random_forest_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aUi1DfbWadfq"
      },
      "source": [
        "# Project: Random Forests"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fbsdViVfadfq"
      },
      "source": [
        "This project will focus on creating and improving a model for the Tanzania 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",
        "\n",
        "# I. Wrangle Data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Imports\n",
        "%%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",
        "import numpy as np\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",
        "from sklearn.ensemble import RandomForestClassifier\n"
      ],
      "metadata": {
        "id": "uTk9tjcQa4Ui"
      },
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hGoreC6cb84T",
        "outputId": "8fbb3ec0-1749-426a-acf7-c6a2f602b369"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n",
            "/content/gdrive/My Drive/Kaggle/bloomtech-water-pump-challenge\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r5nztLqGadfr"
      },
      "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",
        "    # Convert columns to datetime\n",
        "    df[['date_recorded','construction_year']] = df[['date_recorded','construction_year']].apply(pd.to_datetime)\n",
        "\n",
        "    # Feature engineer column 'pump_age' by using 'construction_year' and 'date_recorded'\n",
        "    df['pump-age'] = (df['date_recorded'] - df['construction_year'])/np.timedelta64(1, 'Y')\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",
        "    # Drop 'construction_year' and 'date_recorded' columns\n",
        "    df.drop(columns=['construction_year', 'date_recorded'], inplace= True)               \n",
        "\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V-dakKJZadft"
      },
      "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": [
        "df.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "SzqJpSGrcHSd",
        "outputId": "034afdbf-4b2c-49c2-c113-bffc65877ec3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         amount_tsh  gps_height  longitude   latitude  \\\n",
              "id                                                      \n",
              "43979.0         NaN         NaN  33.584817  -3.195961   \n",
              "39977.0      1000.0       728.0  36.058550 -10.971401   \n",
              "19446.0         NaN      1519.0  36.890395  -3.195145   \n",
              "35370.0        50.0        -8.0  38.659109  -6.804926   \n",
              "50495.0         NaN      1368.0  37.092574  -3.181783   \n",
              "\n",
              "                           basin       region  region_code  district_code  \\\n",
              "id                                                                          \n",
              "43979.0            Lake Victoria    Shinyanga           17            2.0   \n",
              "39977.0  Ruvuma / Southern Coast       Ruvuma           10            5.0   \n",
              "19446.0                 Internal       Arusha            2            7.0   \n",
              "35370.0              Wami / Ruvu        Pwani            6            2.0   \n",
              "50495.0                  Pangani  Kilimanjaro            3            7.0   \n",
              "\n",
              "         population public_meeting  ... water_quality quality_group  \\\n",
              "id                                  ...                               \n",
              "43979.0         NaN           True  ...          soft          good   \n",
              "39977.0       300.0           True  ...          soft          good   \n",
              "19446.0       350.0           True  ...          soft          good   \n",
              "35370.0        50.0           True  ...          soft          good   \n",
              "50495.0         1.0           True  ...          soft          good   \n",
              "\n",
              "             quantity                source           source_type  \\\n",
              "id                                                                  \n",
              "43979.0      seasonal          shallow well          shallow well   \n",
              "39977.0  insufficient          shallow well          shallow well   \n",
              "19446.0  insufficient  rainwater harvesting  rainwater harvesting   \n",
              "35370.0        enough                 river            river/lake   \n",
              "50495.0        enough                spring                spring   \n",
              "\n",
              "        source_class     waterpoint_type waterpoint_type_group  \\\n",
              "id                                                               \n",
              "43979.0  groundwater           hand pump             hand pump   \n",
              "39977.0  groundwater           hand pump             hand pump   \n",
              "19446.0      surface  communal standpipe    communal standpipe   \n",
              "35370.0      surface  communal standpipe    communal standpipe   \n",
              "50495.0  groundwater  communal standpipe    communal standpipe   \n",
              "\n",
              "           status_group   pump-age  \n",
              "id                                  \n",
              "43979.0  non functional        NaN  \n",
              "39977.0      functional  43.154890  \n",
              "19446.0      functional  43.587480  \n",
              "35370.0  non functional  41.158956  \n",
              "50495.0      functional  43.201435  \n",
              "\n",
              "[5 rows x 29 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>amount_tsh</th>\n",
              "      <th>gps_height</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
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              "      <th>status_group</th>\n",
              "      <th>pump-age</th>\n",
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              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>43979.0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.584817</td>\n",
              "      <td>-3.195961</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>Shinyanga</td>\n",
              "      <td>17</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>...</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",
              "      <td>non functional</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39977.0</th>\n",
              "      <td>1000.0</td>\n",
              "      <td>728.0</td>\n",
              "      <td>36.058550</td>\n",
