Bank_Customer_Churn_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Bank Customer Churn Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOTKmS9SACx42y7kJHizrPj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MainakRepositor/Data-Analysis/blob/master/Bank_Customer_Churn_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wQl_1WZy0vOD"
      },
      "source": [
        "# Bank Customer Churn Prediction\n",
        "\n",
        "<hr>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T0xKrx1Z0t8t"
      },
      "source": [
        "## 1. Importing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PYjoanQF0oOQ",
        "outputId": "8599b0f0-02af-420e-b3f1-872420a31b41"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# For visualization\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "pd.options.display.max_rows = None\n",
        "pd.options.display.max_columns = None\n",
        "\n",
        "print(\"Necessary libraries are included successfully!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Necessary libraries are included successfully!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DMvidZLx2F2J"
      },
      "source": [
        "## 2. Importing the datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "ZnrYNVVH1FTM",
        "outputId": "922e2115-f98d-4852-b2b2-dfb24c941f06"
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Churn%20Modeling.csv'\n",
        "df = pd.read_csv(url,error_bad_lines=False)\n",
        "df = df.drop([\"RowNumber\", \"CustomerId\", \"Surname\"], axis = 1)\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>619</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>608</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>502</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>699</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>645</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Male</td>\n",
              "      <td>44</td>\n",
              "      <td>8</td>\n",
              "      <td>113755.78</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>149756.71</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>822</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "      <td>50</td>\n",
              "      <td>7</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10062.80</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>376</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Female</td>\n",
              "      <td>29</td>\n",
              "      <td>4</td>\n",
              "      <td>115046.74</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>119346.88</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>501</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "      <td>44</td>\n",
              "      <td>4</td>\n",
              "      <td>142051.07</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>74940.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>684</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "      <td>27</td>\n",
              "      <td>2</td>\n",
              "      <td>134603.88</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>71725.73</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
              "0          619    France  Female   42       2       0.00              1   \n",
              "1          608     Spain  Female   41       1   83807.86              1   \n",
              "2          502    France  Female   42       8  159660.80              3   \n",
              "3          699    France  Female   39       1       0.00              2   \n",
              "4          850     Spain  Female   43       2  125510.82              1   \n",
              "5          645     Spain    Male   44       8  113755.78              2   \n",
              "6          822    France    Male   50       7       0.00              2   \n",
              "7          376   Germany  Female   29       4  115046.74              4   \n",
              "8          501    France    Male   44       4  142051.07              2   \n",
              "9          684    France    Male   27       2  134603.88              1   \n",
              "\n",
              "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
              "0          1               1        101348.88       1  \n",
              "1          0               1        112542.58       0  \n",
              "2          1               0        113931.57       1  \n",
              "3          0               0         93826.63       0  \n",
              "4          1               1         79084.10       0  \n",
              "5          1               0        149756.71       1  \n",
              "6          1               1         10062.80       0  \n",
              "7          1               0        119346.88       1  \n",
              "8          0               1         74940.50       0  \n",
              "9          1               1         71725.73       0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yqDTDoks2JN1"
      },
      "source": [
        "## 3. Exploring the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L9yLbPKK1Qpz",
        "outputId": "ba32a047-9a0e-4fa8-d1d1-d03c9b8b10a0"
      },
      "source": [
        "r,c = df.shape\n",
        "print(\"Number of customers in the bank   = \",r)\n",
        "print(\"Number of churning  parameters    = \",c)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of customers in the bank   =  10000\n",
            "Number of churning  parameters    =  11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H18WPJkR1xR3",
        "outputId": "c40d02d4-7974-4293-daf8-6315493f026c"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 11 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   CreditScore      10000 non-null  int64  \n",
            " 1   Geography        10000 non-null  object \n",
            " 2   Gender           10000 non-null  object \n",
            " 3   Age              10000 non-null  int64  \n",
            " 4   Tenure           10000 non-null  int64  \n",
            " 5   Balance          10000 non-null  float64\n",
            " 6   NumOfProducts    10000 non-null  int64  \n",
            " 7   HasCrCard        10000 non-null  int64  \n",
            " 8   IsActiveMember   10000 non-null  int64  \n",
            " 9   EstimatedSalary  10000 non-null  float64\n",
            " 10  Exited           10000 non-null  int64  \n",
            "dtypes: float64(2), int64(7), object(2)\n",
            "memory usage: 859.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fy9bZsv1-50",
        "outputId": "2da32e6a-c8df-41d8-f273-9880ac193589"
      },
      "source": [
        "print(\"Are there any missing values in the dataset ?\",df.isnull().values.any())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Are there any missing values in the dataset ? False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gh3jIeMA2cfo",
        "outputId": "d13f6e74-abbf-4576-9764-41e17f2ee79a"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CreditScore        0\n",
              "Geography          0\n",
              "Gender             0\n",
              "Age                0\n",
              "Tenure             0\n",
              "Balance            0\n",
              "NumOfProducts      0\n",
              "HasCrCard          0\n",
              "IsActiveMember     0\n",
              "EstimatedSalary    0\n",
              "Exited             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "w13Pc7oP2VFS",
        "outputId": "bfeb3b92-3630-446d-d9b9-77c257edc8f8"
      },
      "source": [
        "df.describe().T"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CreditScore</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>650.528800</td>\n",
