loan_default_classifier.ipynb

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    "colab": {
      "name": "loan_default_classifier.ipynb",
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      "collapsed_sections": [],
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Blackman9t/Machine_Learning/blob/master/loan_default_classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgqAK0YFlgX_",
        "colab_type": "text"
      },
      "source": [
        "<h1 align=\"center\"><font size=\"6\">Classification with Python</font></h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSUU7S1dl_Jk",
        "colab_type": "text"
      },
      "source": [
        "In this notebook we try to classify bank customers into possible loan-defaulters or non-defaulters using a bunch of classifier algorithms.<br>\n",
        "We shall use The following algorithms:-<br>\n",
        "K-Nearest-Neighbour(KNN)<br>\n",
        "K-Means-Clustering<br>\n",
        "Decision Trees<br>\n",
        "Logistic Regression\n",
        "\n",
        "We load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n",
        "\n",
        "Lets first load required libraries:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u4LYRKCXlS4m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import itertools\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.ticker import NullFormatter\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.ticker as ticker\n",
        "import seaborn as sns\n",
        "from sklearn import preprocessing\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NjqG6zM9m_sI",
        "colab_type": "text"
      },
      "source": [
        "The data set we shall use is from Credit One Bank and it's in Kaggle and can be  seen along side its data dictionary via this [link](https://www.kaggle.com/dataforyou/bankloan)<br>\n",
        "\n",
        "The raw file can be read straight from github via this [link](https://github.com/Blackman9t/Machine_Learning/blob/master/Loan_Data%20for%20Classification.xlsx?raw=true')"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQe62hUWnOxx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = 'https://github.com/Blackman9t/Machine_Learning/blob/master/Loan_Data%20for%20Classification.xlsx?raw=true'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s0nc5t_gn_JP",
        "colab_type": "text"
      },
      "source": [
        "Reading the raw file to a data frame and setting row 2 as the header, since I first viewed the file in excel.<br>\n",
        "\n",
        "Let's define additional NaN values that pandas may not see"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQHYKJGorrrP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "additional_nan_values = ['n/a', '--','?','None','Non','non','none']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UqOKNI8Cn9c-",
        "colab_type": "code",
        "outputId": "efd511de-1dd1-4a6b-d18e-cad5d0fb8b2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "loan_df = pd.read_excel(data, header=2, na_values=additional_nan_values)\n",
        "\n",
        "loan_df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Customer_ID</th>\n",
              "      <th>Status_Checking_Acc</th>\n",
              "      <th>Duration_in_Months</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Purposre_Credit_Taken</th>\n",
              "      <th>Credit_Amount</th>\n",
              "      <th>Savings_Acc</th>\n",
              "      <th>Years_At_Present_Employment</th>\n",
              "      <th>Inst_Rt_Income</th>\n",
              "      <th>Marital_Status_Gender</th>\n",
              "      <th>Other_Debtors_Guarantors</th>\n",
              "      <th>Current_Address_Yrs</th>\n",
              "      <th>Property</th>\n",
              "      <th>Age</th>\n",
              "      <th>Other_Inst_Plans</th>\n",
              "      <th>Housing</th>\n",
              "      <th>Num_CC</th>\n",
              "      <th>Job</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Telephone</th>\n",
              "      <th>Foreign_Worker</th>\n",
              "      <th>Default_On_Payment</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001</td>\n",
              "      <td>A11</td>\n",
              "      <td>6</td>\n",
              "      <td>A34</td>\n",
              "      <td>A43</td>\n",
              "      <td>1169</td>\n",
              "      <td>A65</td>\n",
              "      <td>A75</td>\n",
              "      <td>4</td>\n",
              "      <td>A93</td>\n",
              "      <td>A101</td>\n",
              "      <td>4</td>\n",
              "      <td>A121</td>\n",
              "      <td>67</td>\n",
              "      <td>A143</td>\n",
              "      <td>A152</td>\n",
              "      <td>2</td>\n",
              "      <td>A173</td>\n",
              "      <td>1</td>\n",
              "      <td>A192</td>\n",
              "      <td>A201</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002</td>\n",
              "      <td>A12</td>\n",
              "      <td>48</td>\n",
              "      <td>A32</td>\n",
              "      <td>A43</td>\n",
              "      <td>5951</td>\n",
              "      <td>A61</td>\n",
              "      <td>A73</td>\n",
              "      <td>2</td>\n",
              "      <td>A92</td>\n",
              "      <td>A101</td>\n",
              "      <td>2</td>\n",
              "      <td>A121</td>\n",
              "      <td>22</td>\n",
              "      <td>A143</td>\n",
              "      <td>A152</td>\n",
              "      <td>1</td>\n",
              "      <td>A173</td>\n",
              "      <td>1</td>\n",
              "      <td>A191</td>\n",
              "      <td>A201</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003</td>\n",
              "      <td>A14</td>\n",
              "      <td>12</td>\n",
              "      <td>A34</td>\n",
              "      <td>A46</td>\n",
              "      <td>2096</td>\n",
              "      <td>A61</td>\n",
              "      <td>A74</td>\n",
              "      <td>2</td>\n",
              "      <td>A93</td>\n",
              "      <td>A101</td>\n",
              "      <td>3</td>\n",
              "      <td>A121</td>\n",
              "      <td>49</td>\n",
              "      <td>A143</td>\n",
              "      <td>A152</td>\n",
              "      <td>1</td>\n",
              "      <td>A172</td>\n",
              "      <td>2</td>\n",
              "      <td>A191</td>\n",
              "      <td>A201</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004</td>\n",
              "      <td>A11</td>\n",
              "      <td>42</td>\n",
              "      <td>A32</td>\n",
              "      <td>A42</td>\n",
              "      <td>7882</td>\n",
              "      <td>A61</td>\n",
              "      <td>A74</td>\n",
              "      <td>2</td>\n",
              "      <td>A93</td>\n",
              "      <td>A103</td>\n",
              "      <td>4</td>\n",
              "      <td>A122</td>\n",
              "      <td>45</td>\n",
              "      <td>A143</td>\n",
              "      <td>A153</td>\n",
              "      <td>1</td>\n",
              "      <td>A173</td>\n",
              "      <td>2</td>\n",
              "      <td>A191</td>\n",
              "      <td>A201</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005</td>\n",
              "      <td>A11</td>\n",
              "      <td>24</td>\n",
              "      <td>A33</td>\n",
              "      <td>A40</td>\n",
              "      <td>4870</td>\n",
              "      <td>A61</td>\n",
              "      <td>A73</td>\n",
              "      <td>3</td>\n",
              "      <td>A93</td>\n",
              "      <td>A101</td>\n",
              "      <td>4</td>\n",
              "      <td>A124</td>\n",
              "      <td>53</td>\n",
              "      <td>A143</td>\n",
              "      <td>A153</td>\n",
              "      <td>2</td>\n",
              "      <td>A173</td>\n",
              "      <td>2</td>\n",
              "      <td>A191</td>\n",
              "      <td>A201</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Customer_ID Status_Checking_Acc  ...  Default_On_Payment Count\n",
              "0       100001                 A11  ...                   0     1\n",
              "1       100002                 A12  ...                   0     1\n",
              "2       100003                 A14  ...                   0     1\n",
              "3       100004                 A11  ...                   0     1\n",
              "4       100005                 A11  ...                   1     1\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0t-XhroDpPe4",
        "colab_type": "text"
      },
      "source": [
        "Let's check the shape"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-WtSPhRpWr1",
        "colab_type": "code",
        "outputId": "0f7754a9-d135-4b54-a8c9-d3802d15d0a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loan_df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 23)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NwSPvMJLphiQ",
        "colab_type": "text"
      },
      "source": [
        "Shape is 5000 rows and 23 columns. <br>Pay attention to the data, it has  a lot of alpha-numeric notations<br>The best bet is to open the data dictionary via this [link](https://www.kaggle.com/dataforyou/bankloan) to a browser so one can understand what each notation stands for.\n",
        "\n",
        ">>\n",
        "\n",
        "Let's check the info and dtypes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RM0NWwkqqFPX",
        "colab_type": "code",
        "outputId": "aaf834b9-a23a-49c2-d5eb-50d7ee423531",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "source": [
        "loan_df.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5000 entries, 0 to 4999\n",
            "Data columns (total 23 columns):\n",
            "Customer_ID                    5000 non-null int64\n",
            "Status_Checking_Acc            5000 non-null object\n",
            "Duration_in_Months             5000 non-null int64\n",
            "Credit_History                 5000 non-null object\n",
            "Purposre_Credit_Taken          5000 non-null object\n",
            "Credit_Amount                  5000 non-null int64\n",
            "Savings_Acc                    5000 non-null object\n",
            "Years_At_Present_Employment    5000 non-null object\n",
            "Inst_Rt_Income                 5000 non-null int64\n",
            "Marital_Status_Gender          5000 non-null object\n",
            "Other_Debtors_Guarantors       5000 non-null object\n",
            "Current_Address_Yrs            5000 non-null int64\n",
            "Property                       5000 non-null object\n",
            "Age                            5000 non-null int64\n",
            "Other_Inst_Plans               5000 non-null object\n",
            "Housing                        5000 non-null object\n",
            "Num_CC                         5000 non-null int64\n",
            "Job                            5000 non-null object\n",
            "Dependents                     5000 non-null int64\n",
            "Telephone                      5000 non-null object\n",
            "Foreign_Worker                 5000 non-null object\n",
            "Default_On_Payment             5000 non-null int64\n",
            "Count                          5000 non-null int64\n",
            "dtypes: int64(10), object(13)\n",
            "memory usage: 898.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uo_gigzLq7aM",
        "colab_type": "text"
      },
      "source": [
        "Let's check the stats for columns of object dtype and transpose it for readability"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILF_NwsMqkz3",
        "colab_type": "code",
        "outputId": "a5d0c115-1be6-446c-f913-c4649b55bb2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "source": [
        "loan_df.describe(include=['object','category']).T"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Status_Checking_Acc</th>\n",
              "      <td>5000</td>\n",
              "      <td>4</td>\n",
              "      <td>A14</td>\n",
              "      <td>1970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Credit_History</th>\n",
              "      <td>5000</td>\n",
              "      <td>5</td>\n",
              "      <td>A32</td>\n",
              "      <td>2650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Purposre_Credit_Taken</th>\n",
              "      <td>5000</td>\n",
              "      <td>10</td>\n",
              "      <td>A43</td>\n",
              "      <td>1400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savings_Acc</th>\n",
              "      <td>5000</td>\n",
              "      <td>5</td>\n",
              "      <td>A61</td>\n",
              "      <td>3015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Years_At_Present_Employment</th>\n",
              "      <td>5000</td>\n",
              "      <td>5</td>\n",
              "      <td>A73</td>\n",
              "      <td>1695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Marital_Status_Gender</th>\n",
              "      <td>5000</td>\n",
              "      <td>4</td>\n",
              "      <td>A93</td>\n",
              "      <td>2740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other_Debtors_Guarantors</th>\n",
              "      <td>5000</td>\n",
              "      <td>3</td>\n",
              "      <td>A101</td>\n",
              "      <td>4535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Property</th>\n",
              "      <td>5000</td>\n",
              "      <td>4</td>\n",
              "      <td>A123</td>\n",
              "      <td>1660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other_Inst_Plans</th>\n",
              "      <td>5000</td>\n",
              "      <td>3</td>\n",
              "      <td>A143</td>\n",
              "      <td>4070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Housing</th>\n",
