decision-tree-model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ls2aRdQznH6f"
      },
      "source": [
        "# Project: Decision Trees\n",
        "\n",
        "This project will focus on creating and improving a model for the Tanzanian Water Pump dataset. The goal is to create a model to predict whether a water pump is functional, non-functional, or functional needs repair.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install category_encoders==2.*\n",
        "!pip install pandas_profiling==2.*\n",
        "\n",
        "from category_encoders import OrdinalEncoder\n",
        "from pandas_profiling import ProfileReport\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.tree import DecisionTreeClassifier, plot_tree\n"
      ],
      "metadata": {
        "id": "LsSj39xDnhmQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Downloading dataset\n",
        "\n",
        "# mounting your google drive on colab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "# work directory\n",
        "%cd /content/gdrive/My Drive/Kaggle/bloomtech-water-pump-challenge\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bh5KYj4osXci",
        "outputId": "63b2233c-cf40-47c7-bd51-198475cf9041"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n",
            "/content/gdrive/My Drive/Kaggle/bloomtech-water-pump-challenge\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# List all files in your Kaggle folder on your google drive.\n",
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MclPZA0Uus9q",
        "outputId": "0d58bdc2-d326-4939-d35f-62964ea9220f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "first_submission.csv  sample_submission.csv  train_features.csv\n",
            "new_submission.csv    test_features.csv      train_labels.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Td3KH9HnH6h"
      },
      "source": [
        "\n",
        "# I. Wrangle Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NArsa2gXnH6j"
      },
      "outputs": [],
      "source": [
        "def wrangle(fm_path, tv_path=None):\n",
        "    if tv_path:\n",
        "        df = pd.merge(pd.read_csv(fm_path, \n",
        "                                  na_values=[0, -2.000000e-08]),\n",
        "                      pd.read_csv(tv_path)).set_index('id')\n",
        "    else:\n",
        "        df = pd.read_csv(fm_path, \n",
        "                         na_values=[0, -2.000000e-08],\n",
        "                         index_col='id')\n",
        "\n",
        "    # Drop constant columns\n",
        "    df.drop(columns=['recorded_by'], inplace=True)\n",
        "\n",
        "    # Drop HCCCs\n",
        "    cutoff = 100\n",
        "    drop_cols = [col for col in df.select_dtypes('object').columns\n",
        "                 if df[col].nunique() > cutoff]\n",
        "    df.drop(columns=drop_cols, inplace=True)\n",
        "\n",
        "    # Drop duplicate columns\n",
        "    dupe_cols = [col for col in df.head(100).T.duplicated().index\n",
        "                 if df.head(100).T.duplicated()[col]]\n",
        "    df.drop(columns=dupe_cols, inplace=True) \n",
        "\n",
        "    # Drop columns with high proportion of zeros\n",
        "    df.drop(columns= 'num_private', inplace=True)            \n",
        "\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L-30ON9KnH6l"
      },
      "outputs": [],
      "source": [
        "df = wrangle('/content/gdrive/My Drive/Kaggle/bloomtech-water-pump-challenge/train_features.csv', '/content/gdrive/My Drive/Kaggle/bloomtech-water-pump-challenge/train_labels.csv')\n",
        "X_test = wrangle('/content/gdrive/My Drive/Kaggle/bloomtech-water-pump-challenge/test_features.csv')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check first few rows of df\n",
        "df.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "EkMI7BTruOno",
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            "text/plain": [
              "         amount_tsh  gps_height  longitude  latitude        basin    region  \\\n",
              "id                                                                            \n",
              "454.0          50.0      2092.0  35.426020 -4.227446     Internal   Manyara   \n",
              "510.0           NaN         NaN  35.510074 -5.724555     Internal    Dodoma   \n",
              "14146.0         NaN         NaN  32.499866 -9.081222   Lake Rukwa     Mbeya   \n",
              "47410.0         NaN         NaN  34.060484 -8.830208       Rufiji     Mbeya   \n",
              "1288.0        300.0      1023.0  37.032690 -6.040787  Wami / Ruvu  Morogoro   \n",
              "\n",
              "         region_code  district_code  population public_meeting  ...  \\\n",
              "id                                                              ...   \n",
              "454.0             21            1.0       160.0           True  ...   \n",
              "510.0              1            6.0         NaN           True  ...   \n",
              "14146.0           12            6.0         NaN           True  ...   \n",
              "47410.0           12            7.0         NaN           True  ...   \n",
              "1288.0             5            1.0       120.0           True  ...   \n",
              "\n",
              "        payment_type water_quality  quality_group      quantity        source  \\\n",
              "id                                                                              \n",
