Customer_Churn_Regression_Model.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/drshaheed/Predicting-Customer-Churn-with-Logistic-Regression/blob/main/Customer_Churn_Regression_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LVjB1_3Ov7Sp"
      },
      "outputs": [],
      "source": [
        "# install pycaret\n",
        "!pip install pycaret"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import libraries\n",
        "import pandas as pd\n",
        "import numpy as np# read csv data\n",
        "data = pd.read_csv('https://raw.githubusercontent.com/srees1988/predict-churn-py/main/customer_churn_data.csv')"
      ],
      "metadata": {
        "id": "86J7uGxIxzqx"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "Gk777xPmyL-C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check data types\n",
        "data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U3Gg0kIQyJlG",
        "outputId": "bbe6df57-c755-4729-f5f9-e4fec0fcf543"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customerID           object\n",
              "gender               object\n",
              "SeniorCitizen         int64\n",
              "Partner              object\n",
              "Dependents           object\n",
              "tenure                int64\n",
              "PhoneService         object\n",
              "MultipleLines        object\n",
              "InternetService      object\n",
              "OnlineSecurity       object\n",
              "OnlineBackup         object\n",
              "DeviceProtection     object\n",
              "TechSupport          object\n",
              "StreamingTV          object\n",
              "StreamingMovies      object\n",
              "Contract             object\n",
              "PaperlessBilling     object\n",
              "PaymentMethod        object\n",
              "MonthlyCharges      float64\n",
              "TotalCharges         object\n",
              "Churn                object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# replace blanks with np.nan\n",
        "data['TotalCharges'] = data['TotalCharges'].replace(' ', np.nan)# convert to float64\n",
        "data['TotalCharges'] = data['TotalCharges'].astype('float64')"
      ],
      "metadata": {
        "id": "4Lr1ZS_TyP3i"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notice that TotalCharges is of an object type instead of float64. Upon investigation, I figured out there are some blank spaces in this column which has caused Python to force the data type as object . To fix that, we will have to trim blank spaces before changing the data type."
      ],
      "metadata": {
        "id": "qsh0uWxmyaJ8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Intuitively contract type, tenure (length of stay of the customer), and pricing plans are very important information when it comes to customer churn or retention. Let’s explore the relationship:"
      ],
      "metadata": {
        "id": "JWu8bVwTye82"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "fig = px.scatter(x=data['tenure'], y=data['TotalCharges'], \n",
        "                 color = data['Churn'], template = 'presentation', \n",
        "                 opacity = 0.5, facet_col = data['Contract'], \n",
        "                 title = 'Customer Churn by Tenure, Charges, and Contract Type',\n",
        "                 labels = {'x' : 'Customer Tenure', 'y' : 'Total Charges $'})\n",
        "                \n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "dbc1DSutyTvh",
        "outputId": "97536168-8bc0-4505-a13c-6a9ec22cba62"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
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{\"template\":{\"data\":{\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"width\":3},\"marker\":{\"size\":9},\"type\":\"scatter3d\"}],\"scattergeo\":[{\"line\":{\"width\":3},\"marker\":{\"size\":9},\"type\":\"scattergeo\"}],\"scattergl\":[{\"line\":{\"width\":3},\"marker\":{\"size\":9},\"type\":\"scattergl\"}],\"scatterpolargl\":[{\"line\":{\"width\":3},\"marker\":{\"size\":9},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"line\":{\"width\":3},\"marker\":{\"size\":9},\"type\":\"scatterpolar\"}],\"scatter\":[{\"line\":{\"width\":3},\"marker\":{\"size\":9},\"type\":\"scatter\"}],\"scatterternary\":[{\"line\":{\"width\":3},\"marker\":{\"size\":9},\"type\":\"scatterternary\"}],\"table\":[{\"cells\":{\"height\":30},\"header\":{\"height\":36},\"type\":\"table\"}]},\"layout\":{\"font\":{\"size\":18},\"xaxis\":{\"title\":{\"standoff\":15}},\"yaxis\":{\"title\":{\"standoff\":15}}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,0.31999999999999995],\"title\":{\"text\":\"Customer 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              "                            \n",
              "var gd = document.getElementById('7c8848c8-951d-4001-b094-844985011105');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that most churn can be seen in the contracts that are “Month-to-Month”. Makes sense, ofcourse. Also, I can see that as the tenure increases and so are the total charges, the likelihood of customers with high tenure and low charges is less compared to customers with high tenure and high charges."
      ],
      "metadata": {
        "id": "xYXo3bmJysS_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dealing Missing Values"
      ],
      "metadata": {
        "id": "qPwWfckJy1t7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check missing values\n",
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnQ1WcLsyuBN",
        "outputId": "988f8bbe-b50c-4841-d99e-122583eaff25"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customerID           0\n",
              "gender               0\n",
              "SeniorCitizen        0\n",
              "Partner              0\n",
              "Dependents           0\n",
              "tenure               0\n",
              "PhoneService         0\n",
              "MultipleLines        0\n",
              "InternetService      0\n",
              "OnlineSecurity       0\n",
              "OnlineBackup         0\n",
              "DeviceProtection     0\n",
              "TechSupport          0\n",
              "StreamingTV          0\n",
              "StreamingMovies      0\n",
              "Contract             0\n",
              "PaperlessBilling     0\n",
              "PaymentMethod        0\n",
              "MonthlyCharges       0\n",
              "TotalCharges        11\n",
              "Churn                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that because we replaced blank values with np.nan there are now 11 rows with missing values in TotalCharges. No problem — I will leave it with PyCaret to impute it automatically."