              "      <td>5000</td>\n",
              "      <td>3</td>\n",
              "      <td>A152</td>\n",
              "      <td>3565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Job</th>\n",
              "      <td>5000</td>\n",
              "      <td>4</td>\n",
              "      <td>A173</td>\n",
              "      <td>3150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Telephone</th>\n",
              "      <td>5000</td>\n",
              "      <td>2</td>\n",
              "      <td>A191</td>\n",
              "      <td>2980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Foreign_Worker</th>\n",
              "      <td>5000</td>\n",
              "      <td>2</td>\n",
              "      <td>A201</td>\n",
              "      <td>4815</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            count unique   top  freq\n",
              "Status_Checking_Acc          5000      4   A14  1970\n",
              "Credit_History               5000      5   A32  2650\n",
              "Purposre_Credit_Taken        5000     10   A43  1400\n",
              "Savings_Acc                  5000      5   A61  3015\n",
              "Years_At_Present_Employment  5000      5   A73  1695\n",
              "Marital_Status_Gender        5000      4   A93  2740\n",
              "Other_Debtors_Guarantors     5000      3  A101  4535\n",
              "Property                     5000      4  A123  1660\n",
              "Other_Inst_Plans             5000      3  A143  4070\n",
              "Housing                      5000      3  A152  3565\n",
              "Job                          5000      4  A173  3150\n",
              "Telephone                    5000      2  A191  2980\n",
              "Foreign_Worker               5000      2  A201  4815"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-D8JlANlrJp1",
        "colab_type": "text"
      },
      "source": [
        "Now Let's check the stats for numeric  columns  and transpose it for readability"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0heOSdFrOt4",
        "colab_type": "code",
        "outputId": "04d58006-f965-4f8b-87dd-9ec200503d57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "loan_df.describe().T"
      ],
      "execution_count": 8,
      "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>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>Customer_ID</th>\n",
              "      <td>5000.0</td>\n",
              "      <td>102500.500</td>\n",
              "      <td>1443.520003</td>\n",
              "      <td>100001.0</td>\n",
              "      <td>101250.75</td>\n",
              "      <td>102500.5</td>\n",
              "      <td>103750.25</td>\n",
              "      <td>105000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Duration_in_Months</th>\n",
              "      <td>5000.0</td>\n",
              "      <td>20.903</td>\n",
              "      <td>12.053989</td>\n",
              "      <td>4.0</td>\n",
              "      <td>12.00</td>\n",
              "      <td>18.0</td>\n",
              "      <td>24.00</td>\n",
              "      <td>72.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Credit_Amount</th>\n",
              "      <td>5000.0</td>\n",
              "      <td>3271.258</td>\n",
              "      <td>2821.607329</td>\n",
              "      <td>250.0</td>\n",
              "      <td>1365.50</td>\n",
              "      <td>2319.5</td>\n",
              "      <td>3972.25</td>\n",
              "      <td>18424.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Inst_Rt_Income</th>\n",
              "      <td>5000.0</td>\n",
              "      <td>2.973</td>\n",
              "      <td>1.118267</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Current_Address_Yrs</th>\n",
              "      <td>5000.0</td>\n",
              "      <td>2.845</td>\n",
              "      <td>1.103276</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>5000.0</td>\n",
              "      <td>35.546</td>\n",
              "      <td>11.370917</td>\n",
              "      <td>19.0</td>\n",
              "      <td>27.00</td>\n",
              "      <td>33.0</td>\n",
              "      <td>42.00</td>\n",
              "      <td>75.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Num_CC</th>\n",
              "      <td>5000.0</td>\n",
              "      <td>1.407</td>\n",
              "      <td>0.577423</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependents</th>\n",
              "      <td>5000.0</td>\n",
              "      <td>1.155</td>\n",
              "      <td>0.361941</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Default_On_Payment</th>\n",
              "      <td>5000.0</td>\n",
              "      <td>0.299</td>\n",
              "      <td>0.457866</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Count</th>\n",
              "      <td>5000.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      count        mean  ...        75%       max\n",
              "Customer_ID          5000.0  102500.500  ...  103750.25  105000.0\n",
              "Duration_in_Months   5000.0      20.903  ...      24.00      72.0\n",
              "Credit_Amount        5000.0    3271.258  ...    3972.25   18424.0\n",
              "Inst_Rt_Income       5000.0       2.973  ...       4.00       4.0\n",
              "Current_Address_Yrs  5000.0       2.845  ...       4.00       4.0\n",
              "Age                  5000.0      35.546  ...      42.00      75.0\n",
              "Num_CC               5000.0       1.407  ...       2.00       4.0\n",
              "Dependents           5000.0       1.155  ...       1.00       2.0\n",
              "Default_On_Payment   5000.0       0.299  ...       1.00       1.0\n",
              "Count                5000.0       1.000  ...       1.00       1.0\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMufdpyJsXKp",
        "colab_type": "text"
      },
      "source": [
        "No missing values atall  as both numeric and object dtype columns have complete 5000 records, but let's just confirm.<br>\n",
        "\n",
        "I also notice the count column seems to have only 1s in it and no reason why, let's consult the Data Dictionary to find out what it means  and deal with it as we pre-process the Data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bCA-zSqsgle",
        "colab_type": "code",
        "outputId": "d7b6141c-6a68-4e32-f32e-27e4ef407404",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loan_df.isna().any().all()  # checking if any missing values and checking if any true value in the response"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tr5dvEnxtULz",
        "colab_type": "text"
      },
      "source": [
        "For a data set of 5000 rows and 23 columns, we may need to reduce it's memory consumption as we know pandas stores all floats as float64 and all int as int64 let's reduce some columns to int8 or int16, especially columns with single or double digit entries like Age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-_YmerVpuHz7",
        "colab_type": "code",
        "outputId": "1be71076-ea0e-487a-9d97-fadcb0c3c9ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        }
      },
      "source": [
        "loan_df.dtypes"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Customer_ID                     int64\n",
              "Status_Checking_Acc            object\n",
              "Duration_in_Months              int64\n",
              "Credit_History                 object\n",
              "Purposre_Credit_Taken          object\n",
              "Credit_Amount                   int64\n",
              "Savings_Acc                    object\n",
              "Years_At_Present_Employment    object\n",
              "Inst_Rt_Income                  int64\n",
              "Marital_Status_Gender          object\n",
              "Other_Debtors_Guarantors       object\n",
              "Current_Address_Yrs             int64\n",
              "Property                       object\n",
              "Age                             int64\n",
              "Other_Inst_Plans               object\n",
              "Housing                        object\n",
              "Num_CC                          int64\n",
              "Job                            object\n",
              "Dependents                      int64\n",
              "Telephone                      object\n",
              "Foreign_Worker                 object\n",
              "Default_On_Payment              int64\n",
              "Count                           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GOxq3gHzt5l4",
        "colab_type": "code",
        "outputId": "194035c7-bc5c-4abd-8b78-0fe28f9f4e04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "current_memory = loan_df.memory_usage().sum()\n",
        "print('Current memory is-', current_memory,'Bytes.')\n",
        "\n",
        "loan_df.memory_usage()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Current memory is- 920080 Bytes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index                             80\n",
              "Customer_ID                    40000\n",
              "Status_Checking_Acc            40000\n",
              "Duration_in_Months             40000\n",
              "Credit_History                 40000\n",
              "Purposre_Credit_Taken          40000\n",
              "Credit_Amount                  40000\n",
              "Savings_Acc                    40000\n",
              "Years_At_Present_Employment    40000\n",
              "Inst_Rt_Income                 40000\n",
              "Marital_Status_Gender          40000\n",
              "Other_Debtors_Guarantors       40000\n",
              "Current_Address_Yrs            40000\n",
              "Property                       40000\n",
              "Age                            40000\n",
              "Other_Inst_Plans               40000\n",
              "Housing                        40000\n",
              "Num_CC                         40000\n",
              "Job                            40000\n",
              "Dependents                     40000\n",
              "Telephone                      40000\n",
              "Foreign_Worker                 40000\n",
              "Default_On_Payment             40000\n",
              "Count                          40000\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eBrWdiHWvWdm",
        "colab_type": "code",
        "outputId": "73a1881f-323f-48a0-f8c8-e9c7bb112224",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "# let's change all these columns with a max of 2 digits to int8 to save memory\n",
        "\n",
        "loan_df[['Duration_in_Months','Inst_Rt_Income','Current_Address_Yrs','Age','Num_CC','Dependents','Default_On_Payment','Count']] = \\\n",
        "loan_df[['Duration_in_Months','Inst_Rt_Income','Current_Address_Yrs','Age','Num_CC','Dependents','Default_On_Payment','Count']].astype('int8', inplace=True)\n",
        "\n",
        "# Let's change Credit_Amount to 1nt16 and Customer_ID to int32 to save memory\n",
        "\n",
        "loan_df['Customer_ID'] = loan_df['Customer_ID'].astype('int32')\n",
        "loan_df['Credit_Amount'] = loan_df['Credit_Amount'].astype('int16')\n",
        "new_memory = loan_df.memory_usage().sum()\n",
        "print('New memory is-', new_memory,'Bytes.')\n",
        "\n",
        "loan_df.memory_usage()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "New memory is- 590080 Bytes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index                             80\n",
              "Customer_ID                    20000\n",
              "Status_Checking_Acc            40000\n",
              "Duration_in_Months              5000\n",
              "Credit_History                 40000\n",
              "Purposre_Credit_Taken          40000\n",
              "Credit_Amount                  10000\n",
              "Savings_Acc                    40000\n",
              "Years_At_Present_Employment    40000\n",
              "Inst_Rt_Income                  5000\n",
              "Marital_Status_Gender          40000\n",
              "Other_Debtors_Guarantors       40000\n",
              "Current_Address_Yrs             5000\n",
              "Property                       40000\n",
              "Age                             5000\n",
              "Other_Inst_Plans               40000\n",
              "Housing                        40000\n",
              "Num_CC                          5000\n",
              "Job                            40000\n",
              "Dependents                      5000\n",
              "Telephone                      40000\n",
              "Foreign_Worker                 40000\n",
              "Default_On_Payment              5000\n",
              "Count                           5000\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iZfrkGqv0I4x",
        "colab_type": "text"
      },
      "source": [
        "Let's see how much memory we have saved"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ywON8i4uy6dR",
        "colab_type": "code",
        "outputId": "33091342-79f1-4f13-8326-90e46a9f4824",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "current_memory - new_memory  # We've saved 330,000 Bytes just now."
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "330000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I0ndbPIB0qwi",
        "colab_type": "text"
      },
      "source": [
        "Next we need to organise the Data into a more logical sequence before we start any computation.<br>\n",
        "This means we arrange columns of similar meta-data together starting with categorical, then continuous columns.<br>\n",
        "Except in case where it is more sensible to put a continuous variable before a categorical one."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4K8tc2RV1RE7",
        "colab_type": "code",