              "454.0     per bucket          soft           good  insufficient        spring   \n",
              "510.0      never pay          soft           good        enough  shallow well   \n",
              "14146.0    never pay          soft           good        enough  shallow well   \n",
              "47410.0      monthly          soft           good  insufficient         river   \n",
              "1288.0    on failure         salty          salty        enough  shallow well   \n",
              "\n",
              "          source_type source_class     waterpoint_type waterpoint_type_group  \\\n",
              "id                                                                             \n",
              "454.0          spring  groundwater  communal standpipe    communal standpipe   \n",
              "510.0    shallow well  groundwater           hand pump             hand pump   \n",
              "14146.0  shallow well  groundwater               other                 other   \n",
              "47410.0    river/lake      surface  communal standpipe    communal standpipe   \n",
              "1288.0   shallow well  groundwater               other                 other   \n",
              "\n",
              "           status_group  \n",
              "id                       \n",
              "454.0        functional  \n",
              "510.0        functional  \n",
              "14146.0  non functional  \n",
              "47410.0  non functional  \n",
              "1288.0   non functional  \n",
              "\n",
              "[5 rows x 29 columns]"
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              "      <th>454.0</th>\n",
              "      <td>50.0</td>\n",
              "      <td>2092.0</td>\n",
              "      <td>35.426020</td>\n",
              "      <td>-4.227446</td>\n",
              "      <td>Internal</td>\n",
              "      <td>Manyara</td>\n",
              "      <td>21</td>\n",
              "      <td>1.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>per bucket</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>insufficient</td>\n",
              "      <td>spring</td>\n",
              "      <td>spring</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>functional</td>\n",
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              "    <tr>\n",
              "      <th>510.0</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>35.510074</td>\n",
              "      <td>-5.724555</td>\n",
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              "      <td>1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>never pay</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</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",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14146.0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.499866</td>\n",
              "      <td>-9.081222</td>\n",
              "      <td>Lake Rukwa</td>\n",
              "      <td>Mbeya</td>\n",
              "      <td>12</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>never pay</td>\n",
              "      <td>soft</td>\n",
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              "      <th>47410.0</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>34.060484</td>\n",
              "      <td>-8.830208</td>\n",
              "      <td>Rufiji</td>\n",
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              "      <td>7.0</td>\n",
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              "      <td>monthly</td>\n",
              "      <td>soft</td>\n",
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              "      <td>insufficient</td>\n",
              "      <td>river</td>\n",
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              "      <td>communal standpipe</td>\n",
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              "      <th>1288.0</th>\n",
              "      <td>300.0</td>\n",
              "      <td>1023.0</td>\n",
              "      <td>37.032690</td>\n",
              "      <td>-6.040787</td>\n",
              "      <td>Wami / Ruvu</td>\n",
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              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>on failure</td>\n",
              "      <td>salty</td>\n",
              "      <td>salty</td>\n",
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              "      <td>shallow well</td>\n",
              "      <td>groundwater</td>\n",
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      "source": [
        "# Check first few rows of the features test set\n",
        "X_test.head()"
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            "text/plain": [
              "       amount_tsh  gps_height  longitude  latitude            basin  \\\n",
              "id                                                                    \n",
              "37098         NaN         NaN  31.985658 -3.596360  Lake Tanganyika   \n",
              "14530         NaN         NaN  32.832815 -4.944937  Lake Tanganyika   \n",
              "62607        10.0      1675.0  35.488289 -4.242048         Internal   \n",
              "46053         NaN         NaN  33.140828 -9.059386       Lake Rukwa   \n",
              "47083        50.0      1109.0  34.217077 -4.430529         Internal   \n",
              "\n",
              "          region  region_code  district_code  population public_meeting  ...  \\\n",
