ajaytiwari-isb/severity_model.ipynb

## severity_model.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Positive Severity - Gamma2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyPUoTSn9LUdB4krbotGnzlO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/ajaytiwari-isb/e15bb62ca9e29229df573bbe0981d6c5/positive-severity-gamma2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31kjk-Bf25C4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "from patsy import dmatrices\n",
        "import numpy as np\n",
        "import statsmodels.api as sm\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from statsmodels.compat import lzip\n",
        "from statsmodels.graphics.api import abline_plot"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9oFXygO3V4A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reading csv data\n",
        "df = pd.read_csv('data1.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qWZoRewdaaNl",
        "colab_type": "text"
      },
      "source": [
        "Data Check"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUEN3NORanOZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "328e5ca8-603d-4b50-b273-a7c2bf126087"
      },
      "source": [
        "# Rows containing duplicate data\n",
        "duplicate_rows_df = df[df.duplicated()]\n",
        "print(\"number of duplicate rows:\", duplicate_rows_df.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of duplicate rows: (378, 11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UtHc0LXka3vs",
        "colab_type": "code",
        "outputId": "c5684d62-5afc-46b0-ef9b-b19e4084ac0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "# Dropping duplicate rows\n",
        "df1 = df.drop_duplicates()\n",
        "df1.head(5)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>veh_value</th>\n",
              "      <th>exposure</th>\n",
              "      <th>clm</th>\n",
              "      <th>numclaims</th>\n",
              "      <th>claimcst0</th>\n",
              "      <th>veh_body</th>\n",
              "      <th>veh_age</th>\n",
              "      <th>gender</th>\n",
              "      <th>area</th>\n",
              "      <th>agecat</th>\n",
              "      <th>X_OBSTAT_</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.06</td>\n",
              "      <td>0.303901</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>HBACK</td>\n",
              "      <td>3</td>\n",
              "      <td>F</td>\n",
              "      <td>C</td>\n",
              "      <td>2</td>\n",
              "      <td>01101    0    0    0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.03</td>\n",
              "      <td>0.648871</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>HBACK</td>\n",
              "      <td>2</td>\n",
              "      <td>F</td>\n",
              "      <td>A</td>\n",
              "      <td>4</td>\n",
              "      <td>01101    0    0    0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.26</td>\n",
              "      <td>0.569473</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UTE</td>\n",
              "      <td>2</td>\n",
              "      <td>F</td>\n",
              "      <td>E</td>\n",
              "      <td>2</td>\n",
              "      <td>01101    0    0    0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.14</td>\n",
              "      <td>0.317591</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>STNWG</td>\n",
              "      <td>2</td>\n",
              "      <td>F</td>\n",
              "      <td>D</td>\n",
              "      <td>2</td>\n",
              "      <td>01101    0    0    0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.648871</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>HBACK</td>\n",
              "      <td>4</td>\n",
              "      <td>F</td>\n",
              "      <td>C</td>\n",
              "      <td>2</td>\n",
