InsuranceCompany.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Business Problem\n",
        "An insurance company wants to improve its cash flow forecasting by better predicting patient charges using demographic and basic patient health risk metrics at the time of hospitalization."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vKLrcu1wPv3J"
      },
      "source": [
        "## PyCaret\n",
        "PyCaret is an open source, low-code machine learning library in Python to train and deploy machine learning pipelines and models in production. PyCaret can be installed easily using pip as shown below.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9i_W5GZlG4oR",
        "outputId": "f0cfef80-8def-4c7f-b95e-57d3d92a4829"
      },
      "source": [
        "!pip install pycaret\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pycaret\n",
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            "\u001b[?25hRequirement already satisfied: nltk in /usr/local/lib/python3.6/dist-packages (from pycaret) (3.2.5)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/40/88/ae1f78cf582b707c605c77df49b4c8786a4465edc51adb25d2f98ef4c4de/databricks-cli-0.14.1.tar.gz (54kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/f7/12/ec3f2e203afa394a149911729357aa48affc59c20e2c1c8297a60f33f133/threadpoolctl-2.1.0-py3-none-any.whl\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4a/e2/e588ab9298d4989ce7fdb2b97d18aac878d99dbdc379a4476a09d9271b68/tangled_up_in_unicode-0.0.6-py3-none-any.whl (3.1MB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/12/ae/66029dcaa88ccca77f454dbb29c1178c751ec24fc771ed475a992b49a02d/combo-0.1.2.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4c/5f/f61b420143ed1c8dc69f9eaec5ff1ac36109d52c80de49d66e0c36c3dfdf/websocket_client-0.57.0-py2.py3-none-any.whl (200kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e8/cc/6c96bfb3d3cf4c3bdedfa6b46503223f4c2a4fa388377697e0f8082a4fed/msrest-0.6.21-py2.py3-none-any.whl (85kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/12/9e/6bb67fe85f6a89d71f50c86a0da778a5064f749a485ed9ba498067034227/azure_core-1.10.0-py2.py3-none-any.whl (125kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/11/d1800bca0a3bae820b84b7d813ad1eff15a48a64caea9c823fc8c1b119e8/gitdb-4.0.5-py3-none-any.whl (63kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5c/db/2d2d88b924aa4674a080aae83b59ea19d593250bfe5ed789947c21736785/Mako-1.1.4.tar.gz (479kB)\n",
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            "Installing collected packages: threadpoolctl, scikit-learn, lightgbm, catboost, funcy, pyLDAvis, querystring-parser, gunicorn, websocket-client, docker, isodate, msrest, azure-core, semantic-version, setuptools-rust, cryptography, azure-storage-blob, smmap, gitdb, gitpython, databricks-cli, Mako, python-editor, alembic, prometheus-flask-exporter, mlflow, xgboost, scikit-plot, imbalanced-learn, confuse, phik, tangled-up-in-unicode, imagehash, visions, htmlmin, pandas-profiling, yellowbrick, combo, suod, pyod, kmodes, pycaret\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmJ9s06feBHZ"
      },
      "source": [
        "## Load data from PyCaret’s repository\n",
        "\n",
        "Load the open source datasets insurance dataset hosted by PyCaret. These are hosted on PyCaret’s github and can also be directly loaded using pycaret datasets module."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "mM4iyhqcLE-c",
        "outputId": "47f01011-bea3-43ef-fcf1-67885a5b90a6"
      },
      "source": [
        "from pycaret.datasets import get_data\n",
        "data = get_data('insurance') "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>16884.92400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>male</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1725.55230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>male</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>4449.46200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>male</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>21984.47061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>male</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex     bmi  children smoker     region      charges\n",
              "0   19  female  27.900         0    yes  southwest  16884.92400\n",
              "1   18    male  33.770         1     no  southeast   1725.55230\n",
              "2   28    male  33.000         3     no  southeast   4449.46200\n",
              "3   33    male  22.705         0     no  northwest  21984.47061\n",
              "4   32    male  28.880         0     no  northwest   3866.85520"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ixzadtJPec3G"
      },
      "source": [
        "Set preprocessing tasks such as scaling and normalization, automatic feature engineering and binning continuous data into intervals. The transformed dataset has 55 features for training derived from only 7 features in the original dataset. All of the new features are the result of transformations and automatic feature engineering in PyCaret."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "2943affd58634990bdfcb0da5deee124",
            "92367c9dddea4b41b6709b5459e84bf5",
            "75a5f57bdc704f9e97f89d8befbe8097",
            "86637974ebcd4167929a70288c4abf5d",
            "3ddf352e989b425dba1cfd163be1ec89",
            "a45fb1dd0bbe45c7ac8f33c181803d9c"