              "id                                                                       ...   \n",
              "37098  Shinyanga           17            5.0         NaN           True  ...   \n",
              "14530     Tabora           14            6.0         NaN           True  ...   \n",
              "62607    Manyara           21            1.0       148.0           True  ...   \n",
              "46053      Mbeya           12            6.0         NaN          False  ...   \n",
              "47083    Singida           13            1.0       235.0           True  ...   \n",
              "\n",
              "              payment payment_type  water_quality quality_group      quantity  \\\n",
              "id                                                                              \n",
              "37098         unknown      unknown           soft          good           dry   \n",
              "14530       never pay    never pay          milky         milky  insufficient   \n",
              "62607  pay per bucket   per bucket           soft          good  insufficient   \n",
              "46053       never pay    never pay           soft          good      seasonal   \n",
              "47083  pay per bucket   per bucket           soft          good        enough   \n",
              "\n",
              "             source   source_type source_class              waterpoint_type  \\\n",
              "id                                                                            \n",
              "37098  shallow well  shallow well  groundwater                        other   \n",
              "14530  shallow well  shallow well  groundwater                    hand pump   \n",
              "62607        spring        spring  groundwater           communal standpipe   \n",
              "46053  shallow well  shallow well  groundwater                    hand pump   \n",
              "47083   machine dbh      borehole  groundwater  communal standpipe multiple   \n",
              "\n",
              "      waterpoint_type_group  \n",
              "id                           \n",
              "37098                 other  \n",
              "14530             hand pump  \n",
              "62607    communal standpipe  \n",
              "46053             hand pump  \n",
              "47083    communal standpipe  \n",
              "\n",
              "[5 rows x 28 columns]"
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              "      <th></th>\n",
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              "      <th>id</th>\n",
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              "      <th>37098</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.985658</td>\n",
              "      <td>-3.596360</td>\n",
              "      <td>Lake Tanganyika</td>\n",
              "      <td>Shinyanga</td>\n",
              "      <td>17</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>unknown</td>\n",
              "      <td>unknown</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>dry</td>\n",
              "      <td>shallow well</td>\n",
              "      <td>shallow well</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14530</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.832815</td>\n",
              "      <td>-4.944937</td>\n",
              "      <td>Lake Tanganyika</td>\n",
              "      <td>Tabora</td>\n",
              "      <td>14</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>never pay</td>\n",
              "      <td>never pay</td>\n",
              "      <td>milky</td>\n",
              "      <td>milky</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>62607</th>\n",
              "      <td>10.0</td>\n",
              "      <td>1675.0</td>\n",
              "      <td>35.488289</td>\n",
              "      <td>-4.242048</td>\n",
              "      <td>Internal</td>\n",
              "      <td>Manyara</td>\n",
              "      <td>21</td>\n",
              "      <td>1.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>pay per bucket</td>\n",
              "      <td>per bucket</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>insufficient</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>46053</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.140828</td>\n",
              "      <td>-9.059386</td>\n",
              "      <td>Lake Rukwa</td>\n",
              "      <td>Mbeya</td>\n",
              "      <td>12</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>never pay</td>\n",
              "      <td>never pay</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>seasonal</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>47083</th>\n",
              "      <td>50.0</td>\n",
              "      <td>1109.0</td>\n",
              "      <td>34.217077</td>\n",
              "      <td>-4.430529</td>\n",
              "      <td>Internal</td>\n",
              "      <td>Singida</td>\n",
              "      <td>13</td>\n",
              "      <td>1.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
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              "      <td>per bucket</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>enough</td>\n",
              "      <td>machine dbh</td>\n",
              "      <td>borehole</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>communal standpipe multiple</td>\n",