              "      <td>01101    0    0    0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   veh_value  exposure  clm  ...  area  agecat             X_OBSTAT_\n",
              "0       1.06  0.303901    0  ...     C       2  01101    0    0    0\n",
              "1       1.03  0.648871    0  ...     A       4  01101    0    0    0\n",
              "2       3.26  0.569473    0  ...     E       2  01101    0    0    0\n",
              "3       4.14  0.317591    0  ...     D       2  01101    0    0    0\n",
              "4       0.72  0.648871    0  ...     C       2  01101    0    0    0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZnWa7nghA-d9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Dropping observations with zero vehicle value\n",
        "df2 = df1[df1.veh_value !=0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gm4VQ5mlaeBw",
        "colab_type": "text"
      },
      "source": [
        "Exploring Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vf8L3XCAeFzY",
        "colab_type": "code",
        "outputId": "75a60bc2-27b9-41cc-e361-146f59a532db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "#Gender\n",
        "fig, (ax1, ax2) = plt.subplots(ncols=2)\n",
        "fig.set_size_inches(10,3)\n",
        "g = sns.countplot(x='gender',data=df2.sort_values('gender'), ax=ax1)\n",
        "title = g.set_title('Overall Data')\n",
        "g = sns.countplot(x='gender',data=df2[df2['numclaims']>0].sort_values('gender'), ax=ax2)\n",
        "title = g.set_title('Excluded Zero Values')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sKeavB2wz/w2bv6Zsm/nbb0L+gnFyWN8LuJuA/QY+79tiD1FVy4BlW6pRW6sk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\nOEmSpJ6xgJMkSeoZCzhJkqSe+f8BU2G1B+khIAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vS1tJP16hh-U",
        "colab_type": "code",
        "outputId": "a6094e14-4b70-4e61-88bc-d3dfc3f2c59b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        }
      },
      "source": [
        "# Converting vehcile value as categorical\n",
        "df2['veh_value_cat'] = df2['veh_value'].apply(lambda x:1 if x<=1 else 2 if x<=2 else 3 if x<=3 else 4 if x<=4 else 5)\n",
        "#Graphs\n",
        "fig, (ax1, ax2) = plt.subplots(ncols=2)\n",
        "fig.set_size_inches(10,3)\n",
        "g = sns.countplot(x='veh_value_cat',data=df2, ax=ax1)\n",
        "title = g.set_title('Overall Data')\n",
        "g = sns.countplot(x='veh_value_cat',data=df2[df2['numclaims']>0], ax=ax2)\n",
        "title = g.set_title('Excluded Zero Values')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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z0yOGLGNIIiKi1Ua8C7WF3wtcAhwmaWyZsXpYKYuIiIjoap14AteS7wXa3ijp\nXOC2st85tjeO3GVEREREdMaIJ3Ct/F6g7XnAvFa3MSIiImI0y8fsIyIiout95vRvdLoJgzrtU+9o\net8kcBHRtNEeAIcS/CIi6mw0vcg3IiIiIpqQBC4iIiKiZpLARURERNRMEriIiIiImkkCFxEREVEz\nSeAiIiIiaiYJXERERETNJIGLiIiIqJkkcBERERE1kwQuIiIiomaSwEVERETUTBK4iIiIiJpJAhcR\nERFRM7VP4CTNlHS/pNWSzuh0eyIimpX4FRHDVesETtIY4FLgCGAqcLykqZ1tVUTE4BK/ImJL1DqB\nAw4AVtt+0PbPgQXArA63KSKiGYlfETFsdU/gJgCPNKyvKWUREaNd4ldEDJtsd7oNwybpGGCm7T8t\n6ycCB9o+rdd+c4A5ZfUNwP1tbtoewONtrmOk5FpGn265DhiZa3mt7XFtrmPIEr9GRK5ldOqWaxmp\n6+gzhm0zAhW301pgUsP6xFK2GduXA5ePVKMkLbc9faTqa6dcy+jTLdcB3XUtw5D41Wa5ltGpW66l\n09dR9y7U24ApkvaRtB1wHLCow22KiGhG4ldEDFutn8DZ3iTpNGAJMAaYZ3tlh5sVETGoxK+I2BK1\nTuAAbC8GFne6Hb2MWHfHCMi1jD7dch3QXdcyZIlfbZdrGZ265Vo6eh21nsQQERERsTWq+xi4iIiI\niK1OErgWkjRP0npJ93S6LVtC0iRJN0m6V9JKSR/sdJuGS9IOkm6V9KNyLR/rdJu2lKQxku6U9K+d\nbsuWkPSQpLslrZC0vNPt2dp1S/yC7olhiV+j12iIX+lCbSFJbwWeA662/cZOt2e4JO0J7Gn7Dkm7\nALcDR9u+t8NNGzJJAnay/ZykbYHvAx+0fXOHmzZskv4SmA7savuoTrdnuCQ9BEy33Q3vg6q9bolf\n0D0xLPFr9BoN8StP4FrI9veAjZ1ux5ayvc72HWX5WeA+avqGeFeeK6vbll9t71okTQT+APhCp9sS\n3aVb4hd0TwxL/IqBJIGLAUmaDOwH3NLZlgxfeWS/AlgPLLVd22sB/hn4G+CXnW5ICxj4tqTby9cG\nIlqu7jEs8WvU6nj8SgIX/ZK0M3Aj8CHbz3S6PcNl+0Xb06jedH+ApFp2D0k6Clhv+/ZOt6VFftf2\n/sARwNzShRfRMt0QwxK/Rq2Ox68kcNGnMt7iRuAa21/tdHtawfZTwE3AzE63ZZgOAv6wjL1YABwi\n6cudbdLw2V5b/q4HvgYc0NkWRTfpthiW+DW6jIb4lQQuXqYMnL0SuM/2hZ1uz5aQNE7SbmV5R+Dt\nwI8726rhsX2m7Ym2J1N9dqL/oOoAAAOiSURBVOnfbZ/Q4WYNi6SdyuByJO0EHAbUfvZjjA7dEsMS\nv0an0RK/ksC1kKTrgB8Cb5C0RtIpnW7TMB0EnEh1h7Si/I7sdKOGaU/gJkl3UX17cqntWk9f7xLj\nge9L+hFwK/BN29/qcJu2al0Uv6B7Ylji1+g0KuJXXiMSERERUTN5AhcRERFRM0ngIiIiImomCVxE\nREREzSSBi4iIiKiZJHARERERNZMELiIiIqJmksDFqCBpsqS2vQhR0lWSjmnX+Zuo/72S9upU/RHR\nXolhMdKSwEWMjPcCCX4RUVfvJTFsVEkCF20j6XxJcxvWz5b0V5L+WtJtku6S9LGGQ8ZIukLSSknf\nLp+O6eu8vyHp1ob1yZLuLssfLee+R9Ll5ZM6vY9/SNIeZXm6pO+U5Z0kzZN0q6Q7Jc0a4NrGSPqn\nUs9dkj7QX/3lrnk6cE15I3yf1xURo0tiWGLYaJYELtrpeuDYhvVjgQ3AFKoP/04D3iLprWX7FOBS\n2/sCTwHv7uuktn8MbCdpn1L0R6UugM/Y/i3bbwR2BI4aQnv/lur7fAcAbwP+sXznri9zgMnANNtv\nBq7pr37bNwDLgT+xPc32T4fQpojonMSwxLBRKwlctI3tO4HXSNpL0m8CTwJvovrw753AHcBvUAU9\ngJ/YXlGWb6cKLv1ZSBX0YPPg9zZJt5S72UOAfYfQ5MOAMyStAL4D7ADs3c++/wv4vO1NALY3tqD+\niBhFEsMSw0azbTrdgOh6XwGOAX6NKkC9Fvik7c837iRpMvBCQ9GLVHd//bke+IqkrwK2vUrSDsBn\ngem2H5F0NlUA620TL928NG4X8G7b9zd3aZsbQv0RUR+JYTEq5QlctNv1wHFUAfArwBLgfZJ2BpA0\nQdJrhnpS2w9QBci/56U7155A83g5f38zth4C3lKWG7s4lgAf6BlzImm/AZqwFDhV0jZl390Hqf9Z\nYJdBLisiRp/EsEpi2CiTBC7ayvZKqv/o19peZ/vbwLXAD8sj+hsYflC4HjiBqisC208BVwD3UAWy\n2/o57mPAxZKWUwXQHucC2wJ3SVpZ1vvzBeA/y74/Av54kPqvAj6XAcAR9ZIY9itXkRg2qsh2p9sQ\nEREREUOQJ3ARERERNZNJDDGqSboUOKhX8cW2vzhC9R8OXNCr+Ce23zkS9UdEvSWGRbukCzUiIiKi\nZtKFGhEREVEzSeAiIiIiaiYJXERERETNJIGLiIiIqJkkcBERERE18/8BqMQ459VfmVAAAAAASUVO\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 720x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zuQrIA0GxJo9",
        "colab_type": "code",
        "outputId": "d80ac48f-e18f-4a41-8b80-55f7cd845519",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        }
      },
      "source": [
        "# Vehicle Age\n",
        "fig, (ax1, ax2) = plt.subplots(ncols=2)\n",
        "fig.set_size_inches(10,3)\n",
        "g = sns.countplot(x='veh_age',data=df2,ax=ax1)\n",
        "title = g.set_title('Overall Data')\n",
        "g = sns.countplot(x='veh_age',data=df2[df2['numclaims']>0], ax=ax2)\n",
        "title = g.set_title('Excluded Zero Values')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0jbbvLa19E3DQwOpLW9s2qursqlpRVSuWLFkyuuAl9YoFnCQNQZIA5wA3VtW7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\nLOAkSZJ65v8HpavffvesBBgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mxr3tJf8yaqD",
        "colab_type": "code",
        "outputId": "2a804701-c0cb-443e-8c04-1eaa138c9e8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "# Driver Age\n",
        "fig, (ax1, ax2) = plt.subplots(ncols=2)\n",
        "fig.set_size_inches(10,3)\n",
        "g = sns.countplot(x='agecat',data=df2, ax=ax1)\n",
        "title = g.set_title('Overall Data')\n",
        "g = sns.countplot(x='agecat',data=df2[df2['numclaims']>0],ax=ax2)\n",
        "title = g.set_title('Excluded Zero Values')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/TsNCzgH2l3Q/8M60DDAfWEb2w/Ri4KMlxGxmFVZGdyrw0nEjki4CziIbL3IW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\ncWZmZmYV9P8BxNUausZp1IcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hN67mKCI7YZi",
        "colab_type": "code",
        "outputId": "a03782cb-db69-4bd2-dd79-125fa0ba3442",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        }
      },
      "source": [
        "# Vehicle Body Type\n",
        "fig, (ax1, ax2) = plt.subplots(ncols=2)\n",
        "fig.set_size_inches(10,3)\n",
        "g = sns.countplot(x='veh_body',data=df2,ax=ax1)\n",
        "label = g.set_xticklabels(g.get_xticklabels(), rotation=45)\n",
        "title = g.set_title('Overall Data')\n",
        "g = sns.countplot(x='veh_body',data=df2[df2['numclaims']>0], ax=ax2)\n",
        "label = g.set_xticklabels(g.get_xticklabels(), rotation=45)\n",
        "title = g.set_title('Excluded Zero Values')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JoeQXEWdGxNSImDphwoQaS2pmvcgVOzOzmkhaklKpuyAivpfJf250sebfhzP9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\n9QhX7MzMzMx6hCt2ZmZmZj3i/wMu0wnLldYIrgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_DZcVOIJ8U2t",