              "      <td>96.653299</td>\n",
              "      <td>350.00</td>\n",
              "      <td>584.00</td>\n",
              "      <td>652.000</td>\n",
              "      <td>718.0000</td>\n",
              "      <td>850.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>38.921800</td>\n",
              "      <td>10.487806</td>\n",
              "      <td>18.00</td>\n",
              "      <td>32.00</td>\n",
              "      <td>37.000</td>\n",
              "      <td>44.0000</td>\n",
              "      <td>92.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tenure</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>5.012800</td>\n",
              "      <td>2.892174</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>5.000</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Balance</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>76485.889288</td>\n",
              "      <td>62397.405202</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>97198.540</td>\n",
              "      <td>127644.2400</td>\n",
              "      <td>250898.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumOfProducts</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>1.530200</td>\n",
              "      <td>0.581654</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HasCrCard</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>0.705500</td>\n",
              "      <td>0.455840</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IsActiveMember</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>0.515100</td>\n",
              "      <td>0.499797</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>100090.239881</td>\n",
              "      <td>57510.492818</td>\n",
              "      <td>11.58</td>\n",
              "      <td>51002.11</td>\n",
              "      <td>100193.915</td>\n",
              "      <td>149388.2475</td>\n",
              "      <td>199992.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Exited</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>0.203700</td>\n",
              "      <td>0.402769</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   count           mean           std     min       25%  \\\n",
              "CreditScore      10000.0     650.528800     96.653299  350.00    584.00   \n",
              "Age              10000.0      38.921800     10.487806   18.00     32.00   \n",
              "Tenure           10000.0       5.012800      2.892174    0.00      3.00   \n",
              "Balance          10000.0   76485.889288  62397.405202    0.00      0.00   \n",
              "NumOfProducts    10000.0       1.530200      0.581654    1.00      1.00   \n",
              "HasCrCard        10000.0       0.705500      0.455840    0.00      0.00   \n",
              "IsActiveMember   10000.0       0.515100      0.499797    0.00      0.00   \n",
              "EstimatedSalary  10000.0  100090.239881  57510.492818   11.58  51002.11   \n",
              "Exited           10000.0       0.203700      0.402769    0.00      0.00   \n",
              "\n",
              "                        50%          75%        max  \n",
              "CreditScore         652.000     718.0000     850.00  \n",
              "Age                  37.000      44.0000      92.00  \n",
              "Tenure                5.000       7.0000      10.00  \n",
              "Balance           97198.540  127644.2400  250898.09  \n",
              "NumOfProducts         1.000       2.0000       4.00  \n",
              "HasCrCard             1.000       1.0000       1.00  \n",
              "IsActiveMember        1.000       1.0000       1.00  \n",
              "EstimatedSalary  100193.915  149388.2475  199992.48  \n",
              "Exited                0.000       0.0000       1.00  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYIpZmjQ2YEQ",
        "outputId": "fe256c8f-2ac4-452d-9e18-e5292e6972a0"
      },
      "source": [
        "df.nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CreditScore         460\n",
              "Geography             3\n",
              "Gender                2\n",
              "Age                  70\n",
              "Tenure               11\n",
              "Balance            6382\n",
              "NumOfProducts         4\n",
              "HasCrCard             2\n",
              "IsActiveMember        2\n",
              "EstimatedSalary    9999\n",
              "Exited                2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "TQaSVFdW2hlI",
        "outputId": "fe7187d9-5d52-4e33-d6cd-47aec956e2ca"
      },
      "source": [
        "labels = 'Exited', 'Retained'\n",
        "sizes = [df.Exited[df['Exited']==1].count(), df.Exited[df['Exited']==0].count()]\n",
        "explode = (0, 0.1)\n",
        "fig1, ax1 = plt.subplots(figsize=(10, 8))\n",
        "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax1.axis('equal')\n",
        "plt.title(\"Proportion of customer churned and retained\", size = 20)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "fpfcQp-Q2tmI",
        "outputId": "0fbc8928-341e-4e16-ed28-b2d686835eee"
      },
      "source": [
        "    # We first review the 'Status' relation with categorical variables\n",
        "    fig, axarr = plt.subplots(2, 2, figsize=(20, 12))\n",
        "    sns.countplot(x='Geography', hue = 'Exited',data = df, ax=axarr[0][0])\n",
        "    sns.countplot(x='Gender', hue = 'Exited',data = df, ax=axarr[0][1])\n",
        "    sns.countplot(x='HasCrCard', hue = 'Exited',data = df, ax=axarr[1][0])\n",
        "    sns.countplot(x='IsActiveMember', hue = 'Exited',data = df, ax=axarr[1][1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1fc0e3d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x864 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "Fold2XD72zlB",
        "outputId": "728adbdf-436e-4fbd-fe64-b904470abc80"
      },
      "source": [
        "# Relations based on the continuous data attributes\n",
        "fig, axarr = plt.subplots(3, 2, figsize=(20, 12))\n",
        "sns.boxplot(y='CreditScore',x = 'Exited', hue = 'Exited',data = df, ax=axarr[0][0])\n",
        "sns.boxplot(y='Age',x = 'Exited', hue = 'Exited',data = df , ax=axarr[0][1])\n",
        "sns.boxplot(y='Tenure',x = 'Exited', hue = 'Exited',data = df, ax=axarr[1][0])\n",
        "sns.boxplot(y='Balance',x = 'Exited', hue = 'Exited',data = df, ax=axarr[1][1])\n",
        "sns.boxplot(y='NumOfProducts',x = 'Exited', hue = 'Exited',data = df, ax=axarr[2][0])\n",
        "sns.boxplot(y='EstimatedSalary',x = 'Exited', hue = 'Exited',data = df, ax=axarr[2][1])   "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1fbe882e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x864 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oa3Ljpgm3Bfu",
        "outputId": "7a095372-4182-4e05-a713-22fa95b0c60c"
      },
      "source": [
        "# Split Train, test data\n",
        "df_train = df.sample(frac=0.8,random_state=200)\n",
        "df_test = df.drop(df_train.index)\n",
        "print(len(df_train))\n",
        "print(len(df_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8000\n",
            "2000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "z0t0qYBj3jQ6",
        "outputId": "d6439330-3aa8-453d-8ac5-0aa7cda712ef"
      },
      "source": [
        "df_train['BalanceSalaryRatio'] = df_train.Balance/df_train.EstimatedSalary\n",
        "sns.boxplot(y='BalanceSalaryRatio',x = 'Exited', hue = 'Exited',data = df_train)\n",
