Credit_Card_Default_Prediction_Capstone_Project.ipynb

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      "name": "Credit-Card-Default Prediction - Capstone Project.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/syedsharin/Credit-Card-Default-Prediction/blob/main/Credit_Card_Default_Prediction_Capstone_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "metadata": {
        "id": "tOGC-qoyhJeX"
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      "source": [
        "# <b><u> Project Title : Predicting whether a customer will default on his/her credit card </u></b>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y06xIdG26kRF"
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      "source": [
        "## <b> Problem Description </b>\n",
        "\n",
        "### This project is aimed at predicting the case of customers default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. We can use the [K-S chart](https://www.listendata.com/2019/07/KS-Statistics-Python.html) to evaluate which customers will default on their credit card payments\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlLxAtlziMbP"
      },
      "source": [
        "## <b> Data Description </b>\n",
        "\n",
        "### <b>Attribute Information: </b>\n",
        "\n",
        "### This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 23 variables as explanatory variables:\n",
        "* ### X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit.\n",
        "* ### X2: Gender (1 = male; 2 = female).\n",
        "* ### X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others).\n",
        "* ### X4: Marital status (1 = married; 2 = single; 3 = others).\n",
        "* ### X5: Age (year).\n",
        "* ### X6 - X11: History of past payment. We tracked the past monthly payment records (from April to September, 2005) as follows: X6 = the repayment status in September, 2005; X7 = the repayment status in August, 2005; . . .;X11 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n",
        "* ### X12-X17: Amount of bill statement (NT dollar). X12 = amount of bill statement in September, 2005; X13 = amount of bill statement in August, 2005; . . .; X17 = amount of bill statement in April, 2005.\n",
        "* ### X18-X23: Amount of previous payment (NT dollar). X18 = amount paid in September, 2005; X19 = amount paid in August, 2005; . . .;X23 = amount paid in April, 2005."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9gBQ24vDFK5e"
      },
      "source": [
        "##<b> Import Modules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dByMsuzT8Tnw"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZfrnoFQFMKy"
      },
      "source": [
        "## <b> Loading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uScyOLm_zpKT"
      },
      "source": [
        "data = pd.read_csv(\"https://raw.githubusercontent.com/syedsharin/Credit-Card-Default-Prediction/main/default%20of%20credit%20card%20clients.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9CfZ8fzFSbf"
      },
      "source": [
        "# Copy data for backup\n",
        "df = data.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDndRhnozt76",
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          "base_uri": "https://localhost:8080/",
          "height": 295
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        "outputId": "c8f6cf0d-268d-44bd-bc6a-fd402af4a816"
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      "source": [
        "# Checking top 5 rows of the data\n",
        "df.head()"
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              "  Unnamed: 0         X1   X2  ...       X22       X23                           Y\n",
              "0         ID  LIMIT_BAL  SEX  ...  PAY_AMT5  PAY_AMT6  default payment next month\n",
              "1          1      20000    2  ...         0         0                           1\n",
              "2          2     120000    2  ...         0      2000                           1\n",
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              "\n",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UakdSnb2IOyA"
      },
      "source": [
        "#<b> Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9USqqL_3sNH4"
      },
      "source": [
        "# Droping 'Unnamed: 0' column \n",
        "df.drop(columns='Unnamed: 0',inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWqiXeXm0S3J"
      },
      "source": [
        "# creating list of columns name for renaming data frame column names\n",
        "columns = ['Limit_bal','Gender','Education','Marital_status','Age','Repayment_September','Repayment_August','Repayment_July','Repayment_June','Repayment_May',\n",
        "           'Repayment_April','Sep_Bill','Aug_Bill','July_Bill','June_Bill','May_Bill','Apr_Bill','Pay_Sep','Pay_Aug','Pay_July','Pay_June','Pay_May','Pay_April','Defaulter'] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fh2IVIyP40sN"
      },
      "source": [
        " # replace column name with columns list\n",
        "df.set_axis(columns, axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0LIeTJG15Wve"
      },
      "source": [
        "# droping the axis 0\n",
        "df=df.drop(0,axis=0).reset_index(drop=True)"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
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              "      <td>689</td>\n",