              "      <td>-10.971401</td>\n",
              "      <td>Ruvuma / Southern Coast</td>\n",
              "      <td>Ruvuma</td>\n",
              "      <td>10</td>\n",
              "      <td>5.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</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",
              "      <td>functional</td>\n",
              "      <td>43.154890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19446.0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1519.0</td>\n",
              "      <td>36.890395</td>\n",
              "      <td>-3.195145</td>\n",
              "      <td>Internal</td>\n",
              "      <td>Arusha</td>\n",
              "      <td>2</td>\n",
              "      <td>7.0</td>\n",
              "      <td>350.0</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>insufficient</td>\n",
              "      <td>rainwater harvesting</td>\n",
              "      <td>rainwater harvesting</td>\n",
              "      <td>surface</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>functional</td>\n",
              "      <td>43.587480</td>\n",
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              "    <tr>\n",
              "      <th>35370.0</th>\n",
              "      <td>50.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>38.659109</td>\n",
              "      <td>-6.804926</td>\n",
              "      <td>Wami / Ruvu</td>\n",
              "      <td>Pwani</td>\n",
              "      <td>6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</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",
              "      <td>non functional</td>\n",
              "      <td>41.158956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50495.0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1368.0</td>\n",
              "      <td>37.092574</td>\n",
              "      <td>-3.181783</td>\n",
              "      <td>Pangani</td>\n",
              "      <td>Kilimanjaro</td>\n",
              "      <td>3</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</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",
              "      <td>functional</td>\n",
              "      <td>43.201435</td>\n",
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              "<p>5 rows × 29 columns</p>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# Features with Nan values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8YOpt4_BfkdU",
        "outputId": "1c5781af-dfda-425c-9bee-3dcf86762be7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "amount_tsh               33330\n",
              "gps_height               16274\n",
              "longitude                 1433\n",
              "latitude                  1433\n",
              "basin                        0\n",
              "region                       0\n",
              "region_code                  0\n",
              "district_code               19\n",
              "population               17047\n",
              "public_meeting            2688\n",
              "scheme_management         3102\n",
              "permit                    2439\n",
              "extraction_type              0\n",
              "extraction_type_group        0\n",
              "extraction_type_class        0\n",
              "management                   0\n",
              "management_group             0\n",
              "payment                      0\n",
              "payment_type                 0\n",
              "water_quality                0\n",
              "quality_group                0\n",
              "quantity                     0\n",
              "source                       0\n",
              "source_type                  0\n",
              "source_class                 0\n",
              "waterpoint_type              0\n",
              "waterpoint_type_group        0\n",
              "status_group                 0\n",
              "pump-age                 16502\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RyCyS8fMadfu"
      },
      "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": "woM4HESIadfu"
      },
      "outputs": [],
      "source": [
        "target= 'status_group'\n",
        "X = df.drop(columns=target)\n",
        "y = df[target]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v0q7knU-adfv"
      },
      "outputs": [],
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.20, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5i3Qme3adfw"
      },
      "source": [
        "# III. Establish Baseline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ql8fTA8Nadfw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94f22dac-ae08-40ea-a881-58dc4e4656fa"
      },
      "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": "Xpq7Tu-p6Ogj"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fl_DlOGsadfx"
      },
      "source": [
        "# IV. Build Model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "omPGb8A3adfx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01421421-eab6-4b64-d70d-2e8d3d960812"
      },
      "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",
              "                ('randomforestclassifier',\n",
              "                 RandomForestClassifier(random_state=42))])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "model_rf = make_pipeline(\n",
        "    OrdinalEncoder(),\n",
        "    SimpleImputer(strategy='mean'),\n",
        "    RandomForestClassifier(random_state=42)\n",
        "    )\n",
        "\n",
        "# Fit the model\n",
        "model_rf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VYyPaAWRadfx"
      },
      "source": [
        "# V. Check Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J9U9ftV_adfy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aed593e7-cef2-4e80-ed5a-b44167dcaac3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy Score: 0.9954228593976062\n",
            "Validation Accuracy Score: 0.7901936026936027\n"
          ]
        }
      ],
      "source": [
        "training_acc = accuracy_score(y_train, model_rf.predict(X_train))\n",
        "val_acc = accuracy_score(y_val, model_rf.predict(X_val))\n",
        "\n",
        "print('Training Accuracy Score:', training_acc)\n",
        "print('Validation Accuracy Score:', val_acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights:**\n",
        "\n",
        "- The training accuracy is 99.54%.\n",
        "- The validation accuracy is 79% (lesser than the training).\n",
        "- We could say the model is overfit.\n",
        "- The model generalization isn't great but is acceptable."