        "plt.ylim(-1, 5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1.0, 5.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k2R5ZtgoAAJlIGgA/tv0DScfavl7Sz1XiXEAAgGwlnQvoTtsXSNqpwnWA/xkRa1KtDACQqqQXgU+Q9A9tv/RtD7c9KSLeSLM4AEB6ko7mfVDS77Xbbi2+Nr3sFQFAHzf62DGJ79xJerwkrrjiCq1du1Zbt25VdXW1Fi1apGuvvbbH7SYNgMERsa9tIyL22R7a41Yz0tfmF8lSTU2N5s1jLB/QE0nu2U/DypUry3q8pAHwvu3ZEfGYJNn+sqStZa2kFzQ2Nqph4ya1jijfbVTdNWhfYTD1htfey6yGit3lG8ACoP9KGgB/JulvbC9XYQ7/tyWVZyRCL2sdMUZ7Tirfggr90fCXWHMHQPK7gF6V9LniAu6KiF2pVgUAfUooIlRYybbv+rfp2pJJehfQMSqsBDZJ0uC2v4SIuL175QFA/1Ox823t+GiMjh05tM+GQERo27ZtGjZsWOLPJO0CelSF6Z83SPq4B7UBQL814jd36wNdr/dHT1RvrWT74b5B8uCh+uijjxJ/ZtiwYaqurk68f9IAqI6IixIfFQAGkEH7WjTque/0apvff360jvlMrZYuXZpaG0mngvgn27+bWhUAgF6X9AzgXElX2X5dhS4gS4qIOD21ygAAqUoaABenWgUAoNclvQ30TUmyPV5S8kvMAIA+K9E1ANuzbW+W9LqkZyS9IelnKdYFAEhZ0ovA35D0OUmvRMQJkv5A0rOpVQUASF3SANgfEdskDbI9KCJ+ocIawQCAfirpReAdxWkg1qkwJ9AWFdcHLoXteyV9UdKWiDit1OMBAJJLegbwZUl7JP25pCclvSrpS2Vo/z5JDDADgAwkvQuo/bf9vy5X4xGxzvakch0PAJBcpwFgu0XSkaaXaxsINjqVqg6vYa6kuZJ0/PHHp90cAORGpwEQEZW9VUgnNayQtEKSamtruzfXKQDgqJJeBJb0yYFgEfFW2SsCAPQKBoIBQE4lPQNoGwj284g40/Z5kr5aauO2V0r6vKTjbDdJWhARPyz1uAD6t+bmZn3UUqE7nk/9MmOf9WZLhUY2N6faRtIA2B8R22wfGghm+3ulNh4RV5R6DABAz2Q6EAwAjqSqqkofH3hHt03bmXUpmbnj+dE6pqoq1Ta6MxBst8o/EAwAkJHuDgQ7aPvvJW2L7i4/DwDoUzo9A7D9Odtrbf+d7TNtb5S0UdJ7tpnCAQD6sa7OAJZLuk3SpyQ9LeniiHjW9kmSVqrQHdRvNDc3q2L3hxr+0hNZl5Kpit3b1Nx8IOsyAGSsq2sAgyNidUQ8KOndiHhWkiLipfRLAwCkqaszgIPtnu/p8F6/uwZQVVWldz8erD0nzcq6lEwNf+kJVVVNyLoMABnrKgDOsL1Thcnfhhefq7jN2sAA0I91NRlcRW8VAgDoXUnHAQAABhgCAAByigAAgJwiAAAgpwgAAMgpAgAAcooAAICcIgAAIKcIAADIKQIAAHKKAACAnCIAACCnCAAAyCkCAAByigAAgJwiAAAgpwgAAMiprpaEBIBMvLWrQnc8Pzqz9t/bXfh+PGHEwS72TMdbuyo0OeU2Mg0A2xdJWiqpQtI9EbE4y3oA9A01NTVZl6B9jY2SpGM+k00tk5X+30NmAWC7QtL3JV0gqUnSr20/FhH/mlVNAPqGefPmZV2C6urqJElLly7NuJL0ZHkN4GxJjRHxWkTsk7RK0pczrAcAciXLLqAqSW+3226S9O/TbrRi9wca/tITPf78oL075YP7y1hRz8SgITo4rGf9oxW7P5A0obwFAX3IsmXL1Fjswumpts+3nQn0RE1NTZ84mzmaPn8R2PZcSXMl6fjjjy/pWOXoT2tuPqA9e/aUfJxSDR8+XFVVPf0lPqFP9LECfdnw4cOzLiF1johsGrZnSFoYERcWt+dLUkR8+2ifqa2tjfr6+l6qEAAGBtsbIqK24+tZXgP4taTJtk+wPVTS5ZIey7AeAMiVzLqAIuKA7ZskPaXCbaD3RsSLWdUDAHmT6TWAiHhCUs+vyAIAeoypIAAgpwgAAMgpAgAAcooAAICcIgAAIKcIAADIKQIAAHKKAACAnCIAACCnCAAAyCkCAAByigAAgJwiAAAgpwgAAMgpAgAAcooAAICcIgAAIKcIAADIKQIAAHKKAACAnCIAACCnCAAAyCkCAAByigAAgJwiAAAgpwgAAMgpAgAAcooAAICcyiQAbH/F9ou2D9quzaIGAMi7rM4ANkr6Y0nrMmofAHJvcBaNRsQmSbKdRfMAAGUUAN1he66kucXNXbZfzrKeAeY4SVuzLgI4Av5tltdnjvRiagFg++eSfusIb30tIh5NepyIWCFpRdkKwyG26yOCazDoc/i32TtSC4CI+I9pHRsAUDpuAwWAnMrqNtBLbDdJmiHp720/lUUdoGsNfRb/NnuBIyLrGgAAGaALCAByigAAgJwiAHLI9kW2X7bdaPvWrOsB2ti+1/YW2xuzriUPCICcsV0h6fuSLpZ0iqQrbJ+SbVXAIfdJuijrIvKCAMifsyU1RsRrEbFP0ipJX864JkCSFBHrJH2QdR15QQDkT5Wkt9ttNxVfA5AzBAAA5BQBkD/Nkia2264uvgYgZwiA/Pm1pMm2T7A9VNLlkh7LuCYAGSAAciYiDki6SdJTkjZJ+nFEvJhtVUCB7ZWSfiVpiu0m29dmXdNAxlQQAJBTnAEAQE4RAACQUwQAAOQUAQAAOUUAAEBOEQBAO7ZbbTe0e3Q6W6rtJ2wfW3zc0IP2Ftq+uecVAz2X2qLwQD+1JyKmJt05ImZJku1Jkm6QdFc6ZQHlxxkA0AXbnyqunzCluL3S9vXF52/YPk7SYkm/UzxrWFJ87xbbv7b9z7YXtTve12y/YvuXkqZk8CMBkjgDADoabruh3fa3I+Jvbd8k6T7bSyV9OiLu7vC5WyWd1nb2YPsLkiarMP22JT1me6akj1SYfmOqCv//npe0IdWfCDgKAgA43BG7gCJije2vqLCYzhkJjvOF4uM3xe1RKgRCpaSHI2K3JNlmHiZkhi4gIAHbgySdLGm3pE8n+YgKZw9Ti4+aiPhhqkUC3UQAAMn8uQqT5/2JpB/ZHtLh/RYVvt23eUrSNbZHSZLtKtvjJa2T9Ee2h9uulPSl9EsHjowuIOBwHa8BPCnpR5Kuk3R2RLTYXifp65IWtO0UEdts/2NxMfOfRcQttk+W9CvbkrRL0lcj4nnbfyvpBUlbVJieG8gEs4ECQE7RBQQAOUUAAEBOEQAAkFMEAADkFAEAADlFAABAThEAAJBT/x+tJrb57cff4AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "kEnLBju23lWe",
        "outputId": "8f59cc2a-be8a-40b0-ee15-9ed96ab65dab"
      },
      "source": [
        "# Given that tenure is a 'function' of age, we introduce a variable aiming to standardize tenure over age:\n",
        "df_train['TenureByAge'] = df_train.Tenure/(df_train.Age)\n",
        "sns.boxplot(y='TenureByAge',x = 'Exited', hue = 'Exited',data = df_train)\n",
        "plt.ylim(-1, 1)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uprnfU8W3nbp"
      },
      "source": [
        "df_train['CreditScoreGivenAge'] = df_train.CreditScore/(df_train.Age)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "BiL1rqwR4_wZ",
        "outputId": "e2811668-4ba6-4bf5-e600-25a1bf771274"
      },
      "source": [
        "df_train.head()"
      ],
      "execution_count": null,
      "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>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "      <th>BalanceSalaryRatio</th>\n",
              "      <th>TenureByAge</th>\n",
              "      <th>CreditScoreGivenAge</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8159</th>\n",
              "      <td>461</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>25</td>\n",
              "      <td>6</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>15306.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.240000</td>\n",
              "      <td>18.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6332</th>\n",
              "      <td>619</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>4</td>\n",
              "      <td>90413.12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>20555.21</td>\n",
              "      <td>0</td>\n",
              "      <td>4.398550</td>\n",
              "      <td>0.114286</td>\n",
              "      <td>17.685714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8895</th>\n",
              "      <td>699</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>40</td>\n",
              "      <td>8</td>\n",
              "      <td>122038.34</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>102085.35</td>\n",
              "      <td>0</td>\n",
              "      <td>1.195454</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>17.475000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5351</th>\n",
              "      <td>558</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Male</td>\n",
              "      <td>41</td>\n",
              "      <td>2</td>\n",
              "      <td>124227.14</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>111184.67</td>\n",
              "      <td>0</td>\n",
              "      <td>1.117305</td>\n",
              "      <td>0.048780</td>\n",
              "      <td>13.609756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4314</th>\n",
              "      <td>638</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "      <td>34</td>\n",
              "      <td>5</td>\n",
              "      <td>133501.36</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>155643.04</td>\n",
              "      <td>0</td>\n",
              "      <td>0.857741</td>\n",
              "      <td>0.147059</td>\n",
              "      <td>18.764706</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
              "8159          461     Spain  Female   25       6       0.00              2   \n",
              "6332          619    France  Female   35       4   90413.12              1   \n",
              "8895          699    France  Female   40       8  122038.34              1   \n",
              "5351          558   Germany    Male   41       2  124227.14              1   \n",
              "4314          638    France    Male   34       5  133501.36              1   \n",
              "\n",
              "      HasCrCard  IsActiveMember  EstimatedSalary  Exited  BalanceSalaryRatio  \\\n",
              "8159          1               1         15306.29       0            0.000000   \n",
              "6332          1               1         20555.21       0            4.398550   \n",
              "8895          1               0        102085.35       0            1.195454   \n",
              "5351          1               1        111184.67       0            1.117305   \n",
              "4314          0               1        155643.04       0            0.857741   \n",
              "\n",
              "      TenureByAge  CreditScoreGivenAge  \n",