          ]
        },
        "id": "85EI_bGZGtzF",
        "outputId": "848c8e87-6d2d-4193-a591-cb7b5eab776c"
      },
      "source": [
        "from pycaret.regression import *\n",
        "\n",
        "r2 = setup(data, target = 'charges', session_id = 123,\n",
        "           normalize = True,\n",
        "           polynomial_features = True, trigonometry_features = True,\n",
        "           feature_interaction=True, \n",
        "           bin_numeric_features= ['age', 'bmi'])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Target</td>\n",
              "      <td>charges</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(1338, 7)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Numeric Features</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Categorical Features</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ordinal Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>High Cardinality Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>High Cardinality Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Transformed Train Set</td>\n",
              "      <td>(936, 57)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Transformed Test Set</td>\n",
              "      <td>(402, 57)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Shuffle Train-Test</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Stratify Train-Test</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>KFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Fold Number</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>USI</td>\n",
              "      <td>c559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>constant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Normalize</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Normalize Method</td>\n",
              "      <td>zscore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Transformation Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>PCA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>PCA Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>PCA Components</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Combine Rare Levels</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Rare Level Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Numeric Binning</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Remove Outliers</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Outliers Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Remove Multicollinearity</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Clustering</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Transform Target</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Transform Target Method</td>\n",
              "      <td>box-cox</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Description             Value\n",
              "0                               session_id               123\n",
              "1                                   Target           charges\n",
              "2                            Original Data         (1338, 7)\n",
              "3                           Missing Values             False\n",
              "4                         Numeric Features                 2\n",
              "5                     Categorical Features                 4\n",
              "6                         Ordinal Features             False\n",
              "7                High Cardinality Features             False\n",
              "8                  High Cardinality Method              None\n",
              "9                    Transformed Train Set         (936, 57)\n",
              "10                    Transformed Test Set         (402, 57)\n",
              "11                      Shuffle Train-Test              True\n",
              "12                     Stratify Train-Test             False\n",
              "13                          Fold Generator             KFold\n",
              "14                             Fold Number                10\n",
              "15                                CPU Jobs                -1\n",
              "16                                 Use GPU             False\n",
              "17                          Log Experiment             False\n",
              "18                         Experiment Name  reg-default-name\n",
              "19                                     USI              c559\n",
              "20                         Imputation Type            simple\n",
              "21          Iterative Imputation Iteration              None\n",
              "22                         Numeric Imputer              mean\n",
              "23      Iterative Imputation Numeric Model              None\n",
              "24                     Categorical Imputer          constant\n",
              "25  Iterative Imputation Categorical Model              None\n",
              "26           Unknown Categoricals Handling    least_frequent\n",
              "27                               Normalize              True\n",
              "28                        Normalize Method            zscore\n",
              "29                          Transformation             False\n",
              "30                   Transformation Method              None\n",
              "31                                     PCA             False\n",