              "      <td>communal standpipe</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 28 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check data types and missing values in the df\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vCuHEFdOtjZZ",
        "outputId": "937d2bc8-26a0-4ab7-d3ee-02523bdb41a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Float64Index: 47519 entries, 454.0 to 23812.0\n",
            "Data columns (total 29 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   amount_tsh             14189 non-null  float64\n",
            " 1   gps_height             31245 non-null  float64\n",
            " 2   longitude              46086 non-null  float64\n",
            " 3   latitude               46086 non-null  float64\n",
            " 4   basin                  47519 non-null  object \n",
            " 5   region                 47519 non-null  object \n",
            " 6   region_code            47519 non-null  int64  \n",
            " 7   district_code          47500 non-null  float64\n",
            " 8   population             30472 non-null  float64\n",
            " 9   public_meeting         44831 non-null  object \n",
            " 10  scheme_management      44417 non-null  object \n",
            " 11  permit                 45080 non-null  object \n",
            " 12  construction_year      31017 non-null  float64\n",
            " 13  extraction_type        47519 non-null  object \n",
            " 14  extraction_type_group  47519 non-null  object \n",
            " 15  extraction_type_class  47519 non-null  object \n",
            " 16  management             47519 non-null  object \n",
            " 17  management_group       47519 non-null  object \n",
            " 18  payment                47519 non-null  object \n",
            " 19  payment_type           47519 non-null  object \n",
            " 20  water_quality          47519 non-null  object \n",
            " 21  quality_group          47519 non-null  object \n",
            " 22  quantity               47519 non-null  object \n",
            " 23  source                 47519 non-null  object \n",
            " 24  source_type            47519 non-null  object \n",
            " 25  source_class           47519 non-null  object \n",
            " 26  waterpoint_type        47519 non-null  object \n",
            " 27  waterpoint_type_group  47519 non-null  object \n",
            " 28  status_group           47519 non-null  object \n",
            "dtypes: float64(7), int64(1), object(21)\n",
            "memory usage: 10.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VdH0pWNLnH6l"
      },
      "source": [
        "# II. Split Data\n",
        "\n",
        "Split DataFrame `df` into a feature matrix `X` and the target vector `y`. We want to predict `'status_group'`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UMpX_VtFnH6m"
      },
      "outputs": [],
      "source": [
        "target = 'status_group'\n",
        "X = df.drop(columns=target)\n",
        "y = df[target]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jjrzHvG8nH6n"
      },
      "outputs": [],
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size= .2, random_state= 42) "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sanity check \n",
        "len(X_train) + len(X_val) == len(X)\n",
        "len(y_train) + len(y_val) == len(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vQULxAsyB0f",
        "outputId": "2757433d-ba41-4281-9f3e-e63159a8929a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjRqQEpSnH6n"
      },
      "source": [
        "# III. Establish Baseline\n",
        "Since this is a **classification** problem, our baseline will be the accuracy score. We'll figure out what is the majority class in `y_train` and what percentage of the training observations it represents."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kM_AsyIGnH6n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fdfd8845-6694-4fc8-9223-bdabc46bf9d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Baseline Accuracy Score: 0.5425489938182296\n"
          ]
        }
      ],
      "source": [
        "baseline_acc = y_train.value_counts(normalize=True).max()\n",
        "print('Baseline Accuracy Score:', baseline_acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights:** If we predicted that all of the incoming new observations are functional, we would probably be accurate by around 54%."
      ],
      "metadata": {
        "id": "3QXYyGD94EO0"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qiln4vUnH6o"
      },
      "source": [
        "# IV. Build Model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RWDks_zMnH6o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00540cbb-f6e8-4bfd-a674-d88fe25c0185"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('ordinalencoder',\n",
              "                 OrdinalEncoder(cols=['basin', 'region', 'public_meeting',\n",
              "                                      'scheme_management', 'permit',\n",
              "                                      'extraction_type',\n",
              "                                      'extraction_type_group',\n",
              "                                      'extraction_type_class', 'management',\n",
              "                                      'management_group', 'payment',\n",
              "                                      'payment_type', 'water_quality',\n",