              "8159     0.240000            18.440000  \n",
              "6332     0.114286            17.685714  \n",
              "8895     0.200000            17.475000  \n",
              "5351     0.048780            13.609756  \n",
              "4314     0.147059            18.764706  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "aqG6dXPy5CUX",
        "outputId": "213e4d8f-dfbc-4b5e-8a97-a452a0d8f0a5"
      },
      "source": [
        "# Arrange columns by data type for easier manipulation\n",
        "continuous_vars = ['CreditScore',  'Age', 'Tenure', 'Balance','NumOfProducts', 'EstimatedSalary', 'BalanceSalaryRatio',\n",
        "                   'TenureByAge','CreditScoreGivenAge']\n",
        "cat_vars = ['HasCrCard', 'IsActiveMember','Geography', 'Gender']\n",
        "df_train = df_train[['Exited'] + continuous_vars + cat_vars]\n",
        "df_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Exited</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>BalanceSalaryRatio</th>\n",
              "      <th>TenureByAge</th>\n",
              "      <th>CreditScoreGivenAge</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8159</th>\n",
              "      <td>0</td>\n",
              "      <td>461</td>\n",
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              "      <td>6</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>18.440000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6332</th>\n",
              "      <td>0</td>\n",
              "      <td>619</td>\n",
              "      <td>35</td>\n",
              "      <td>4</td>\n",
              "      <td>90413.12</td>\n",
              "      <td>1</td>\n",
              "      <td>20555.21</td>\n",
              "      <td>4.398550</td>\n",
              "      <td>0.114286</td>\n",
              "      <td>17.685714</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8895</th>\n",
              "      <td>0</td>\n",
              "      <td>699</td>\n",
              "      <td>40</td>\n",
              "      <td>8</td>\n",
              "      <td>122038.34</td>\n",
              "      <td>1</td>\n",
              "      <td>102085.35</td>\n",
              "      <td>1.195454</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>17.475000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5351</th>\n",
              "      <td>0</td>\n",
              "      <td>558</td>\n",
              "      <td>41</td>\n",
              "      <td>2</td>\n",
              "      <td>124227.14</td>\n",
              "      <td>1</td>\n",
              "      <td>111184.67</td>\n",
              "      <td>1.117305</td>\n",
              "      <td>0.048780</td>\n",
              "      <td>13.609756</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4314</th>\n",
              "      <td>0</td>\n",
              "      <td>638</td>\n",
              "      <td>34</td>\n",
              "      <td>5</td>\n",
              "      <td>133501.36</td>\n",
              "      <td>1</td>\n",
              "      <td>155643.04</td>\n",
              "      <td>0.857741</td>\n",
              "      <td>0.147059</td>\n",
              "      <td>18.764706</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Exited  CreditScore  Age  Tenure    Balance  NumOfProducts  \\\n",
              "8159       0          461   25       6       0.00              2   \n",
              "6332       0          619   35       4   90413.12              1   \n",
              "8895       0          699   40       8  122038.34              1   \n",
              "5351       0          558   41       2  124227.14              1   \n",
              "4314       0          638   34       5  133501.36              1   \n",
              "\n",
              "      EstimatedSalary  BalanceSalaryRatio  TenureByAge  CreditScoreGivenAge  \\\n",
              "8159         15306.29            0.000000     0.240000            18.440000   \n",
              "6332         20555.21            4.398550     0.114286            17.685714   \n",
              "8895        102085.35            1.195454     0.200000            17.475000   \n",
              "5351        111184.67            1.117305     0.048780            13.609756   \n",
              "4314        155643.04            0.857741     0.147059            18.764706   \n",
              "\n",
              "      HasCrCard  IsActiveMember Geography  Gender  \n",
              "8159          1               1     Spain  Female  \n",
              "6332          1               1    France  Female  \n",
              "8895          1               0    France  Female  \n",
              "5351          1               1   Germany    Male  \n",
              "4314          0               1    France    Male  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "LdmtL4c-5FwY",
        "outputId": "9b8047c7-8749-4f9e-dc45-9e4b770e27e6"
      },
      "source": [
        "df_train.loc[df_train.HasCrCard == 0, 'HasCrCard'] = -1\n",
        "df_train.loc[df_train.IsActiveMember == 0, 'IsActiveMember'] = -1\n",
        "df_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Exited</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>BalanceSalaryRatio</th>\n",
              "      <th>TenureByAge</th>\n",
              "      <th>CreditScoreGivenAge</th>\n",
              "      <th>HasCrCard</th>\n",
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              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8159</th>\n",
              "      <td>0</td>\n",
              "      <td>461</td>\n",
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              "      <td>6</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
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              "    <tr>\n",
              "      <th>6332</th>\n",
              "      <td>0</td>\n",
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              "      <td>35</td>\n",
              "      <td>4</td>\n",
              "      <td>90413.12</td>\n",
              "      <td>1</td>\n",
              "      <td>20555.21</td>\n",
              "      <td>4.398550</td>\n",
              "      <td>0.114286</td>\n",
              "      <td>17.685714</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8895</th>\n",
              "      <td>0</td>\n",
              "      <td>699</td>\n",
              "      <td>40</td>\n",
              "      <td>8</td>\n",
              "      <td>122038.34</td>\n",
              "      <td>1</td>\n",
              "      <td>102085.35</td>\n",
              "      <td>1.195454</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>17.475000</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5351</th>\n",
              "      <td>0</td>\n",
              "      <td>558</td>\n",
              "      <td>41</td>\n",
              "      <td>2</td>\n",
              "      <td>124227.14</td>\n",
              "      <td>1</td>\n",
              "      <td>111184.67</td>\n",
              "      <td>1.117305</td>\n",
              "      <td>0.048780</td>\n",
              "      <td>13.609756</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4314</th>\n",
              "      <td>0</td>\n",
              "      <td>638</td>\n",
              "      <td>34</td>\n",
              "      <td>5</td>\n",
              "      <td>133501.36</td>\n",
              "      <td>1</td>\n",
              "      <td>155643.04</td>\n",
              "      <td>0.857741</td>\n",
              "      <td>0.147059</td>\n",
              "      <td>18.764706</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Exited  CreditScore  Age  Tenure    Balance  NumOfProducts  \\\n",