        "colab_type": "code",
        "outputId": "c3cf81bc-d312-42b8-9285-5ba8285e2b93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "source": [
        "#Grouping bottom categories as \"others\"\n",
        "series = pd.value_counts(df2.veh_body)\n",
        "mask = (series/series.sum() * 100).lt(1)\n",
        "# To replace df['column'] use np.where I.e \n",
        "df2['veh_body'] = np.where(df2['veh_body'].isin(series[mask].index),'Other',df2['veh_body'])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxJw6CQLTHfh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Converting into string\n",
        "df3 = df2.astype({\"veh_body\":str,\"veh_age\":str,\"gender\":str,\"area\":str,\"agecat\":str,\"veh_value_cat\":str})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3C-SO2BtNsH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Extracting subset of data having a claim\n",
        "df4 = df3[df3.clm == 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6me4e2uyLVCH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Removing observations beyond 99th percentile of claim cost\n",
        "df_excl_0_99 = df4[df4.claimcst0 < np.percentile(df4.claimcst0,99)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWStEIj0r7JV",
        "colab_type": "code",
        "outputId": "918c63c1-4b03-4583-ca1f-4a430018a5b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "# Density Plot and Histogram of all arrival delays\n",
        "sns.distplot(df_excl_0_99['claimcst0'], hist=True, kde=True,\n",
        "             bins=10,              \n",
        "             color = 'darkblue', \n",
        "             hist_kws={'edgecolor':'black'},\n",
        "             kde_kws={'linewidth': 2})"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3e59ae4e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jPfXcZxEZrxQYBjHUu54Bpk0rAeDYsY6s9ElEJNsUGAYx8HnPUUydWkJBgXH8eCfd3b3Z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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Tt6FEd9r8ok",
        "colab_type": "code",
        "outputId": "92690dc5-c41f-4ff1-d506-4979b4ad15a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        }
      },
      "source": [
        "# Create a figure instance\n",
        "fig = plt.figure(1, figsize=(5, 8))\n",
        "\n",
        "# Create an axes instance\n",
        "ax = fig.add_subplot(111)\n",
        "\n",
        "# Create the boxplot\n",
        "bp = ax.boxplot(df_excl_0_99['claimcst0'])\n",
        "\n",
        "# Save the figure\n",
        "fig.savefig('fig1.png', bbox_inches='tight')\n",
        "## add patch_artist=True option to ax.boxplot() \n",
        "## to get fill color\n",
        "bp = ax.boxplot(df_excl_0_99['claimcst0'], patch_artist=True)\n",
        "\n",
        "## change outline color, fill color and linewidth of the boxes\n",
        "for box in bp['boxes']:\n",
        "    # change outline color\n",
        "    box.set( color='#7570b3', linewidth=2)\n",
        "    # change fill color\n",
        "    box.set( facecolor = '#1b9e77' )\n",
        "\n",
        "## change color and linewidth of the whiskers\n",
        "for whisker in bp['whiskers']:\n",
        "    whisker.set(color='#7570b3', linewidth=2)\n",
        "\n",
        "## change color and linewidth of the caps\n",
        "for cap in bp['caps']:\n",
        "    cap.set(color='#7570b3', linewidth=2)\n",
        "\n",
        "## change color and linewidth of the medians\n",
        "for median in bp['medians']:\n",
        "    median.set(color='#b2df8a', linewidth=2)\n",
        "\n",
        "## change the style of fliers and their fill\n",
        "for flier in bp['fliers']:\n",