              "32                              PCA Method              None\n",
              "33                          PCA Components              None\n",
              "34                     Ignore Low Variance             False\n",
              "35                     Combine Rare Levels             False\n",
              "36                    Rare Level Threshold              None\n",
              "37                         Numeric Binning              True\n",
              "38                         Remove Outliers             False\n",
              "39                      Outliers Threshold              None\n",
              "40                Remove Multicollinearity             False\n",
              "41             Multicollinearity Threshold              None\n",
              "42                              Clustering             False\n",
              "43                    Clustering Iteration              None\n",
              "44                     Polynomial Features              True\n",
              "45                       Polynomial Degree                 2\n",
              "46                    Trignometry Features              True\n",
              "47                    Polynomial Threshold               0.1\n",
              "48                          Group Features             False\n",
              "49                       Feature Selection             False\n",
              "50            Features Selection Threshold              None\n",
              "51                     Feature Interaction              True\n",
              "52                           Feature Ratio             False\n",
              "53                   Interaction Threshold              0.01\n",
              "54                        Transform Target             False\n",
              "55                 Transform Target Method           box-cox"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n8ErSIeDe_1i"
      },
      "source": [
        "Sample code for model training and validation in PyCaret using linear regression."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_cw3Ghs_NlYA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
          "referenced_widgets": [
            "1ec44f97b2ca475e9c2af443db4a2d27",
            "6d37e8dba2ea429eac5d864a8488b2ef",
            "b5e87763390442a0973e0f166a1cf0b2"
          ]
        },
        "outputId": "edc6da1f-c7f1-48c5-ead0-2b5dde0fc217"
      },
      "source": [
        "# Model Training and Validation \n",
        "lr = create_model('lr')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3036.0027</td>\n",
              "      <td>2.143462e+07</td>\n",
              "      <td>4629.7534</td>\n",
              "      <td>0.8720</td>\n",
              "      <td>0.3896</td>\n",
              "      <td>0.3137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3486.8723</td>\n",
              "      <td>3.270450e+07</td>\n",
              "      <td>5718.7847</td>\n",
              "      <td>0.8098</td>\n",
              "      <td>0.4405</td>\n",
              "      <td>0.3345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3034.7471</td>\n",
              "      <td>2.337602e+07</td>\n",
              "      <td>4834.8755</td>\n",
              "      <td>0.7040</td>\n",
              "      <td>0.5614</td>\n",
              "      <td>0.4033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2899.4216</td>\n",
              "      <td>1.940902e+07</td>\n",
              "      <td>4405.5664</td>\n",
              "      <td>0.8379</td>\n",
              "      <td>0.4674</td>\n",
              "      <td>0.3893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3234.3174</td>\n",
              "      <td>2.714745e+07</td>\n",
              "      <td>5210.3213</td>\n",
              "      <td>0.7981</td>\n",
              "      <td>0.4538</td>\n",
              "      <td>0.3108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3436.4258</td>\n",
              "      <td>2.675808e+07</td>\n",
              "      <td>5172.8213</td>\n",
              "      <td>0.8279</td>\n",
              "      <td>0.4876</td>\n",
              "      <td>0.3124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2939.4294</td>\n",
              "      <td>2.508074e+07</td>\n",
              "      <td>5008.0674</td>\n",
              "      <td>0.8293</td>\n",
              "      <td>0.3771</td>\n",
              "      <td>0.3151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3551.7922</td>\n",
              "      <td>3.083264e+07</td>\n",
              "      <td>5552.7144</td>\n",
              "      <td>0.8305</td>\n",
              "      <td>0.4757</td>\n",
              "      <td>0.3165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3132.7239</td>\n",
              "      <td>2.292745e+07</td>\n",
              "      <td>4788.2622</td>\n",
              "      <td>0.8519</td>\n",
              "      <td>0.3998</td>\n",
              "      <td>0.3320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3295.1824</td>\n",
              "      <td>2.914138e+07</td>\n",
              "      <td>5398.2754</td>\n",
              "      <td>0.8123</td>\n",
              "      <td>0.5079</td>\n",
              "      <td>0.3647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>3204.6915</td>\n",