        "outputId": "e2a316ec-feb8-4c2c-864e-68370b96540f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "# Let's see the total columns in the data set\n",
        "\n",
        "loan_df.columns"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Customer_ID', 'Status_Checking_Acc', 'Duration_in_Months',\n",
              "       'Credit_History', 'Purposre_Credit_Taken', 'Credit_Amount',\n",
              "       'Savings_Acc', 'Years_At_Present_Employment', 'Inst_Rt_Income',\n",
              "       'Marital_Status_Gender', 'Other_Debtors_Guarantors',\n",
              "       'Current_Address_Yrs', 'Property', 'Age', 'Other_Inst_Plans ',\n",
              "       'Housing', 'Num_CC', 'Job', 'Dependents', 'Telephone', 'Foreign_Worker',\n",
              "       'Default_On_Payment', 'Count'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QXkfOA61tNQ",
        "colab_type": "text"
      },
      "source": [
        "Next, let's try to group them into subgroups using python lists. \n",
        "<br>\n",
        "For exanmple we can have a list for bio-data, one for financial date and so on"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEf7hgrI1o5J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "customer_data = ['Customer_ID', 'Marital_Status_Gender', 'Age', 'Dependents', 'Housing','Telephone', 'Property','Current_Address_Yrs']\n",
        "\n",
        "job_and_personal_finance = ['Job', 'Foreign_Worker', 'Years_At_Present_Employment', 'Savings_Acc','Status_Checking_Acc', 'Credit_History']\n",
        "\n",
        "loan_credit_data = ['Credit_Amount', 'Purposre_Credit_Taken', 'Duration_in_Months','Inst_Rt_Income','Other_Debtors_Guarantors','Other_Inst_Plans ','Num_CC','Default_On_Payment', \"Count\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rAyfKQVX9bwN",
        "colab_type": "text"
      },
      "source": [
        "The above process is highly manual and subject to errors let's confirm we made no errors before assigning the order"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNzePw-j9pgk",
        "colab_type": "code",
        "outputId": "c32732b4-66fd-4f42-9946-6a79667cc047",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# lets concatenate the lists\n",
        "\n",
        "new_columns = customer_data + job_and_personal_finance + loan_credit_data\n",
        "\n",
        "# Let's check we did not omit any column\n",
        "\n",
        "set(loan_df.columns).difference(set(new_columns))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "set()"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xUftRy5n-sLW",
        "colab_type": "text"
      },
      "source": [
        "The empty set returned above means no difference and we did not omit any thing. so let's go ahead and re-order the columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRHiLbC82b3_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loan_df = loan_df[new_columns]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n_zpaPeACcEP",
        "colab_type": "text"
      },
      "source": [
        "Next let's strip off white-spaces either at the beginning or end of all column names, incase we mistakenly left it during the manual process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JF_HeZqK5gyl",
        "colab_type": "code",
        "outputId": "2e088a98-7e65-4274-a932-4fd63f8e99d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "loan_df.head(2)"
      ],
      "execution_count": 18,
      "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>Customer_ID</th>\n",
              "      <th>Marital_Status_Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Housing</th>\n",
              "      <th>Telephone</th>\n",
              "      <th>Property</th>\n",
              "      <th>Current_Address_Yrs</th>\n",
              "      <th>Job</th>\n",
              "      <th>Foreign_Worker</th>\n",
              "      <th>Years_At_Present_Employment</th>\n",
              "      <th>Savings_Acc</th>\n",
              "      <th>Status_Checking_Acc</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Credit_Amount</th>\n",
              "      <th>Purposre_Credit_Taken</th>\n",
              "      <th>Duration_in_Months</th>\n",
              "      <th>Inst_Rt_Income</th>\n",
              "      <th>Other_Debtors_Guarantors</th>\n",
              "      <th>Other_Inst_Plans</th>\n",
              "      <th>Num_CC</th>\n",
              "      <th>Default_On_Payment</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001</td>\n",
              "      <td>A93</td>\n",
              "      <td>67</td>\n",
              "      <td>1</td>\n",
              "      <td>A152</td>\n",
              "      <td>A192</td>\n",
              "      <td>A121</td>\n",
              "      <td>4</td>\n",
              "      <td>A173</td>\n",
              "      <td>A201</td>\n",
              "      <td>A75</td>\n",
              "      <td>A65</td>\n",
              "      <td>A11</td>\n",
              "      <td>A34</td>\n",
              "      <td>1169</td>\n",
              "      <td>A43</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>A101</td>\n",
              "      <td>A143</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002</td>\n",
              "      <td>A92</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>A152</td>\n",
              "      <td>A191</td>\n",
              "      <td>A121</td>\n",
              "      <td>2</td>\n",
              "      <td>A173</td>\n",
              "      <td>A201</td>\n",
              "      <td>A73</td>\n",
              "      <td>A61</td>\n",
              "      <td>A12</td>\n",
              "      <td>A32</td>\n",
              "      <td>5951</td>\n",
              "      <td>A43</td>\n",
              "      <td>48</td>\n",
              "      <td>2</td>\n",
              "      <td>A101</td>\n",
              "      <td>A143</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Customer_ID Marital_Status_Gender  Age  ...  Num_CC Default_On_Payment Count\n",
              "0       100001                   A93   67  ...       2                  0     1\n",
              "1       100002                   A92   22  ...       1                  0     1\n",
              "\n",
              "[2 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6xkTbWaB2cZg",
        "colab_type": "code",
        "outputId": "54f6b241-3c9f-4405-b5a2-a98544bff552",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "stripped_columns = [str.strip(x, ' ') for x in loan_df.columns if x[0] or x[-1] == ' '] \n",
        "stripped_columns[:5]"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Customer_ID', 'Marital_Status_Gender', 'Age', 'Dependents', 'Housing']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WUpjSzfr8PNr",
        "colab_type": "text"
      },
      "source": [
        "Now let's assign the stripped-off-white-spaces column names to the data frame as we rename the data frame columns using a dict and the rename function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CAmG_IjW6YtE",
        "colab_type": "code",
        "outputId": "457dad29-4272-4ea8-91ff-3d7d63e9454f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "columns_dict = {}\n",
        "for i in range(len(loan_df.columns)):\n",
        "    x = loan_df.columns[i]\n",
        "    columns_dict[x] = stripped_columns[i]\n",
        "    \n",
        "loan_df.rename(columns = columns_dict, inplace=True)\n",
        "loan_df.head(3)"
      ],
      "execution_count": 20,
      "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>Customer_ID</th>\n",
              "      <th>Marital_Status_Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Housing</th>\n",
              "      <th>Telephone</th>\n",
              "      <th>Property</th>\n",
              "      <th>Current_Address_Yrs</th>\n",
              "      <th>Job</th>\n",
              "      <th>Foreign_Worker</th>\n",
              "      <th>Years_At_Present_Employment</th>\n",
              "      <th>Savings_Acc</th>\n",
              "      <th>Status_Checking_Acc</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Credit_Amount</th>\n",
              "      <th>Purposre_Credit_Taken</th>\n",
              "      <th>Duration_in_Months</th>\n",
              "      <th>Inst_Rt_Income</th>\n",
              "      <th>Other_Debtors_Guarantors</th>\n",
              "      <th>Other_Inst_Plans</th>\n",
              "      <th>Num_CC</th>\n",
              "      <th>Default_On_Payment</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001</td>\n",
              "      <td>A93</td>\n",
              "      <td>67</td>\n",
              "      <td>1</td>\n",
              "      <td>A152</td>\n",
              "      <td>A192</td>\n",
              "      <td>A121</td>\n",
              "      <td>4</td>\n",
              "      <td>A173</td>\n",
              "      <td>A201</td>\n",
              "      <td>A75</td>\n",
              "      <td>A65</td>\n",
              "      <td>A11</td>\n",
              "      <td>A34</td>\n",
              "      <td>1169</td>\n",
              "      <td>A43</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>A101</td>\n",
              "      <td>A143</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002</td>\n",
              "      <td>A92</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>A152</td>\n",
              "      <td>A191</td>\n",
              "      <td>A121</td>\n",
              "      <td>2</td>\n",
              "      <td>A173</td>\n",
              "      <td>A201</td>\n",
              "      <td>A73</td>\n",
              "      <td>A61</td>\n",
              "      <td>A12</td>\n",
              "      <td>A32</td>\n",
              "      <td>5951</td>\n",
              "      <td>A43</td>\n",
              "      <td>48</td>\n",
              "      <td>2</td>\n",
              "      <td>A101</td>\n",
              "      <td>A143</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003</td>\n",
              "      <td>A93</td>\n",
              "      <td>49</td>\n",
              "      <td>2</td>\n",
              "      <td>A152</td>\n",
              "      <td>A191</td>\n",
              "      <td>A121</td>\n",
              "      <td>3</td>\n",
              "      <td>A172</td>\n",
              "      <td>A201</td>\n",
              "      <td>A74</td>\n",
              "      <td>A61</td>\n",
              "      <td>A14</td>\n",
              "      <td>A34</td>\n",
              "      <td>2096</td>\n",
              "      <td>A46</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>A101</td>\n",
              "      <td>A143</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Customer_ID Marital_Status_Gender  Age  ...  Num_CC Default_On_Payment Count\n",
              "0       100001                   A93   67  ...       2                  0     1\n",
              "1       100002                   A92   22  ...       1                  0     1\n",
              "2       100003                   A93   49  ...       1                  0     1\n",
              "\n",
              "[3 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-4XSnHb_S9G",
        "colab_type": "text"
      },
      "source": [
        "This is beautiful our loan_df columns are ordered in a logical fashion, let's start feature binarization and pre-processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ClelReV5e_Au",
        "colab_type": "text"
      },
      "source": [
        "### First Let's **visualize** The relationships between the categorical variables and Default on payments column (our target variable)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VMxyXWX5Ekam",
        "colab_type": "code",
        "outputId": "3fe72915-e019-4700-bf9e-cbaaf14c097f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1363
        }
      },
      "source": [
        "import matplotlib.gridspec as gridspec\n",
        "\n",
        "fig = plt.figure(constrained_layout=True, figsize=(18,18))\n",
        "sns.set_style('ticks')\n",
        "\n",
        "specs = gridspec.GridSpec(ncols=3, nrows=4, figure=fig)\n",
        "ax1 = fig.add_subplot(specs[0, 0])\n",
        "ax2 = fig.add_subplot(specs[0, 1])\n",
        "ax3 = fig.add_subplot(specs[0, 2])\n",
        "ax4 = fig.add_subplot(specs[1, 0])\n",
        "ax5 = fig.add_subplot(specs[1, 1])\n",
        "ax6 = fig.add_subplot(specs[1, 2])\n",
        "ax7 = fig.add_subplot(specs[2, 0])\n",
        "ax8 = fig.add_subplot(specs[2, 1])\n",
        "ax9 = fig.add_subplot(specs[2, 2])\n",
        "ax10 = fig.add_subplot(specs[3, 0])\n",
        "ax11 = fig.add_subplot(specs[3, 1])\n",
        "ax12 = fig.add_subplot(specs[3, 2])\n",
        "\n",
        "plt.suptitle('Percentage of Non-Defaulters Per Categorical Variable', fontsize=18, y=1.03)\n",
        "\n",
        "\n",
        "ax1 = sns.violinplot('Marital_Status_Gender', 'Default_On_Payment', data=loan_df, order=['A91','A92','A93','A94'], ax=ax1)\n",
        "ax1.set_title('Marital-Status-Gender % of Non-Defaulters')\n",
        "yposlist = loan_df.groupby(['Marital_Status_Gender'])['Default_On_Payment'].value_counts(normalize=True).tolist()\n",
        "yposlist = [round(i,2) for i in yposlist[0::2]]\n",
        "xposlist = range(len(yposlist))\n",
        "for i in range(len(yposlist)):\n",
        "    ax1.text(xposlist[i], yposlist[i], str(round(yposlist[i], 2)), fontsize=14)\n",
        "\n",
        "\n",
        "ax2 = sns.violinplot('Housing', 'Default_On_Payment', data=loan_df, order=['A151','A152','A153'], ax=ax2)\n",
        "ax2.set_title('Housing-Conditions % of Non-Defaulters')\n",
        "yposlist = loan_df.groupby(['Housing'])['Default_On_Payment'].value_counts(normalize=True).tolist()\n",
        "yposlist = [round(i,2) for i in yposlist[0::2]]\n",
        "xposlist = range(len(yposlist))\n",
        "for i in range(len(yposlist)):\n",
        "    ax2.text(xposlist[i], yposlist[i], str(round(yposlist[i], 2)), fontsize=14)\n",
        "\n",
        "\n",
        "ax3 = sns.violinplot('Telephone', 'Default_On_Payment', data=loan_df,order=['A191','A192'], ax=ax3)\n",
        "ax3.set_title('Registered-Telephone % of Non-Defaulters')\n",