              "                                      'quality_group', 'quantity', 'source',\n",
              "                                      'source_type', 'source_class',\n",
              "                                      'waterpoint_type',\n",
              "                                      'waterpoin...\n",
              "communal standpipe             2\n",
              "communal standpipe multiple    3\n",
              "improved spring                4\n",
              "other                          5\n",
              "cattle trough                  6\n",
              "dam                            7\n",
              "NaN                           -2\n",
              "dtype: int64},\n",
              "                                         {'col': 'waterpoint_type_group',\n",
              "                                          'data_type': dtype('O'),\n",
              "                                          'mapping': hand pump             1\n",
              "communal standpipe    2\n",
              "improved spring       3\n",
              "other                 4\n",
              "cattle trough         5\n",
              "dam                   6\n",
              "NaN                  -2\n",
              "dtype: int64}])),\n",
              "                ('simpleimputer', SimpleImputer()),\n",
              "                ('decisiontreeclassifier',\n",
              "                 DecisionTreeClassifier(random_state=42))])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "model_dt = make_pipeline(\n",
        "    OrdinalEncoder(),\n",
        "    SimpleImputer(strategy= 'mean'),\n",
        "    DecisionTreeClassifier(random_state=42)\n",
        ")\n",
        "\n",
        "# Fit the model to the training data\n",
        "model_dt.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrain model after tuning hyperparemeter\n",
        "\n",
        "# model_dt = make_pipeline(\n",
        "#     OrdinalEncoder(),\n",
        "#     SimpleImputer(strategy= 'mean'),\n",
        "#     DecisionTreeClassifier(max_depth= 17, random_state=42) # max_depth calculated on task 8\n",
        "# )\n",
        "\n",
        "# # Fit the model to the training data\n",
        "# model_dt.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "k5Z9r9CTDU8i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize= (60, 15))\n",
        "plot_tree(model_dt.named_steps['decisiontreeclassifier'], \n",
        "          max_depth=3,\n",
        "          feature_names=X_train.columns,\n",
        "          filled=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 914
        },
        "id": "GeQasnMTZC_F",
        "outputId": "38091d02-185e-4fc2-aabe-f38b8eab349e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "[Text(0.5, 0.9, 'quantity <= 1.5\\ngini = 0.553\\nsamples = 38015\\nvalue = [20625, 2779, 14611]'),\n",
              " Text(0.25, 0.7, 'region <= 1.5\\ngini = 0.063\\nsamples = 4056\\nvalue = [104, 27, 3925]'),\n",
              " Text(0.125, 0.5, 'longitude <= 32.736\\ngini = 0.257\\nsamples = 232\\nvalue = [34, 1, 197]'),\n",
              " Text(0.0625, 0.3, 'longitude <= 32.421\\ngini = 0.483\\nsamples = 44\\nvalue = [26, 0, 18]'),\n",
              " Text(0.03125, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.09375, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.1875, 0.3, 'extraction_type <= 2.5\\ngini = 0.092\\nsamples = 188\\nvalue = [8, 1, 179]'),\n",
              " Text(0.15625, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.21875, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.375, 0.5, 'construction_year <= 2011.5\\ngini = 0.049\\nsamples = 3824\\nvalue = [70, 26, 3728]'),\n",
              " Text(0.3125, 0.3, 'extraction_type_class <= 1.5\\ngini = 0.045\\nsamples = 3779\\nvalue = [61, 26, 3692]'),\n",
              " Text(0.28125, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.34375, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.4375, 0.3, 'latitude <= -10.751\\ngini = 0.32\\nsamples = 45\\nvalue = [9, 0, 36]'),\n",
              " Text(0.40625, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.46875, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.75, 0.7, 'waterpoint_type_group <= 3.5\\ngini = 0.529\\nsamples = 33959\\nvalue = [20521, 2752, 10686]'),\n",
              " Text(0.625, 0.5, 'payment <= 2.5\\ngini = 0.5\\nsamples = 30704\\nvalue = [19932, 2564, 8208]'),\n",
              " Text(0.5625, 0.3, 'extraction_type_class <= 2.5\\ngini = 0.547\\nsamples = 15749\\nvalue = [9023, 1318, 5408]'),\n",
              " Text(0.53125, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.59375, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.6875, 0.3, 'waterpoint_type <= 2.5\\ngini = 0.426\\nsamples = 14955\\nvalue = [10909, 1246, 2800]'),\n",
              " Text(0.65625, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.71875, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.875, 0.5, 'extraction_type_class <= 3.5\\ngini = 0.384\\nsamples = 3255\\nvalue = [589, 188, 2478]'),\n",
              " Text(0.8125, 0.3, 'latitude <= -3.167\\ngini = 0.569\\nsamples = 980\\nvalue = [323, 112, 545]'),\n",
              " Text(0.78125, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.84375, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.9375, 0.3, 'source_class <= 1.5\\ngini = 0.263\\nsamples = 2275\\nvalue = [266, 76, 1933]'),\n",
              " Text(0.90625, 0.1, '\\n  (...)  \\n'),\n",