              "8159       0          461   25       6       0.00              2   \n",
              "6332       0          619   35       4   90413.12              1   \n",
              "8895       0          699   40       8  122038.34              1   \n",
              "5351       0          558   41       2  124227.14              1   \n",
              "4314       0          638   34       5  133501.36              1   \n",
              "\n",
              "      EstimatedSalary  BalanceSalaryRatio  TenureByAge  CreditScoreGivenAge  \\\n",
              "8159         15306.29            0.000000     0.240000            18.440000   \n",
              "6332         20555.21            4.398550     0.114286            17.685714   \n",
              "8895        102085.35            1.195454     0.200000            17.475000   \n",
              "5351        111184.67            1.117305     0.048780            13.609756   \n",
              "4314        155643.04            0.857741     0.147059            18.764706   \n",
              "\n",
              "      HasCrCard  IsActiveMember Geography  Gender  \n",
              "8159          1               1     Spain  Female  \n",
              "6332          1               1    France  Female  \n",
              "8895          1              -1    France  Female  \n",
              "5351          1               1   Germany    Male  \n",
              "4314         -1               1    France    Male  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "EKKD-H4G5KMr",
        "outputId": "fba77ef3-5e18-4c0e-b70c-f52679d706a4"
      },
      "source": [
        "# One hot encode the categorical variables\n",
        "lst = ['Geography', 'Gender']\n",
        "remove = list()\n",
        "for i in lst:\n",
        "    if (df_train[i].dtype == np.str or df_train[i].dtype == np.object):\n",
        "        for j in df_train[i].unique():\n",
        "            df_train[i+'_'+j] = np.where(df_train[i] == j,1,-1)\n",
        "        remove.append(i)\n",
        "df_train = df_train.drop(remove, axis=1)\n",
        "df_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Exited</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>EstimatedSalary</th>\n",
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              "      <th>TenureByAge</th>\n",
              "      <th>CreditScoreGivenAge</th>\n",
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              "      <th>Geography_Spain</th>\n",
              "      <th>Geography_France</th>\n",
              "      <th>Geography_Germany</th>\n",
              "      <th>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8159</th>\n",
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              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>15306.29</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.240000</td>\n",
              "      <td>18.440000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6332</th>\n",
              "      <td>0</td>\n",
              "      <td>619</td>\n",
              "      <td>35</td>\n",
              "      <td>4</td>\n",
              "      <td>90413.12</td>\n",
              "      <td>1</td>\n",
              "      <td>20555.21</td>\n",
              "      <td>4.398550</td>\n",
              "      <td>0.114286</td>\n",
              "      <td>17.685714</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8895</th>\n",
              "      <td>0</td>\n",
              "      <td>699</td>\n",
              "      <td>40</td>\n",
              "      <td>8</td>\n",
              "      <td>122038.34</td>\n",
              "      <td>1</td>\n",
              "      <td>102085.35</td>\n",
              "      <td>1.195454</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>17.475000</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5351</th>\n",
              "      <td>0</td>\n",
              "      <td>558</td>\n",
              "      <td>41</td>\n",
              "      <td>2</td>\n",
              "      <td>124227.14</td>\n",
              "      <td>1</td>\n",
              "      <td>111184.67</td>\n",
              "      <td>1.117305</td>\n",
              "      <td>0.048780</td>\n",
              "      <td>13.609756</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4314</th>\n",
              "      <td>0</td>\n",
              "      <td>638</td>\n",
              "      <td>34</td>\n",
              "      <td>5</td>\n",
              "      <td>133501.36</td>\n",
              "      <td>1</td>\n",
              "      <td>155643.04</td>\n",
              "      <td>0.857741</td>\n",
              "      <td>0.147059</td>\n",
              "      <td>18.764706</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Exited  CreditScore  Age  Tenure    Balance  NumOfProducts  \\\n",
              "8159       0          461   25       6       0.00              2   \n",
              "6332       0          619   35       4   90413.12              1   \n",
              "8895       0          699   40       8  122038.34              1   \n",
              "5351       0          558   41       2  124227.14              1   \n",
              "4314       0          638   34       5  133501.36              1   \n",
              "\n",
              "      EstimatedSalary  BalanceSalaryRatio  TenureByAge  CreditScoreGivenAge  \\\n",
              "8159         15306.29            0.000000     0.240000            18.440000   \n",
              "6332         20555.21            4.398550     0.114286            17.685714   \n",
              "8895        102085.35            1.195454     0.200000            17.475000   \n",
              "5351        111184.67            1.117305     0.048780            13.609756   \n",
              "4314        155643.04            0.857741     0.147059            18.764706   \n",
              "\n",
              "      HasCrCard  IsActiveMember  Geography_Spain  Geography_France  \\\n",
              "8159          1               1                1                -1   \n",
              "6332          1               1               -1                 1   \n",
              "8895          1              -1               -1                 1   \n",
              "5351          1               1               -1                -1   \n",
              "4314         -1               1               -1                 1   \n",
              "\n",
              "      Geography_Germany  Gender_Female  Gender_Male  \n",
              "8159                 -1              1           -1  \n",
              "6332                 -1              1           -1  \n",
              "8895                 -1              1           -1  \n",
              "5351                  1             -1            1  \n",
              "4314                 -1             -1            1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "NkaJv3Cc5MmS",
        "outputId": "7de9847a-f946-4265-f05e-4e4208d976b0"
      },
      "source": [
        "# minMax scaling the continuous variables\n",
        "minVec = df_train[continuous_vars].min().copy()\n",
        "maxVec = df_train[continuous_vars].max().copy()\n",
        "df_train[continuous_vars] = (df_train[continuous_vars]-minVec)/(maxVec-minVec)\n",
        "df_train.head()"
      ],
      "execution_count": null,
      "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>Exited</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>BalanceSalaryRatio</th>\n",
              "      <th>TenureByAge</th>\n",
              "      <th>CreditScoreGivenAge</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>Geography_Spain</th>\n",