              "      <td>679</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Limit_bal Gender Education  ... Pay_May Pay_April Defaulter\n",
              "0     20000      2         2  ...       0         0         1\n",
              "1    120000      2         2  ...       0      2000         1\n",
              "2     90000      2         2  ...    1000      5000         0\n",
              "3     50000      2         2  ...    1069      1000         0\n",
              "4     50000      1         2  ...     689       679         0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZaB-Buv6quA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe2a89e3-5bc2-417c-cd2f-96ead7eaa468"
      },
      "source": [
        "# Checking the shape of data\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30000, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4rpSVlcgENSj",
        "outputId": "a3f6930c-29f1-4d62-f4f8-021123f0232f"
      },
      "source": [
        "# checking information about each column, but all columns dtype is in object\n",
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30000 entries, 0 to 29999\n",
            "Data columns (total 24 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   Limit_bal            30000 non-null  object\n",
            " 1   Gender               30000 non-null  object\n",
            " 2   Education            30000 non-null  object\n",
            " 3   Marital_status       30000 non-null  object\n",
            " 4   Age                  30000 non-null  object\n",
            " 5   Repayment_September  30000 non-null  object\n",
            " 6   Repayment_August     30000 non-null  object\n",
            " 7   Repayment_July       30000 non-null  object\n",
            " 8   Repayment_June       30000 non-null  object\n",
            " 9   Repayment_May        30000 non-null  object\n",
            " 10  Repayment_April      30000 non-null  object\n",
            " 11  Sep_Bill             30000 non-null  object\n",
            " 12  Aug_Bill             30000 non-null  object\n",
            " 13  July_Bill            30000 non-null  object\n",
            " 14  June_Bill            30000 non-null  object\n",
            " 15  May_Bill             30000 non-null  object\n",
            " 16  Apr_Bill             30000 non-null  object\n",
            " 17  Pay_Sep              30000 non-null  object\n",
            " 18  Pay_Aug              30000 non-null  object\n",
            " 19  Pay_July             30000 non-null  object\n",
            " 20  Pay_June             30000 non-null  object\n",
            " 21  Pay_May              30000 non-null  object\n",
            " 22  Pay_April            30000 non-null  object\n",
            " 23  Defaulter            30000 non-null  object\n",
            "dtypes: object(24)\n",
            "memory usage: 5.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_9i7IxhEw6y"
      },
      "source": [
        "# Converting all columns from Object to int \n",
        "for i in df.columns:\n",
        "  df[i]=df[i].astype('int')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "h-2f0lSEEaoS",
        "outputId": "05138f9e-852d-463d-99a6-5321b2e0e604"
      },
      "source": [
        "# Describing data set\n",
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Limit_bal</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Education</th>\n",
              "      <th>Marital_status</th>\n",
              "      <th>Age</th>\n",
              "      <th>Repayment_September</th>\n",
              "      <th>Repayment_August</th>\n",
              "      <th>Repayment_July</th>\n",
              "      <th>Repayment_June</th>\n",
              "      <th>Repayment_May</th>\n",
              "      <th>Repayment_April</th>\n",
              "      <th>Sep_Bill</th>\n",
              "      <th>Aug_Bill</th>\n",
              "      <th>July_Bill</th>\n",
              "      <th>June_Bill</th>\n",
              "      <th>May_Bill</th>\n",
              "      <th>Apr_Bill</th>\n",
              "      <th>Pay_Sep</th>\n",
              "      <th>Pay_Aug</th>\n",
              "      <th>Pay_July</th>\n",
              "      <th>Pay_June</th>\n",
              "      <th>Pay_May</th>\n",
              "      <th>Pay_April</th>\n",
              "      <th>Defaulter</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>3.000000e+04</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>3.000000e+04</td>\n",
              "      <td>30000.00000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>167484.322667</td>\n",
              "      <td>1.603733</td>\n",
              "      <td>1.853133</td>\n",
              "      <td>1.551867</td>\n",
              "      <td>35.485500</td>\n",
              "      <td>-0.016700</td>\n",
              "      <td>-0.133767</td>\n",
              "      <td>-0.166200</td>\n",
              "      <td>-0.220667</td>\n",
              "      <td>-0.266200</td>\n",
              "      <td>-0.291100</td>\n",
              "      <td>51223.330900</td>\n",
              "      <td>49179.075167</td>\n",
              "      <td>4.701315e+04</td>\n",
              "      <td>43262.948967</td>\n",
              "      <td>40311.400967</td>\n",
              "      <td>38871.760400</td>\n",
              "      <td>5663.580500</td>\n",
              "      <td>5.921163e+03</td>\n",
              "      <td>5225.68150</td>\n",