      ],
      "metadata": {
        "id": "CWufpnWiy_tZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preparation"
      ],
      "metadata": {
        "id": "Tj_es1g6zHVa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Common to all modules in PyCaret, the setup is the first and the only mandatory step in any machine learning experiment performed in PyCaret. This function takes care of all the data preparation required prior to training models. Besides performing some basic default processing tasks, PyCaret also offers a wide array of pre-processing features. To learn more about all the preprocessing functionalities in PyCaret"
      ],
      "metadata": {
        "id": "Nm_Z7kD0zJgn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# initial setup\n",
        "from pycaret.classification import *\n",
        "s = setup(data, target = 'Churn', ignore_features = ['customerID'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "7CfPeAScy7JI",
        "outputId": "f3790dea-a108-4a09-d0b2-4f50bab05b26"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f06141596a0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_4758d_row13_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_4758d\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_4758d_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_4758d_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_4758d_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_4758d_row0_col1\" class=\"data row0 col1\" >5266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_4758d_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_4758d_row1_col1\" class=\"data row1 col1\" >Churn</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_4758d_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_4758d_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_4758d_row3_col0\" class=\"data row3 col0\" >Target mapping</td>\n",
              "      <td id=\"T_4758d_row3_col1\" class=\"data row3 col1\" >No: 0, Yes: 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_4758d_row4_col0\" class=\"data row4 col0\" >Original data shape</td>\n",
              "      <td id=\"T_4758d_row4_col1\" class=\"data row4 col1\" >(7043, 21)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_4758d_row5_col0\" class=\"data row5 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_4758d_row5_col1\" class=\"data row5 col1\" >(7043, 41)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_4758d_row6_col0\" class=\"data row6 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_4758d_row6_col1\" class=\"data row6 col1\" >(4930, 41)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_4758d_row7_col0\" class=\"data row7 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_4758d_row7_col1\" class=\"data row7 col1\" >(2113, 41)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_4758d_row8_col0\" class=\"data row8 col0\" >Ignore features</td>\n",
              "      <td id=\"T_4758d_row8_col1\" class=\"data row8 col1\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_4758d_row9_col0\" class=\"data row9 col0\" >Ordinal features</td>\n",
              "      <td id=\"T_4758d_row9_col1\" class=\"data row9 col1\" >5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_4758d_row10_col0\" class=\"data row10 col0\" >Numeric features</td>\n",
              "      <td id=\"T_4758d_row10_col1\" class=\"data row10 col1\" >4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_4758d_row11_col0\" class=\"data row11 col0\" >Categorical features</td>\n",
              "      <td id=\"T_4758d_row11_col1\" class=\"data row11 col1\" >15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_4758d_row12_col0\" class=\"data row12 col0\" >Rows with missing values</td>\n",
              "      <td id=\"T_4758d_row12_col1\" class=\"data row12 col1\" >0.2%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_4758d_row13_col0\" class=\"data row13 col0\" >Preprocess</td>\n",
              "      <td id=\"T_4758d_row13_col1\" class=\"data row13 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_4758d_row14_col0\" class=\"data row14 col0\" >Imputation type</td>\n",
              "      <td id=\"T_4758d_row14_col1\" class=\"data row14 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_4758d_row15_col0\" class=\"data row15 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_4758d_row15_col1\" class=\"data row15 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_4758d_row16_col0\" class=\"data row16 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_4758d_row16_col1\" class=\"data row16 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_4758d_row17_col0\" class=\"data row17 col0\" >Maximum one-hot encoding</td>\n",
              "      <td id=\"T_4758d_row17_col1\" class=\"data row17 col1\" >25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_4758d_row18_col0\" class=\"data row18 col0\" >Encoding method</td>\n",
              "      <td id=\"T_4758d_row18_col1\" class=\"data row18 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "      <td id=\"T_4758d_row19_col0\" class=\"data row19 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_4758d_row19_col1\" class=\"data row19 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "      <td id=\"T_4758d_row20_col0\" class=\"data row20 col0\" >Fold Number</td>\n",
              "      <td id=\"T_4758d_row20_col1\" class=\"data row20 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
              "      <td id=\"T_4758d_row21_col0\" class=\"data row21 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_4758d_row21_col1\" class=\"data row21 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
              "      <td id=\"T_4758d_row22_col0\" class=\"data row22 col0\" >Use GPU</td>\n",
              "      <td id=\"T_4758d_row22_col1\" class=\"data row22 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
              "      <td id=\"T_4758d_row23_col0\" class=\"data row23 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_4758d_row23_col1\" class=\"data row23 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
              "      <td id=\"T_4758d_row24_col0\" class=\"data row24 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_4758d_row24_col1\" class=\"data row24 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4758d_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
              "      <td id=\"T_4758d_row25_col0\" class=\"data row25 col0\" >USI</td>\n",
              "      <td id=\"T_4758d_row25_col1\" class=\"data row25 col1\" >bc04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Whenever you initialize the setup function in PyCaret, it profiles the dataset and infers the data types for all input features. In this case, we can see except for tenure MonthlyCharges and TotalCharges , everything else is categorical, which is correct, we can now press enter to continue. If data types are not inferred correctly (which can happen sometimes), we can use numeric_feature and categorical_feature to overwrite the data types.\n",
        "\n",
        "Also, I have passed ignore_features = ['customerID'] in the setup function so that it is not considered when training the models. The good thing about this is PyCaret will not remove the column from the dataset, it will just ignore it behind the scene for model training. As such when you generate predictions at the end, you don’t need to worry about joining IDs back by yourself."
      ],
      "metadata": {
        "id": "50KwSkd-zdZy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Training & Selection"
      ],
      "metadata": {
        "id": "3p7JJyQkzwWu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that data preparation is done, let’s start the training process by using compare_models functionality. This function trains all the algorithms available in the model library and evaluates multiple performance metrics using cross-validation."