              " Text(0.96875, 0.1, '\\n  (...)  \\n')]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 4320x1080 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aIacXTOEnH6o"
      },
      "source": [
        "# V. Check Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n70ScxrqnH6p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6298cda0-3b02-49d8-86dd-01ed550c922a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy Score: 0.995475470209128\n",
            "Validation Accuracy Score: 0.7411616161616161\n"
          ]
        }
      ],
      "source": [
        "training_acc = accuracy_score(y_train, model_dt.predict(X_train))\n",
        "val_acc = accuracy_score(y_val, model_dt.predict(X_val))\n",
        "\n",
        "print('Training Accuracy Score:', training_acc)\n",
        "print('Validation Accuracy Score:', val_acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights:**\n",
        "- The accuracy score of 99.55% for the training set is almost perfect.\n",
        "- The validation accuracy score of 74.12% is lesser than the training. So, we could say the model is overfit.\n",
        "- The model generalization isn't great but it's acceptable. "
      ],
      "metadata": {
        "id": "496YrcDF5vNW"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zZ7MPhPRnH6p"
      },
      "source": [
        "# VI. Tune Model\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Depth of the tree\n",
        "print('Initial depth of the tree:', model_dt.named_steps['decisiontreeclassifier'].get_depth())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXgDdIgL-Ocd",
        "outputId": "c6b486f0-bb30-43c8-8bb3-d63bb5470e11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial depth of the tree: 38\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "khrrTJl7nH6p"
      },
      "outputs": [],
      "source": [
        "# Use this cell to experiment and then change the model hyperparameters \n",
        "\n",
        "depths = range(5, 50, 2)\n",
        "list(depths)\n",
        "\n",
        "train_acc = []\n",
        "val_acc = []\n",
        "\n",
        "for depth in depths:\n",
        "  tree_model = make_pipeline(\n",
        "      OrdinalEncoder(),\n",
        "      SimpleImputer(),\n",
        "      DecisionTreeClassifier(max_depth=depth, random_state=42)\n",
        "  )\n",
        "\n",
        "  tree_model.fit(X_train, y_train)\n",
        "  train_acc.append(tree_model.score(X_train, y_train))\n",
        "  val_acc.append(tree_model.score(X_val, y_val))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams[\"figure.figsize\"] = (10,5)\n",
        "plt.plot(depths, train_acc, color='blue', label='training')\n",
        "plt.plot(depths, val_acc, color='orange', label='validation')\n",
        "plt.xlabel('max_depth')\n",
        "plt.ylabel('accuracy')\n",
        "plt.title('Validation Curves')\n",
        "plt.legend()\n",
        "plt.show;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "OHy-mjHp9N8z",
        "outputId": "d2a89ecc-cb68-4938-e70a-82a50a85f092"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(depths, val_acc);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "6gtIDIH8R4OG",
        "outputId": "e621caab-a135-45b1-e547-8e234b8d5337"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tuning hyperparameter max_depth and retraining the model\n",
        "model_dt = make_pipeline(\n",
        "    OrdinalEncoder(),\n",
        "    SimpleImputer(strategy= 'mean'),\n",
        "    DecisionTreeClassifier(max_depth= 17, random_state=42) # max_depth calculated on task 8\n",
        ")\n",
        "\n",
        "# Fit the model to the training data\n",
        "model_dt.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gFoO7FA0arIf",
        "outputId": "58811f3a-f11c-4428-e0a9-1dd45302c55c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('ordinalencoder',\n",
              "                 OrdinalEncoder(cols=['basin', 'region', 'public_meeting',\n",
              "                                      'scheme_management', 'permit',\n",
              "                                      'extraction_type',\n",
              "                                      'extraction_type_group',\n",
              "                                      'extraction_type_class', 'management',\n",
              "                                      'management_group', 'payment',\n",
              "                                      'payment_type', 'water_quality',\n",
              "                                      'quality_group', 'quantity', 'source',\n",
              "                                      'source_type', 'source_class',\n",
              "                                      'waterpoint_type',\n",
              "                                      'waterpoin...\n",
              "communal standpipe             2\n",
              "communal standpipe multiple    3\n",
              "improved spring                4\n",
              "other                          5\n",
              "cattle trough                  6\n",
              "dam                            7\n",
              "NaN                           -2\n",
              "dtype: int64},\n",
              "                                         {'col': 'waterpoint_type_group',\n",
              "                                          'data_type': dtype('O'),\n",
              "                                          'mapping': hand pump             1\n",
              "communal standpipe    2\n",