              "      <td>4826.076867</td>\n",
              "      <td>4799.387633</td>\n",
              "      <td>5215.502567</td>\n",
              "      <td>0.221200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>129747.661567</td>\n",
              "      <td>0.489129</td>\n",
              "      <td>0.790349</td>\n",
              "      <td>0.521970</td>\n",
              "      <td>9.217904</td>\n",
              "      <td>1.123802</td>\n",
              "      <td>1.197186</td>\n",
              "      <td>1.196868</td>\n",
              "      <td>1.169139</td>\n",
              "      <td>1.133187</td>\n",
              "      <td>1.149988</td>\n",
              "      <td>73635.860576</td>\n",
              "      <td>71173.768783</td>\n",
              "      <td>6.934939e+04</td>\n",
              "      <td>64332.856134</td>\n",
              "      <td>60797.155770</td>\n",
              "      <td>59554.107537</td>\n",
              "      <td>16563.280354</td>\n",
              "      <td>2.304087e+04</td>\n",
              "      <td>17606.96147</td>\n",
              "      <td>15666.159744</td>\n",
              "      <td>15278.305679</td>\n",
              "      <td>17777.465775</td>\n",
              "      <td>0.415062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-165580.000000</td>\n",
              "      <td>-69777.000000</td>\n",
              "      <td>-1.572640e+05</td>\n",
              "      <td>-170000.000000</td>\n",
              "      <td>-81334.000000</td>\n",
              "      <td>-339603.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>3558.750000</td>\n",
              "      <td>2984.750000</td>\n",
              "      <td>2.666250e+03</td>\n",
              "      <td>2326.750000</td>\n",
              "      <td>1763.000000</td>\n",
              "      <td>1256.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>8.330000e+02</td>\n",
              "      <td>390.00000</td>\n",
              "      <td>296.000000</td>\n",
              "      <td>252.500000</td>\n",
              "      <td>117.750000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>140000.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22381.500000</td>\n",
              "      <td>21200.000000</td>\n",
              "      <td>2.008850e+04</td>\n",
              "      <td>19052.000000</td>\n",
              "      <td>18104.500000</td>\n",
              "      <td>17071.000000</td>\n",
              "      <td>2100.000000</td>\n",
              "      <td>2.009000e+03</td>\n",
              "      <td>1800.00000</td>\n",
              "      <td>1500.000000</td>\n",
              "      <td>1500.000000</td>\n",
              "      <td>1500.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>240000.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>67091.000000</td>\n",
              "      <td>64006.250000</td>\n",
              "      <td>6.016475e+04</td>\n",
              "      <td>54506.000000</td>\n",
              "      <td>50190.500000</td>\n",
              "      <td>49198.250000</td>\n",
              "      <td>5006.000000</td>\n",
              "      <td>5.000000e+03</td>\n",
              "      <td>4505.00000</td>\n",
              "      <td>4013.250000</td>\n",
              "      <td>4031.500000</td>\n",
              "      <td>4000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>964511.000000</td>\n",
              "      <td>983931.000000</td>\n",
              "      <td>1.664089e+06</td>\n",
              "      <td>891586.000000</td>\n",
              "      <td>927171.000000</td>\n",
              "      <td>961664.000000</td>\n",
              "      <td>873552.000000</td>\n",
              "      <td>1.684259e+06</td>\n",
              "      <td>896040.00000</td>\n",
              "      <td>621000.000000</td>\n",
              "      <td>426529.000000</td>\n",
              "      <td>528666.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Limit_bal        Gender  ...      Pay_April     Defaulter\n",
              "count    30000.000000  30000.000000  ...   30000.000000  30000.000000\n",
              "mean    167484.322667      1.603733  ...    5215.502567      0.221200\n",
              "std     129747.661567      0.489129  ...   17777.465775      0.415062\n",
              "min      10000.000000      1.000000  ...       0.000000      0.000000\n",
              "25%      50000.000000      1.000000  ...     117.750000      0.000000\n",
              "50%     140000.000000      2.000000  ...    1500.000000      0.000000\n",
              "75%     240000.000000      2.000000  ...    4000.000000      0.000000\n",
              "max    1000000.000000      2.000000  ...  528666.000000      1.000000\n",
              "\n",
              "[8 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTiCavk9nAqq"
      },
      "source": [
        "The average value for the amount of credit card limit is 167,484 NT dollars. The standard deviation is 129,747 NT dollars, ranging from 10,000 to 1M NT dollars.\n",
        "Education level is mostly graduate school (1) and university (2). Most of the clients are either marrined or single (less frequent the other status). Average age is 35.5 years, with a standard deviation of 9.2 years."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBDI2r1iFCva",
        "outputId": "65f0096e-fc43-45a8-d158-850d0f76e378"