      ],
      "metadata": {
        "id": "SjpbcEgfzyqu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# compare all models\n",
        "best_model = compare_models(sort='AUC')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520,
          "referenced_widgets": [
            "8a451e7651134d708385c06c34cead6b",
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            "ef15b11c7e0b4a64bcc90e176680a414",
            "d08cfe06dfa746939ee4dd21f09b006d",
            "2e1e89444589497e8ae39ad129645904",
            "c883b386608b4b02ac28a25d8dc8dbb9",
            "ce1a10bc7ade4478bd91795937c7b03a",
            "dc95790dae444215893f280eb5f1b795",
            "8151d60e148b4b319877db8809936dca",
            "b556e03a1f074e70a969db146d99f297"
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        "id": "8q8TzEQVzZe_",
        "outputId": "f6ac96f2-755e-409d-d4cc-2e02d453988a"
      },
      "execution_count": 9,
      "outputs": [
        {
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              "#T_2d16a_row0_col0, #T_2d16a_row0_col1, #T_2d16a_row0_col3, #T_2d16a_row0_col5, #T_2d16a_row0_col6, #T_2d16a_row0_col7, #T_2d16a_row1_col0, #T_2d16a_row1_col1, #T_2d16a_row1_col2, #T_2d16a_row1_col3, #T_2d16a_row1_col4, #T_2d16a_row1_col5, #T_2d16a_row1_col6, #T_2d16a_row1_col7, #T_2d16a_row2_col0, #T_2d16a_row2_col2, #T_2d16a_row2_col3, #T_2d16a_row2_col4, #T_2d16a_row3_col0, #T_2d16a_row3_col1, #T_2d16a_row3_col2, #T_2d16a_row3_col3, #T_2d16a_row3_col4, #T_2d16a_row3_col5, #T_2d16a_row3_col6, #T_2d16a_row3_col7, #T_2d16a_row4_col0, #T_2d16a_row4_col1, #T_2d16a_row4_col2, #T_2d16a_row4_col3, #T_2d16a_row4_col4, #T_2d16a_row4_col5, #T_2d16a_row4_col6, #T_2d16a_row4_col7, #T_2d16a_row5_col0, #T_2d16a_row5_col1, #T_2d16a_row5_col2, #T_2d16a_row5_col3, #T_2d16a_row5_col4, #T_2d16a_row5_col5, #T_2d16a_row5_col6, #T_2d16a_row5_col7, #T_2d16a_row6_col0, #T_2d16a_row6_col1, #T_2d16a_row6_col2, #T_2d16a_row6_col3, #T_2d16a_row6_col4, #T_2d16a_row6_col5, #T_2d16a_row6_col6, #T_2d16a_row6_col7, #T_2d16a_row7_col0, #T_2d16a_row7_col1, #T_2d16a_row7_col2, #T_2d16a_row7_col4, #T_2d16a_row7_col5, #T_2d16a_row7_col6, #T_2d16a_row7_col7, #T_2d16a_row8_col0, #T_2d16a_row8_col1, #T_2d16a_row8_col2, #T_2d16a_row8_col3, #T_2d16a_row8_col4, #T_2d16a_row8_col5, #T_2d16a_row8_col6, #T_2d16a_row8_col7, #T_2d16a_row9_col0, #T_2d16a_row9_col1, #T_2d16a_row9_col2, #T_2d16a_row9_col3, #T_2d16a_row9_col4, #T_2d16a_row9_col5, #T_2d16a_row9_col6, #T_2d16a_row9_col7, #T_2d16a_row10_col0, #T_2d16a_row10_col1, #T_2d16a_row10_col2, #T_2d16a_row10_col3, #T_2d16a_row10_col4, #T_2d16a_row10_col5, #T_2d16a_row10_col6, #T_2d16a_row10_col7, #T_2d16a_row11_col0, #T_2d16a_row11_col1, #T_2d16a_row11_col2, #T_2d16a_row11_col3, #T_2d16a_row11_col4, #T_2d16a_row11_col5, #T_2d16a_row11_col6, #T_2d16a_row11_col7, #T_2d16a_row12_col0, #T_2d16a_row12_col1, #T_2d16a_row12_col2, #T_2d16a_row12_col3, #T_2d16a_row12_col4, #T_2d16a_row12_col5, #T_2d16a_row12_col6, #T_2d16a_row12_col7, #T_2d16a_row13_col0, #T_2d16a_row13_col1, #T_2d16a_row13_col2, #T_2d16a_row13_col3, #T_2d16a_row13_col4, #T_2d16a_row13_col5, #T_2d16a_row13_col6, #T_2d16a_row13_col7, #T_2d16a_row14_col0, #T_2d16a_row14_col1, #T_2d16a_row14_col2, #T_2d16a_row14_col3, #T_2d16a_row14_col4, #T_2d16a_row14_col5, #T_2d16a_row14_col6, #T_2d16a_row14_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_2d16a_row0_col2, #T_2d16a_row0_col4, #T_2d16a_row2_col1, #T_2d16a_row2_col5, #T_2d16a_row2_col6, #T_2d16a_row2_col7, #T_2d16a_row7_col3 {\n",
              "  text-align: left;\n",
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              "}\n",
              "#T_2d16a_row0_col8, #T_2d16a_row1_col8, #T_2d16a_row2_col8, #T_2d16a_row3_col8, #T_2d16a_row4_col8, #T_2d16a_row5_col8, #T_2d16a_row6_col8, #T_2d16a_row7_col8, #T_2d16a_row8_col8, #T_2d16a_row9_col8, #T_2d16a_row10_col8, #T_2d16a_row11_col8, #T_2d16a_row12_col8, #T_2d16a_row13_col8 {\n",
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              "#T_2d16a_row14_col8 {\n",
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              "</style>\n",
              "<table id=\"T_2d16a\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_2d16a_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_2d16a_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_2d16a_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_2d16a_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_2d16a_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_2d16a_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_2d16a_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_2d16a_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_2d16a_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row0\" class=\"row_heading level0 row0\" >gbc</th>\n",
              "      <td id=\"T_2d16a_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_2d16a_row0_col1\" class=\"data row0 col1\" >0.8057</td>\n",
              "      <td id=\"T_2d16a_row0_col2\" class=\"data row0 col2\" >0.8487</td>\n",