        "    flier.set(marker='o', color='#e7298a', alpha=0.5)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2jLBEzriBMBKJGwgjW9xAGGNOXV3dBdWBEJ8YmGb2rJkdMrPdGbX/ama/NbN3zOwVM5sc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\n",
            "text/plain": [
              "<Figure size 360x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UopMv-fiTSJV",
        "colab_type": "code",
        "outputId": "e3f73b7f-d2df-4284-e7a1-ade03d1da42f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "source": [
        "# Comparing original and log transformed values\n",
        "fig, (ax1, ax2) = plt.subplots(1,2)\n",
        "fig.set_size_inches(16,5)\n",
        "ax1.hist(df_excl_0_99['claimcst0'], bins=20)\n",
        "ax1.set_title('Histogram - Claim cost including zero values')\n",
        "ax1.grid(True)\n",
        "ax2.hist(np.log(df_excl_0_99['claimcst0']), bins=20, color='g')\n",
        "ax2.set_title('Histogram - Claim cost excluding zero values')\n",
        "ax2.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wrC0vA24FaNvvbfklSVqytjo7sSRJGoYkLwXurKqrkqzscb+rgFUAExMTTE5O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\nYBjESpIkSZIGwyBWkiRJkjQY/wfpEZhNN+5NkQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5S9rsYgg3a8K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##from sklearn.preprocessing import MinMaxScaler\n",
        "#scaler = MinMaxScaler() \n",
        "#df_excl_0_99[['veh_value']]= scaler.fit_transform(df_excl_0_99[['veh_value']])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0VWa8WeK3gJ6",
        "colab_type": "code",
        "outputId": "05c5d298-8263-4c80-90c7-de2bc5db4f35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# Training and test split\n",
        "mask = np.random.rand(len(df_excl_0_99)) < 0.8\n",
        "df_train = df_excl_0_99[mask]\n",
        "df_test = df_excl_0_99[~mask]\n",
        "print('Training data set length='+str(len(df_train)))\n",
        "print('Testing data set length='+str(len(df_test)))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data set length=3664\n",
            "Testing data set length=907\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SemoNia-9is8",
        "colab_type": "text"
      },
      "source": [
        "Gauss - Log"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bflV40u-tRp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Model expression\n",
        "expr = \"\"\"claimcst0 ~ veh_value+veh_age+gender+area+agecat\"\"\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6AmUtGZ-wbS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Converting data into dmatrices\n",
        "y_train, X_train = dmatrices(expr, df_train, return_type='dataframe')\n",
        "y_test, X_test = dmatrices(expr, df_test, return_type='dataframe')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4twDOVF8LAa",
        "colab_type": "code",
        "outputId": "3f87a66e-7095-4751-87f5-d8d78a58b7d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "# Training model\n",
        "gauss_log = sm.GLM(y_train, X_train, exposure=df_train.numclaims,family=sm.families.Gaussian(sm.families.links.log)).fit()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: Calling Family(..) with a link class as argument is deprecated.\n",
            "Use an instance of a link class instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4bqVVSW3pce",
        "colab_type": "code",
        "outputId": "69e50abf-7a16-4b8a-cdba-b9b66e79cb61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        }
      },
      "source": [
        "print(gauss_log.summary2())"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                  Results: Generalized linear model\n",
            "=====================================================================\n",
            "Model:              GLM              AIC:            67667.3580      \n",
            "Link Function:      log              BIC:            22300044605.7269\n",