              "      <th>Geography_France</th>\n",
              "      <th>Geography_Germany</th>\n",
              "      <th>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8159</th>\n",
              "      <td>0</td>\n",
              "      <td>0.222</td>\n",
              "      <td>0.094595</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.076118</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.432000</td>\n",
              "      <td>0.323157</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6332</th>\n",
              "      <td>0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>0.229730</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.360358</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.102376</td>\n",
              "      <td>0.003317</td>\n",
              "      <td>0.205714</td>\n",
              "      <td>0.305211</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8895</th>\n",
              "      <td>0</td>\n",
              "      <td>0.698</td>\n",
              "      <td>0.297297</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.486406</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.510225</td>\n",
              "      <td>0.000901</td>\n",
              "      <td>0.360000</td>\n",
              "      <td>0.300198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5351</th>\n",
              "      <td>0</td>\n",
              "      <td>0.416</td>\n",
              "      <td>0.310811</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.495130</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.555744</td>\n",
              "      <td>0.000843</td>\n",
              "      <td>0.087805</td>\n",
              "      <td>0.208238</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4314</th>\n",
              "      <td>0</td>\n",
              "      <td>0.576</td>\n",
              "      <td>0.216216</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.532094</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.778145</td>\n",
              "      <td>0.000647</td>\n",
              "      <td>0.264706</td>\n",
              "      <td>0.330882</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Exited  CreditScore       Age  Tenure   Balance  NumOfProducts  \\\n",
              "8159       0        0.222  0.094595     0.6  0.000000       0.333333   \n",
              "6332       0        0.538  0.229730     0.4  0.360358       0.000000   \n",
              "8895       0        0.698  0.297297     0.8  0.486406       0.000000   \n",
              "5351       0        0.416  0.310811     0.2  0.495130       0.000000   \n",
              "4314       0        0.576  0.216216     0.5  0.532094       0.000000   \n",
              "\n",
              "      EstimatedSalary  BalanceSalaryRatio  TenureByAge  CreditScoreGivenAge  \\\n",
              "8159         0.076118            0.000000     0.432000             0.323157   \n",
              "6332         0.102376            0.003317     0.205714             0.305211   \n",
              "8895         0.510225            0.000901     0.360000             0.300198   \n",
              "5351         0.555744            0.000843     0.087805             0.208238   \n",
              "4314         0.778145            0.000647     0.264706             0.330882   \n",
              "\n",
              "      HasCrCard  IsActiveMember  Geography_Spain  Geography_France  \\\n",
              "8159          1               1                1                -1   \n",
              "6332          1               1               -1                 1   \n",
              "8895          1              -1               -1                 1   \n",
              "5351          1               1               -1                -1   \n",
              "4314         -1               1               -1                 1   \n",
              "\n",
              "      Geography_Germany  Gender_Female  Gender_Male  \n",
              "8159                 -1              1           -1  \n",
              "6332                 -1              1           -1  \n",
              "8895                 -1              1           -1  \n",
              "5351                  1             -1            1  \n",
              "4314                 -1             -1            1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R3KG_Dsm5O61"
      },
      "source": [
        "# data prep pipeline for test data\n",
        "def DfPrepPipeline(df_predict,df_train_Cols,minVec,maxVec):\n",
        "    # Add new features\n",
        "    df_predict['BalanceSalaryRatio'] = df_predict.Balance/df_predict.EstimatedSalary\n",
        "    df_predict['TenureByAge'] = df_predict.Tenure/(df_predict.Age - 18)\n",
        "    df_predict['CreditScoreGivenAge'] = df_predict.CreditScore/(df_predict.Age - 18)\n",
        "    # Reorder the columns\n",
        "    continuous_vars = ['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary','BalanceSalaryRatio',\n",
        "                   'TenureByAge','CreditScoreGivenAge']\n",
        "    cat_vars = ['HasCrCard','IsActiveMember',\"Geography\", \"Gender\"] \n",
        "    df_predict = df_predict[['Exited'] + continuous_vars + cat_vars]\n",
        "    # Change the 0 in categorical variables to -1\n",
        "    df_predict.loc[df_predict.HasCrCard == 0, 'HasCrCard'] = -1\n",
        "    df_predict.loc[df_predict.IsActiveMember == 0, 'IsActiveMember'] = -1\n",
        "    # One hot encode the categorical variables\n",
        "    lst = [\"Geography\", \"Gender\"]\n",
        "    remove = list()\n",
        "    for i in lst:\n",
        "        for j in df_predict[i].unique():\n",
        "            df_predict[i+'_'+j] = np.where(df_predict[i] == j,1,-1)\n",
        "        remove.append(i)\n",
        "    df_predict = df_predict.drop(remove, axis=1)\n",
        "    # Ensure that all one hot encoded variables that appear in the train data appear in the subsequent data\n",
        "    L = list(set(df_train_Cols) - set(df_predict.columns))\n",
        "    for l in L:\n",
        "        df_predict[str(l)] = -1        \n",
        "    # MinMax scaling coontinuous variables based on min and max from the train data\n",
        "    df_predict[continuous_vars] = (df_predict[continuous_vars]-minVec)/(maxVec-minVec)\n",
        "    # Ensure that The variables are ordered in the same way as was ordered in the train set\n",
        "    df_predict = df_predict[df_train_Cols]\n",
        "    return df_predict"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWs-Tmar5Tlu"
      },
      "source": [
        "# Support functions\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from scipy.stats import uniform\n",
        "\n",
        "# Fit models\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "# Scoring functions\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import roc_curve"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEi3YLFv5V_t"
      },
      "source": [
        "# Function to give best model score and parameters\n",
        "def best_model(model):\n",
        "    print(model.best_score_)    \n",
        "    print(model.best_params_)\n",
        "    print(model.best_estimator_)\n",
        "def get_auc_scores(y_actual, method,method2):\n",
        "    auc_score = roc_auc_score(y_actual, method); \n",
        "    fpr_df, tpr_df, _ = roc_curve(y_actual, method2); \n",
        "    return (auc_score, fpr_df, tpr_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bgH9oju5asu",
        "outputId": "6cfb61cd-e4df-4544-dea5-c90ec60d39fd"
      },
      "source": [
        "# Fit primal logistic regression\n",