              "      <td id=\"T_2d16a_row0_col3\" class=\"data row0 col3\" >0.5199</td>\n",
              "      <td id=\"T_2d16a_row0_col4\" class=\"data row0 col4\" >0.6728</td>\n",
              "      <td id=\"T_2d16a_row0_col5\" class=\"data row0 col5\" >0.5853</td>\n",
              "      <td id=\"T_2d16a_row0_col6\" class=\"data row0 col6\" >0.4615</td>\n",
              "      <td id=\"T_2d16a_row0_col7\" class=\"data row0 col7\" >0.4687</td>\n",
              "      <td id=\"T_2d16a_row0_col8\" class=\"data row0 col8\" >0.5510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row1\" class=\"row_heading level0 row1\" >ada</th>\n",
              "      <td id=\"T_2d16a_row1_col0\" class=\"data row1 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_2d16a_row1_col1\" class=\"data row1 col1\" >0.7988</td>\n",
              "      <td id=\"T_2d16a_row1_col2\" class=\"data row1 col2\" >0.8440</td>\n",
              "      <td id=\"T_2d16a_row1_col3\" class=\"data row1 col3\" >0.5184</td>\n",
              "      <td id=\"T_2d16a_row1_col4\" class=\"data row1 col4\" >0.6524</td>\n",
              "      <td id=\"T_2d16a_row1_col5\" class=\"data row1 col5\" >0.5770</td>\n",
              "      <td id=\"T_2d16a_row1_col6\" class=\"data row1 col6\" >0.4474</td>\n",
              "      <td id=\"T_2d16a_row1_col7\" class=\"data row1 col7\" >0.4529</td>\n",
              "      <td id=\"T_2d16a_row1_col8\" class=\"data row1 col8\" >0.4350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row2\" class=\"row_heading level0 row2\" >lr</th>\n",
              "      <td id=\"T_2d16a_row2_col0\" class=\"data row2 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_2d16a_row2_col1\" class=\"data row2 col1\" >0.8059</td>\n",
              "      <td id=\"T_2d16a_row2_col2\" class=\"data row2 col2\" >0.8419</td>\n",
              "      <td id=\"T_2d16a_row2_col3\" class=\"data row2 col3\" >0.5444</td>\n",
              "      <td id=\"T_2d16a_row2_col4\" class=\"data row2 col4\" >0.6635</td>\n",
              "      <td id=\"T_2d16a_row2_col5\" class=\"data row2 col5\" >0.5975</td>\n",
              "      <td id=\"T_2d16a_row2_col6\" class=\"data row2 col6\" >0.4714</td>\n",
              "      <td id=\"T_2d16a_row2_col7\" class=\"data row2 col7\" >0.4757</td>\n",
              "      <td id=\"T_2d16a_row2_col8\" class=\"data row2 col8\" >1.2330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row3\" class=\"row_heading level0 row3\" >lightgbm</th>\n",
              "      <td id=\"T_2d16a_row3_col0\" class=\"data row3 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_2d16a_row3_col1\" class=\"data row3 col1\" >0.7972</td>\n",
              "      <td id=\"T_2d16a_row3_col2\" class=\"data row3 col2\" >0.8375</td>\n",
              "      <td id=\"T_2d16a_row3_col3\" class=\"data row3 col3\" >0.5107</td>\n",
              "      <td id=\"T_2d16a_row3_col4\" class=\"data row3 col4\" >0.6492</td>\n",
              "      <td id=\"T_2d16a_row3_col5\" class=\"data row3 col5\" >0.5708</td>\n",
              "      <td id=\"T_2d16a_row3_col6\" class=\"data row3 col6\" >0.4408</td>\n",
              "      <td id=\"T_2d16a_row3_col7\" class=\"data row3 col7\" >0.4467</td>\n",
              "      <td id=\"T_2d16a_row3_col8\" class=\"data row3 col8\" >0.3330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row4\" class=\"row_heading level0 row4\" >lda</th>\n",
              "      <td id=\"T_2d16a_row4_col0\" class=\"data row4 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_2d16a_row4_col1\" class=\"data row4 col1\" >0.7953</td>\n",
              "      <td id=\"T_2d16a_row4_col2\" class=\"data row4 col2\" >0.8335</td>\n",
              "      <td id=\"T_2d16a_row4_col3\" class=\"data row4 col3\" >0.5459</td>\n",
              "      <td id=\"T_2d16a_row4_col4\" class=\"data row4 col4\" >0.6329</td>\n",
              "      <td id=\"T_2d16a_row4_col5\" class=\"data row4 col5\" >0.5857</td>\n",
              "      <td id=\"T_2d16a_row4_col6\" class=\"data row4 col6\" >0.4509</td>\n",
              "      <td id=\"T_2d16a_row4_col7\" class=\"data row4 col7\" >0.4534</td>\n",
              "      <td id=\"T_2d16a_row4_col8\" class=\"data row4 col8\" >0.4290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row5\" class=\"row_heading level0 row5\" >xgboost</th>\n",
              "      <td id=\"T_2d16a_row5_col0\" class=\"data row5 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_2d16a_row5_col1\" class=\"data row5 col1\" >0.7872</td>\n",
              "      <td id=\"T_2d16a_row5_col2\" class=\"data row5 col2\" >0.8253</td>\n",
              "      <td id=\"T_2d16a_row5_col3\" class=\"data row5 col3\" >0.5100</td>\n",
              "      <td id=\"T_2d16a_row5_col4\" class=\"data row5 col4\" >0.6208</td>\n",
              "      <td id=\"T_2d16a_row5_col5\" class=\"data row5 col5\" >0.5589</td>\n",
              "      <td id=\"T_2d16a_row5_col6\" class=\"data row5 col6\" >0.4207</td>\n",