              "improved spring       3\n",
              "other                 4\n",
              "cattle trough         5\n",
              "dam                   6\n",
              "NaN                  -2\n",
              "dtype: int64}])),\n",
              "                ('simpleimputer', SimpleImputer()),\n",
              "                ('decisiontreeclassifier',\n",
              "                 DecisionTreeClassifier(max_depth=17, random_state=42))])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy after tuning the hyperparamenther max depth\n",
        "# We reduced overfitting and improved the val accuracy score\n",
        "training_acc = accuracy_score(y_train, model_dt.predict(X_train))\n",
        "val_acc = accuracy_score(y_val, model_dt.predict(X_val))\n",
        "\n",
        "print('Training Accuracy Score:', training_acc)\n",
        "print('Validation Accuracy Score:', val_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_94lULzC9SN",
        "outputId": "a0572ff3-79f3-4d5b-c778-a29852b2f08a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy Score: 0.8958568985926608\n",
            "Validation Accuracy Score: 0.7644149831649831\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights:**  By tuning one hyperparameter, which in this case was max depth of the tree, we were able to reduce overfitting and increase the generalizability of the model. Our validation has gone up from 74.12% to 76.44%."
      ],
      "metadata": {
        "id": "pMJbeZe0DdBH"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sOZxx9_znH6p"
      },
      "source": [
        "# VII. Communicate Results\n",
        "\n",
        "These are the 10 most important features for model_dt, sorted by value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2ZHbDOU7nH6q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "058dc3a4-5494-4a67-9718-34b4c86cec74"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "features = model_dt.named_steps['ordinalencoder'].get_feature_names()\n",
        "gini_importances = model_dt.named_steps['decisiontreeclassifier'].feature_importances_\n",
        "pd.Series(data=gini_importances, index=features).sort_values(key=abs).tail(10).plot(kind='barh')\n",
        "plt.ylabel('features')\n",
        "plt.xlabel('gini importance')\n",
        "plt.title('Gini Feature Importance');"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insigths:** According to this plot, the most important feature that helped the model make a prediction about the water pump status, was quantity. "
      ],
      "metadata": {
        "id": "HL4T4mjqFFHl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Kaggle Submission\n",
        "predictions = pd.DataFrame(data=model_dt.predict(X_test), index=X_test.index)"
      ],
      "metadata": {
        "id": "xIifIYfSEz0O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions.columns = ['status_group']"
      ],
      "metadata": {
        "id": "UuBnLB-PFaCB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "4wvtzKbCFfMh",
        "outputId": "446fbd4c-9468-40b6-bbb6-f4358a76c5d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         status_group\n",
              "id                   \n",
              "37098  non functional\n",
              "14530      functional\n",
              "62607      functional\n",
              "46053      functional\n",
              "47083      functional\n",
              "...               ...\n",
              "26092      functional\n",
              "919    non functional\n",
              "47444  non functional\n",
              "61128      functional\n",
              "8075       functional\n",
              "\n",
              "[11880 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0f3c190a-6cbc-4c13-9ee0-fe42f51d5430\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>status_group</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>37098</th>\n",
              "      <td>non functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14530</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62607</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46053</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47083</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26092</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>919</th>\n",
              "      <td>non functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47444</th>\n",
              "      <td>non functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61128</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8075</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11880 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f3c190a-6cbc-4c13-9ee0-fe42f51d5430')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0f3c190a-6cbc-4c13-9ee0-fe42f51d5430 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0f3c190a-6cbc-4c13-9ee0-fe42f51d5430');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate CSV file\n",
        "predictions.to_csv('first_submission.csv')\n",
        "\n",
        "# Download CSV file\n",
        "from google.colab import files\n",
        "files.download('first_submission.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "mrlkmPZcFgdp",
        "outputId": "bd4a863e-22d3-45bb-a858-5c712a176a1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_f761edfc-75b8-4cc0-9935-cb06195b9bde\", \"first_submission.csv\", 224853)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.9"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}