              "      <td>2.588119e+07</td>\n",
              "      <td>5071.9442</td>\n",
              "      <td>0.8174</td>\n",
              "      <td>0.4561</td>\n",
              "      <td>0.3392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>221.1203</td>\n",
              "      <td>4.017185e+06</td>\n",
              "      <td>395.6903</td>\n",
              "      <td>0.0428</td>\n",
              "      <td>0.0542</td>\n",
              "      <td>0.0326</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
              "0     3036.0027  2.143462e+07  4629.7534  0.8720  0.3896  0.3137\n",
              "1     3486.8723  3.270450e+07  5718.7847  0.8098  0.4405  0.3345\n",
              "2     3034.7471  2.337602e+07  4834.8755  0.7040  0.5614  0.4033\n",
              "3     2899.4216  1.940902e+07  4405.5664  0.8379  0.4674  0.3893\n",
              "4     3234.3174  2.714745e+07  5210.3213  0.7981  0.4538  0.3108\n",
              "5     3436.4258  2.675808e+07  5172.8213  0.8279  0.4876  0.3124\n",
              "6     2939.4294  2.508074e+07  5008.0674  0.8293  0.3771  0.3151\n",
              "7     3551.7922  3.083264e+07  5552.7144  0.8305  0.4757  0.3165\n",
              "8     3132.7239  2.292745e+07  4788.2622  0.8519  0.3998  0.3320\n",
              "9     3295.1824  2.914138e+07  5398.2754  0.8123  0.5079  0.3647\n",
              "Mean  3204.6915  2.588119e+07  5071.9442  0.8174  0.4561  0.3392\n",
              "SD     221.1203  4.017185e+06   395.6903  0.0428  0.0542  0.0326"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xn6A3QYLIvE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376,
          "referenced_widgets": [
            "a9f593a97afc4a088f13b82818ead733",
            "a5dde38d4dc5460a8cfefe0e57dfdc6f",
            "53d0bd6cd5784f48a47497033ef0fe23"
          ]
        },
        "outputId": "22cba17f-2b3e-4412-8b51-b225723f44cc"
      },
      "source": [
        "# plot residuals of trained model\n",
        "plot_model(lr, plot = 'residuals')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IFhorK27f6_x"
      },
      "source": [
        "At this stage, the model is still only an object within the notebook. Next, save the model as a file in Google Drive that can be transferred and consumed by other applications."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QXa1NDMQVDGO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec31a44a-2cf8-4e97-c08f-a0f56a5ed6d3"
      },
      "source": [
        "# save transformation pipeline and model \n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0ZvK_YLYqNQ"
      },
      "source": [
        "import os\n",
        "model_dir = '/content/drive/My Drive/Colab Notebooks/Insurance Company Model/'\n",
        "os.makedirs(model_dir, exist_ok=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_klvas2KWnsu",
        "outputId": "924158a2-a039-42ef-dc58-35748c3a0e28"
      },
      "source": [
        "save_model(lr, model_dir + 'deployment_28042020')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Succesfully Saved\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Pipeline(memory=None,\n",
              "          steps=[('dtypes',\n",
              "                  DataTypes_Auto_infer(categorical_features=[],\n",
              "                                       display_types=True, features_todrop=[],\n",
              "                                       id_columns=[], ml_usecase='regression',\n",
              "                                       numerical_features=[], target='charges',\n",
              "                                       time_features=[])),\n",
              "                 ('imputer',\n",
              "                  Simple_Imputer(categorical_strategy='not_available',\n",
              "                                 fill_value_categorical=None,\n",
              "                                 fill_value_numerical=None,\n",
              "                                 numeric_strategy...\n",
              "                 ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
              "                 ('dfs',\n",
              "                  DFS_Classic(interactions=['multiply'], ml_usecase='regression',\n",
              "                              random_state=123, subclass='binary',\n",
              "                              target='charges',\n",
              "                              top_features_to_pick_percentage=None)),\n",
              "                 ('pca', 'passthrough'),\n",
              "                 ['trained_model',\n",
              "                  LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1,\n",
              "                                   normalize=False)]],\n",
              "          verbose=False),\n",
              " '/content/drive/My Drive/Colab Notebooks/Insurance Company Model/deployment_28042020.pkl')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRX-1QqahDE6"
      },
      "source": [
        "When a model is saved in PyCaret, the entire transformation pipeline based on the configuration defined in the setup() function is created . All inter-dependencies are orchestrated automatically. The first task of training a model for development has been finished\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lX2DgSxpZ5tD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}