              "      <td id=\"T_2d16a_row5_col7\" class=\"data row5 col7\" >0.4248</td>\n",
              "      <td id=\"T_2d16a_row5_col8\" class=\"data row5 col8\" >0.2770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row6\" class=\"row_heading level0 row6\" >rf</th>\n",
              "      <td id=\"T_2d16a_row6_col0\" class=\"data row6 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_2d16a_row6_col1\" class=\"data row6 col1\" >0.7970</td>\n",
              "      <td id=\"T_2d16a_row6_col2\" class=\"data row6 col2\" >0.8209</td>\n",
              "      <td id=\"T_2d16a_row6_col3\" class=\"data row6 col3\" >0.5015</td>\n",
              "      <td id=\"T_2d16a_row6_col4\" class=\"data row6 col4\" >0.6536</td>\n",
              "      <td id=\"T_2d16a_row6_col5\" class=\"data row6 col5\" >0.5662</td>\n",
              "      <td id=\"T_2d16a_row6_col6\" class=\"data row6 col6\" >0.4369</td>\n",
              "      <td id=\"T_2d16a_row6_col7\" class=\"data row6 col7\" >0.4442</td>\n",
              "      <td id=\"T_2d16a_row6_col8\" class=\"data row6 col8\" >0.4700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row7\" class=\"row_heading level0 row7\" >qda</th>\n",
              "      <td id=\"T_2d16a_row7_col0\" class=\"data row7 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_2d16a_row7_col1\" class=\"data row7 col1\" >0.6513</td>\n",
              "      <td id=\"T_2d16a_row7_col2\" class=\"data row7 col2\" >0.8176</td>\n",
              "      <td id=\"T_2d16a_row7_col3\" class=\"data row7 col3\" >0.8440</td>\n",
              "      <td id=\"T_2d16a_row7_col4\" class=\"data row7 col4\" >0.4260</td>\n",
              "      <td id=\"T_2d16a_row7_col5\" class=\"data row7 col5\" >0.5609</td>\n",
              "      <td id=\"T_2d16a_row7_col6\" class=\"data row7 col6\" >0.3232</td>\n",
              "      <td id=\"T_2d16a_row7_col7\" class=\"data row7 col7\" >0.3830</td>\n",
              "      <td id=\"T_2d16a_row7_col8\" class=\"data row7 col8\" >0.4040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row8\" class=\"row_heading level0 row8\" >nb</th>\n",
              "      <td id=\"T_2d16a_row8_col0\" class=\"data row8 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_2d16a_row8_col1\" class=\"data row8 col1\" >0.6892</td>\n",
              "      <td id=\"T_2d16a_row8_col2\" class=\"data row8 col2\" >0.8144</td>\n",
              "      <td id=\"T_2d16a_row8_col3\" class=\"data row8 col3\" >0.8409</td>\n",
              "      <td id=\"T_2d16a_row8_col4\" class=\"data row8 col4\" >0.4539</td>\n",
              "      <td id=\"T_2d16a_row8_col5\" class=\"data row8 col5\" >0.5895</td>\n",
              "      <td id=\"T_2d16a_row8_col6\" class=\"data row8 col6\" >0.3736</td>\n",
              "      <td id=\"T_2d16a_row8_col7\" class=\"data row8 col7\" >0.4200</td>\n",
              "      <td id=\"T_2d16a_row8_col8\" class=\"data row8 col8\" >0.2720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row9\" class=\"row_heading level0 row9\" >et</th>\n",
              "      <td id=\"T_2d16a_row9_col0\" class=\"data row9 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_2d16a_row9_col1\" class=\"data row9 col1\" >0.7746</td>\n",
              "      <td id=\"T_2d16a_row9_col2\" class=\"data row9 col2\" >0.7896</td>\n",
              "      <td id=\"T_2d16a_row9_col3\" class=\"data row9 col3\" >0.4686</td>\n",
              "      <td id=\"T_2d16a_row9_col4\" class=\"data row9 col4\" >0.5965</td>\n",
              "      <td id=\"T_2d16a_row9_col5\" class=\"data row9 col5\" >0.5235</td>\n",
              "      <td id=\"T_2d16a_row9_col6\" class=\"data row9 col6\" >0.3791</td>\n",
              "      <td id=\"T_2d16a_row9_col7\" class=\"data row9 col7\" >0.3845</td>\n",
              "      <td id=\"T_2d16a_row9_col8\" class=\"data row9 col8\" >0.6060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row10\" class=\"row_heading level0 row10\" >knn</th>\n",
              "      <td id=\"T_2d16a_row10_col0\" class=\"data row10 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_2d16a_row10_col1\" class=\"data row10 col1\" >0.7653</td>\n",
              "      <td id=\"T_2d16a_row10_col2\" class=\"data row10 col2\" >0.7465</td>\n",
              "      <td id=\"T_2d16a_row10_col3\" class=\"data row10 col3\" >0.4457</td>\n",
              "      <td id=\"T_2d16a_row10_col4\" class=\"data row10 col4\" >0.5736</td>\n",
              "      <td id=\"T_2d16a_row10_col5\" class=\"data row10 col5\" >0.5012</td>\n",
              "      <td id=\"T_2d16a_row10_col6\" class=\"data row10 col6\" >0.3511</td>\n",
              "      <td id=\"T_2d16a_row10_col7\" class=\"data row10 col7\" >0.3560</td>\n",
              "      <td id=\"T_2d16a_row10_col8\" class=\"data row10 col8\" >0.3090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row11\" class=\"row_heading level0 row11\" >dt</th>\n",