            "Dependent Variable: claimcst0        Log-Likelihood: -33818.         \n",
            "Date:               2020-03-29 18:03 LL-Null:        -33862.         \n",
            "No. Observations:   3664             Deviance:       2.2300e+10      \n",
            "Df Model:           15               Pearson chi2:   2.23e+10        \n",
            "Df Residuals:       3648             Scale:          6.1130e+06      \n",
            "Method:             IRLS                                             \n",
            "----------------------------------------------------------------------\n",
            "                   Coef.   Std.Err.     z     P>|z|    [0.025   0.975]\n",
            "----------------------------------------------------------------------\n",
            "Intercept          7.6122    0.1127  67.5414  0.0000   7.3914   7.8331\n",
            "veh_age[T.2]      -0.0006    0.0794  -0.0080  0.9936  -0.1564   0.1551\n",
            "veh_age[T.3]       0.0739    0.0794   0.9307  0.3520  -0.0817   0.2294\n",
            "veh_age[T.4]       0.1338    0.0866   1.5446  0.1224  -0.0360   0.3036\n",
            "gender[T.M]        0.1188    0.0468   2.5372  0.0112   0.0270   0.2106\n",
            "area[T.B]          0.0433    0.0703   0.6158  0.5380  -0.0946   0.1812\n",
            "area[T.C]          0.0399    0.0642   0.6211  0.5345  -0.0859   0.1656\n",
            "area[T.D]          0.0166    0.0897   0.1845  0.8536  -0.1593   0.1925\n",
            "area[T.E]          0.1201    0.0895   1.3423  0.1795  -0.0553   0.2955\n",
            "area[T.F]          0.3115    0.0916   3.3994  0.0007   0.1319   0.4911\n",
            "agecat[T.2]       -0.2898    0.0766  -3.7841  0.0002  -0.4400  -0.1397\n",
            "agecat[T.3]       -0.2602    0.0734  -3.5461  0.0004  -0.4039  -0.1164\n",
            "agecat[T.4]       -0.3021    0.0749  -4.0333  0.0001  -0.4489  -0.1553\n",
            "agecat[T.5]       -0.2975    0.0865  -3.4410  0.0006  -0.4670  -0.1281\n",
            "agecat[T.6]       -0.3367    0.1034  -3.2551  0.0011  -0.5394  -0.1339\n",
            "veh_value         -0.0786    0.0295  -2.6679  0.0076  -0.1364  -0.0209\n",
            "=====================================================================\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usuDaaLSMnMc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predict\n",
        "ypred = gauss_log.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8jzqm_RMu7R",
        "colab_type": "code",
        "outputId": "3806c160-952a-4eaf-dcbc-54f9c3c5baee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Calculating RMSE\n",
        "from sklearn.metrics import mean_squared_error \n",
        "rmse =np.sqrt(mean_squared_error(y_test,ypred))\n",
        "rmse"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2731.8863497474526"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q1j3c1mk9xPr",
        "colab_type": "text"
      },
      "source": [
        "Gamma - Log"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHnhzNgU-75i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Model Expression\n",
        "expr = \"\"\"claimcst0 ~ veh_value+veh_age+gender+area+agecat\"\"\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95jd8ipO--nB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Converting data into dmatrices\n",
        "y_train, X_train = dmatrices(expr, df_train, return_type='dataframe')\n",
        "y_test, X_test = dmatrices(expr, df_test, return_type='dataframe')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZK0Nl-no879k",
        "colab_type": "code",
        "outputId": "d9f0d826-8f32-43ac-cdb5-4466b2671941",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "# Training model\n",
        "model_gamma = sm.GLM(y_train, X_train,exposure=df_train.numclaims,family=sm.families.Gamma(link=sm.families.links.log)).fit()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: Calling Family(..) with a link class as argument is deprecated.\n",