      },
      "source": [
        "# Checking duplicates in the data\n",
        "# we have duplicates of non defaulter - 29 and defaulter 6\n",
        "duplicates = df[df.duplicated()]\n",
        "duplicates.Defaulter.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    29\n",
              "1     6\n",
              "Name: Defaulter, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvHo4WGTnVMq"
      },
      "source": [
        "# Drop Duplicates\n",
        "df.drop_duplicates(inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrZx7KEXFywX",
        "outputId": "a8efe570-c24b-472a-850b-882bbbdd1f4b"
      },
      "source": [
        "# Checking null values. So we don't have any missing values\n",
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Limit_bal              0\n",
              "Gender                 0\n",
              "Education              0\n",
              "Marital_status         0\n",
              "Age                    0\n",
              "Repayment_September    0\n",
              "Repayment_August       0\n",
              "Repayment_July         0\n",
              "Repayment_June         0\n",
              "Repayment_May          0\n",
              "Repayment_April        0\n",
              "Sep_Bill               0\n",
              "Aug_Bill               0\n",
              "July_Bill              0\n",
              "June_Bill              0\n",
              "May_Bill               0\n",
              "Apr_Bill               0\n",
              "Pay_Sep                0\n",
              "Pay_Aug                0\n",
              "Pay_July               0\n",
              "Pay_June               0\n",
              "Pay_May                0\n",
              "Pay_April              0\n",
              "Defaulter              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k2ov9hHhnzpA"
      },
      "source": [
        "So we don't have any missing value"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HRgG_yBqWZK1"
      },
      "source": [
        "#<b> Feature Engineering on Categorical Feature"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ubcWrQbrn_jS",
        "outputId": "98ff92fb-e26b-4472-e1d8-b86e250e1377"
      },
      "source": [
        "# Value count of Gender Column\n",
        "df.Gender.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    18091\n",
              "1    11874\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzou51x_n_gy"
      },
      "source": [
        "# converting Gender 1 & 2 value in Male & Female for better understanding  \n",
        "df['Gender'] = df['Gender'].apply(lambda x:'Male'  if x == 1 else 'Female')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "10TDTBg1n_eN",
        "outputId": "39f663b6-3261-43b0-9fb7-42b40912044d"
      },
      "source": [
        "# Value count of Education Column\n",
        "df.Education.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    14019\n",
              "1    10563\n",
              "3     4915\n",
              "5      280\n",
              "4      123\n",
              "6       51\n",
              "0       14\n",
              "Name: Education, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05ag2Lc3qK3C"
      },
      "source": [
        "In data given values are  1 = graduate school; 2 = university; 3 = high school; 4 = others so all other are not given in data we include them on others"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lsPKME6Tn_bX"
      },
      "source": [
        "# \n",
        "df['Education']=df['Education'].apply(lambda x :'graduate' if x==1 else ('university' if x==2 else ('high school' if x==3  else 'others')))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Iy3z3Z3rZn4",
        "outputId": "3bc9e5e1-e26b-4e91-8c02-cbca99b72d26"
      },
      "source": [
        "# Value count of Marital_status Column\n",
        "df.Marital_status.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    15945\n",
              "1    13643\n",
              "3      323\n",
              "0       54\n",
              "Name: Marital_status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Ag4hpuJrmVX"
      },
      "source": [
        "data given values are 1 = married; 2 = single; 3 = others so 0 is  not given in data, we include them on others"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ChLsIvfD5iyM"
      },
      "source": [
        "# \n",
        "df['Marital_status']=df['Marital_status'].apply(lambda x : \"married\" if x==1 else (\"single\" if x==2 else 'others'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eqxSXnZjr9DX",
        "outputId": "ebf7d99c-e709-450d-91ce-476350c142b5"
      },
      "source": [
        "# Value count percentage of Age Column\n",
        "df.Age.value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29    0.053462\n",
              "27    0.049224\n",
              "28    0.046921\n",
              "30    0.046521\n",
              "26    0.041782\n",
              "31    0.040481\n",
              "25    0.039546\n",
              "34    0.038745\n",
              "32    0.038612\n",
              "33    0.038245\n",
              "24    0.037577\n",
              "35    0.037143\n",
              "36    0.036943\n",
              "37    0.034741\n",
              "39    0.031737\n",
              "38    0.031470\n",
              "23    0.031036\n",
              "40    0.029034\n",