        "yposlist = loan_df.groupby(['Telephone'])['Default_On_Payment'].value_counts(normalize=True).tolist()\n",
        "yposlist = [round(i,2) for i in yposlist[0::2]]\n",
        "xposlist = range(len(yposlist))\n",
        "for i in range(len(yposlist)):\n",
        "    ax3.text(xposlist[i], yposlist[i], str(round(yposlist[i], 2)), fontsize=14)\n",
        "\n",
        "\n",
        "ax4 = sns.violinplot('Property', 'Default_On_Payment', data=loan_df,order=['A121','A122','A123','A124'], ax=ax4)\n",
        "ax4.set_title('Property-Type % of Non-Defaulters')\n",
        "yposlist = loan_df.groupby(['Property'])['Default_On_Payment'].value_counts(normalize=True).tolist()\n",
        "yposlist = [round(i,2) for i in yposlist[0::2]]\n",
        "xposlist = range(len(yposlist))\n",
        "for i in range(len(yposlist)):\n",
        "    ax4.text(xposlist[i], yposlist[i], str(round(yposlist[i], 2)), fontsize=14)\n",
        "\n",
        "\n",
        "ax5 = sns.violinplot('Job', 'Default_On_Payment', data=loan_df, order=['A171','A172','A173','A174'], ax=ax5)\n",
        "ax5.set_title('Job-Status % of Non-Defaulters')\n",
        "yposlist = loan_df.groupby(['Job'])['Default_On_Payment'].value_counts(normalize=True).tolist()\n",
        "yposlist = [round(i,2) for i in yposlist[0::2]]\n",
        "xposlist = range(len(yposlist))\n",
        "for i in range(len(yposlist)):\n",
        "    ax5.text(xposlist[i], yposlist[i], str(round(yposlist[i], 2)), fontsize=14)\n",
        "\n",
        "\n",
        "ax6 = sns.violinplot('Foreign_Worker', 'Default_On_Payment', data=loan_df, ax=ax6)\n",
        "ax6.set_title('Foreign-Worker-Status % of Non-Defaulterst')\n",
        "yposlist = loan_df.groupby(['Foreign_Worker'])['Default_On_Payment'].value_counts(normalize=True).tolist()\n",
        "yposlist = [round(i,2) for i in yposlist[0::2]]\n",
        "xposlist = range(len(yposlist))\n",
        "for i in range(len(yposlist)):\n",
        "    ax6.text(xposlist[i], yposlist[i], str(round(yposlist[i], 2)), fontsize=14)\n",
        "\n",
        "\n",
        "ax7 = sns.violinplot('Years_At_Present_Employment', 'Default_On_Payment', order=['A71','A72','A73','A74','A75'], data=loan_df, ax=ax7)\n",
        "ax7.set_title('Years@Present-Employment % of Non-Defaulters')\n",
        "yposlist = loan_df.groupby(['Years_At_Present_Employment'])['Default_On_Payment'].value_counts(normalize=True).tolist()\n",
        "yposlist = [round(i,2) for i in yposlist[0::2]]\n",
        "xposlist = range(len(yposlist))\n",
        "for i in range(len(yposlist)):\n",
        "    ax7.text(xposlist[i], yposlist[i], str(round(yposlist[i], 2)), fontsize=14)\n",
        "\n",
        "\n",
        "ax8 = sns.violinplot('Savings_Acc', 'Default_On_Payment', data=loan_df, order=['A61','A62','A63','A64','A65'], ax=ax8)\n",
        "ax8.set_title('Savings-Acc-Condition % of Non-Defaulters')\n",
        "yposlist = loan_df.groupby(['Savings_Acc'])['Default_On_Payment'].value_counts(normalize=True).tolist()\n",
        "yposlist = [round(i,2) for i in yposlist[0::2]]\n",
        "xposlist = range(len(yposlist))\n",
        "for i in range(len(yposlist)):\n",
        "    ax8.text(xposlist[i], yposlist[i], str(round(yposlist[i], 2)), fontsize=14)\n",
        "\n",
        "\n",
        "ax9 = sns.violinplot('Status_Checking_Acc', 'Default_On_Payment', data=loan_df,order=['A11','A12','A13','A14'], ax=ax9)\n",
        "ax9.set_title('Status-Checking-Acc % of Non-Defaulters')\n",
        "yposlist = loan_df.groupby(['Status_Checking_Acc'])['Default_On_Payment'].value_counts(normalize=True).tolist()\n",
        "yposlist = [round(i,2) for i in yposlist[0::2]]\n",
        "xposlist = range(len(yposlist))\n",
        "for i in range(len(yposlist)):\n",
        "    ax9.text(xposlist[i], yposlist[i], str(round(yposlist[i], 2)), fontsize=14)\n",
        "\n",
        "\n",
        "ax10 = sns.violinplot('Credit_History', 'Default_On_Payment', data=loan_df, order=['A30','A31','A32','A33','A34'], ax=ax10)\n",
        "ax10.set_title('Credit-History % of Non-Defaulters')\n",
        "yposlist = loan_df.groupby(['Credit_History'])['Default_On_Payment'].value_counts(normalize=True).tolist()\n",
        "yposlist = [round(i,2) for i in yposlist[0::2]]\n",
        "xposlist = range(len(yposlist))\n",
        "for i in range(len(yposlist)):\n",
        "    ax10.text(xposlist[i], yposlist[i], str(round(yposlist[i], 2)), fontsize=14)\n",
        "\n",
        "\n",
        "ax11 = sns.violinplot('Other_Inst_Plans', 'Default_On_Payment', data=loan_df,order=['A141','A142','A143'], ax=ax11)\n",
        "ax11.set_title('Other-Inst-Plans % of Non-Defaulters')\n",
        "yposlist = loan_df.groupby(['Other_Inst_Plans'])['Default_On_Payment'].value_counts(normalize=True).tolist()\n",
        "yposlist = [round(i,2) for i in yposlist[0::2]]\n",
        "xposlist = range(len(yposlist))\n",
        "for i in range(len(yposlist)):\n",
        "    ax11.text(xposlist[i], yposlist[i], str(round(yposlist[i], 2)), fontsize=14)\n",
        "\n",
        "\n",
        "ax12 = sns.violinplot('Other_Debtors_Guarantors', 'Default_On_Payment', data=loan_df, order=['A101','A102','A103'], ax=ax12)\n",
        "ax12.set_title('Other-Debtors-Guarantors % of Non-Defaulters')\n",
        "yposlist = loan_df.groupby(['Other_Debtors_Guarantors'])['Default_On_Payment'].value_counts(normalize=True).tolist()\n",
        "yposlist = [round(i,2) for i in yposlist[0::2]]\n",
        "xposlist = range(len(yposlist))\n",
        "for i in range(len(yposlist)):\n",
        "    ax12.text(xposlist[i], yposlist[i], str(round(yposlist[i], 2)), fontsize=14)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yFzBNVX8SkbeBc8P0C4FXM2VlhO0aFLYLLLtkQFjftDDtRxH5SFUPSrOY28K+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QWZZIJpM1HYhsJTOb6O6Dsh1HY6BjXXt0rGuPjrXUdzqHGwd9zw2fvmOR7NDfnjREOq/rHg2R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19p+8qVK4mPjy97nJOTw7Rp01i9ejWZmZm0aNGCUaNGcccdd3jsfbjBIzlLnJtzgdH5sRBC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MxhhjjDHGGGOMMcaYPIsr6gIUtiVLlmwRkdVFXY5cqg5sKepClBG2rwuP7evCU1L3dcOi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IUrr7wSvaUR8/GPvHtyeyuRxfvJyBzP4sWLvXtu4bH8/HxiY4bwXqn3Eozf1Bopb0BWUw1Q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\nhiORJ92nAH2Svaxds5oHHnjAgugkyd5kLpMk6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88e5s2bhxkZd9LtZnQi5WVlfPbZZxZF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Tte+zYZ\nfD3/KwC+nv81Q7teH5Tjegw3ye2Tg3KscKCq6n3AfQDp6ekWR+NzvE9wU9uANJahc931vpN1gMlf\nLQrMcQBTiQj7vsf+5q+PxRMHZQAr57d8hlhNbOE0ENZ1HaIJzbP3Ey6IAkxdMNXigJpHxAv0ytAs\nDjXCfuDEpRNdqm+r7zH5qqpGAEn45vA05rktGpO1a9cO4XHjKD+CmRDMLf9azkjpdtK5qJHS0+KI\nmsE0iTx2kLZtQ3vFQn2ioqLwtLAYCDCoveek3/PlaS27KOoxfDFFR0e3OLZQ1i61HcdcxxDNGgn6\nl0gTzP16LgBzv56LGGp9TABKlUK7du2sDgOw/hQl4InMDhSHg9olND2tL6DgLDuEkdgRPa3PyU+q\nLkiG04lxfWoKg/WtfL2yswcF0EoiUJN1ruhc90O65nmhUGBMSfGtjVYqFL99QIqzBBVUMOWbKZDh\n+95fHJUO2nawz0lEdHQ0XkWpvxp9Cj8HcLlYOW0ag2q+b4Kaa4bBTO5dunRB27EL3fAS4Qz8duPt\nEjoztOv17Fydx9Cu19MuIfAT6AxT51hVAWd2OS/gxwqCoOQyqy+W1afmIoQSoJmFwRyUOUyvrS6q\n2JnD4cChKBh++iv096AMOB6b0xlSLU1PKSEhASrw9f6IsTqapgnoBdFgEeAodvh+D+FpPdBLVdVM\nfHnoNuD2Wo+ZA9wFrAFuApZqmiZUVZ0DfKmq6htAOtALWOfP4G688UZmzZpN4d4VVPT7JQTh/Mhf\nvF2HUpoLk+cvxUjqibdryHRCOS7ywCaUikIeePIFq0Pxu7g4/8ycH9mzCoWjrFswiSvSvNzWs6mN\nmU5WZdTEF9fi2EJZ5/TO5PyYgylMgtyKv65UqBhawZQdU3zFxVSL4wFfbjrmoHNPeyyAsroqczyR\nqaoahS+Rzan1mJpEBicksurbb6vemTOTACQyf4mMjASz1rQ1RUHv0Bd3z8vRO/StMw1AEeb/nhvG\noqJ8yxy8Rt1PC0WBK7t4+GP/Sq7s4ql3pkQoXdlp37496Z3TUQ778ZNRATFQIH4mEAOF/z50PcBR\nOPfcc/30gi2XkpJCRTMK7goK16Dwp+p/m7pJTBkQERER1AHF8OHDcXtd7C3cErRjtkvoTN/084NS\nXATYV7Qdl6eCa6+9NijHC7BWkcvqEx8fT3RMDIonMLuBe7sOpTSpJ5PnL6U0kIMyIVA8FbRv3z4w\nrx+GYmNjqPT6J+mM7FnFFWk1g7KjjGzhoAw4PlgMp6Lx7bffjkM4UH5s+sU2q4mzBBUZvguiFRkV\nfr0gGizKbgWOwqi7R1kdSkBUt+94AFgI7ACmaJq2TVXVv6uq+svqh30EpKqquht4DHiy+rnbgCnA\ndmAB8CdN0/zavyg+Pp4nn3wCxVVC/I65KFUh1P5AUfB2O4+qASPwdjvPf9O/g0GYRO1bS9T+jVx1\n1VVceumlVkfkd8nJbTnmafnvRFFgZK8qXj6/jJG9qlr8az7mcRAdFfm/VXCt1LBhwxAVAkqsjqRa\nKr5WJXYoLgIUg6gQDBs2zOpIAItnMGqapquqWpPInMDHNYkM2KBp2hx8iezz6kR2FN/AjerH1SQy\nnQAkMn+Jjo6uW2A8HdO3/CLcP1BqTvzdzezb6g6hAiPAsIuGMXX6VEyXCTYe8yh5vsHLBRdcYHUo\nx7Vr145jpokBOIN4+aoUSE1JCeps4oEDBzKg/wB2aGvonNyTuOg2QTt2MLi85Ww7sIpevXoxZMgQ\nq8NpsdaSyxrSNaMr2uEALemvHpQFuvuQ4i5H6B4yMoKzsVI4aJeaSom71C+vVTMoG9mr5YXFGsVu\n3zX0cCoad+vWjd/e9Vs++ugjzPUm4lzh+8QJBdUXREOS8BUXHT85GDJkCFdffbXVEQWMpmnzgfm1\nbnvuhK+rgJsbeO5LwEuBjG/o0KG89tprPDd6NMr22bgyL8ZI6RbIQ7Zqiruc6L3LcZYeYMSIETzw\nwANWhxQQndLT2bxvu9Vh1FHoctCxY4ewX9F4OsOGDcPpdGJmV+c96SRKtoLT6ZQFxhp2T2T+EBMT\ng1LetOGRYrSOAmPNlO/KZk5Lr5mhECrLVUaMGMG0adNQNAVxtk0/IE1wak769O3DmWeeaXU0x3Xv\n3h0TKAQ6BPG4BYpCZo8eQTyiz+NPPM59993Hmuw5XKbehjNQPe6CzDQN1u75ClPx8vTTT4fNSVNr\nyGUNGTToHHZPnea7MBaif6fOsoMAnHNOCO5OapF2aR0o2rXP6jAaVFTlKzDapSeRv/zmN7/BMAzG\njx8PFWCcb9j6gmXI00HZpODY6+Ciiy7i/9u77/ioqvTx459bkklPINTQ6wkIGjqIVEFgRZEiTdB1\n1Z+oq7Lq2tBVFLuo6NcVxfVr7+h+Xdl1dZVVAbEgIPUg0kLvJZ0k8/vjTjRAQtrM3MnM8369eJHM\n3Ln3mXtv5sx97nPOufvuu8Om3aqtevbsyYvz5nHnjBls2fAZRSnNyG/eC2+sjOnsN8WFRO38Cc/u\nn7BNk5tvvz1cepyUqXHjxizMgePFEOV2/85S9uTZpLVp6nYYrktKSmLUqFF88OEHFLUvgkS3Iwoh\nR8HcYnLRmItITAyNHRNCf0LhKykxserjUxU5swCFyokSKMnJyQBkFVTvVDx2vHYlGNPS0hg2bBjW\nJguOuh1N2YwNBt5sL5dffnlIfYlu40vyBXNe60K87ANat24dxK06WrRowYwZMziQtYslG/+PwuLa\nP4NcUXEhS375iH3HtnPrrbf+ekxF7datWze8xUVYh0NyGORKsQ5tJaVOHVq2bOl2KLVG69at2Z5t\nlTmGcijYlmURFWXTpElojEnkL6Zp8oc//IGZM2cSdTQK+xMbY73h1wnkBE4X9EywP7UxN5tMnTqV\nBx54IOLHQgsVTZs25W8vvsh1111HQv4B4lZ9QPTWbwI2XEfE8BZj7/uZ+FXzid7xIwP6ncPrr78W\n1slFgLZt21Lkhe1ZoVMSXlAEO7JM2rVr53YoIeGyyy7D4/FgLbdq3RAhAeMFa4VFTEwMl112WcXL\nB4kkGIMgKSkRs7hqCUaj0EkwxsfHByKkkBEVFUVcbAxHqzmOU9Zxg8T4OGy79lTNTJs2jfi4eKzv\nLahm1/CAOQrWGov+/fvTq1doTb7RokULYmNiCGa9zHagyOt1rZKzf//+3Hrrrew+uplFP8/neIAm\n0giGwqICFm/8kJ2HN3LjjTeGdRezSNO9e3dS6tTB3q/dDqVajIIc7MPbGDF8eEjdVAl1bdu2paAI\ndueE5lfJbccsWrVsWau+H1TFoEGDeO3V1zi719mYq0zsz2xnWg658Kq5Q2B9ZWEttWjVqBXPPPMM\nV111Va2YUDCSREVFMWHCBN5++y1Gnv87ovesJW7lO0RvXoSRF6J38UNVcSH2nnXE//Qenk1f0rpJ\nA+bMmcN9991H48aN3Y4u4JRSAGw6GjoJxm1ZFkVeaN++vduhhIQ6depw3bXXwR6nGEaAoQ3YA9dd\nex0pKaFTwS0tZRAkJydj+hKGlWUU5hOfkBhWsx+WJzU1lcP51TsVD+WbpNay7k916tTh5ptuhoNg\nrA6hD8hCsJfaxMfHc9NNN4XchbZt22RkZLA5iF/wN+HM5J6RkRG0bZ5s5MiR3H333ezP2sEX69/g\nWN4h12Kpruz8IyzUb7Hn6FZuu+02xo4d63ZIwo9s2+b83/0O+3Bmrbyos/euB6837Cs0/K1Dhw4A\n6MOhl8ArLIaNR6PpeEYnt0MJqLS0NB566CEef/xxmiQ3wVpiYf/Hhkwk0VgdB8BcZGL9xyI+x/ku\n9LcX/8ZZZ53ldmTiNOrUqcOtt97Km2++yYUjzyfm4EbifnoPz8aFmFn73A4vtB3PI2rnShJWvotn\ny2LSWzbloYce4qW//S2ihgxp3LgxdeuksP5Q6Eyuuv6Q07aG0nBVbhs1ahT9+/fHXG1CpP9p7wNz\njcmAAQO48MILK14+iCTBGAR169bFW5AD3sp/2zMKcqhTp04Aowod9Rs05FBB9RKphwos6tVv4OeI\nAu/cc89l1KhRmNrE2BYCiTwvmN+bcBTum3kfdevWdTuiMnXv0YP9xcXsD9KV0wbDIF0p14cqGDJk\nCLNnz6bYzOfzda+z+8hmV+Opij1Ht/Kfda+R783m0Ucf5fzzz3c7JBEAY8eOJcqOImrHcrdDqZrC\nfDx71nDOOefQooVMFFAVLVq0ILVuHdYcDJ0LshKbjlrkFXrp2rWr26EERc+ePXn11VeZMWMGTRKa\nYC21sD+1MbZI1+kKeYE9vorFLywSsxK56qqreO/d97jooosi4kZ/uGjSpAm33HIL7737LuMvvpj4\n7B3Ervk/Ytf+A+vAL1Acat2G3GPkHCB609ckrHib6Mzv6dK5A0899RTPPz+Xvn37Rly1rmEYdO/R\nkzWHo0Nm2I/VB6No1bJF2I0jXBOGYXDbbbfRNK0p9hLbmYkzEh0Be4lN0yZNufXWW0OuKCiyPj1c\nkpqa6swiXYUqRrMwl/r1QmXu88Bq2LAh+/OqVwGxP8+mUaNGfo4oOG644QY6de6E9YPlzFziImO1\ngbHd4JprrqF79+7uBnMa/fv3B5zpdgPtMF52eL30HzAgCFurWLdu3Zj34jyaNkvjqw3z+Wn7VxR7\nQ/fLcrG3mNU7FvPVhvdo2Lg+8+a9EHLd7oX/1KtXj9GjLyLqwEaMnNpTZRu16ye8hflcccUVbodS\n65RckK0+5KEwxD6KVu6PwjSMiEkwglNJPGzYMF577TVmzpxJy3otMb83sf9pY6wxwH8TdIeHQmfm\nTfszG+sri5SCFK699lref+99pk6dWmvG9hanqlevHn/84x/5+4cfcv3115OWYBGzcSEJP71D1I7l\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if3ofa//GgE4GY+QeJkZ/Suz6f9Ewwebuu+/m9ddfY9iwYdKWBdCgQYNIrVuHf24LXnv2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\navv/MnyfEkcXlIIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1296x1296 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sryI1LOc8GMM",
        "colab_type": "text"
      },
      "source": [
        "Let's visualize The last categorical variable that we could not squeeze into the grid above for uniformity's sakes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFXhY1wX8Gpy",
        "colab_type": "code",
        "outputId": "83a7be53-6476-4f76-d91d-d926c459d272",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.violinplot('Purposre_Credit_Taken', 'Default_On_Payment', data=loan_df,order=['A40','A41','A410','A42','A43','A44','A45','A46','A48','A49'])\n",
        "plt.title('Purposre-Credit-Taken % of Non-Defaulters', fontsize=15)\n",
        "yposlist = loan_df.groupby(['Purposre_Credit_Taken'])['Default_On_Payment'].value_counts(normalize=True).tolist()\n",
        "yposlist = [round(i,2) for i in yposlist[0::2]]\n",
        "xposlist = range(len(yposlist))\n",
        "for i in range(len(yposlist)):\n",
        "    plt.text(xposlist[i], yposlist[i], str(round(yposlist[i], 2)), fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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npKB79913OXLkCCaTiWnTppGbmxvvLgkSBCHoBIJW1NTUELAEAFCtqr6te/fu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\n5/8BBqJ2lll0RekAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2PKb_Iej-HZk",
        "colab_type": "text"
      },
      "source": [
        "The essence of visualizing each categorical variable is to identify the variables that have a high percent of Non-defaulters or a low percent of Non-defaulters.<br>\n",
        "\n",
        "My threshold is <br>\n",
        "\n",
        "###$Non-defaulting <= 0.3\\: or\\: >= 0.7$<br>\n",
        "\n",
        "Thus, I will select only those categorical variables that indicate a pretty high Non-defaulting score equal or larger than 70% or a pretty-low Non-defaulting score less than or equal to 30%.<br>\n",
        "\n",
        "Note that for a given variable, a high Non-defaulting Variable score simply indicates  a low-Defaulting Variable score and Vice-Versa"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Six6q9Ug2MzV",
        "colab_type": "text"
      },
      "source": [
        "### Feature Binarization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0VMhKoHmE_K-",
        "colab_type": "code",
        "outputId": "ec128e15-eb4a-432b-b50d-06e9175d5ce1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "loan_df.head(3)"
      ],
