufficient</th>\n",
              "      <th>quantity_seasonal</th>\n",
              "      <th>quantity_unknown</th>\n",
              "      <th>source_dam</th>\n",
              "      <th>source_hand dtw</th>\n",
              "      <th>source_lake</th>\n",
              "      <th>source_machine dbh</th>\n",
              "      <th>source_other</th>\n",
              "      <th>source_rainwater harvesting</th>\n",
              "      <th>source_river</th>\n",
              "      <th>source_shallow well</th>\n",
              "      <th>source_spring</th>\n",
              "      <th>source_unknown</th>\n",
              "      <th>source_class_groundwater</th>\n",
              "      <th>source_class_surface</th>\n",
              "      <th>source_class_unknown</th>\n",
              "      <th>waterpoint_type_cattle trough</th>\n",
              "      <th>waterpoint_type_communal standpipe</th>\n",
              "      <th>waterpoint_type_communal standpipe multiple</th>\n",
              "      <th>waterpoint_type_dam</th>\n",
              "      <th>waterpoint_type_hand pump</th>\n",
              "      <th>waterpoint_type_improved spring</th>\n",
              "      <th>waterpoint_type_other</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>69572</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1390.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>34.938093</td>\n",
              "      <td>-9.856322</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8776</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1399.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>34.698766</td>\n",
              "      <td>-2.147466</td>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34310</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>686.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>37.460664</td>\n",
              "      <td>-3.821329</td>\n",
              "      <td>21</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67743</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38.486161</td>\n",
              "      <td>-11.155298</td>\n",
              "      <td>90</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>1986.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19728</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>31.130847</td>\n",
              "      <td>-1.825359</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 113 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       funder_1  ...  waterpoint_type_other\n",
              "id               ...                       \n",
              "69572         0  ...                    0.0\n",
              "8776          0  ...                    0.0\n",
              "34310         0  ...                    0.0\n",
              "67743         0  ...                    0.0\n",
              "19728         0  ...                    0.0\n",
              "\n",
              "[5 rows x 113 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oShc7rpNUa7g"
      },
      "source": [
        "# Normalization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CSaciIOtUmvX"
      },
      "source": [
        "Min max normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TekRmnHyUc-5"
      },
      "source": [
        "df=(df - df.min())/(df.max() - df.min())"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hKmY4mcUokV"
      },
      "source": [
        "Standard normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VjJSfwNvUqXU"
      },
      "source": [
        "df = (df - mean(df)) / std(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCVDy-i7whhA"
      },
      "source": [
        "# Separate training and test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "20LkdJiFwj70"
      },
      "source": [
        "X = df[:len(features_df)]\n",
        "X_test = df[len(features_df):]"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VIYVn75G9AIz"
      },
      "source": [
        "X_train, X_valid, y_train, y_valid = train_test_split(X, y.status_group, test_size=0.1, random_state=10)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "sbyjIAEGj02d",
        "outputId": "22394d57-7ac9-46ea-e1c7-0d43f0acf8bc"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>water_quality_fluoride</th>\n",
              "      <th>water_quality_fluoride abandoned</th>\n",
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              "      <th>source_spring</th>\n",
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              "      <th>source_class_unknown</th>\n",
              "      <th>waterpoint_type_cattle trough</th>\n",
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              "      <th>waterpoint_type_communal standpipe multiple</th>\n",
              "      <th>waterpoint_type_dam</th>\n",
              "      <th>waterpoint_type_hand pump</th>\n",
              "      <th>waterpoint_type_improved spring</th>\n",
              "      <th>waterpoint_type_other</th>\n",
              "    </tr>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.771010</td>\n",
              "      <td>0.681492</td>\n",
              "      <td>0.020408</td>\n",
              "      <td>0.0375</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.968254</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36293</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.449720</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.354400</td>\n",
              "      <td>0.813389</td>\n",
              "      <td>0.183673</td>\n",
              "      <td>0.0500</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 113 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       funder_1  ...  waterpoint_type_other\n",
              "id               ...                       \n",
              "32154       0.0  ...                    0.0\n",
              "6131        0.0  ...                    0.0\n",
              "34216       0.0  ...                    0.0\n",
              "2744        0.0  ...                    0.0\n",
              "36293       0.0  ...                    0.0\n",