              "41    0.027432\n",
              "42    0.026431\n",
              "44    0.023361\n",
              "43    0.022326\n",
              "45    0.020591\n",
              "46    0.019022\n",
              "22    0.018688\n",
              "47    0.016720\n",
              "48    0.015551\n",
              "49    0.014984\n",
              "50    0.013716\n",
              "51    0.011347\n",
              "53    0.010846\n",
              "52    0.010145\n",
              "54    0.008243\n",
              "55    0.006975\n",
              "56    0.005940\n",
              "58    0.004071\n",
              "57    0.004071\n",
              "59    0.002770\n",
              "60    0.002236\n",
              "21    0.002236\n",
              "61    0.001869\n",
              "62    0.001468\n",
              "63    0.001035\n",
              "64    0.001035\n",
              "66    0.000834\n",
              "65    0.000801\n",
              "67    0.000534\n",
              "69    0.000501\n",
              "70    0.000334\n",
              "68    0.000167\n",
              "73    0.000133\n",
              "71    0.000100\n",
              "72    0.000100\n",
              "75    0.000100\n",
              "74    0.000033\n",
              "79    0.000033\n",
              "Name: Age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "oIHbU1oWsQtm",
        "outputId": "76ce1e80-1132-4a07-b321-28d15fc48bf0"
      },
      "source": [
        "# visualizing the Age column for better understanding with histplot\n",
        "sns.histplot(df['Age'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe48dcf54d0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EYXSVGWOGndg"
      },
      "source": [
        "From this hist plot we can divide age into 9 bins"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBXvpScoHr2O"
      },
      "source": [
        "# we are using Bining in Age column\n",
        "df['Age_group'] = pd.cut(df.Age, bins=[21,25,30,35,40,45,50,55,60,80],labels=['21-25','26-30','31-35','36-40','41-45','46-50','51-55','56-60','61 and Above'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzhkHXz1EGyw"
      },
      "source": [
        "# droping Age column\n",
        "df.drop(columns=['Age'],inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4d2l_3PJOuWs"
      },
      "source": [
        "# Feature Engineering on Repayments status Columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eaubDCmPlUB"
      },
      "source": [
        "# creating a new data frame of Repayments status\n",
        "repayment_df = df[['Repayment_September','Repayment_August','Repayment_July','Repayment_June','Repayment_May','Repayment_April']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BZcwXf2POpjE",
        "outputId": "a23e1d42-a1a8-4bb6-e0d8-03345d16793f"
      },
      "source": [
        "#value counts of all Repayments status Columns \n",
        "for i in repayment_df.columns:\n",
        "  print('column name = ', i)\n",
        "  print(repayment_df[i].value_counts())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "column name =  Repayment_September\n",
            " 0    14737\n",
            "-1     5682\n",
            " 1     3667\n",
            "-2     2750\n",
            " 2     2666\n",
            " 3      322\n",
            " 4       76\n",
            " 5       26\n",
            " 8       19\n",
            " 6       11\n",
            " 7        9\n",
            "Name: Repayment_September, dtype: int64\n",
            "column name =  Repayment_August\n",
            " 0    15730\n",
            "-1     6046\n",
            " 2     3926\n",
            "-2     3752\n",
            " 3      326\n",
            " 4       99\n",
            " 1       28\n",
            " 5       25\n",
            " 7       20\n",
            " 6       12\n",
            " 8        1\n",
            "Name: Repayment_August, dtype: int64\n",
            "column name =  Repayment_July\n",
            " 0    15764\n",
            "-1     5934\n",
            "-2     4055\n",
            " 2     3819\n",
            " 3      240\n",
            " 4       75\n",
            " 7       27\n",
            " 6       23\n",
            " 5       21\n",
            " 1        4\n",
            " 8        3\n",
            "Name: Repayment_July, dtype: int64\n",
            "column name =  Repayment_June\n",
            " 0    16455\n",
            "-1     5683\n",
            "-2     4318\n",
            " 2     3159\n",
            " 3      180\n",
            " 4       68\n",
            " 7       58\n",
            " 5       35\n",
            " 6        5\n",
            " 8        2\n",
            " 1        2\n",
            "Name: Repayment_June, dtype: int64\n",
            "column name =  Repayment_May\n",
            " 0    16947\n",
            "-1     5535\n",
            "-2     4516\n",
            " 2     2626\n",
            " 3      178\n",
            " 4       83\n",
            " 7       58\n",
            " 5       17\n",
            " 6        4\n",
            " 8        1\n",
            "Name: Repayment_May, dtype: int64\n",
            "column name =  Repayment_April\n",
            " 0    16286\n",
            "-1     5736\n",
            "-2     4865\n",
            " 2     2766\n",
            " 3      184\n",
            " 4       48\n",
            " 7       46\n",