      "execution_count": 23,
      "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>Customer_ID</th>\n",
              "      <th>Marital_Status_Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Housing</th>\n",
              "      <th>Telephone</th>\n",
              "      <th>Property</th>\n",
              "      <th>Current_Address_Yrs</th>\n",
              "      <th>Job</th>\n",
              "      <th>Foreign_Worker</th>\n",
              "      <th>Years_At_Present_Employment</th>\n",
              "      <th>Savings_Acc</th>\n",
              "      <th>Status_Checking_Acc</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Credit_Amount</th>\n",
              "      <th>Purposre_Credit_Taken</th>\n",
              "      <th>Duration_in_Months</th>\n",
              "      <th>Inst_Rt_Income</th>\n",
              "      <th>Other_Debtors_Guarantors</th>\n",
              "      <th>Other_Inst_Plans</th>\n",
              "      <th>Num_CC</th>\n",
              "      <th>Default_On_Payment</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001</td>\n",
              "      <td>A93</td>\n",
              "      <td>67</td>\n",
              "      <td>1</td>\n",
              "      <td>A152</td>\n",
              "      <td>A192</td>\n",
              "      <td>A121</td>\n",
              "      <td>4</td>\n",
              "      <td>A173</td>\n",
              "      <td>A201</td>\n",
              "      <td>A75</td>\n",
              "      <td>A65</td>\n",
              "      <td>A11</td>\n",
              "      <td>A34</td>\n",
              "      <td>1169</td>\n",
              "      <td>A43</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>A101</td>\n",
              "      <td>A143</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002</td>\n",
              "      <td>A92</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>A152</td>\n",
              "      <td>A191</td>\n",
              "      <td>A121</td>\n",
              "      <td>2</td>\n",
              "      <td>A173</td>\n",
              "      <td>A201</td>\n",
              "      <td>A73</td>\n",
              "      <td>A61</td>\n",
              "      <td>A12</td>\n",
              "      <td>A32</td>\n",
              "      <td>5951</td>\n",
              "      <td>A43</td>\n",
              "      <td>48</td>\n",
              "      <td>2</td>\n",
              "      <td>A101</td>\n",
              "      <td>A143</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003</td>\n",
              "      <td>A93</td>\n",
              "      <td>49</td>\n",
              "      <td>2</td>\n",
              "      <td>A152</td>\n",
              "      <td>A191</td>\n",
              "      <td>A121</td>\n",
              "      <td>3</td>\n",
              "      <td>A172</td>\n",
              "      <td>A201</td>\n",
              "      <td>A74</td>\n",
              "      <td>A61</td>\n",
              "      <td>A14</td>\n",
              "      <td>A34</td>\n",
              "      <td>2096</td>\n",
              "      <td>A46</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>A101</td>\n",
              "      <td>A143</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Customer_ID Marital_Status_Gender  Age  ...  Num_CC Default_On_Payment Count\n",
              "0       100001                   A93   67  ...       2                  0     1\n",
              "1       100002                   A92   22  ...       1                  0     1\n",
              "2       100003                   A93   49  ...       1                  0     1\n",
              "\n",
              "[3 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xOB7uLVJDk4",
        "colab_type": "text"
      },
      "source": [
        "Let's binarize categorical variables below and save it in a new variable loan_df_dummies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jvuwGVLWILsC",
        "colab_type": "code",
        "outputId": "57b2ae05-0f75-406d-ebd4-3bf97ee53ef1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "loan_df_dummies = pd.get_dummies(loan_df)\n",
        "loan_df_dummies.head(3)"
      ],
      "execution_count": 24,
      "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>Customer_ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Current_Address_Yrs</th>\n",
              "      <th>Credit_Amount</th>\n",
              "      <th>Duration_in_Months</th>\n",
              "      <th>Inst_Rt_Income</th>\n",
              "      <th>Num_CC</th>\n",
              "      <th>Default_On_Payment</th>\n",
              "      <th>Count</th>\n",
              "      <th>Marital_Status_Gender_A91</th>\n",
              "      <th>Marital_Status_Gender_A92</th>\n",
              "      <th>Marital_Status_Gender_A93</th>\n",
              "      <th>Marital_Status_Gender_A94</th>\n",
              "      <th>Housing_A151</th>\n",
              "      <th>Housing_A152</th>\n",
              "      <th>Housing_A153</th>\n",
              "      <th>Telephone_A191</th>\n",
              "      <th>Telephone_A192</th>\n",
              "      <th>Property_A121</th>\n",
              "      <th>Property_A122</th>\n",
              "      <th>Property_A123</th>\n",
              "      <th>Property_A124</th>\n",
              "      <th>Job_A171</th>\n",
              "      <th>Job_A172</th>\n",
              "      <th>Job_A173</th>\n",
              "      <th>Job_A174</th>\n",
              "      <th>Foreign_Worker_A201</th>\n",
              "      <th>Foreign_Worker_A202</th>\n",
              "      <th>Years_At_Present_Employment_A71</th>\n",
              "      <th>Years_At_Present_Employment_A72</th>\n",
              "      <th>Years_At_Present_Employment_A73</th>\n",
              "      <th>Years_At_Present_Employment_A74</th>\n",
              "      <th>Years_At_Present_Employment_A75</th>\n",
              "      <th>Savings_Acc_A61</th>\n",
              "      <th>Savings_Acc_A62</th>\n",
              "      <th>Savings_Acc_A63</th>\n",
              "      <th>Savings_Acc_A64</th>\n",
              "      <th>Savings_Acc_A65</th>\n",
              "      <th>Status_Checking_Acc_A11</th>\n",
              "      <th>Status_Checking_Acc_A12</th>\n",
              "      <th>Status_Checking_Acc_A13</th>\n",
              "      <th>Status_Checking_Acc_A14</th>\n",
              "      <th>Credit_History_A30</th>\n",
              "      <th>Credit_History_A31</th>\n",
              "      <th>Credit_History_A32</th>\n",
              "      <th>Credit_History_A33</th>\n",
              "      <th>Credit_History_A34</th>\n",
              "      <th>Purposre_Credit_Taken_A40</th>\n",
              "      <th>Purposre_Credit_Taken_A41</th>\n",
              "      <th>Purposre_Credit_Taken_A410</th>\n",
              "      <th>Purposre_Credit_Taken_A42</th>\n",
              "      <th>Purposre_Credit_Taken_A43</th>\n",
              "      <th>Purposre_Credit_Taken_A44</th>\n",
              "      <th>Purposre_Credit_Taken_A45</th>\n",
              "      <th>Purposre_Credit_Taken_A46</th>\n",
              "      <th>Purposre_Credit_Taken_A48</th>\n",
              "      <th>Purposre_Credit_Taken_A49</th>\n",
              "      <th>Other_Debtors_Guarantors_A101</th>\n",
              "      <th>Other_Debtors_Guarantors_A102</th>\n",
              "      <th>Other_Debtors_Guarantors_A103</th>\n",
              "      <th>Other_Inst_Plans_A141</th>\n",
              "      <th>Other_Inst_Plans_A142</th>\n",
              "      <th>Other_Inst_Plans_A143</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001</td>\n",
              "      <td>67</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003</td>\n",
              "      <td>49</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Customer_ID  Age  ...  Other_Inst_Plans_A142  Other_Inst_Plans_A143\n",
              "0       100001   67  ...                      0                      1\n",
              "1       100002   22  ...                      0                      1\n",
              "2       100003   49  ...                      0                      1\n",
              "\n",
              "[3 rows x 64 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JAWgQNPaoKD",
        "colab_type": "text"
      },
      "source": [
        "Let's see howmany total columns we have in loan_df_dummies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3srr0ZXRauwp",
        "colab_type": "code",
        "outputId": "351cd63b-2098-496f-e921-5c318d79b422",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loan_df_dummies.shape  # we now have 64 total columns from 23 of before"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cBsAtFWsJLG7",
        "colab_type": "text"
      },
      "source": [
        "Now let's drop categorical columns whose elements are not  upto 30%  Non-defaulters or above 70% as we visualized earlier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oeRnMpbUU5pm",
        "colab_type": "code",
        "outputId": "6573ff09-a92c-41bc-d7ba-d8b1eb19440e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "categorical_cols = loan_df.select_dtypes(include ='object').columns\n",
        "categorical_cols"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Marital_Status_Gender', 'Housing', 'Telephone', 'Property', 'Job',\n",
              "       'Foreign_Worker', 'Years_At_Present_Employment', 'Savings_Acc',\n",
              "       'Status_Checking_Acc', 'Credit_History', 'Purposre_Credit_Taken',\n",
              "       'Other_Debtors_Guarantors', 'Other_Inst_Plans'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-jHs_2VzZk7M",
        "colab_type": "text"
      },
      "source": [
        "Next let's loop through each categorical column and create a list of columns to be deleted whose elements do not show a strong defaulting or non-defaulting characteristics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oL_2BJ-7Jn-o",
        "colab_type": "code",
        "outputId": "efe5c90e-008b-413b-8931-0f4f783f0231",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "# Let's define a simple column that we can use to sort out the features to delete\n",
        "\n",
        "def delete_categorical_feature(categorical_cols):\n",
        "    \"\"\" This method takes a pandas index object column and sorts it,\n",
        "    and returns a list of feature attributes to delete \"\"\"\n",
        "    \n",
        "    delete_list = []\n",
        "    for x in categorical_cols:\n",
        "        Non_default_df = loan_df.groupby(x)['Default_On_Payment'].value_counts(normalize=True).to_frame()[0::2]\n",
        "        delete_feature = Non_default_df[(Non_default_df.Default_On_Payment > 0.3) & (Non_default_df.Default_On_Payment < 0.7)]\n",
        "        delete = [delete_feature.index[i][0] for i in range(len(delete_feature))]\n",
        "        for i in range(len(delete)):\n",
        "            delete[i] = x + '_' + delete[i]\n",
        "        delete_list += delete\n",
        "    return delete_list\n",
        "\n",
        "delete_list = delete_categorical_feature(categorical_cols)\n",
        "print('Total cols to be deleted-', len(delete_list))\n",
        "delete_list[:5]"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total cols to be deleted- 32\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Marital_Status_Gender_A91',\n",
              " 'Marital_Status_Gender_A92',\n",
              " 'Housing_A151',\n",
              " 'Housing_A153',\n",
              " 'Telephone_A191']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yMLgsA97bCc1",
        "colab_type": "text"
      },
      "source": [
        "Now let's delete the weak relationship cols"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ru9O720ZbHmS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loan_df_dummies = loan_df_dummies.drop(delete_list, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fb6zetXfbRKU",
        "colab_type": "code",
        "outputId": "d8290b13-2679-40b9-ae8e-52e3593f3226",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loan_df_dummies.shape"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-_SDoIpdD6h",
        "colab_type": "code",
        "outputId": "7a788299-d8fe-40c1-937f-dfe99c26c465",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "loan_df_dummies.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Customer_ID</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Customer_ID  Age  ...  Other_Debtors_Guarantors_A103  Other_Inst_Plans_A143\n",
              "0       100001   67  ...                              0                      1\n",
              "1       100002   22  ...                              0                      1\n",
              "2       100003   49  ...                              0                      1\n",
              "3       100004   45  ...                              1                      1\n",
              "4       100005   53  ...                              0                      1\n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afCah-_ihGjW",
        "colab_type": "text"
      },
      "source": [
        "Okay so back to the count column, I have checked the Data Dictionary and frankly do not see any mention of it, lemme investigate further"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XaHj4_gChUpW",
        "colab_type": "code",
        "outputId": "8307ea52-7724-442f-eda9-ce627a7444c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loan_df_dummies.Count.nunique()\n",
        "\n",
        "# this shows it contains only one unique item which is 1,  5000 times.\n",
        "# I don't see any use for it and will delete it in the dummies data frame so it is not one of our features"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F5yS16Qc2N0c",
        "colab_type": "text"
      },
      "source": [
        "Let's drop the Count column as well as the customer Id column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJ3oqgvzh62T",
        "colab_type": "code",