              "\n",
              "[5 rows x 113 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsFsdfXVZnWp"
      },
      "source": [
        "PCA heatmap"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1E1gRUeXZpKo",
        "outputId": "8479c5a9-e893-46a6-c832-0c1f48ffca49"
      },
      "source": [
        "pca = PCA(n_components = 2)\n",
        "pca.fit(X_train)\n",
        "x_pca = pca.transform(X_train)\n",
        "  \n",
        "x_pca.shape"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(53460, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "qRV8r4TV64F-",
        "outputId": "0337e6f9-89b7-4855-f874-a49eb0a661ac"
      },
      "source": [
        "df_comp = pd.DataFrame(pca.components_, columns = X_train.columns)\n",
        "  \n",
        "plt.figure(figsize =(20, 6))\n",
        "  \n",
        "# plotting heatmap\n",
        "sns.heatmap(df_comp)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f868d64e1d0>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rAT3Nl2suv3R"
      },
      "source": [
        "# Hyperparameter Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuJHnqrDN4ft"
      },
      "source": [
        "def grid_search(model, params, X_tune, y_tune):\n",
        "  clf =  GridSearchCV(model, params, cv=5, scoring='accuracy')\n",
        "  clf.fit(X_tune, y_tune)\n",
        "  return clf.best_score_, clf.best_params_"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oywkj_-QLBo7"
      },
      "source": [
        "X_tune=X\n",
        "y_tune = y.status_group"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GPMPBv8zzlNY"
      },
      "source": [
        "model_params = {\n",
        "    'random_forest': {\n",
        "        'model': RandomForestClassifier(),\n",
        "        'params' : {\n",
        "            'n_estimators': [100, 500, 1000, 1100],\n",
        "            'max_depth': [6, 10, 12, 14, 20, 22]\n",
        "        }\n",
        "    },\n",
        "    'xgboost' : {\n",
        "        'model': XGBClassifier(),\n",
        "        'params': {\n",
        "            'max_depth': [10, 12, 14, 20],\n",
        "            'eta': [0.1, 0.2, 0.3, 0.4]\n",
        "        }\n",
        "    }\n",
        "}\n"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9yPRZSPswO-8"
      },
      "source": [
        "Grid Search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9trEWjmu0kq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3bd5d0b-15eb-4b99-9951-573f0386a84d"
      },
      "source": [
        "best_score, best_params = grid_search(model_params['random_forest']['model'], model_params['random_forest']['params'], X_tune, y_tune)\n",
        "print(best_score, best_params)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8133670033670034 {'max_depth': 20, 'n_estimators': 1000}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wo2ofF8CMBUi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00df160d-fe94-49c7-ee48-b9b36c35995b"
      },
      "source": [
        "best_score, best_params = grid_search(model_params['xgboost']['model'], model_params['xgboost']['params'], X_tune, y_tune)\n",
        "print(best_score, best_params)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8123400673400674 {'eta': 0.1, 'max_depth': 14}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l5bpz_l69Uxg"
      },
      "source": [
        "# **Model training**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Vi6J7EOvyY5"
      },
      "source": [
        "Define models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ahVrAeTQrapa"
      },
      "source": [
        "modelRFC = RandomForestClassifier(n_estimators=1300, min_samples_split=10, criterion='gini')\n",
        "\n",
        "modelETC = ExtraTreesClassifier(n_estimators=1000, min_samples_split=10)\n",
        "\n",
        "modelXGB = XGBClassifier(objective = 'multi:softmax', \n",
        "                      num_class = 3, maximize = False, eval_metric = 'merror', eta = 0.1,\n",
        "                      max_depth = 14, colsample_bytree = 0.4, num_boost_round=1000, nfold=4, early_stopping_rounds=10)\n",
        "\n",
        "modelGB = GradientBoostingClassifier(random_state=8, n_estimators=1000)\n",
        "\n",
        "modelLGB = lgb.LGBMClassifier(learning_rate=0.03, n_estimators=478, max_depth=5, min_samples_split=100, min_samples_leaf=100, random_state=27, gamma=0.1,\n",
        "                                                       subsample=0.8, reg_alpha = 0.001, reg_lambda=10, objective = 'multi:softmax', boosting_type='goss')\n",
        "\n",
        "modelSVM = SVC(gamma='auto', kernel='rbf')\n",
        "\n",
        "modelCAT = CatBoostClassifier(silent=True, max_ctr_complexity=5, task_type='CPU', iterations=10000,\n",
        "        eval_metric='AUC', od_type='Iter', od_wait=500, loss_function = 'MultiClass', random_seed=42)\n",
        "\n",
        "# neural network\n",
        "modelMLP = MLPClassifier(hidden_layer_sizes=(200,100,), learning_rate='invscaling', learning_rate_init=0.01,\n",
        "                          power_t=0.5, max_iter=1000, shuffle=True, tol=1e-4, activation='logistic', \n",
        "                          early_stopping=True, validation_fraction=0.1, n_iter_no_change=12, max_fun=15000,\n",
        "                         random_state=8, solver='adam',batch_size=1024)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bktl17K6rGz1"
      },
      "source": [
        "Single scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "odOmT2XO9XhE",
        "outputId": "398cb8aa-53ce-4e36-d06c-e92d78de27e7"
      },
      "source": [
        "modelRFC.fit(X_train, y_train)\n",
        "scoreRFC= modelRFC.score(X_valid,y_valid)\n",
        "scoreRFC"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8164983164983165"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOaAmZxgIWvH",
        "outputId": "0750a471-c3ff-46ca-c0ba-c280522755f5"
      },
      "source": [
        "modelETC.fit(X_train, y_train)\n",
        "scoreETC= modelETC.score(X_valid,y_valid)\n",