              "      <td id=\"T_2d16a_row11_col0\" class=\"data row11 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_2d16a_row11_col1\" class=\"data row11 col1\" >0.7304</td>\n",
              "      <td id=\"T_2d16a_row11_col2\" class=\"data row11 col2\" >0.6600</td>\n",
              "      <td id=\"T_2d16a_row11_col3\" class=\"data row11 col3\" >0.5068</td>\n",
              "      <td id=\"T_2d16a_row11_col4\" class=\"data row11 col4\" >0.4912</td>\n",
              "      <td id=\"T_2d16a_row11_col5\" class=\"data row11 col5\" >0.4981</td>\n",
              "      <td id=\"T_2d16a_row11_col6\" class=\"data row11 col6\" >0.3141</td>\n",
              "      <td id=\"T_2d16a_row11_col7\" class=\"data row11 col7\" >0.3147</td>\n",
              "      <td id=\"T_2d16a_row11_col8\" class=\"data row11 col8\" >0.3330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row12\" class=\"row_heading level0 row12\" >dummy</th>\n",
              "      <td id=\"T_2d16a_row12_col0\" class=\"data row12 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_2d16a_row12_col1\" class=\"data row12 col1\" >0.7347</td>\n",
              "      <td id=\"T_2d16a_row12_col2\" class=\"data row12 col2\" >0.5000</td>\n",
              "      <td id=\"T_2d16a_row12_col3\" class=\"data row12 col3\" >0.0000</td>\n",
              "      <td id=\"T_2d16a_row12_col4\" class=\"data row12 col4\" >0.0000</td>\n",
              "      <td id=\"T_2d16a_row12_col5\" class=\"data row12 col5\" >0.0000</td>\n",
              "      <td id=\"T_2d16a_row12_col6\" class=\"data row12 col6\" >0.0000</td>\n",
              "      <td id=\"T_2d16a_row12_col7\" class=\"data row12 col7\" >0.0000</td>\n",
              "      <td id=\"T_2d16a_row12_col8\" class=\"data row12 col8\" >0.4280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row13\" class=\"row_heading level0 row13\" >svm</th>\n",
              "      <td id=\"T_2d16a_row13_col0\" class=\"data row13 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_2d16a_row13_col1\" class=\"data row13 col1\" >0.6807</td>\n",
              "      <td id=\"T_2d16a_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
              "      <td id=\"T_2d16a_row13_col3\" class=\"data row13 col3\" >0.6215</td>\n",
              "      <td id=\"T_2d16a_row13_col4\" class=\"data row13 col4\" >0.5246</td>\n",
              "      <td id=\"T_2d16a_row13_col5\" class=\"data row13 col5\" >0.5128</td>\n",
              "      <td id=\"T_2d16a_row13_col6\" class=\"data row13 col6\" >0.3124</td>\n",
              "      <td id=\"T_2d16a_row13_col7\" class=\"data row13 col7\" >0.3514</td>\n",
              "      <td id=\"T_2d16a_row13_col8\" class=\"data row13 col8\" >0.4070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2d16a_level0_row14\" class=\"row_heading level0 row14\" >ridge</th>\n",
              "      <td id=\"T_2d16a_row14_col0\" class=\"data row14 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_2d16a_row14_col1\" class=\"data row14 col1\" >0.8008</td>\n",
              "      <td id=\"T_2d16a_row14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
              "      <td id=\"T_2d16a_row14_col3\" class=\"data row14 col3\" >0.5054</td>\n",
              "      <td id=\"T_2d16a_row14_col4\" class=\"data row14 col4\" >0.6632</td>\n",
              "      <td id=\"T_2d16a_row14_col5\" class=\"data row14 col5\" >0.5732</td>\n",
              "      <td id=\"T_2d16a_row14_col6\" class=\"data row14 col6\" >0.4465</td>\n",
              "      <td id=\"T_2d16a_row14_col7\" class=\"data row14 col7\" >0.4537</td>\n",
              "      <td id=\"T_2d16a_row14_col8\" class=\"data row14 col8\" >0.2390</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/65 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a451e7651134d708385c06c34cead6b"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print best_model parameters\n",
        "print(best_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6OgZTerM2470",
        "outputId": "fa374deb-8117-4125-d90f-6c2559caaabd"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
            "                           learning_rate=0.1, loss='log_loss', max_depth=3,\n",
            "                           max_features=None, max_leaf_nodes=None,\n",
            "                           min_impurity_decrease=0.0, min_samples_leaf=1,\n",
            "                           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
            "                           n_estimators=100, n_iter_no_change=None,\n",
            "                           random_state=5266, subsample=1.0, tol=0.0001,\n",
            "                           validation_fraction=0.1, verbose=0,\n",
            "                           warm_start=False)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hyperparameter Tuning"
      ],
      "metadata": {
        "id": "_SxwIuB93BxJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "we can use the tune_model function from PyCaret to automatically tune the hyperparameters of the model."