        "outputId": "4042faf4-935e-4cb0-d009-199595c4b25c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loan_df_dummies.drop(['Count', 'Customer_ID'], axis=1, inplace=True)\n",
        "\n",
        "# let's confirm that the delete is done.\n",
        "'Count' in loan_df_dummies.columns"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fPNlF1fddfTS",
        "colab_type": "text"
      },
      "source": [
        "Then finally for this phase, let's reposition our target variable to the last column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1sUzUPPdpNi",
        "colab_type": "code",
        "outputId": "ae349350-e364-4a61-b09e-e605a8ea2215",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "temp = loan_df_dummies.Default_On_Payment\n",
        "\n",
        "loan_df_dummies = loan_df_dummies.drop('Default_On_Payment', axis=1)\n",
        "\n",
        "loan_df_dummies['Default_On_Payment'] = temp\n",
        "\n",
        "loan_df_dummies.head()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ],
            "text/plain": [
              "   Age  Dependents  ...  Other_Inst_Plans_A143  Default_On_Payment\n",
              "0   67           1  ...                      1                   0\n",
              "1   22           1  ...                      1                   0\n",
              "2   49           2  ...                      1                   0\n",
              "3   45           2  ...                      1                   0\n",
              "4   53           2  ...                      1                   1\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yg05Hc8pJqNm",
        "colab_type": "text"
      },
      "source": [
        "### How Balanced is our data?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sPj77UizT2Ei",
        "colab_type": "text"
      },
      "source": [
        "Let's check the ratio of defaulters to non-defaulters in the data set. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7kilMc3Sbip",
        "colab_type": "code",
        "outputId": "1dbdb6a3-fe15-4f1e-e8b6-820432e3c1fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "loan_df.Default_On_Payment.value_counts(normalize=True)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.701\n",
              "1    0.299\n",
              "Name: Default_On_Payment, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rPdk1vFVUAQv",
        "colab_type": "text"
      },
      "source": [
        "70% of the data set customers do not default and only 30% do.<br>\n",
        "So we have an imbalanced data set.\n",
        "\n",
        "An imbalanced Data set is one in which the elements within it are not evenly or fairly arranged.<br>\n",
        "Just as in this case, we have 70% of elements in Non-default-class and 30% in Default-class."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yf78rGqSJwMy",
        "colab_type": "text"
      },
      "source": [
        "let's visualize that"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NpCaEPkEuIq",
        "colab_type": "code",
        "outputId": "e1581113-cf02-4305-cccf-15235c9e75a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "x = loan_df_dummies.Default_On_Payment.replace(to_replace=[0, 1], value=['Non-Defaulters','Defaulters'])\n",
        "sns.countplot(x)\n",
        "plt.title('Count of Defaulters and Non-Defaulters')\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EhFauXKkpU6bo4Ycfdt7/wgsvqLi4WL1791a7du00ceLES565iImJUaNGjdS5c2dFRkZe\n8Jr60KFDVbNmTXXs2FFPPvmk8xeki4mNjdX+/fvVtm1bjR07Vu7u7nrttdf01VdfqVu3burQoYOe\nfvrpS8ZJkjIzMxUZGamgoCDNmDFD8+bNU+3atS9Yb+jQoSosLFSHDh0UFxenzp07/97T5hQYGKip\nU6fqscceU+fOnVW/fv0LXnL4LX369CnzMoIkzZ07V0eOHFHnzp01fvx4TZgwwXl0/ke89957CgoK\nUtu2bTVmzBj5+PgoOTnZeRahZcuWeu6555SQkKB27dopIiJCycnJl9zftGnTtGDBAgUFBenll18u\n86bJevXqadq0aXr66afVpUsXeXl5XfL58PLy0ujRo3Xfffepbdu22r17t8LDwzVy5EhNnjxZd999\nt/r06aOtW7deci5nzpzR008/rXvuuUddu3aVj4+PRowY8QefKVxp3KzynHcDAADVAkf8AAAYhPAD\nAGAQwg8AgEEIPwAABiH8AAAYpFp+gE/79u3LfPIaAADV3ZEjR7R9+/bfXa9ahr9x48a/+feyAABU\nNzabrVzrcaofAACDEH4AAAxC+AEAMAjhBwDAIIQfAACDEH4AAAxC+AEAMAjhBwDAIIQfAACDEH4A\nAAxC+AEAMAjhBwDAIIQfAACDEH4AAAxC+AEAMIjLwl9YWKjY2Fj17dtXkZGRWrBggSQpPj5eYWFh\nio6OVnR0tPbt2ydJsixL06dPV3h4uKKiovTFF18497V69WpFREQoIiJCq1evdtWUAQCo9jxctWNP\nT08lJiaqTp06Ki4u1v33368uXbpIkp544gn17NmzzPpbt27VgQMHtGnTJn322Wd65plntHLlSp04\ncUKLFi3SO++8Izc3N9lsNoWFhcnb29tVUwcAoNpy2RG/m5ub6tSpI0kqKSlRSUmJ3NzcLrl+enq6\nYmJi5ObmptatW+vkyZPKycnRtm3b1KlTJ/n4+Mjb21udOnVSZmamq6YNAEC15tLX+B0Oh6Kjo9Wx\nY0d17NhRd911lyRp3rx5ioqK0syZM1VUVCRJstvtatiwoXPbhg0bym63X7A8ICBAdrvdldMGAKDa\ncmn43d3dlZKSooyMDO3Zs0fffPONJk+erA0bNuidd95RQUGBlixZUiFjJSUlyWazyWazKT8/v0L2\neTGFxQ6X7RuoLHwfA+Zy2Wv8v1S/fn21b99emZmZGjFihKTz7wGw2Wx64403JJ0/ks/OznZuk52d\nrYCAAAUEBGjHjh3O5Xa7Xffcc88FY8TFxSkuLk6SZLPZXPZYatV0V5vH33TZ/oHK8N8Xh1T1FABU\nEZcd8efl5enkyZOSpJ9++kkffvihmjZtqpycHEnn38WflpamwMBASVJYWJjWrFkjy7K0e/du1atX\nT/7+/goJCdG2bdtUUFCggoICbdu2TSEhIa6aNgAA1ZrLjvhzcnIUHx8vh8Mhy7LUs2dPde3aVUOG\nDFF+fr4sy9Jtt92mZ599VpIUGhqqjIwMhYeHy8vLSzNnzpQk+fj4aOzYsYqNjZUkjRs3Tj4+Pq6a\nNgAA1ZqbZVlWVU+iotlsNiUnJ7ts/5zqx9WOU/1A9VPe9vHJfQAAGITwAwBgEMIPAIBBCD8AAAYh\n/AAAGITwAwBgEMIPAIBBCD8AAAYh/AAAGITwAwBgEMIPAIBBCD8AAAYh/AAAGITwAwBgEMIPAIBB\nCD8AAAYh/AAAGITwAwBgEMIPAIBBCD8AAAYh/AAAGITwAwBgEMIPAIBBCD8AAAYh/AAAGITwAwBg\nEMIPAIBBCD8AAAYh/AAAGITwAwBgEMIPAIBBCD8AAAYh/AAAGITwAwBgEMIPAIBBCD8AAAYh/AAA\nGITwAwBgEMIPAIBBCD8AAAYh/AAAGITwAwBgEMIPAIBBCD8AAAYh/AAAGITwAwBgEMIPAIBBCD8A\nAAYh/AAAGITwAwBgEMIPAIBBXBb+wsJCxcbGqm/fvoqMjNSCBQskSVlZWRowYIDCw8M1adIkFRUV\nSZKKioo0adIkhYeHa8CAATp8+LBzX4sXL1Z4eLh69OihzMxMV00ZAIBqz2Xh9/T0VGJiotauXas1\na9YoMzNTu3fv1pw5czRs2DBt3rxZ9evX16pVqyRJK1euVP369bV582YNGzZMc+bMkSTt379fqamp\nSk1N1dKlS/Xss8/K4XC4atoAAFRrLgu/m5ub6tSpI0kqKSlRSUmJ3Nzc9PHHH6tHjx6SpH79+ik9\nPV2S9J///Ef9+vWTJPXo0UMfffSRLMtSenq6IiMj5enpqSZNmuimm27Snj17XDVtAACqNQ9X7tzh\ncMhms+nQoUO6//771aRJE9WvX18eHueHbdiwoex2uyTJbrfr+uuvPz8pDw/Vq1dP+fn5stvtuuuu\nu5z7DAgIcG7zS0lJSUpKSpIk5efnu/JhAQBw1XLpm/vc3d2VkpKijIwM7dmzR99//73LxoqLi1Ny\ncrKSk5Pl6+vrsnEAALiaVcq7+uvXr6/27dtr9+7dOnnypEpKSiRJ2dnZCggIkHT+SP7YsWOSzr80\ncOrUKfn6+iogIEDZ2dnOfdntduc2AADg8rgs/Hl5eTp58qQk6aefftKHH36oW265Re3bt9fGjRsl\nSatXr1ZYWJgkKSwsTKtXr5Ykbdy4UR06dJCbm5vCwsKUmpqqoqIiZWVl6cCBA2rVqpWrpg0AQLXm\nstf4c3JyFB8fL4fDIcuy1LNnT3Xt2lXNmjXTI488ovnz5+v222/XgAEDJEmxsbF6/PHHFR4eLm9v\nb82bN0+SFBgYqF69eql3795yd3fX1KlT5e7u7qppAwBQrblZlmVV9SQqms1mU3Jyssv23+bxN122\nb6Ay/PfFIVU9BQAVrLzt45P7AAAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAA\nDEL4AQAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8A\nAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEH\nAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAADEL4\nAQAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEHAMAgLgv/sWPH\nNHjwYPXu3VuRkZFKTEyUJC1cuFCdO3dWdHS0oqOjlZGR4dxm8eLFCg8PV48ePZSZmelcvnXrVvXo\n0UPh4eFasmSJq6YMAEC15+GqHbu7uys+Pl4tWrTQ6dOn1b9/f3Xq1EmSNGzYMI0YMaLM+vv371dq\naqpSU1Nlt9s1fPhwbdy4UZKUkJCgZcuWKSAgQLGxsQoLC1OzZs1cNXUAAKotl4Xf399f/v7+kqS6\ndeuqadOmstvtl1w/PT1dkZGR8vT0VJMmTXTTTTdpz549kqSbbrpJTZo0kSRFRkYqPT2d8AMA8AdU\nymv8hw8f1r59+3TXXXdJkpYvX66oqChNmTJFBQUFkiS73a6GDRs6twkICJDdbr/k8l9LSkqSzWaT\nzWZTfn6+ix8RAABXJ5eH/8yZM5o4caKeeuop1a1bV/fdd582b96slJQU+fv7a9asWRUyTlxcnJKT\nk5WcnCxfX98K2ScAANWNS8NfXFysiRMnKioqShEREZKka6+9Vu7u7qpRo4YGDBigzz//XNL5I/ns\n7Gzntna7XQEBAZdcDgAALp/Lwm9Zlv7617+qadOmGj58uHN5Tk6O8+u0tDQFBgZKksLCwpSamqqi\noiJlZWXpwIEDatWqlVq2bKkDBw4oKytLRUVFSk1NVVhYmKumDQBAteayN/f997//VUpKim699VZF\nR0dLkiZPnqx3331XX331lSSpcePGSkhIkCQFBgaqV69e6t27t9zd3TV16lS5u7tLkqZOnaqRI0fK\n4XCof//+zl8WAADA5XFZ+Nu2bauvv/76guWhoaGX3GbMmDEaM2bMRbf5re0AAED58Ml9AAAYhPAD\nAGAQwg8AgEEIPwAABiH8AAAYhPADAGAQwg8AgEEIPwAABiH8AAAYhPADAGAQwg8AgEEIPwAABiH8\nAAAYhPADAGAQwg8AgEEIPwAABiH8AAAYhPADAGAQwg8AgEEIPwAABiH8AAAYhPADAGAQwg8AgEEI\nPwAABiH8AAAYhPADAGAQwg8AgEEIPwAABiH8AAAYhPADAGAQwg8AgEEIPwAABiH8AAAYhPADAGAQ\nwg8AgEEIPwAABiH8AAAYhPADAGAQwg8AgEEIPwAABiH8AAAYhPADAGAQwg8AgEEIPwAABiH8AAAY\nhPADAGAQwg8AgEEIPwAABiH8AAAYhPADAGAQl4X/2LFjGjx4sHr37q3IyEglJiZKkk6cOKHhw4cr\nIiJCw4cPV0FBgSTJsixNnz5d4eHhioqK0hdffOHc1+rVqxUREaGIiAitXr3aVVMGAKDac1n43d3d\nFR8fr/Xr1yspKUn//ve/tX//fi1ZskTBwcHatGmTgoODtWTJEknS1q1bdeDAAW3atEnPPfecnnnm\nGUnnf1FYtGiRVqxYoZUrV2rRokXOXxYAAMDlcVn4/f391aJFC0lS3bp11bRpU9ntdqWnpysmJkaS\nFBMTo7S0NElyLndzc1Pr1q118uRJ5eTkaNu2berUqZN8fHzk7e2tTp06KTMz01XTBgCgWquU1/gP\nHz6sffv26a677lJubq78/f0lSdddd51yc3MlSXa7XQ0bNnRu07BhQ9nt9guWBwQEyG63V8a0AQCo\ndjxcPcCZM2c0ceJEPfXUU6pbt26Z+9zc3OTm5lYh4yQlJSkpKUmSlJ+fXyH7BACgunHpEX9xcbEm\nTpyoqKgoRURESJIaNGignJwcSVJOTo78/PwknT+Sz87Odm6bnZ2tgICAC5bb7XYFBARcMFZcXJyS\nk5OVnJwsX19fVz4sAACuWi4Lv2VZ+utf/6qmTZtq+PDhzuVhYWFas2aNJGnNmjXq1q1bmeWWZWn3\n7t2qV6+e/P39FRISom3btqmgoEAFBQXatm2bQkJCXDVtAACqNZed6v/vf/+rlJQU3XrrrYqOjpYk\nTZ48WaNGjdKkSZO0atUqNWrUSPPnz5ckhYaGKiMjQ+Hh4fLy8tLMmTMlST4+Pho7dqxiY2MlSePG\njZOPj4+rpg0AQLXmsvC3bdtWX3/99UXv+/lv+n/Jzc1N06ZNu+j6sbGxzvADAIA/jk/uAwDAIIQf\nAACDlCv8Q4cOLdcyAABwZfvN1/gLCwt17tw55efnq6CgQJZlSZJOnz7Nh+gAAHAV+s3wv/3220pM\nTFROTo5sNpsz/HXr1tUDDzxQKRMEAAAV5zfDP3ToUA0dOlT/+te/NHjw4MqaEwAAcJFy/Tnf4MGD\n9emnn+rIkSNyOBzO5T9fbAcAAFwdyhX+xx9/XFlZWbrtttvk7u4u6fzf3RN+AACuLuUK/969e7V+\n/foKu6AOAACoGuX6c77AwED9+OOPrp4LAABwsXId8efn5ysyMlKtWrVSzZo1nctfe+01l00MAABU\nvHKFf8KECa6eBwAAqATlCv8999zj6nkAAIBKUK7wBwUFOd/YV1xcrJKSEnl5eenTTz916eQAAEDF\nKlf4d+3a5fzasiylp6dr9+7dLpsUAABwjcu+Op+bm5u6d++ubdu2uWI+AADAhcp1xL9p0ybn16Wl\npdq7d69q1arlskkBAADXKFf433//fefX7u7uaty4sV555RWXTQoAALhGucL//PPPu3oeAACgEpTr\nNf7s7GyNGzdOwcHBCg4O1oQJE5Sdne3quQEAgApWrvBPmTJFYWFhyszMVGZmprp27aopU6a4em4A\nAKCClSv8eXl56t+/vzw8POTh4SGbzaa8vDxXzw0AAFSwcoXfx8dHKSkpcjgccjgcSklJkY+Pj6vn\nBgAAKli5wj9z5ky999576tSpk0JCQrRx40bNmjXL1XMDAAAVrFzv6l+wYIFmz54tb29vSdKJEyc0\ne/Zs3u0PAMBVplxH/F9//bUz+tL5U//79u1z2aQAAIBrlCv8paWlKigocN4+ceKEHA6HyyYFAABc\no1yn+h988EHFxcWpZ8+ekqQNGzZo9OjRLp0YAACoeOUKf0xMjO688059/PHHkqRFixapWbNmLp0Y\nAACoeOUKvyQ1a9aM2AMAcJWz2rfXAAAXmUlEQVS77MvyAgCAqxfhBwDAIIQfAACDEH4AAAxC+AEA\nMAjhBwDAIIQfAACDEH4AAAxC+AEAMAjhBwDAIIQfAACDEH4AAAxC+AEAMAjhBwDAIIQfAACDEH4A\nAAxC+AEAMAjhBwDAIIQfAACDEH4AAAzisvBPmTJFwcHB6tOnj3PZwoUL1blzZ0VHRys6OloZGRnO\n+xYvXqzw8HD16NFDmZmZzuVbt25Vjx49FB4eriVLlrhqugAAGMHDVTu22Wx64IEH9OSTT5ZZPmzY\nMI0YMaLMsv379ys1NVWpqamy2+0aPny4Nm7cKElKSEjQsmXLFBAQoNjYWIWFhalZs2aumjYAANWa\ny8Lfrl07HT58uFzrpqenKzIyUp6enmrSpIluuukm7dmzR5J00003qUmTJpKkyMhIpaenE34AAP6g\nSn+Nf/ny5YqKitKUKVNUUFAgSbLb7WrYsKFznYCAANnt9ksuBwAAf0ylhv++++7T5s2blZKSIn9/\nf82aNavC9p2UlCSbzSabzab8/PwK2y+AK4NVUljVUwAqRFV/L7vsVP/FXHvttc6vBwwYoNGjR0s6\nfySfnZ3tvM9utysgIECSLrn81+Li4hQXFyfp/PsLAFQvbh61dCihZVVPA/jTbpz6eZWOX6lH/Dk5\nOc6v09LSFBgYKEkKCwtTamqqioqKlJWVpQMHDqhVq1Zq2bKlDhw4oKysLBUVFSk1NVVhYWGVOWUA\nAKoVlx3xT548WTt27FB+fr66dOmiCRMmaMeOHfrqq68kSY0bN1ZCQoIkKTAwUL169VLv3r3l7u6u\nqVOnyt3dXZI0depUjRw5Ug6HQ/3793f+sgAAAC6fy8I/d+7cC5YNGDDgkuuPGTNGY8aMuWB5aGio\nQkNDK3RuAACYik/uAwDAIIQfAACDEH4AAAxC+AEAMAjhBwDAIIQfAACDEH4AAAxC+AEAMAjhBwDA\nIIQfAACDEH4AAAxC+AEAMAjhBwDAIIQfAACDEH4AAAxC+AEAMAjhBwDAIIQfAACDEH4AAAxC+AEA\nMAjhBwDAIIQfAACDEH4AAAxC+AEAMAjhBwDAIIQfAACDEH4AAAxC+AEAMAjhBwDAIIQfAACDEH4A\nAAxC+AEAMAjhBwDAIIQfAACDEH4AAAxC+AEAMAjhBwDAIIQfAACDEH4AAAxC+AEAMAjhBwDAIIQf\nAACDEH4AAAxC+AEAMAjhBwDAIIQfAACDEH4AAAxC+AEAMAjhBwDAIIQfAACDuCz8U6ZMUXBwsPr0\n6eNcduLECQ0fPlwREREaPny4CgoKJEmWZWn69OkKDw9XVFSUvvjiC+c2q1evVkREhCIiIrR69WpX\nTRcAACO4LPw2m01Lly4ts2zJkiUKDg7Wpk2bFBwcrCVLlkiStm7dqgMHDmjTpk167rnn9Mwzz0g6\n/4vCokWLtGLFCq1cuVKLFi1y/rIAAAAun8vC365dO3l7e5dZlp6erpiYGElSTEyM0tLSyix3c3NT\n69atdfLkSeXk5Gjbtm3q1KmTfHx85O3trU6dOikzM9NVUwYAoNqr1Nf4c3Nz5e/vL0m67rrrlJub\nK0my2+1q2LChc72GDRvKbrdfsDwgIEB2u70ypwwAQLXiUVUDu7m5yc3NrcL2l5SUpKSkJElSfn5+\nhe0XAIDqpFKP+Bs0aKCcnBxJUk5Ojvz8/CSdP5LPzs52rpedna2AgIALltvtdgUEBFx033FxcUpO\nTlZycrJ8fX1d+CgAALh6VWr4w8LCtGbNGknSmjVr1K1btzLLLcvS7t27Va9ePfn7+yskJETbtm1T\nQUGBCgoKtG3bNoWEhFTmlAEAqFZcdqp/8uTJ2rFjh/Lz89WlSxdNmDBBo0aN0qRJk7Rq1So1atRI\n8+fPlySFhoYqIyND4eHh8vLy0syZMyVJPj4+Gjt2rGJjYyVJ48aNk4+Pj6umDABAteey8M+dO/ei\nyxMTEy9Y5ubmpmnTpl10/djYWGf4AQDAn8Mn9wEAYBDCDwCAQQg/AAAGIfwAABiE8AMAYBDCDwCA\nQQg/AAAGIfwAABiE8AMAYBDCDwCAQQg/AAAGIfwAABiE8AMAYBDCDwCAQQg/AAAGIfwAABiE8AMA\nYBDCDwCAQQg/AAAGIfwAABiE8AMAYBDCDwCAQQg/AAAGIfwAABiE8AMAYBDCDwCAQQg/AAAGIfwA\nABiE8AMAYBDCDwCAQQg/AAAGIfwAABiE8AMAYBDCDwCAQQg/AAAGIfwAABiE8AMAYBDCDwCAQQg/\nAAAGIfwAABiE8AMAYBDCDwCAQQg/AAAGIfwAABiE8AMAYBDCDwCAQQg/AAAGIfwAABiE8AMAYBDC\nDwCAQTyqYtCwsDDVqVNHNWrUkLu7u5KTk3XixAk98sgjOnLkiBo3bqz58+fL29tblmVpxowZysjI\nUO3atTVr1iy1aNGiKqYNAMBVr8qO+BMTE5WSkqLk5GRJ0pIlSxQcHKxNmzYpODhYS5YskSRt3bpV\nBw4c0KZNm/Tcc8/pmWeeqaopAwBw1btiTvWnp6crJiZGkhQTE6O0tLQyy93c3NS6dWudPHlSOTk5\nVTlVAACuWlUW/hEjRshmsykpKUmSlJubK39/f0nSddddp9zcXEmS3W5Xw4YNnds1bNhQdru98icM\nAEA1UCWv8b/11lsKCAhQbm6uhg8frqZNm5a5383NTW5ubpe1z6SkJOcvEfn5+RU2VwAAqpMqOeIP\nCAiQJDVo0EDh4eHas2ePGjRo4DyFn5OTIz8/P+e62dnZzm2zs7Od2/9SXFyckpOTlZycLF9f30p4\nFAAAXH0qPfxnz57V6dOnnV9/8MEHCgwMVFhYmNasWSNJWrNmjbp16yZJzuWWZWn37t2qV6+e8yUB\nAABweSr9VH9ubq7GjRsnSXI4HOrTp4+6dOmili1batKkSVq1apUaNWqk+fPnS5JCQ0OVkZGh8PBw\neXl5aebMmZU9ZQAAqo1KD3+TJk20du3aC5b7+voqMTHxguVubm6aNm1aZUwNAIBq74r5cz4AAOB6\nhB8AAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAADEL4AQAw\nCOEHAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAA\nDEL4AQAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8A\nAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8AAIMQfgAADEL4AQAwCOEH\nAMAghB8AAIMQfgAADEL4AQAwCOEHAMAghB8AAINcNeHfunWrevToofDwcC1ZsqSqpwMAwFXpqgi/\nw+FQQkKCli5dqtTUVL377rvav39/VU8LAICrzlUR/j179uimm25SkyZN5OnpqcjISKWnp1f1tAAA\nuOpcFeG32+1q2LCh83ZAQIDsdnsVzggAgKuTR1VPoKIkJSUpKSlJkvTDDz/IZrO5bKybXLZnSFJ+\nfr58fX2rehrVms22pqqn8AcFVvUEqjX+71USF/XpyJEj5Vrvqgh/QECAsrOznbftdrsCAgLKrBMX\nF6e4uLjKnhpcwGazKTk5uaqnARiH/3tmuCpO9bds2VIHDhxQVlaWioqKlJqaqrCwsKqeFgAAV52r\n4ojfw8NDU6dO1ciRI+VwONS/f38FBnLKDwCAy3VVhF+SQkNDFRoaWtXTQCXgJRugavB/zwxulmVZ\nVT0JAABQOa6K1/gBAEDFIPyGa968uWbNmuW8/frrr2vhwoUVsu+FCxeqc+fOio6OVkREhMaPH1+u\nT1z87rvvFB0drZiYGB06dOgPjfv6669LkpKTk/nMB1QLt99+u6KjoxUZGam+ffvqjTfeUGlp6e9u\nN3v2bEVGRmr27Nl/aNygoCBJ0uHDh7Vu3bo/tA9cWQi/4Tw9PbVp0ybl5eW5ZP/Dhg1TSkqKNm3a\npN69e2vo0KG/O1Z6erp69OihNWvW6MYbb/xT469evVo5OTmXtU1JScmfGhNwhdq1ayslJUWpqala\ntmyZtm7dqkWLFv3uditWrNDatWv15JNP/qnxjxw5onffffeytuH/0pWJ8BvOw8NDcXFxSkxMvOC+\nw4cPa8iQIYqKitLQoUN19OhRSVJ8fLymT5+ugQMHqlu3btqwYUO5xurdu7c6derkPGrYu3evHnjg\nAdlsNo0YMUI5OTnKyMhQYmKi3nrrLQ0ePFiSNHbsWNlsNkVGRjo/pEn6vyMRSdqwYYPi4+PLjLdh\nwwbt3btXjz32mKKjo/XTTz9ddExJGjx4sGbMmCGbzaY333xT7733nvr06aO+fftq0KBBl/GMAq7X\noEEDPffcc1q+fLksy5LD4dDs2bPVv39/RUVF6e2335YkjR49WmfPnpXNZtP69ev1n//8RwMGDFBM\nTIyGDRum48ePSyp7lkyS+vTpo8OHD5cZ8+9//7t27typ6Oho/fOf/7zkmNu3b9f999+v0aNHKzIy\nUmfPntWoUaPUt29f9enTR+vXr6+kZwmXctW8qx+uM2jQIPXt21cjR44ss3z69Onq16+f+vXrp1Wr\nVmn69Ol65ZVXJEk5OTn697//re+//15jxoxRz549yzXWHXfcoe+//17FxcXO/fn5+Wn9+vWaN2+e\nnn/+eQ0cOFDXXHONRowYIUmaOXOmfHx89NNPPyk2NlYRERHl+nSxnj17avny5XriiSfUsmXL3xxT\nkoqLi50fXhIVFaXXX39dAQEBOnnyZLmfS6CyNGnSRA6HQ7m5uUpPT1e9evX0zjvvqKioSAMHDlSn\nTp302muvKSgoSCkpKZKkgoICrVixQm5ublq5cqWWLl16wS/Ml/Loo4/qjTfe0OLFiyWd/7TUi40p\nSV9++aXWrVunJk2aaOPGjfL393deVfXUqVMueDZwOQg/VLduXUVHR+vNN99U7dq1nct37drlfL0/\nOjpaL774ovO+7t27q0aNGmrWrJnzqOFy/PDDD/rmm280fPhwSVJpaamuu+66i677r3/9S5s3b5Yk\nHTt2TAcPHvxDHyv6e2P27t3b+XVQUJDi4+PVq1cvhYeHX/ZYQGX64IMP9PXXX2vjxo2Szsf14MGD\natKkSZn1srOz9cgjj+jHH39UUVGRbrjhhgofs2bNmmrZsqVz7FtvvVWzZ8/Wiy++qK5du6pt27Z/\neExUDMIPSdLQoUNls9nKfY0DT0/PC5bNmzdPW7ZskSTnEcavffnll7rzzjtlWZYCAwPLnLq/mO3b\nt+vDDz9UUlKSvLy8NHjwYBUWFl6w3sWW/drvjenl5eX8OiEhQZ999pm2bNmi/v3765133uEzzHFF\nycrKkru7uxo0aCDLsvT000+rc+fOv7nN9OnTNWzYMHXr1k3bt293vkfA3d29zBsFy/v/6WJjbt++\nXddcc43z9s0336zk5GRlZGRo/vz56tChg8aPH385DxUVjNf4IUny8fFRz549tWrVKueyoKAgpaam\nSpLWrVv3u7+pP/LII0pJSblk9Ddu3KgPPvhAffr00c0336y8vDzt2rVL0vnT7N9+++0F25w6dUre\n3t7y8vLSd999p927dzvvu/baa/Xdd9+ptLRUaWlpFx2zTp06OnPmjCSVe0xJOnTokO666y49/PDD\n8vX1LXOtCKCq5eXladq0aRo0aJDc3NwUEhKit956S8XFxZLOn906e/bsBdudOnXKeZ2TNWv+70JN\njRs31pdffilJ+uKLLy54fV8q+39JUrnHtNvt8vLyUnR0tEaMGOEcB1WHI344Pfjgg1q+fLnz9t/+\n9jdNmTJFr7/+uvz8/JyvhV+Of/7zn1q7dq3OnTunwMBAJSYmys/PT5K0YMECTZ8+XadOnZLD4dDQ\noUMv+CjmLl266O2331avXr108803q3Xr1s77Hn30UT300EPy8/PTnXfeedEfOv369dO0adNUu3Zt\nJSUllWtMSXrhhRd08OBBWZalDh066Lbbbrvsxw5UpJ9++knR0dEqKSmRu7u7oqOjnS9bDRgwQEeO\nHJHNZpNlWfL19XW+H+eXxo8fr4cfflje3t5q3769M/A9evRQSkqKIiMj1apVK/3lL3+5YNvmzZur\nRo0a6tu3r2w2m4YMGVKuMb/55hu98MILqlGjhjw8PPTMM89U6POCy8cn9wEAYBBO9QMAYBDCDwCA\nQQg/AAAGIfwAABiE8AMAYBDCDwCAQQg/UMWulsut7ty5U7GxserZs6d69uz5u5+6eCmHDx9Wq1at\nFB0drd69e2vq1KnleryV5eTJk2U+zwKobvgAH6CK/Xy5VUnKzc3Vo48+qtOnT2vixIm/ud2KFSu0\nY8cOubu7/6nxf77calRU1CXX+fHHH/XYY4/p5ZdfVosWLZSXl6eRI0cqICBA995772WPeeONNyol\nJUUlJSUaOnSo0tLSFBER8SceRcU5efKk3nrrLa7KiGqL8ANXkJ8vtxobG6sJEyaotLRUc+bM0Y4d\nO1RUVKRBgwZp4MCBZS63+tBDD6l27dp69dVXVVxcLB8fH82ZM0fXXnutFi5cWOZKh3369NFrr71W\n5uIsf//73/Xdd98pOjpa/fr107Bhwy6Y1/Lly9WvXz+1aNFCkuTn56fHH39cCxcu1L333qv4+HjV\nrVtXe/fu1Y8//qjHH3+8XFds9PDwUFBQkA4ePKgzZ85o7NixOnnypEpKSvTwww+re/fueumll+Tt\n7e2c17x58+Tn56fbbrtNCxcuVL169fTNN9+oV69euvXWW/Xmm2+qsLBQL7/8sm688Ubnx9v+fFnp\np556Sm3atNHChQt19OhRHT58WEePHtXQoUM1ZMgQ/f3vf9ehQ4cUHR2tjh07/unr2ANXHAtAlWrd\nuvUFy9q0aWP9+OOP1ttvv229/PLLlmVZVmFhodWvXz/r0KFDF2x34sQJq7S01LIsy1qxYoX1/PPP\nW5ZlWQsWLLCWLl3qXC8yMtLKysoqs/3HH39sjRo16jfnOG7cOGvz5s1llp08edJq166dZVmW9eST\nT1oTJkywHA6H9e2331rdu3e/5L6ysrKsyMhIy7Is6+zZs5bNZrO2bNliFRcXW6dOnbIsy7Jyc3Ot\n7t27W6WlpVZWVpYVExNjWZZlORwOq1u3blZeXp718ccfW23atLHsdrtVWFhohYSEWC+99JJlWZb1\nz3/+05o+fbplWZY1efJk65NPPrEsy7KOHDli9ezZ0/ncxMXFWYWFhVZubq51zz33WEVFRWXmB1RH\nHPEDV7CquNzqH3U5l2r++Yjazc1N3bp1U2hoqIqLizV37lx98sknqlGjhux2u44fP64bbrhBPj4+\n+vLLL3X8+HHdcccdzisltmzZUv7+/pLOv3zw8/Xgb731Vm3fvl2S9OGHH2r//v3OsU+fPu282Exo\naKg8PT3l5+cnPz8/5ebmVvjzAlxpCD9whanqy61ezC233KIvvvhC3bt3dy7bu3evmjVr5rx9sUs1\nX8rPr/H/0rp165SXl6fk5GTVrFlTYWFhzvkOGDBAycnJOn78uPr373/RMWvUqOG8XaNGDTkcDklS\naWmpVqxYoVq1al0wj19u7+7urpKSknI/BuBqxbv6gSvIlXC51YsZNGiQVq9erX379kmS8vPzNWfO\nHI0cOfKPPdCLOHXqlBo0aKCaNWvq448/1pEjR5z3de/eXZmZmfr8888VEhJyWfsNCQnRv/71L+ft\nnx/DpZTn+QCuZhzxA1XsSrvc6sXe3Ofv768XX3xRTz/9tM6cOSPLsjR06FCFhYVV2PMQFRWlMWPG\nKCoqSnfeeaeaNm3qvM/T01Pt27dX/fr1L/uvGP76178qISFBUVFRcjgcatu2rRISEi65vq+vr+6+\n+2716dNHnTt35s19qHa4LC+AK15paan69eunl1566aK/vAAoP071A7ii7d+/X+Hh4QoODib6QAXg\niB+AU2ZmpubMmVNm2Q033KCXX375svf19ddf64knniizzNPTUytXrvxTcwTw5xB+AAAMwql+AAAM\nQvgBADAI4QcAwCCEHwAAgxB+AAAM8v8BuaPvr54CkJ4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U9PTMVbOYXeG",
        "colab_type": "text"
      },
      "source": [
        "Let's see the correlation of variables in our data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yi67iNG-YdOe",
        "colab_type": "code",
        "outputId": "c7a13b79-bf7f-4290-b3ee-ec907f5fdeac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        }
      },
      "source": [
        "corr_data = loan_df.corr()\n",
        "\n",
        "# Lets visualize the correlation using a heat map from SNS\n",
        "plt.figure(figsize=(12,8))\n",
        "sns.heatmap(corr_data, annot=True, square=True, linewidths=2.5)\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VO7l5PvsQ4suysDYn5t7jz1BsRCzm1hbEP/E5M7e2IDZCt41F1ucM4CufnrR2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\nQmCC\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lb1FziTqMZLv",
        "colab_type": "text"
      },
      "source": [
        "### Now Let's balance The Data set by Over-Sampling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4p7012ekgZe",
        "colab_type": "text"
      },
      "source": [
        "We need to balance The Data set because in an imbalanced Data set Accuracy is not a reliable metric for the real performance of a classifier, because it will yield misleading results (that is, when the numbers of observations in different classes vary greatly).<br>\n",
        "For example, 70% non-defaulters and 30% defaulters, a particular classifier might classify all or most of the observations as Non-defaulters.<br> \n",
        "This is due to the classifier or model having a high recognition rate (sensitivity) for the dominant class.<br>\n",
        "[F1 score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) too, would even be more unreliable in an imbalanced Data set.\n",
        "See [link](https://en.wikipedia.org/wiki/Confusion_matrix)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "brjQcpQKMssM",
        "colab_type": "code",
        "outputId": "96471d35-4717-4297-c0bf-62200012a4e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "sm = SMOTE(sampling_strategy='minority', random_state=19, k_neighbors=5)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RcwKxwtPr4R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "over_sampled_features, over_sampled_target = sm.fit_resample(loan_df_dummies.drop('Default_On_Payment', axis=1), loan_df_dummies.Default_On_Payment)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CekWebPwSnXd",
        "colab_type": "code",
        "outputId": "12b13354-6066-4e79-ad19-94bd34d8d591",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print('Shape of resampled feature set is:',over_sampled_features.shape)\n",
        "print('Shape of resampled target data is:',over_sampled_target.shape)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of resampled feature set is: (7010, 29)\n",
            "Shape of resampled target data is: (7010,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clEjDC-ITuKH",
        "colab_type": "text"
      },
      "source": [
        "Let's concat the features and target back into one data frame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ze6BEXjBZVpE",
        "colab_type": "code",
        "outputId": "580eadb6-959b-48bd-bbdd-521b456d3ceb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "over_sampled_features = pd.DataFrame(over_sampled_features)\n",
        "\n",
        "over_sampled_features['x'] = over_sampled_target\n",
        "\n",
        "resampled_data = over_sampled_features\n",
        "\n",
        "resampled_data.head(2)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    0  1  2     3   4  5  6  7  8  9  ...  20  21  22  23  24  25  26  27  28  x\n",
              "0  67  1  4  1169   6  4  2  1  0  1  ...   0   0   1   0   1   0   1   0   1  0\n",
              "1  22  1  2  5951  48  2  1  0  0  1  ...   0   0   0   0   1   0   1   0   1  0\n",
              "\n",
              "[2 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60luVcdQk6Q4",
        "colab_type": "text"
      },
      "source": [
        "And let's input the column headers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61T9Ml-dbaSo",
        "colab_type": "code",
        "outputId": "b77fb0ae-acf4-4967-cd5e-c96f9c5af079",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "resampled_data.columns = loan_df_dummies.columns\n",
        "\n",
        "resampled_data.head()"
      ],
      "execution_count": 41,
      "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",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Current_Address_Yrs</th>\n",
              "      <th>Credit_Amount</th>\n",
              "      <th>Duration_in_Months</th>\n",
              "      <th>Inst_Rt_Income</th>\n",
              "      <th>Num_CC</th>\n",
              "      <th>Marital_Status_Gender_A93</th>\n",
              "      <th>Marital_Status_Gender_A94</th>\n",
              "      <th>Housing_A152</th>\n",
              "      <th>Telephone_A192</th>\n",
              "      <th>Property_A121</th>\n",
              "      <th>Job_A172</th>\n",
              "      <th>Job_A173</th>\n",
              "      <th>Foreign_Worker_A202</th>\n",
              "      <th>Years_At_Present_Employment_A74</th>\n",
              "      <th>Years_At_Present_Employment_A75</th>\n",
              "      <th>Savings_Acc_A63</th>\n",
              "      <th>Savings_Acc_A64</th>\n",
              "      <th>Savings_Acc_A65</th>\n",
              "      <th>Status_Checking_Acc_A13</th>\n",
              "      <th>Status_Checking_Acc_A14</th>\n",
              "      <th>Credit_History_A34</th>\n",
              "      <th>Purposre_Credit_Taken_A41</th>\n",
              "      <th>Purposre_Credit_Taken_A43</th>\n",
              "      <th>Purposre_Credit_Taken_A48</th>\n",
              "      <th>Other_Debtors_Guarantors_A101</th>\n",
              "      <th>Other_Debtors_Guarantors_A103</th>\n",
              "      <th>Other_Inst_Plans_A143</th>\n",
              "      <th>Default_On_Payment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Dependents  ...  Other_Inst_Plans_A143  Default_On_Payment\n",
              "0   67           1  ...                      1                   0\n",
              "1   22           1  ...                      1                   0\n",
              "2   49           2  ...                      1                   0\n",
              "3   45           2  ...                      1                   0\n",
              "4   53           2  ...                      1                   1\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cm8zt0a8lWp2",
        "colab_type": "text"
      },
      "source": [
        "Let's revisualize the resampled target column that shows the number of Defaulters and Non-defaulters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fJTysgnizkX",
        "colab_type": "code",
        "outputId": "ab67a6b0-6c63-4aa7-e794-63897b02e094",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "x = resampled_data.Default_On_Payment.replace(to_replace=[0, 1], value=['Non-Defaulters','Defaulters'])\n",
        "sns.countplot(x)\n",
        "plt.title('Count of Defaulters and Non-Defaulters- Balanced Data')\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WLVumiIgITZ48WXl5eZIku92u+vXrO5bx9/eX3W6/4PQ/SkhIkM1mk81mU25u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\nwg8AgEH+P+8JPEmRBz3tAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8eedcRMGiVYc",
        "colab_type": "text"
      },
      "source": [
        "## Splitting The Data set into Training, Validation and Testing sets\n",
        "\n",
        "\n",
        "We shall use 70% for training,  15% for validation and 15% reserved only for final testing "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6elWdwHIsePf",
        "colab_type": "text"
      },
      "source": [
        "**Let's define the Training sets**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDql7I1jsdAX",
        "colab_type": "code",
        "outputId": "2fd57558-6989-4650-f8f8-7ec66d506b19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "X_train = resampled_data.sample(frac=0.7)\n",
        "\n",
        "y_train = X_train.Default_On_Payment\n",
        "\n",
        "X_train.drop('Default_On_Payment', axis=1, inplace=True)\n",
        "\n",
        "print('X_train shape is:', X_train.shape)\n",
        "print('y_train shape is:', y_train.shape)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train shape is: (4907, 29)\n",
            "y_train shape is: (4907,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qp5mlvzswg2J",
        "colab_type": "text"
      },
      "source": [
        "**Lets define the Validation set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7RSCRhKvwkya",
        "colab_type": "code",
        "outputId": "f17fa00d-2a4d-44a7-a831-b33a89f07a61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "Spare_set_index_df = resampled_data.iloc[list(set(resampled_data.index).difference(set(X_train.index))),]\n",
        "\n",
        "print(len(Spare_set_index_df))\n",
        "Spare_set_index_df.head(3)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2103\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Current_Address_Yrs</th>\n",
              "      <th>Credit_Amount</th>\n",
              "      <th>Duration_in_Months</th>\n",
              "      <th>Inst_Rt_Income</th>\n",
              "      <th>Num_CC</th>\n",
              "      <th>Marital_Status_Gender_A93</th>\n",
              "      <th>Marital_Status_Gender_A94</th>\n",
              "      <th>Housing_A152</th>\n",
              "      <th>Telephone_A192</th>\n",
              "      <th>Property_A121</th>\n",
              "      <th>Job_A172</th>\n",
              "      <th>Job_A173</th>\n",
              "      <th>Foreign_Worker_A202</th>\n",
              "      <th>Years_At_Present_Employment_A74</th>\n",
              "      <th>Years_At_Present_Employment_A75</th>\n",
              "      <th>Savings_Acc_A63</th>\n",
              "      <th>Savings_Acc_A64</th>\n",
              "      <th>Savings_Acc_A65</th>\n",
              "      <th>Status_Checking_Acc_A13</th>\n",
              "      <th>Status_Checking_Acc_A14</th>\n",
              "      <th>Credit_History_A34</th>\n",
              "      <th>Purposre_Credit_Taken_A41</th>\n",
              "      <th>Purposre_Credit_Taken_A43</th>\n",