            " 6       19\n",
            " 5       13\n",
            " 8        2\n",
            "Name: Repayment_April, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CTp3s5c2UD53"
      },
      "source": [
        "In data description we have not given information about 0 and -2 values "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50z3AVNQSgwz",
        "outputId": "79711a2c-6f31-4727-b3b0-869c2a71f068"
      },
      "source": [
        "# Value count percentage of Repayments status columns with respect of Defaulter\n",
        "for i in repayment_df.columns:\n",
        "  print('column name = ',i)\n",
        "  print(df[repayment_df[i]<=0].Defaulter.value_counts(normalize=True))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "column name =  Repayment_September\n",
            "0    0.861625\n",
            "1    0.138375\n",
            "Name: Defaulter, dtype: float64\n",
            "column name =  Repayment_August\n",
            "0    0.837238\n",
            "1    0.162762\n",
            "Name: Defaulter, dtype: float64\n",
            "column name =  Repayment_July\n",
            "0    0.82802\n",
            "1    0.17198\n",
            "Name: Defaulter, dtype: float64\n",
            "column name =  Repayment_June\n",
            "0    0.820381\n",
            "1    0.179619\n",
            "Name: Defaulter, dtype: float64\n",
            "column name =  Repayment_May\n",
            "0    0.815468\n",
            "1    0.184532\n",
            "Name: Defaulter, dtype: float64\n",
            "column name =  Repayment_April\n",
            "0    0.813293\n",
            "1    0.186707\n",
            "Name: Defaulter, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2VOlHjBTgFD"
      },
      "source": [
        "# Defining function for all Repayments status value less than 0 consider as 0\n",
        "def repaymentsstatus(delay):\n",
        "    if delay <= 0:\n",
        "      return 0\n",
        "    else:\n",
        "      return delay"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nuwXLIFVxJy"
      },
      "source": [
        "# Apply function on Repayments status columns\n",
        "for i in repayment_df.columns:\n",
        "  df[i]=df[i].apply(repaymentsstatus)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yrc2ET9wfdEk"
      },
      "source": [
        "# Divide data into Categorical and numerical feature \n",
        "cat_features = df[['Age_group','Gender','Education','Marital_status','Repayment_September','Repayment_August','Repayment_July','Repayment_May','Repayment_April','Defaulter']]\n",
        "num_feature  = df[['Limit_bal','Sep_Bill','Aug_Bill', 'July_Bill', 'June_Bill', 'May_Bill', 'Apr_Bill', 'Pay_Sep',\n",
        "       'Pay_Aug', 'Pay_July', 'Pay_June', 'Pay_May', 'Pay_April']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OCLVLs4u5xXI"
      },
      "source": [
        "#<b> EDA "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JfrwzFsfSn9"
      },
      "source": [
        "#<b> Categorical Feature"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "T0fws8uxfSIc",
        "outputId": "764ef0d8-8198-45dd-89fa-fb233bb2d7a5"
      },
      "source": [
        "cat_features.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age_group</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Education</th>\n",
              "      <th>Marital_status</th>\n",
              "      <th>Repayment_September</th>\n",
              "      <th>Repayment_August</th>\n",
              "      <th>Repayment_July</th>\n",
              "      <th>Repayment_May</th>\n",
              "      <th>Repayment_April</th>\n",
              "      <th>Defaulter</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21-25</td>\n",
              "      <td>Female</td>\n",
              "      <td>university</td>\n",
              "      <td>married</td>\n",
              "      <td>2</td>\n",
              "      <td>2</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>26-30</td>\n",
              "      <td>Female</td>\n",
              "      <td>university</td>\n",
              "      <td>single</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31-35</td>\n",
              "      <td>Female</td>\n",
              "      <td>university</td>\n",
              "      <td>single</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>36-40</td>\n",
              "      <td>Female</td>\n",
              "      <td>university</td>\n",
              "      <td>married</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56-60</td>\n",
              "      <td>Male</td>\n",
              "      <td>university</td>\n",
              "      <td>married</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Age_group  Gender   Education  ... Repayment_May  Repayment_April  Defaulter\n",
              "0     21-25  Female  university  ...             0                0          1\n",
              "1     26-30  Female  university  ...             0                2          1\n",
              "2     31-35  Female  university  ...             0                0          0\n",
              "3     36-40  Female  university  ...             0                0          0\n",
              "4     56-60    Male  university  ...             0                0          0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 966
        },
        "id": "fVv7BeJdqejq",