      ],
      "metadata": {
        "id": "BVADxlP6tUCo"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "du9MQYtNadfy"
      },
      "source": [
        "# VI. Tune Model\n",
        "\n",
        "Tune `n_estimators` and `max_depth` hyperparameters for the `RandomForestClassifier` to get the best validation accuracy score for `model_rf`. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zPwEzue0adfy"
      },
      "outputs": [],
      "source": [
        "train_acc = []\n",
        "val_acc = []\n",
        "max_depth_list = np.arange(1, 100, 10)\n",
        "\n",
        "for depth in max_depth_list:\n",
        "  model_rf_tune = make_pipeline(\n",
        "      OrdinalEncoder(),\n",
        "      SimpleImputer(),\n",
        "      RandomForestClassifier(random_state=42, max_depth=depth))\n",
        "\n",
        "  model_rf_tune.fit(X_train, y_train)\n",
        "\n",
        "  train_acc.append(model_rf_tune.score(X_train, y_train))\n",
        "  val_acc.append(model_rf_tune.score(X_val, y_val))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(max_depth_list, train_acc, color='blue', label= 'training')\n",
        "plt.plot(max_depth_list, val_acc, color='orange', label= 'validation')\n",
        "plt.xlabel('max_depth')\n",
        "plt.ylabel('accuracy')\n",
        "plt.legend()\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "KmliLMyIwUP5",
        "outputId": "7d2b7708-16fe-427c-c13a-1199d3b60eb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(max_depth_list, val_acc, color='orange', label= 'validation')\n",
        "plt.xlabel('max_depth')\n",
        "plt.ylabel('accuracy')\n",
        "plt.legend()\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "BRsotMyaw8Oc",
        "outputId": "f737c997-f904-43cb-8755-2b01bba4c592"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retraining Random Forest after tuning max_depth\n",
        "\n",
        "model_rf = make_pipeline(\n",
        "    OrdinalEncoder(),\n",
        "    SimpleImputer(strategy= 'mean'),\n",
        "    RandomForestClassifier(max_depth = 18, random_state=42)\n",
        ")\n",
        "\n",
        "model_rf.fit(X_train, y_train);"
      ],
      "metadata": {
        "id": "orBtkfmyxqQt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('FOREST: Training Accuracy:', model_rf.score(X_train, y_train))\n",
        "print('FOREST: Validation Accuracy:', model_rf.score(X_val, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dPE-hQ_8x0pi",
        "outputId": "eec23b49-e25f-4399-9732-e832cc9c6609"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FOREST: Training Accuracy: 0.9162698934631067\n",
            "FOREST: Validation Accuracy: 0.7961910774410774\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insight:** \n",
        "- Our model is still overfit but it's able to generalize slightly better. The validation accuracy has improved from 79.01% to 79.62%. \n",
        "- It seems like the overfitting was reduced quite a bit. There's a decrease from 99.54% to close to 91.62%. "
      ],
      "metadata": {
        "id": "DUOhSfor8A9s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_acc = []\n",
        "val_acc = []\n",
        "n_estimators_list = np.arange(1, 100, 20)\n",
        "\n",
        "for estimator in n_estimators_list:\n",
        "  model_rf = make_pipeline(\n",
        "    OrdinalEncoder(),\n",
        "    SimpleImputer(strategy= 'mean'),\n",
        "    RandomForestClassifier(max_depth=18, n_estimators=estimator, random_state=42) \n",
        "    )\n",
        "  \n",
        "  model_rf.fit(X_train, y_train);\n",
        "\n",
        "  train_acc.append(model_rf.score(X_train, y_train))\n",
        "  val_acc.append(model_rf.score(X_val, y_val))\n"
      ],
      "metadata": {
        "id": "xYhJNZQwyORI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(n_estimators_list, train_acc, color='blue', label= 'training')\n",
        "plt.plot(n_estimators_list, val_acc, color='orange', label= 'validation')\n",
        "plt.xlabel('estimator')\n",
        "plt.ylabel('accuracy')\n",
        "plt.legend()\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "ENUGujFTyg49",
        "outputId": "ca3a422b-6fa1-4d78-e1f2-1977f2cb875c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(n_estimators_list, val_acc, color='orange', label= 'validation')\n",
        "plt.xlabel('estimator')\n",
        "plt.ylabel('accuracy')\n",