      ],
      "metadata": {
        "id": "ROjfGbqF3DVU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# tune best model\n",
        "tuned_best_model = tune_model(best_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492,
          "referenced_widgets": [
            "f8f680437e70478ca1b5da2573ad8100",
            "5f7c848596134f71a8b8d5b0d1df1091",
            "e018d498a06b4ca8b5c1e30c4184b42c",
            "166a08d3d96c473f8cbceee03b4521cc",
            "91ac39e4139d4e4b8dd273108ba1a58c",
            "eab348f9cd444e009cfbeb95969bfea6",
            "f895cea712a249858872f932f8ab5813",
            "586929ab726c44939711e0a28be1f75a",
            "6a5b55decc674965afedd35993b44fbd",
            "7b7b718154b24c9b8b18ea6cdd63725b",
            "67950cdafdb9460a84b296aad6fbbd02"
          ]
        },
        "id": "2oCfHHGW29mR",
        "outputId": "27acd8df-d3e8-42db-85e5-3131bba22343"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f061503d040>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_c8fb8_row10_col0, #T_c8fb8_row10_col1, #T_c8fb8_row10_col2, #T_c8fb8_row10_col3, #T_c8fb8_row10_col4, #T_c8fb8_row10_col5, #T_c8fb8_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_c8fb8\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_c8fb8_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_c8fb8_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_c8fb8_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_c8fb8_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_c8fb8_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_c8fb8_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_c8fb8_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_c8fb8_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_c8fb8_row0_col0\" class=\"data row0 col0\" >0.8114</td>\n",
              "      <td id=\"T_c8fb8_row0_col1\" class=\"data row0 col1\" >0.8415</td>\n",
              "      <td id=\"T_c8fb8_row0_col2\" class=\"data row0 col2\" >0.5154</td>\n",
              "      <td id=\"T_c8fb8_row0_col3\" class=\"data row0 col3\" >0.6907</td>\n",
              "      <td id=\"T_c8fb8_row0_col4\" class=\"data row0 col4\" >0.5903</td>\n",
              "      <td id=\"T_c8fb8_row0_col5\" class=\"data row0 col5\" >0.4711</td>\n",
              "      <td id=\"T_c8fb8_row0_col6\" class=\"data row0 col6\" >0.4796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8fb8_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_c8fb8_row1_col0\" class=\"data row1 col0\" >0.7931</td>\n",
              "      <td id=\"T_c8fb8_row1_col1\" class=\"data row1 col1\" >0.8455</td>\n",
              "      <td id=\"T_c8fb8_row1_col2\" class=\"data row1 col2\" >0.4538</td>\n",
              "      <td id=\"T_c8fb8_row1_col3\" class=\"data row1 col3\" >0.6556</td>\n",
              "      <td id=\"T_c8fb8_row1_col4\" class=\"data row1 col4\" >0.5364</td>\n",
              "      <td id=\"T_c8fb8_row1_col5\" class=\"data row1 col5\" >0.4088</td>\n",
              "      <td id=\"T_c8fb8_row1_col6\" class=\"data row1 col6\" >0.4203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8fb8_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_c8fb8_row2_col0\" class=\"data row2 col0\" >0.7890</td>\n",
              "      <td id=\"T_c8fb8_row2_col1\" class=\"data row2 col1\" >0.8313</td>\n",
              "      <td id=\"T_c8fb8_row2_col2\" class=\"data row2 col2\" >0.4733</td>\n",
              "      <td id=\"T_c8fb8_row2_col3\" class=\"data row2 col3\" >0.6392</td>\n",
              "      <td id=\"T_c8fb8_row2_col4\" class=\"data row2 col4\" >0.5439</td>\n",
              "      <td id=\"T_c8fb8_row2_col5\" class=\"data row2 col5\" >0.4106</td>\n",
              "      <td id=\"T_c8fb8_row2_col6\" class=\"data row2 col6\" >0.4184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8fb8_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_c8fb8_row3_col0\" class=\"data row3 col0\" >0.8093</td>\n",
              "      <td id=\"T_c8fb8_row3_col1\" class=\"data row3 col1\" >0.8609</td>\n",
              "      <td id=\"T_c8fb8_row3_col2\" class=\"data row3 col2\" >0.5496</td>\n",
              "      <td id=\"T_c8fb8_row3_col3\" class=\"data row3 col3\" >0.6729</td>\n",
              "      <td id=\"T_c8fb8_row3_col4\" class=\"data row3 col4\" >0.6050</td>\n",
              "      <td id=\"T_c8fb8_row3_col5\" class=\"data row3 col5\" >0.4811</td>\n",
              "      <td id=\"T_c8fb8_row3_col6\" class=\"data row3 col6\" >0.4853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8fb8_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_c8fb8_row4_col0\" class=\"data row4 col0\" >0.8235</td>\n",
              "      <td id=\"T_c8fb8_row4_col1\" class=\"data row4 col1\" >0.8596</td>\n",
              "      <td id=\"T_c8fb8_row4_col2\" class=\"data row4 col2\" >0.6107</td>\n",
              "      <td id=\"T_c8fb8_row4_col3\" class=\"data row4 col3\" >0.6897</td>\n",
              "      <td id=\"T_c8fb8_row4_col4\" class=\"data row4 col4\" >0.6478</td>\n",
              "      <td id=\"T_c8fb8_row4_col5\" class=\"data row4 col5\" >0.5306</td>\n",
              "      <td id=\"T_c8fb8_row4_col6\" class=\"data row4 col6\" >0.5324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8fb8_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_c8fb8_row5_col0\" class=\"data row5 col0\" >0.7911</td>\n",
              "      <td id=\"T_c8fb8_row5_col1\" class=\"data row5 col1\" >0.8227</td>\n",
              "      <td id=\"T_c8fb8_row5_col2\" class=\"data row5 col2\" >0.4962</td>\n",
              "      <td id=\"T_c8fb8_row5_col3\" class=\"data row5 col3\" >0.6373</td>\n",
              "      <td id=\"T_c8fb8_row5_col4\" class=\"data row5 col4\" >0.5579</td>\n",
              "      <td id=\"T_c8fb8_row5_col5\" class=\"data row5 col5\" >0.4239</td>\n",
              "      <td id=\"T_c8fb8_row5_col6\" class=\"data row5 col6\" >0.4296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8fb8_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_c8fb8_row6_col0\" class=\"data row6 col0\" >0.8114</td>\n",
              "      <td id=\"T_c8fb8_row6_col1\" class=\"data row6 col1\" >0.8596</td>\n",
              "      <td id=\"T_c8fb8_row6_col2\" class=\"data row6 col2\" >0.5115</td>\n",
              "      <td id=\"T_c8fb8_row6_col3\" class=\"data row6 col3\" >0.6979</td>\n",
              "      <td id=\"T_c8fb8_row6_col4\" class=\"data row6 col4\" >0.5903</td>\n",
              "      <td id=\"T_c8fb8_row6_col5\" class=\"data row6 col5\" >0.4715</td>\n",