            "Use an instance of a link class instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8KdPdWwbxj7",
        "colab_type": "code",
        "outputId": "50223756-d8a8-4e32-f925-1c345b91f7e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        }
      },
      "source": [
        "print(model_gamma.summary2())"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               Results: Generalized linear model\n",
            "================================================================\n",
            "Model:              GLM              AIC:            62560.9053 \n",
            "Link Function:      log              BIC:            -24946.5856\n",
            "Dependent Variable: claimcst0        Log-Likelihood: -31264.    \n",
            "Date:               2020-03-29 18:03 LL-Null:        -31303.    \n",
            "No. Observations:   3664             Deviance:       4990.0     \n",
            "Df Model:           15               Pearson chi2:   7.55e+03   \n",
            "Df Residuals:       3648             Scale:          2.0693     \n",
            "Method:             IRLS                                        \n",
            "----------------------------------------------------------------\n",
            "                  Coef.  Std.Err.    z    P>|z|   [0.025  0.975]\n",
            "----------------------------------------------------------------\n",
            "Intercept         7.5433   0.1176 64.1649 0.0000  7.3129  7.7737\n",
            "veh_age[T.2]      0.0842   0.0735  1.1446 0.2524 -0.0600  0.2283\n",
            "veh_age[T.3]      0.1673   0.0767  2.1806 0.0292  0.0169  0.3177\n",
            "veh_age[T.4]      0.2157   0.0861  2.5048 0.0123  0.0469  0.3846\n",
            "gender[T.M]       0.0843   0.0491  1.7167 0.0860 -0.0119  0.1804\n",
            "area[T.B]         0.0647   0.0716  0.9041 0.3659 -0.0756  0.2051\n",
            "area[T.C]         0.0751   0.0652  1.1526 0.2491 -0.0526  0.2028\n",
            "area[T.D]        -0.0485   0.0875 -0.5549 0.5789 -0.2199  0.1229\n",
            "area[T.E]         0.1432   0.0955  1.4994 0.1338 -0.0440  0.3304\n",
            "area[T.F]         0.2757   0.1119  2.4648 0.0137  0.0565  0.4949\n",
            "agecat[T.2]      -0.3092   0.0910 -3.3984 0.0007 -0.4875 -0.1309\n",
            "agecat[T.3]      -0.2626   0.0886 -2.9632 0.0030 -0.4363 -0.0889\n",
            "agecat[T.4]      -0.3145   0.0890 -3.5350 0.0004 -0.4889 -0.1401\n",
            "agecat[T.5]      -0.3236   0.0982 -3.2962 0.0010 -0.5160 -0.1312\n",
            "agecat[T.6]      -0.3826   0.1123 -3.4074 0.0007 -0.6027 -0.1625\n",
            "veh_value        -0.0487   0.0252 -1.9292 0.0537 -0.0981  0.0008\n",
            "================================================================\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3np18wBNkCol",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predict\n",
        "ypred = model_gamma.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lxM_fKpsdUFM",
        "colab_type": "code",
        "outputId": "d1f05b86-50e5-4c3d-b557-b0745e63052f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# RMSE\n",
        "from sklearn.metrics import mean_squared_error \n",
        "rmse =np.sqrt(mean_squared_error(y_test,ypred))\n",
        "rmse"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2720.253527526858"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PAghfX5EdjZ2",
        "colab_type": "code",
        "outputId": "09aeeb31-74bd-4b89-d342-a1173b839d2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "model_predictions = model_gamma.get_prediction(X_train)\n",
        "#.summary_frame() returns a pandas DataFrame\n",
        "predictions_summary_frame = model_predictions.summary_frame()\n",
        "print(predictions_summary_frame)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              mean     mean_se  mean_ci_lower  mean_ci_upper\n",
            "14     1629.771557  174.745554    1320.870219    2010.913176\n",
            "16     1994.841870  233.776772    1585.461249    2509.928319\n",
            "40     2141.831259  249.183346    1705.121747    2690.389207\n",
            "64     1857.901086  223.669761    1467.397540    2352.325359\n",
            "65     1523.047881  118.533329    1307.578008    1774.024060\n",
            "...            ...         ...            ...            ...\n",
            "67770  1563.197424  172.955676    1258.447143    1941.747177\n",
            "67797  1791.556703  190.202464    1454.996233    2205.968199\n",
            "67815  1627.277305  131.488915    1388.934977    1906.519362\n",
            "67847  1898.622093  173.801387    1586.787823    2271.737783\n",
            "67854  1684.725841  133.250133    1442.796557    1967.222022\n",
            "\n",
            "[3664 rows x 4 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThHGha15dwVq",
        "colab_type": "code",
        "outputId": "e68df2ce-d41a-403f-e05e-f55aa83091c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "predicted_counts=predictions_summary_frame['mean']\n",
        "actual_counts = y_train['claimcst0']\n",
        "fig = plt.figure()\n",
        "fig.suptitle('Predicted versus actual')\n",
        "predicted, = plt.plot(X_train.index, predicted_counts, 'go-', label='Predicted counts')\n",
        "actual, = plt.plot(X_train.index, actual_counts, 'ro-', label='Actual counts')\n",
        "plt.legend(handles=[predicted, actual])\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UQaDEFbBJzMujCBtvOci7zOdEbQqNhgYYPtxanj8//CUXWb1QJLIeSExSWXkr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\nRMYIDYPBYDBE5v8D327+B2TNW0YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1g_idmlvzMN",
        "colab_type": "code",
        "outputId": "b86173a4-5530-4b0a-da61-ce60cc13f037",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        }
      },
      "source": [
        "plt.clf()\n",
        "fig = plt.figure()\n",
        "fig.suptitle('Scatter plot of Actual versus Predicted counts')\n",
        "plt.scatter(x=predicted_counts, y=actual_counts, marker='.')\n",
        "plt.xlabel('Predicted counts')\n",
        "plt.ylabel('Actual counts')\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WMFt6AqvOslQErodVUrAeViXCuj5begK7ErFUBNMn1PHAV2bmVL0Uqprp76oc6/ps6Qms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\nccOpGzyAtdsPsPvQUQR4Yd3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203VTtPtXF24FxKj079fMmsg9r2wiZdRGH2sY7f3g/QPx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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRzSy-VLHZNM",
        "colab_type": "code",
        "outputId": "31e18229-cc03-4205-9724-18ad866cf826",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "from scipy import stats\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "resid = model_gamma.resid_deviance.copy()\n",
        "resid_std = stats.zscore(resid)\n",
        "ax.hist(resid_std, bins=25)\n",
        "ax.set_title('Histogram of standardized deviance residuals');\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsTXvXKwHeRx",
        "colab_type": "code",
        "outputId": "df9e0460-0e80-4655-939b-179a84b91546",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "from statsmodels import graphics\n",
        "graphics.gofplots.qqplot(resid, line='r')"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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8WpP/4sXQvz+0bAmjR0PPnjB3LgwbpuQvkgMSaQDMbISZzTGzD8zsyWilcUrK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\neRqeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8WpP/4sXQvz+0bAmjR0PPnjB3LgwbpuQvkgMSaQDMbISZzTGzD8zsyWilcUrK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\neRqeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}