        "param_grid = {'C': [0.1,0.5,1,10,50,100], 'max_iter': [250], 'fit_intercept':[True],'intercept_scaling':[1],\n",
        "              'penalty':['l2'], 'tol':[0.00001,0.0001,0.000001]}\n",
        "log_primal_Grid = GridSearchCV(LogisticRegression(solver='lbfgs'),param_grid, cv=10, refit=True, verbose=0)\n",
        "log_primal_Grid.fit(df_train.loc[:, df_train.columns != 'Exited'],df_train.Exited)\n",
        "best_model(log_primal_Grid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8149999999999998\n",
            "{'C': 100, 'fit_intercept': True, 'intercept_scaling': 1, 'max_iter': 250, 'penalty': 'l2', 'tol': 1e-05}\n",
            "LogisticRegression(C=100, class_weight=None, dual=False, fit_intercept=True,\n",
            "                   intercept_scaling=1, l1_ratio=None, max_iter=250,\n",
            "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
            "                   random_state=None, solver='lbfgs', tol=1e-05, verbose=0,\n",
            "                   warm_start=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ugz1PxB5d_J",
        "outputId": "765dbe42-24e1-49d8-bbce-b15adbc3a48c"
      },
      "source": [
        "# Fit logistic regression with degree 2 polynomial kernel\n",
        "param_grid = {'C': [0.1,10,50], 'max_iter': [300,500], 'fit_intercept':[True],'intercept_scaling':[1],'penalty':['l2'],\n",
        "              'tol':[0.0001,0.000001]}\n",
        "poly2 = PolynomialFeatures(degree=2)\n",
        "df_train_pol2 = poly2.fit_transform(df_train.loc[:, df_train.columns != 'Exited'])\n",
        "log_pol2_Grid = GridSearchCV(LogisticRegression(solver = 'liblinear'),param_grid, cv=5, refit=True, verbose=0)\n",
        "log_pol2_Grid.fit(df_train_pol2,df_train.Exited)\n",
        "best_model(log_pol2_Grid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8553750000000001\n",
            "{'C': 50, 'fit_intercept': True, 'intercept_scaling': 1, 'max_iter': 300, 'penalty': 'l2', 'tol': 0.0001}\n",
            "LogisticRegression(C=50, class_weight=None, dual=False, fit_intercept=True,\n",
            "                   intercept_scaling=1, l1_ratio=None, max_iter=300,\n",
            "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
            "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
            "                   warm_start=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtFoRlWm5okf",
        "outputId": "a49cae34-e461-4f6a-c108-2e1de62cdb6d"
      },
      "source": [
        "# Fit SVM with RBF Kernel\n",
        "param_grid = {'C': [0.5,100,150], 'gamma': [0.1,0.01,0.001],'probability':[True],'kernel': ['rbf']}\n",
        "SVM_grid = GridSearchCV(SVC(), param_grid, cv=3, refit=True, verbose=0)\n",
        "SVM_grid.fit(df_train.loc[:, df_train.columns != 'Exited'],df_train.Exited)\n",
        "best_model(SVM_grid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8518747609662071\n",
            "{'C': 100, 'gamma': 0.1, 'kernel': 'rbf', 'probability': True}\n",
            "SVC(C=100, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n",
            "    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n",
            "    verbose=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hq7H8cUe6kg2",
        "outputId": "237d9b17-3d99-4a4e-d15f-3a25496a0d5d"
      },
      "source": [
        "# Fit SVM with pol kernel\n",
        "param_grid = {'C': [0.5,1,10,50,100], 'gamma': [0.1,0.01,0.001],'probability':[True],'kernel': ['poly'],'degree':[2,3] }\n",
        "SVM_grid = GridSearchCV(SVC(), param_grid, cv=3, refit=True, verbose=0)\n",
        "SVM_grid.fit(df_train.loc[:, df_train.columns != 'Exited'],df_train.Exited)\n",
        "best_model(SVM_grid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8544999485716948\n",
            "{'C': 100, 'degree': 2, 'gamma': 0.1, 'kernel': 'poly', 'probability': True}\n",
            "SVC(C=100, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=2, gamma=0.1, kernel='poly',\n",
            "    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n",
            "    verbose=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A_TCvTc37ffd",
        "outputId": "9fcf6d76-1ae9-456e-8637-990a80f9dc70"
      },
      "source": [
        "# Fit random forest classifier\n",
        "param_grid = {'max_depth': [3, 5, 6, 7, 8], 'max_features': [2,4,6,7,8,9],'n_estimators':[50,100],'min_samples_split': [3, 5, 6, 7]}\n",
        "RanFor_grid = GridSearchCV(RandomForestClassifier(), param_grid, cv=5, refit=True, verbose=0)\n",
        "RanFor_grid.fit(df_train.loc[:, df_train.columns != 'Exited'],df_train.Exited)\n",
        "best_model(RanFor_grid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8636250000000001\n",
            "{'max_depth': 8, 'max_features': 7, 'min_samples_split': 5, 'n_estimators': 50}\n",
            "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
            "                       criterion='gini', max_depth=8, max_features=7,\n",
            "                       max_leaf_nodes=None, max_samples=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=5,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=50,\n",
            "                       n_jobs=None, oob_score=False, random_state=None,\n",
            "                       verbose=0, warm_start=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3mdfwgD_inw",
        "outputId": "33fa744d-88a8-4aaf-a8b7-917548b9df25"
      },
      "source": [
        "# Fit Extreme Gradient boosting classifier\n",
        "param_grid = {'max_depth': [5,6,7,8], 'gamma': [0.01,0.001,0.001],'min_child_weight':[1,5,10], 'learning_rate': [0.05,0.1, 0.2, 0.3], 'n_estimators':[5,10,20,100]}\n",
        "xgb_grid = GridSearchCV(XGBClassifier(), param_grid, cv=5, refit=True, verbose=0)\n",
        "xgb_grid.fit(df_train.loc[:, df_train.columns != 'Exited'],df_train.Exited)\n",
        "best_model(xgb_grid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8629999999999999\n",
            "{'gamma': 0.001, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'n_estimators': 5}\n",
            "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
            "              colsample_bynode=1, colsample_bytree=1, gamma=0.001,\n",
            "              learning_rate=0.2, max_delta_step=0, max_depth=7,\n",
            "              min_child_weight=1, missing=None, n_estimators=5, n_jobs=1,\n",
            "              nthread=None, objective='binary:logistic', random_state=0,\n",
            "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
            "              silent=None, subsample=1, verbosity=1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CAG_SAPoIoJI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ad47f94-b982-4bbc-c3ff-f8dd2ea3cde8"
      },
      "source": [
        "print(classification_report(df_train.Exited, log_primal_Grid.predict(df_train.loc[:, df_train.columns != 'Exited'])))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.97      0.89      6353\n",
            "           1       0.64      0.24      0.35      1647\n",
            "\n",
            "    accuracy                           0.82      8000\n",
            "   macro avg       0.73      0.60      0.62      8000\n",
            "weighted avg       0.79      0.82      0.78      8000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UivZa4966BMM",
        "outputId": "ed799298-2985-4ff1-9282-14837cbb38a5"
      },
      "source": [
        "print(classification_report(df_train.Exited,  log_pol2_Grid.predict(df_train_pol2)))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.96      0.92      6353\n",
            "           1       0.76      0.48      0.59      1647\n",
            "\n",
            "    accuracy                           0.86      8000\n",