        "plt.legend()\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "fj6jzPenzSbw",
        "outputId": "61bb00f1-d1fb-4f0d-efcb-7f98ca76d7ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retraining Random Forest after tuning n_estimators\n",
        "\n",
        "model_rf = make_pipeline(\n",
        "    OrdinalEncoder(),\n",
        "    SimpleImputer(strategy= 'mean'),\n",
        "    RandomForestClassifier(n_estimators= 17, max_depth = 18, random_state=42)\n",
        ")\n",
        "\n",
        "model_rf.fit(X_train, y_train);"
      ],
      "metadata": {
        "id": "xqtks6xC-bzx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('FOREST: Training Accuracy:', model_rf.score(X_train, y_train))\n",
        "print('FOREST: Validation Accuracy:', model_rf.score(X_val, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0mROspt2-u29",
        "outputId": "99344cc0-1d37-48df-baf1-aea9e980334a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FOREST: Training Accuracy: 0.9127186636853873\n",
            "FOREST: Validation Accuracy: 0.7948232323232324\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights:** Tuning the n_estimators did not improve the model's performance. Since this is not helpful, we decided to use the default value of estimators."
      ],
      "metadata": {
        "id": "b5yAkALk_ivv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_rf = make_pipeline(\n",
        "    OrdinalEncoder(),\n",
        "    SimpleImputer(strategy= 'mean'),\n",
        "    RandomForestClassifier(max_depth = 18, random_state=42)\n",
        ")\n",
        "\n",
        "model_rf.fit(X_train, y_train);"
      ],
      "metadata": {
        "id": "jlQGRHjDAQjK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('FOREST: Training Accuracy:', model_rf.score(X_train, y_train))\n",
        "print('FOREST: Validation Accuracy:', model_rf.score(X_val, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCywd04jAj-N",
        "outputId": "372b6411-d257-429d-9f83-8a33d38983b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FOREST: Training Accuracy: 0.9162698934631067\n",
            "FOREST: Validation Accuracy: 0.7961910774410774\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TvVQ6NELadfy"
      },
      "source": [
        "# VII. Communicate Results\n",
        "\n",
        "Generate a list of predictions for `X_test`. The list is named `y_pred`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pVyhoMaPadfy"
      },
      "outputs": [],
      "source": [
        "y_pred = model_rf.predict(X_test)\n",
        "\n",
        "assert len(y_pred) == len(X_test), f'Your list of predictions should have {len(X_test)} items in it. '"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ubfR-isRadfz"
      },
      "outputs": [],
      "source": [
        "submission = pd.DataFrame(data=model_rf.predict(X_test), index=X_test.index)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission.columns = ['status_group']\n",
        "submission"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "jWzsu7bi2mmR",
        "outputId": "72643974-cf99-46a0-cc0d-a4cd18d3025d"
      },
      "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  non functional\n",
              "8075       functional\n",
              "\n",
              "[11880 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae780473-4917-4610-99b3-d17eecdfa992\">\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",
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              "</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>non 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-ae780473-4917-4610-99b3-d17eecdfa992')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae780473-4917-4610-99b3-d17eecdfa992 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-ae780473-4917-4610-99b3-d17eecdfa992');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate CSV file\n",
        "submission.to_csv('second_submission.csv')\n",
        "\n",
        "# Download CSV file\n",
        "from google.colab import files\n",
        "files.download('second_submission.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "nVGfZPZm2pIO",
        "outputId": "cdd4df6d-8874-4d2d-f813-05cdcd055bd2"
      },
      "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_52e85213-ce3f-41aa-901c-52910c2f72ea\", \"second_submission.csv\", 223482)"
            ]
          },
          "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
}