              "      <th>Purposre_Credit_Taken_A48</th>\n",
              "      <th>Other_Debtors_Guarantors_A101</th>\n",
              "      <th>Other_Debtors_Guarantors_A103</th>\n",
              "      <th>Other_Inst_Plans_A143</th>\n",
              "      <th>Default_On_Payment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "      <td>24</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Age  Dependents  ...  Other_Inst_Plans_A143  Default_On_Payment\n",
              "0    67           1  ...                      1                   0\n",
              "1    22           1  ...                      1                   0\n",
              "15   32           1  ...                      1                   1\n",
              "\n",
              "[3 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TwEwZcUz4AB4",
        "colab_type": "code",
        "outputId": "06d9c0d0-a5f8-49be-8fc7-b012078dd142",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "X_val_test = Spare_set_index_df.sample(frac=0.5)\n",
        "y_val_test = X_val_test.Default_On_Payment\n",
        "\n",
        "X_val_test.drop('Default_On_Payment', axis=1, inplace=True)\n",
        "\n",
        "print('X_val_test shape is:', X_val_test.shape)\n",
        "print('y_val_test shape is:', y_val_test.shape)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_val_test shape is: (1052, 29)\n",
            "y_val_test shape is: (1052,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iUBpT1fx3Mqf",
        "colab_type": "text"
      },
      "source": [
        "**Now let's define the Testing set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1TyzG9sEcK_x",
        "colab_type": "code",
        "outputId": "b21520a7-f074-49bd-e984-e9e38e6b9089",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "X_test_set = resampled_data.iloc[list(Spare_set_index_df.index.difference(X_val_test.index)),]\n",
        "\n",
        "y_test_set = X_test_set.Default_On_Payment\n",
        "\n",
        "X_test_set.drop('Default_On_Payment', axis=1, inplace=True)\n",
        "\n",
        "print('X_test_set shape is:', X_test_set.shape)\n",
        "print('y_test_set shape is:', y_test_set.shape)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_test_set shape is: (1051, 29)\n",
            "y_test_set shape is: (1051,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:3940: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  errors=errors)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qnoXH_waidrZ",
        "colab_type": "text"
      },
      "source": [
        "## **Feature Normalization**\n",
        "\n",
        "Let's simply divide each feature element by its max element. This will return a number between 0 and 1 for all features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xSIR1-b_iiRY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train / X_train.max()\n",
        "\n",
        "X_val_test = X_val_test / X_val_test.max()\n",
        "\n",
        "X_test_set = X_test_set / X_test_set.max()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVd1dA5it_9N",
        "colab_type": "code",
        "outputId": "8da82b60-79bd-46ca-a834-1e67e3ba5bdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "X_train.head(3)"
      ],
      "execution_count": 48,
      "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",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Dependents</th>\n",
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              "      <th>Inst_Rt_Income</th>\n",
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              "      <th>Other_Debtors_Guarantors_A103</th>\n",
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              "    <tr>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Age  ...  Other_Inst_Plans_A143\n",
              "4379  0.693333  ...                    1.0\n",
              "3889  0.533333  ...                    0.0\n",
              "4532  0.546667  ...                    1.0\n",
              "\n",
              "[3 rows x 29 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DT7Ewo5nuFH7",
        "colab_type": "code",
        "outputId": "e53b5c41-c4dd-4e26-86e2-efc938121ed8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "X_val_test.head(3)"
      ],
      "execution_count": 49,
      "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>Age</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Current_Address_Yrs</th>\n",
              "      <th>Credit_Amount</th>\n",
              "      <th>Duration_in_Months</th>\n",
              "      <th>Inst_Rt_Income</th>\n",
              "      <th>Num_CC</th>\n",
              "      <th>Marital_Status_Gender_A93</th>\n",
              "      <th>Marital_Status_Gender_A94</th>\n",
              "      <th>Housing_A152</th>\n",
              "      <th>Telephone_A192</th>\n",
              "      <th>Property_A121</th>\n",
              "      <th>Job_A172</th>\n",
              "      <th>Job_A173</th>\n",
              "      <th>Foreign_Worker_A202</th>\n",
              "      <th>Years_At_Present_Employment_A74</th>\n",
              "      <th>Years_At_Present_Employment_A75</th>\n",
              "      <th>Savings_Acc_A63</th>\n",
              "      <th>Savings_Acc_A64</th>\n",
              "      <th>Savings_Acc_A65</th>\n",
              "      <th>Status_Checking_Acc_A13</th>\n",
              "      <th>Status_Checking_Acc_A14</th>\n",
              "      <th>Credit_History_A34</th>\n",
              "      <th>Purposre_Credit_Taken_A41</th>\n",
              "      <th>Purposre_Credit_Taken_A43</th>\n",
              "      <th>Purposre_Credit_Taken_A48</th>\n",
              "      <th>Other_Debtors_Guarantors_A101</th>\n",
              "      <th>Other_Debtors_Guarantors_A103</th>\n",
              "      <th>Other_Inst_Plans_A143</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1782</th>\n",
              "      <td>0.418919</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.076531</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1656</th>\n",
              "      <td>0.554054</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.048198</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5875</th>\n",
              "      <td>0.608108</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.217434</td>\n",
              "      <td>0.388889</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Age  ...  Other_Inst_Plans_A143\n",
              "1782  0.418919  ...                    1.0\n",
              "1656  0.554054  ...                    0.0\n",
              "5875  0.608108  ...                    1.0\n",
              "\n",
              "[3 rows x 29 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGtdX7vHuLSs",
        "colab_type": "code",
        "outputId": "bb8b85fe-35d7-4bf9-c663-3eb57cc21a11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "X_test_set.head(3)"
      ],
      "execution_count": 50,
      "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>Age</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Current_Address_Yrs</th>\n",
              "      <th>Credit_Amount</th>\n",
              "      <th>Duration_in_Months</th>\n",
              "      <th>Inst_Rt_Income</th>\n",
              "      <th>Num_CC</th>\n",
              "      <th>Marital_Status_Gender_A93</th>\n",
              "      <th>Marital_Status_Gender_A94</th>\n",
              "      <th>Housing_A152</th>\n",
              "      <th>Telephone_A192</th>\n",
              "      <th>Property_A121</th>\n",
              "      <th>Job_A172</th>\n",
              "      <th>Job_A173</th>\n",
              "      <th>Foreign_Worker_A202</th>\n",
              "      <th>Years_At_Present_Employment_A74</th>\n",
              "      <th>Years_At_Present_Employment_A75</th>\n",
              "      <th>Savings_Acc_A63</th>\n",
              "      <th>Savings_Acc_A64</th>\n",
              "      <th>Savings_Acc_A65</th>\n",
              "      <th>Status_Checking_Acc_A13</th>\n",
              "      <th>Status_Checking_Acc_A14</th>\n",
              "      <th>Credit_History_A34</th>\n",
              "      <th>Purposre_Credit_Taken_A41</th>\n",
              "      <th>Purposre_Credit_Taken_A43</th>\n",
              "      <th>Purposre_Credit_Taken_A48</th>\n",
              "      <th>Other_Debtors_Guarantors_A101</th>\n",
              "      <th>Other_Debtors_Guarantors_A103</th>\n",
              "      <th>Other_Inst_Plans_A143</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.426667</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.069583</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.586667</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.097916</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.346667</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.112299</td>\n",
              "      <td>0.138889</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Age  Dependents  ...  Other_Debtors_Guarantors_A103  Other_Inst_Plans_A143\n",
              "15  0.426667         0.5  ...                            0.0                    1.0\n",
              "23  0.586667         0.5  ...                            0.0                    1.0\n",
              "24  0.346667         0.5  ...                            0.0                    1.0\n",
              "\n",
              "[3 rows x 29 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypER3GvG4Ahr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import log_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kico6L2HsWrb",
        "colab_type": "text"
      },
      "source": [
        "Let's define a function for plotting and visualizing The Confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmQ0teQmjrDk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S5-qx-aUBk5R",
        "colab_type": "text"
      },
      "source": [
        "## Decision Tree Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I3fQ6NMvsvsW",
        "colab_type": "text"
      },
      "source": [
        "Let's define a function that returns the best decison tree classifier model and outputs its parameters and  a plot of the confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jhxn-mWi-GK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# set max depth range limit for DTree model to iterate through from 1 to 100 to find best parameters\n",
        "max_depth = 100\n",
        "\n",
        "def decision_tree_classifier(max_depth, X_train, y_train, X_val_test, y_val_test):\n",
        "    \"\"\"Method takes max_depth an int, and X_train and y_train 2D/1D arrays,\n",
        "    as well as test data x_test and y_test, also arrays, and returns,\n",
        "    The best max_depth value, from 1 to max_depth, and its best test_accuracy score\"\"\"\n",
        "    \n",
        "    accuracy_dict={'max_depth':0, 'val_acc':0, 'f1_Score':0, 'log_loss':0}\n",
        "\n",
        "    \n",
        "    for i in range(1, max_depth+1):\n",
        "        # Let's instantiate a model\n",
        "        decision_model = DecisionTreeClassifier(criterion='entropy', max_depth = i)\n",
        "        \n",
        "        # Let's train the model\n",
        "        decision_model.fit(X_train, y_train)\n",
        "        \n",
        "        # Let's make prediction on the test data\n",
        "        y_hat = decision_model.predict(X_val_test)\n",
        "        \n",
        "        # Let's measure accuracy of predictions on test data\n",
        "        val_accu = round(metrics.accuracy_score(y_val_test, y_hat),4)\n",
        "        \n",
        "        # Let's Measure the F1 Score\n",
        "        val_f1 = round(f1_score(y_val_test, y_hat, average='weighted'),4)\n",
        "        \n",
        "        # Let's Measure Logloss\n",
        "        logloss = round(log_loss(y_val_test, y_hat, normalize=True),4)\n",
        "        \n",
        "        if accuracy_dict['val_acc'] < val_accu or accuracy_dict['f1_Score'] < val_f1:\n",
        "            accuracy_dict['max_depth'] = i\n",
        "            accuracy_dict['val_acc'] = val_accu\n",
        "            accuracy_dict['f1_Score'] = val_f1\n",
        "            accuracy_dict['log_loss'] = logloss\n",
        "            decision_model2 = decision_model\n",
        "            \n",
        "    print('Model Evaluation:')\n",
        "    print(accuracy_dict)\n",
        "    \n",
        "    # Compute confusion matrix\n",
        "    Dtrees_cnf_matrix = confusion_matrix(y_val_test, y_hat, labels=[1,0])\n",
        "    np.set_printoptions(precision=2)\n",
        "\n",
        "\n",
        "    # Plot non-normalized confusion matrix\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.set(font_scale=1.3)\n",
        "    plot_confusion_matrix(Dtrees_cnf_matrix, \n",
        "                          classes=['Default=1','Non-Default=0'],\n",
        "                          normalize= False,  \n",
        "                          title='Confusion Matrix for Decision-Tree-Classifier')\n",
        "\n",
        "    return decision_model2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CfWzSKck_vQe",
        "colab_type": "code",
        "outputId": "f2febc3f-8729-4005-fd38-548b5a2be59c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        }
      },
      "source": [
        "Decision_Tree_Model = decision_tree_classifier(max_depth, X_train, y_train, X_val_test, y_val_test)\n",
        "Decision_Tree_Model"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Evaluation:\n",
            "{'max_depth': 29, 'val_acc': 0.9962, 'f1_Score': 0.9962, 'log_loss': 0.1313}\n",
            "Confusion matrix, without normalization\n",
            "[[519   5]\n",
            " [  0 528]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=29,\n",
              "                       max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort=False,\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZsLGxwbRp0wqdRJeWlgZDQ0O5YRAAMDIyku3Pp6qqqjBXQENDQ9RMZnt7e1haWmLTpk2I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\nmQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}