              "      <td id=\"T_c8fb8_row6_col6\" class=\"data row6 col6\" >0.4811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8fb8_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_c8fb8_row7_col0\" class=\"data row7 col0\" >0.8093</td>\n",
              "      <td id=\"T_c8fb8_row7_col1\" class=\"data row7 col1\" >0.8417</td>\n",
              "      <td id=\"T_c8fb8_row7_col2\" class=\"data row7 col2\" >0.5038</td>\n",
              "      <td id=\"T_c8fb8_row7_col3\" class=\"data row7 col3\" >0.6947</td>\n",
              "      <td id=\"T_c8fb8_row7_col4\" class=\"data row7 col4\" >0.5841</td>\n",
              "      <td id=\"T_c8fb8_row7_col5\" class=\"data row7 col5\" >0.4644</td>\n",
              "      <td id=\"T_c8fb8_row7_col6\" class=\"data row7 col6\" >0.4745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8fb8_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_c8fb8_row8_col0\" class=\"data row8 col0\" >0.8053</td>\n",
              "      <td id=\"T_c8fb8_row8_col1\" class=\"data row8 col1\" >0.8652</td>\n",
              "      <td id=\"T_c8fb8_row8_col2\" class=\"data row8 col2\" >0.4809</td>\n",
              "      <td id=\"T_c8fb8_row8_col3\" class=\"data row8 col3\" >0.6923</td>\n",
              "      <td id=\"T_c8fb8_row8_col4\" class=\"data row8 col4\" >0.5676</td>\n",
              "      <td id=\"T_c8fb8_row8_col5\" class=\"data row8 col5\" >0.4471</td>\n",
              "      <td id=\"T_c8fb8_row8_col6\" class=\"data row8 col6\" >0.4595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8fb8_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_c8fb8_row9_col0\" class=\"data row9 col0\" >0.7850</td>\n",
              "      <td id=\"T_c8fb8_row9_col1\" class=\"data row9 col1\" >0.8344</td>\n",
              "      <td id=\"T_c8fb8_row9_col2\" class=\"data row9 col2\" >0.4656</td>\n",
              "      <td id=\"T_c8fb8_row9_col3\" class=\"data row9 col3\" >0.6289</td>\n",
              "      <td id=\"T_c8fb8_row9_col4\" class=\"data row9 col4\" >0.5351</td>\n",
              "      <td id=\"T_c8fb8_row9_col5\" class=\"data row9 col5\" >0.3993</td>\n",
              "      <td id=\"T_c8fb8_row9_col6\" class=\"data row9 col6\" >0.4069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8fb8_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_c8fb8_row10_col0\" class=\"data row10 col0\" >0.8028</td>\n",
              "      <td id=\"T_c8fb8_row10_col1\" class=\"data row10 col1\" >0.8463</td>\n",
              "      <td id=\"T_c8fb8_row10_col2\" class=\"data row10 col2\" >0.5061</td>\n",
              "      <td id=\"T_c8fb8_row10_col3\" class=\"data row10 col3\" >0.6699</td>\n",
              "      <td id=\"T_c8fb8_row10_col4\" class=\"data row10 col4\" >0.5758</td>\n",
              "      <td id=\"T_c8fb8_row10_col5\" class=\"data row10 col5\" >0.4508</td>\n",
              "      <td id=\"T_c8fb8_row10_col6\" class=\"data row10 col6\" >0.4588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8fb8_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_c8fb8_row11_col0\" class=\"data row11 col0\" >0.0119</td>\n",
              "      <td id=\"T_c8fb8_row11_col1\" class=\"data row11 col1\" >0.0138</td>\n",
              "      <td id=\"T_c8fb8_row11_col2\" class=\"data row11 col2\" >0.0437</td>\n",
              "      <td id=\"T_c8fb8_row11_col3\" class=\"data row11 col3\" >0.0258</td>\n",
              "      <td id=\"T_c8fb8_row11_col4\" class=\"data row11 col4\" >0.0334</td>\n",
              "      <td id=\"T_c8fb8_row11_col5\" class=\"data row11 col5\" >0.0388</td>\n",
              "      <td id=\"T_c8fb8_row11_col6\" class=\"data row11 col6\" >0.0374</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8f680437e70478ca1b5da2573ad8100"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now the AUC has slightly increased from *0.8472 to 0.8478*"
      ],
      "metadata": {
        "id": "vhbINisI3S7n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Analysis"
      ],
      "metadata": {
        "id": "TvndH5Sy3ZtQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# AUC Plot\n",
        "plot_model(tuned_best_model, plot = 'auc')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "EbbJ-IzK3C9b",
        "outputId": "69f8b786-3bae-44e0-fb2e-6e3190ab6c4b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Importance Plot"
      ],
      "metadata": {
        "id": "vM-DLFFv3m_3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Importance Plot\n",
        "plot_model(tuned_best_model, plot = 'feature')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "902qRG1H3oGu",
        "outputId": "41951292-24fb-4912-c4e6-3728ee658683"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Confusion Matrix"
      ],
      "metadata": {
        "id": "yQXToq3_3xZB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix\n",
        "plot_model(tuned_best_model, plot = 'confusion_matrix')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "vSiCNVBg3su9",
        "outputId": "5ff382af-317e-4ced-b3e1-70f15520be4f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This confusion matrix is on the test set which includes 30% of our data (2,113 rows) We have 309 True Positives (15%) — these are the customers for which we will be able to extend the lifetime value. If we wouldn’t have predicted, then there was no opportunity for intervention.\n",
        "\n",
        "We also have 138 (7%) False Positives where we will lose money because the promotion offered to these customers will just be an extra cost.\n",
        "\n",
        "1,388 (66%) are True Negatives (good customers) and 278 (13%) are False Negative (this is a missed opportunity).\n",
        "\n",
        "So far we have trained multiple models to select the best model giving the highest AUC, followed by tuning the hyperparameters of the best model to squeeze a little more performance in terms of AUC. However, the best AUC doesn’t necessarily translate into the best model for business.\n",
        "\n",
        "In a churn model, often the reward of true positives is way different than the cost of false positives. Let’s use the following assumptions:\n",
        "\n",
        "    $1,000 voucher will be offered to all the customers identified as churn (True Positive + False Positive);\n",
        "    If we are able to stop the churn, we will gain $5,000 in customer lifetime value."
      ],
      "metadata": {
        "id": "SE5AWUdy4ADC"
      }
    }
  ]
}