        "scoreETC"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8141414141414142"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsIB-bapnq2Y",
        "outputId": "3bc15d18-b659-44e0-ceff-522a25525cb4"
      },
      "source": [
        "modelXGB.fit(X_train, y_train)\n",
        "scoreXGB= modelXGB.score(X_valid,y_valid)\n",
        "scoreXGB"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8191919191919191"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aafa4xiNsx62",
        "outputId": "c05f988b-7e49-43e5-b636-7f12d288e7b5"
      },
      "source": [
        "modelSVM.fit(X_train, y_train)\n",
        "scoreSVM= modelSVM.score(X_valid,y_valid)\n",
        "scoreSVM"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7449494949494949"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaOQbn8Cv7WF",
        "outputId": "edaa02c4-5eac-4ec1-a1a7-fa59d34444a6"
      },
      "source": [
        "modelMLP.fit(X_train, y_train)\n",
        "scoreMLP = modelMLP.score(X_valid,y_valid)\n",
        "scoreMLP"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7850168350168351"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZprdXTx0v9k5",
        "outputId": "4e8265cb-d24e-4180-f6b7-188a43924041"
      },
      "source": [
        "modelCAT.fit(X_train, y_train)\n",
        "scoreCAT = modelCAT.score(X_valid,y_valid)\n",
        "scoreCAT"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8092592592592592"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSxSavS4xn2F",
        "outputId": "ed61f5c9-5e0c-40eb-c330-f803bdad4dde"
      },
      "source": [
        "modelLGB.fit(X_train, y_train)\n",
        "scoreLGB = modelLGB.score(X_valid,y_valid)\n",
        "scoreLGB"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7863636363636364"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "owB1dD4zyH0t",
        "outputId": "ba5610cc-31e2-4c3a-80a9-a389b347af8a"
      },
      "source": [
        "modelGB.fit(X_train, y_train)\n",
        "scoreGB = modelGB.score(X_valid,y_valid)\n",
        "scoreGB"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7764309764309765"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jKg-fJXGrKeK"
      },
      "source": [
        "Ensembling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BU3y-5BcrP7F"
      },
      "source": [
        "estimators_stacked = [\n",
        "              ('xgb', modelXGB),\n",
        "              ('rn', modelRFC),\n",
        "              # ('xt', modelETC),\n",
        "              # ('mlp', modelMLP),\n",
        "              # ('lgb', modelLGB),\n",
        "              #('svc', modelSVM),\n",
        "              #('cat', modelCAT),\n",
        "              #('gb', modelGB),\n",
        "             ]\n",
        "\n",
        "# Stacking Classifier\n",
        "stacked_estimator =  StackingClassifier(estimators=estimators_stacked,\n",
        "                                    final_estimator=LogisticRegression()  \n",
        "                                  )\n",
        "# Voting Classifier\n",
        "voting_estimator = VotingClassifier(estimators=estimators_stacked, \n",
        "                        voting='hard')"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I1MCN6G-eh3X",
        "outputId": "acb38662-91ff-4add-8230-6fdd24d5ad53"
      },
      "source": [
        "stacked_estimator.fit(X_train, y_train)\n",
        "scoreStack = stacked_estimator.score(X_valid,y_valid)\n",
        "scoreStack"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8202020202020202"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dkz5ePnVHtRh",
        "outputId": "63fd52aa-2a6f-4874-bb67-a0c6ff295b9b"
      },
      "source": [
        "voting_estimator.fit(X_train, y_train)\n",
        "scoreVoting = voting_estimator.score(X_valid,y_valid)\n",
        "scoreVoting"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8191919191919191"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EgNdUPvclpeh"
      },
      "source": [
        "# Train on entire dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HmU19tsaltBs"
      },
      "source": [
        "model=stacked_estimator\n",
        "X_train=X\n",
        "y_train = y.status_group\n",
        "\n",
        "model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CdeLxc1XwxJ_"
      },
      "source": [
        "# Predict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SEefkErrwyqT"
      },
      "source": [
        "predict=model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1-RTAKVp-iSS"
      },
      "source": [
        "# Submission"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FN8eZOuQBA0P"
      },
      "source": [
        "submission_df = pd.read_csv(\"/content/drive/MyDrive/PumpItUp/SubmissionFormat.csv\", \n",
        "                            index_col=\"id\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JS2Zm91_BQay"
      },
      "source": [
        "# Make sure we have the rows in the same order\n",
        "np.testing.assert_array_equal(test_df.index.values, \n",
        "                              submission_df.index.values)\n",
        "\n",
        "# Save predictions to submission data frame\n",
        "submission_df[\"status_group\"] = predict"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7FGyKBz-26A"
      },
      "source": [
        "submission_df['status_group'].unique()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pC6OLQR1-5-w"
      },
      "source": [
        "submission_df.to_csv('my_submission.csv', index=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "kDviNFhA6Y1_",
        "outputId": "fb359b91-2b45-4f8c-8646-a0e04fd9bfb0"
      },
      "source": [
        "from google.colab import files\n",
        "files.download('my_submission.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "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",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": [
              "download(\"download_7da47766-a9a8-4ee9-9cde-3a30e262bb84\", \"my_submission.csv\", 277866)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    }
  ]
}