            "   macro avg       0.82      0.72      0.75      8000\n",
            "weighted avg       0.85      0.86      0.85      8000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cErIBUXR6A7l",
        "outputId": "50e40efa-3e48-4169-9f41-526cd14c3742"
      },
      "source": [
        "print(classification_report(df_train.Exited,  SVM_grid.predict(df_train.loc[:, df_train.columns != 'Exited'])))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.98      0.92      6353\n",
            "           1       0.84      0.38      0.52      1647\n",
            "\n",
            "    accuracy                           0.86      8000\n",
            "   macro avg       0.85      0.68      0.72      8000\n",
            "weighted avg       0.85      0.86      0.83      8000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "im6y9vDB6MCb",
        "outputId": "dc7d762c-63d8-4a35-bee4-89d3f71f0f47"
      },
      "source": [
        "print(classification_report(df_train.Exited,  RanFor_grid.predict(df_train.loc[:, df_train.columns != 'Exited'])))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.98      0.93      6353\n",
            "           1       0.88      0.52      0.65      1647\n",
            "\n",
            "    accuracy                           0.89      8000\n",
            "   macro avg       0.88      0.75      0.79      8000\n",
            "weighted avg       0.88      0.89      0.87      8000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zgkmu7JY6L1v",
        "outputId": "fadfbc11-7226-4dd7-f73b-3848aefda6a5"
      },
      "source": [
        "print(classification_report(df_train.Exited,  xgb_grid.predict(df_train.loc[:, df_train.columns != 'Exited'])))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.97      0.93      6353\n",
            "           1       0.83      0.52      0.64      1647\n",
            "\n",
            "    accuracy                           0.88      8000\n",
            "   macro avg       0.86      0.75      0.78      8000\n",
            "weighted avg       0.87      0.88      0.87      8000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3Peaa7w6QY-"
      },
      "source": [
        "y = df_train.Exited\n",
        "X = df_train.loc[:, df_train.columns != 'Exited']\n",
        "X_pol2 = df_train_pol2\n",
        "auc_log_primal_Grid, fpr_log_primal_Grid, tpr_log_primal_Grid = get_auc_scores(y, log_primal_Grid.predict(X),log_primal_Grid.predict_proba(X)[:,1])\n",
        "auc_log_pol2_Grid, fpr_log_pol2_Grid, tpr_log_pol2_Grid = get_auc_scores(y, log_pol2_Grid.predict(X_pol2),log_pol2_Grid.predict_proba(X_pol2)[:,1])\n",
        "auc_SVM_grid, fpr_SVM_grid, tpr_SVM_grid = get_auc_scores(y, SVM_grid.predict(X),SVM_grid.predict_proba(X)[:,1])\n",
        "auc_SVM_grid, fpr_SVM_grid, tpr_SVM_grid = get_auc_scores(y, SVM_grid.predict(X),SVM_grid.predict_proba(X)[:,1])\n",
        "auc_RanFor_grid, fpr_RanFor_grid, tpr_RanFor_grid = get_auc_scores(y, RanFor_grid.predict(X),RanFor_grid.predict_proba(X)[:,1])\n",
        "auc_xgb_grid, fpr_xgb_grid, tpr_xgb_grid = get_auc_scores(y, xgb_grid.predict(X),xgb_grid.predict_proba(X)[:,1])"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "7yoOs0Z46QLF",
        "outputId": "2a2ecc75-4aba-4249-a95d-dc21a85d434a"
      },
      "source": [
        "auc_xgb_grid, fpr_xgb_grid, tpr_xgb_grid = get_auc_scores(y, xgb_grid.predict(X),xgb_grid.predict_proba(X)[:,1])\n",
        "plt.figure(figsize = (12,6), linewidth= 1)\n",
        "plt.plot(fpr_log_primal_Grid, tpr_log_primal_Grid, label = 'log primal Score: ' + str(round(auc_log_primal_Grid, 5)))\n",
        "plt.plot(fpr_log_pol2_Grid, tpr_log_pol2_Grid, label = 'log pol2 score: ' + str(round(auc_log_pol2_Grid, 5)))\n",
        "plt.plot(fpr_SVM_grid, tpr_SVM_grid, label = 'SVM RBF Score: ' + str(round(auc_SVM_grid, 5)))\n",
        "plt.plot(fpr_SVM_grid, tpr_SVM_grid, label = 'SVM POL Score: ' + str(round(auc_SVM_grid, 5)))\n",
        "plt.plot(fpr_RanFor_grid, tpr_RanFor_grid, label = 'RF score: ' + str(round(auc_RanFor_grid, 5)))\n",
        "plt.plot(fpr_xgb_grid, tpr_xgb_grid, label = 'XGB score: ' + str(round(auc_xgb_grid, 5)))\n",
        "plt.plot([0,1], [0,1], 'k--', label = 'Random: 0.5')\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.legend(loc='best')\n",
        "#plt.savefig('roc_results_ratios.png')\n",
        "plt.show()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rbNnkoYt8xbk"
      },
      "source": [
        "### From the above results, my main aim is to predict the customers that will possibly churn so they can be put in some sort of scheme to prevent churn hence the recall measures on the 1's is of more importance to me than the overall accuracy score of the model.\n",
        "### Given that in the data we only had 20% of churn, a recall greater than this baseline will already be an improvement but we want to get as high as possible while trying to maintain a high precision so that the bank can train its resources effectively towards clients highlighted by the model without wasting too much resources on the false positives.\n",
        "### From the review of the fitted models above, the best model that gives a decent balance of the recall and precision is the random forest where according to the fit on the training set, with a precision score on 1's of 0.88, out of all customers that the model thinks will churn, 88% do actually churn and with the recall score of 0.53 on the 1's, the model is able to highlight 53% of all those who churned."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Bg6AeFT6P89",
        "outputId": "139f3d41-3cb1-46a2-f511-0fdccfe410f9"
      },
      "source": [
        "print(classification_report(df_test.Exited,  RanFor_grid.predict(df_test.loc[:, df_test.columns != 'Exited'])))"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.94      0.91      1607\n",
            "           1       0.68      0.52      0.59       389\n",
            "\n",
            "    accuracy                           0.86      1996\n",
            "   macro avg       0.78      0.73      0.75      1996\n",
            "weighted avg       0.85      0.86      0.85      1996\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "4AkZoVur6gMz",
        "outputId": "1e4eea47-35b4-446d-8a2d-72df87c06bdb"
      },
      "source": [
        "auc_RanFor_grid_test, fpr_RanFor_grid_test, tpr_RanFor_grid_test = get_auc_scores(df_test.Exited, RanFor_grid.predict(df_test.loc[:, df_test.columns != 'Exited']),\n",
        "                                                       RanFor_grid.predict_proba(df_test.loc[:, df_test.columns != 'Exited'])[:,1])\n",
        "plt.figure(figsize = (12,6), linewidth= 1)\n",
        "plt.plot(fpr_RanFor_grid_test, tpr_RanFor_grid_test, label = 'RF score: ' + str(round(auc_RanFor_grid_test, 5)))\n",
        "plt.plot([0,1], [0,1], 'k--', label = 'Random: 0.5')\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.legend(loc='best')\n",
        "#plt.savefig('roc_results_ratios.png')\n",
        "plt.show()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TOwnxXVd6jrh"
      },
      "source": [
        "## 7. Conclusion\n",
        "### The precision of the model on previousy unseen test data is slightly higher with regard to predicting 1's i.e. those customers that churn. However, in as much as the model has a high accuracy, it still misses about half of those who end up churning. This could be imprved by providing retraining the model with more data over time while in the meantime working with the model to save the 41% that would have churned "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NG7IPUfZ6ru1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}