        "outputId": "d0b1ee66-2de5-4b62-8175-73ab4e117094"
      },
      "source": [
        "\n",
        "f, axes = plt.subplots(3, 3, figsize=(20, 15), facecolor='white')\n",
        "f.suptitle('FREQUENCY OF CATEGORICAL VARIABLES (BY TARGET)')\n",
        "ax1 = sns.countplot(x=\"Age_group\", hue=\"Defaulter\", data=cat_features, ax=axes[0,0])\n",
        "ax2 = sns.countplot(x=\"Gender\", hue=\"Defaulter\", data=cat_features,ax=axes[0,1])\n",
        "ax3 = sns.countplot(x=\"Education\", hue=\"Defaulter\", data=cat_features,ax=axes[0,2])\n",
        "ax4 = sns.countplot(x=\"Marital_status\", hue=\"Defaulter\", data=cat_features,  ax=axes[1,0])\n",
        "ax5 = sns.countplot(x=\"Repayment_September\", hue=\"Defaulter\", data=cat_features, ax=axes[1,1])\n",
        "ax6 = sns.countplot(x=\"Repayment_August\", hue=\"Defaulter\", data=cat_features,  ax=axes[1,2])\n",
        "ax7 = sns.countplot(x=\"Repayment_July\", hue=\"Defaulter\", data=cat_features, ax=axes[2,0])\n",
        "ax8 = sns.countplot(x=\"Repayment_May\", hue=\"Defaulter\", data=cat_features,  ax=axes[2,1])\n",
        "ax9 = sns.countplot(x=\"Repayment_April\", hue=\"Defaulter\", data=cat_features,  ax=axes[2,2]);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1440x1080 with 9 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "tGw-kW3buLVG",
        "outputId": "bbd69565-53ec-4e07-d454-cd05fd0a99f4"
      },
      "source": [
        "plt.pie(df['Defaulter'].value_counts(normalize=True),autopct='%.0f%%')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVC0lEQVR4nO3deZQV5Z3G8e+93XTTNDuyilJIBAQ0LphoQlzGZdAaYyTJ0eMaNTExGhcmS01iJjeJmVQ8MWNU1JgNo5IxiUk01kSNjBFoQRAFRAi4UCwKYlhu0zS93po/6qqNsjTd99bvrarf55x7WgH7ffDwUNtb75sJggCllHmy0gGUUnum5VTKUFpOpQyl5VTKUFpOpQyl5VTKUFpOpQyl5VTKUFpOpQyl5VTKUFpOpQyl5VTKUFpOpQyl5VTKUFpOpQyl5VTKUFpOpQyl5VTKUFpOpQyl5VTKUFpOpQyl5VTKUFpOpQyl5VTKUFpOpQyl5VTKUFpOpQxVKR1A7Z3leJXAwcBI4JDiZ2Txx3oDNXv4VAAtQFPxswOoL342AeuAtR2+bvZdWzfMMVBGNzIyg+V4I4BjgKM7fB1N+c9umgAfWAosBl4AXvBde1uZx1X7oeUUYDleBTAZOBU4GTgWGCIa6oPWEJZ1LvCE79qrhPOkjpYzIpbjjQTOKn5OA/rKJjpgPvBE8TPbd+162TjJp+UsI8vxhgIXABcBxwvHKaU2YB7wEPB737W3COdJJC1niVmO1weYRljIfyG8QZNkbcCTwH3AI75rNwvnSQwtZ4lYjncccAPwacK7pmm0DZgF3O679mrpMHGn5ewGy/GywCeBG4GThOOYJAD+AvzYd+250mHiSsvZBZbj1QJXANcDY4TjmG4hcCvwsO/a7dJh4kTLeQAsx6sCvgTcBAwWjhM3rwPfAR7USQ+do+XsBMvxMsCFwPcJJwaornsB+Lrv2rOlg5hOy7kfluP9K+ASzthRpfMEYUmXSQcxlZZzLyzHGw3cCZwtnSXBCsBM4Gu+a28VzmIcLef7FCebfxX4T9L7SCRqbwHX+K79sHQQk2g5O7Ac72jg1+gprJQ/EpZ0k3QQE2g5efdo+W3gm+hrdNK2AdN9154pHURa6stpOd7BhHNEPy6dRe3mEeDyNL+6lupyFu/EPgAcJJ1F7dFa4ALftRdIB5GQynIW36f8LuFpbEY4jtq3VuDffde+QzpI1FJXTsvxhhCexp4iHEUdmAeAq3zX3iUdJCqpKqfleGOBvwKHSWdRXfI8YPuuvVk6SBRSs/qe5XgfA55Fixlnk4FnLcdLxcsGqSin5XjTgNnAIOksqtvGEBZ0snSQckt8OS3Huw74PdBTOosqmSHA3y3HmyodpJwSXU7L8W4CfkrCf58pVQv8xXK8C6WDlEtibwhZjvc14BbpHKrs2gmfhf5BOkipJbKcluNdD9wmnUNFphU4z3dtTzpIKSWunJbjfQm4WzqHilwz4WOWxLzEnahyWo53BfALdNZPWu0EpvquPU86SCkkppyW450NPEry14lV+1YPfNx37eXSQborEeW0HO8owhXI+0hnUUZYAxwf95XoY/+IobjlwWNoMdV7RgN/sByvh3SQ7oh1OYtLVT5MuG+lUh2dAtwuHaI7Yl1OwgW49CVptTdfshzvaukQXRXba07L8S4BfiOdQxmvDTg1jndwY1nO4rKVS4jfHpdKxjrgw75rb5cOciBid1pbXMXgAbSYqvMOBe6VDnGgYldOwlXyPiYdQsXOZy3Hu0w6xIGI1Wmt5XgnAnPRiQaqa/LAkb5rr5cO0hmxOXJajldDeDqrxVRd1Q/4lXSIzopNOQlXytMlRlR3nV6802+8WJzWWo73IWA5UC2dRSXCJmCs79o7pIPsS1yOnHegxVSlM4xwI1+jGX/ktBzvPMINbpQqpVbCZ58rpYPsjdFHTsvxegH/LZ1DJVIPDJ97a3Q5gRuAUdIhVGKdXlw21UjGntZajteX8L28gdJZVKItB47yXdu4Iph85LwOLaYqv0mAkUdPI4+cxaOmDwwQjqLSYSlwjGlHT1N3cb6eiIvZumUDbz/6o3f/vW37JvpPuZiehx7JlidmELS3kMlWMPCMq6keMY6dq+rIz32QbE1vBk+7iYqavrRu28j2Ob9h8LnfiDK66r4PA+cCf5YO0pFxR07L8foRHjX7S2UICu1suOsyhl/yE7Y8fgd9J59LzZjJ7HptEfnnHmbYhS6bZjkM+WyOxtXzKTQ10Pe4c3j70VvoP+Uiegw8WCq66roXfdc+VjpERyZec16FYDEBmtYupUf/4VT2GwJAoaUx/NrcSEXv4l5ImSxBextBazOZbAVN65dTUTtAixlfx1iOZ0uH6MioclqOlwW+LJ1j58o59DriJAAGnnYV257+NRvu+hzbnv4lA04O3zrqd8Jn2fw/32LXq89RO+Fk8s8+RL+PXSAZW3XfNdIBOjLqtNZyvE8Cj0hmCNpb2TDjMkZcOYOK2gFsfepnVB8yidpxH2fnyrk0LH2coRf8YLf/pmH5bAq7GqgeMY76hX8k27M3A06/imwP3dgsZgrAYb5rr5UOAoYdOQHxxZh2vb6YqqFjqKgN70c1vDSbXmPDd7t7jZ9C88bVu/36QmsTDS/Nps+xNtvnPcggezrVIyey8+W/Rx1ddV8W+Lx0iHcYU07L8UYBZ0rn2LniGWqLp7QAFb0H0rz+JaB4LTpgxG6/vv65P9L3uHPIVFQStLWEG0FkMgRtzVHGVqVzpeV4RjzFMCJE0ZUI/2VRaGmiyV/CoKnXvvtjg876Ctueupeg0E6msoqBU7/y7s+17dhCy8bV9J8SbhHZ57hz2HTfdLI9axk87abI86uSGA6cA/xJOogx15yW4/noPFplhid81xbfNduIclqONxlYJJ1DqaJ2YLjv2m9LhjDlmvPT0gGU6qACOE86hJZTqT37jHQA8dNay/EmAS+JhlDqg1qBwb5r56UCmHDk1KOmMlEP4CzJACaU81PSAZTai3MkBxctp+V4Awhf11HKRGdIDi595JxCOKdGKRMNthxvnNTg0uU8af+/RClRU6QG1nIqtW9iO6eLldNyvFrAqDfPldqDVB45T8SsifdK7cnhluMNkRhYspzHCY6t1IE4UWJQyXIeITi2UgdiosSgWk6l9k/kcYqWU6n9S085LccbCfSRGFupLkhPOdGjpoqX/pbjDY16UKlyik2JUqqLxkc9oFQ5RwqNq1RXWVEPKFXOYULjKtVVg6IeUKqcw4XGVaqrDop6QKlyDhYaV6muSk05dcdqFTepOa3Vcqq4Sc2Rs1ZoXKW6KjVHTumXvJU6UJHv5xh5SYob5CoVNxVRDyhRFF3QS8VR5OWUWIlAj5xlUkF72+rqS7dkwgWRVQkVyNTDtkjH1HImSDsVlS30qK/JtBwunSVpsgT10Y8ZPT2tLaN1wZDN0hkSqi3qASMvp+/aTQj8RtPihcLh+v+2PNqjHlDqFHO70LiJN69wZG/pDAkV+W5jUuXcKjRu4i0sjBslnSGh3op6QC1nwrzNgIPag4zodukJFfm1vFQ5o70nnTJb6LtOOkMCpaaceuQso5WFUQ3SGRIoNae1eru/jOYXJug2F6WXmiPna0LjpsKzhYkie3skXGqOnKuFxk2FFcEoKwhokc6RMG9GPaBUOV8RGjcV2qjssYsqXzpHgrQAr0Y9qFQ51wHNQmOnwrpgqF7Xl85KcvnkT98D8F27gF53ltXzhbGRTzdLsJckBpV8Q0SvO8toXmGS7kVTOqkr5zLBsRNvYWG8TuMrndSV8znBsRNvK/0GtQeZyG//J5TIgUSynAsFx06Ff9JvvXSGBNhGLv+GxMBi5fRd+58I3J5Ok5cLlk7j6746qYGllwyZKzx+oi0oTKiSzpAAf5MaWMuZYHU6ja8UnpIaWLqcfxceP9H+ERxqBYFO9uiGN8nlV0gNLlpO37XXAGK/+aRrp6KykerXpXPEmNhRE+SPnACPSAdIMj8YtkU6Q4xpOaUDJNlincbXHakv50Jgo3SIpJpXmNRXOkNMPU8uL/rnUrycvmsHwGPSOZJqYWG8JZ0hph6QDiBezqI/SwdIqu30GdAeZDdJ54iZNuC30iFMKeff0HWFyubtCKbxrc8XOPW+nUyY0cDEuxr46YLwCc7Xnmxi/J0NHHV3A+c91Mj2pgCAunVtHHV3A5PvbeCVLeFl8famgDPv30khCModd3+eJJcX//NoRDl9124FZkrnSKrlBWtnuceozMKtZ/ZkxTW9WXBlLTMWtbLi7XbOGFPJ8i/Xsuzq3owdmOWHc8PS3jq/hf+9qBe3Te3JPc+3AnDznGa++Ylqshnx7XTulw4AhpSz6BfSAZJqfmFidbnHGN4ny7HDwy0s+1RnOGJwljfqA84cU0llNizbCSMr2LCjAECPCmhsDWhsDf/5ta0F1tcXOMUSXzhwB4Y8QTCmnL5rv4LOGCqLqFfj87cXeHFjOx8duft+s79a0spZHwrL9x9Tqrn0T038cF4z136kim/9XxM3n1r2v0M642Fy+V3SIUBmf859+TlwinSIpFkVHGIFAU2ZDD3LPVZDS8Cnf9fIbVN70rf6vdPTH8xppjILFx0Z7ut79LAKFny+FoA5a9sY3jtLAJz/h0Z6ZDPcemY1Q3uLHDt+LjHonhhz5Cx6GF0NvuQKZCt20rPs0/ha28NiXnRkD6Yd8d7m2jOXtPDYK208OK2GzPuuJ4Mg4OY5zXz75Gq++0wzt5zeky8c24PbnxNZ2bOOXP5ZiYH3xKhy+q7dDNwtnSOJ/GBYWf/SC4KAKx9t4oiDKph+4nunp4+/2sYtdS08ekENvXp88EbPb5a2cvbhlQysydDYCtlM+GlsLWfavbpFZNS9MO20FuA24AagVjpIkiwqjGuflPXL9v3r1rdz/7JWjhyS5eh7wne8/+u0aq77axPN7XDG/Y1AeFPonn+rAcIbQjOXtvLkxb0AmH5CFWfPaqSqAmZNqylb1r1YAfwl6kH3JRPIP1P6AMvxbgWmS+dIktOyi5f8surWo6VzGOwKcvlfS4foyKjT2g5+DDRJh0iSRYVxo6UzGOwN4EHpEO9nZDl9194I/Eo6R5LU07tfW5CNfL+PmLiNXN64vWWMLGfRjwCZ2wIJtZn+G6QzGOhNDL0JaWw5fddeB9wjnSNJlhdGN0pnMJBDLl/26Y1dYWw5i3LoFvUlUxfBNL6YeQ4DXg3bG6PL6bv2VuB70jmSYkFhwjDpDAYJgBvI5c17XFFkdDmLZqCLgJXE6mDkqCBAT21Ds8jlF0iH2Bfjy1l8nexa6RxJEJDNNlCzRjqHARoBRzrE/hhfTgDftZ/GgDfTk+D1YLjOXYYfkssbf+c6FuUsug7Q5Ta6aVFhnLHXWBF5HnClQ3RGbMpZ3Pjo89I54q6uMKmfdAZBu4CLJbaQ74rYlBPAd20Pg963i6PnC2Mt6QyCvk4uv0o6RGfFqpxF0wHdYqCLdlDbry3Iiuw3KewJwjv/sRG7cvqu3QBcChSks8TVWwxIWzm3Er51Eqvr7diVE8B37Trgu9I54uqlwmFpe9Z5Nbl87Cb9x7KcRd8H/iQdIo7qChPLvpaQQX5KLv876RBdEdtyFrdxuBRYLp0lbuYXJgyXzhCR2cBXpUN0VWzLCe9ef56LLgp2QF4LRhwSBBj5JkYJrQHOj8tjkz2JdTkBfNd+HTgf0K3uOikgm92R7Gl82wGbXD7We5PGvpwAvms/BVwvnSNOXgtGJPVsoxX4DLn8Sukg3ZWIcgL4rj0D+LZ0jrhYVBgvHaEcAuCL5PKzpYOUQmLKCeC79s0YtvaoqeYVJvWXzlAG15q2gl53JKqcAL5rfwND14QxyeLC2NFBQKweyu/HteTyd0mHKKXElbPoGgxefsIEO6np00ZF2fftjMhXyOVjNTWvMxJZzuIz0MuBWdJZTLYpGLhROkMJXEcuf6d0iHJIZDkBfNduAy4GEnWqU0rLgsOM2OquG64nl79DOkS5JLacEB5Bfde+Bl0kbI9iPI2vBbicXP526SDllOhyvsN37e8AX0QnKuxmfmHCCOkMXbAFOINcfqZ0kHJLRTkBfNe+F/gU0CCdxRRrguGHBAE7pHMcgFXACeTyc6SDRCE15QTwXfsx4CNA7GePlEYmU08vXzpFJ80mLOar0kGikqpyAviuvZKwoLF8jajUXg0OjsOK+vcCU8nlt0sHiVLqygnh2yy+a58P3AjE9q2FUlhYGP/B7abNkSdckOuLcX67pKtSWc53+K59G3Aq4f6MqVRn7jS+OcCHyeWN2zczKqkuJ4Dv2vOAScBM4SgiXix8yLRpfK3AN4FTyeXXSoeRZOS281Isx5tKuPTmSOksUVpdfcnaqkz7KOkchHdjLyKXXywdxASpP3J25Lv248BE4BfSWaK0MRgkPY2vhXCz5GO1mO/Rcr6P79r1vmt/ATiDlOxutiw4rElw+MeAieTyDrl82lYF3Cct514UV1c4Crga2Cwcp6zqCpNqBIZdBZxFLn9Omp5dHgi95uwEy/H6EG4ZdyMg8Qe5rEZlNm14pnp6VNfZ9YRznW8nl2+NaMxY0nIeAMvxRhKul3sxUCkcp6TWVF9Yn8nQt4xDbAPuJCzlP8s4TmJoObvAcjwL+DrhO6NxfbNjN0uqv7Csf2bnUWX41huBnwA/I5eP0zxecVrObrAcbyjhqgtXAwcJx+mW31fl5hyfXX1SCb/la4TrOd1HLt9cwu+bGlrOErAcryfhqe4VwInCcbrkq5UPzb228pFPdPPbtAGPE07o+DO5vL6i1w1azhKzHG8s4TYRlwCHCsfptBOzL7/826ofTOzif74UuA+YRS7/VgljpZqWs0wsx8sApwCXAZ8EBogG2o8amhtXVF/eM5Pp9OO1TcBvCU9bl5YxWmppOSNgOV4F8FHgLOBs4BjAuLdBVldfuqYq0zZ6Lz/dAtQRbkL7JLAkbvtdxo2WU0DxRtJU4DTgeGAsBkwIebrqxvmjs2+9c80cAP8A/kZYyGfI5ZO++ZFRtJwGKE5yOA6Y3OEzmugK2wysylXOfPxzlU9uBhYDL5LL5yMaX+2BltNQluNVExZ0TPHrIcXPCKBP8dO7+LWWD54mtxCWrqn42Uz43uobwJsd/nkt8Krv2npn1TBazgQo3nyqBSoIi9hSXFhbxZiWUylDid+EUErtmZZTKUNpOZUylJZTKUNpOZUylJZTKUNpOZUylJZTKUNpOZUylJZTKUNpOZUylJZTKUNpOZUylJZTKUNpOZUylJZTKUNpOZUylJZTKUNpOZUylJZTKUNpOZUylJZTKUNpOZUylJZTKUNpOZUylJZTKUNpOZUy1P8Dkn0JAD9vgW8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "NTnP2d0n6Ryb",
        "outputId": "cb989e7e-a69f-4333-d12f-37e261b79a2a"
      },
      "source": [
        "plt.figure(figsize=(20,15))\n",
        "sns.catplot(x=\"Age_group\", hue=\"Gender\", col=\"Defaulter\",\n",
        "                data=cat_features, kind=\"count\",\n",
        "                height=4, aspect=.7)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 475.325x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I0QqurPqwWEj"
      },
      "source": [
        "#<b> Numerical feature"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8QsnJaqGv7Ly",
        "outputId": "fc21ca25-47a3-4b9d-bfba-18155dbee2fe"
      },
      "source": [
        "num_feature.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Limit_bal</th>\n",
              "      <th>Sep_Bill</th>\n",
              "      <th>Aug_Bill</th>\n",
              "      <th>July_Bill</th>\n",
              "      <th>June_Bill</th>\n",
              "      <th>May_Bill</th>\n",
              "      <th>Apr_Bill</th>\n",
              "      <th>Pay_Sep</th>\n",
              "      <th>Pay_Aug</th>\n",
              "      <th>Pay_July</th>\n",
              "      <th>Pay_June</th>\n",
              "      <th>Pay_May</th>\n",
              "      <th>Pay_April</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20000</td>\n",
              "      <td>3913</td>\n",
              "      <td>3102</td>\n",
              "      <td>689</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>689</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>120000</td>\n",
              "      <td>2682</td>\n",
              "      <td>1725</td>\n",
              "      <td>2682</td>\n",
              "      <td>3272</td>\n",
              "      <td>3455</td>\n",
              "      <td>3261</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>90000</td>\n",
              "      <td>29239</td>\n",
              "      <td>14027</td>\n",
              "      <td>13559</td>\n",
              "      <td>14331</td>\n",
              "      <td>14948</td>\n",
              "      <td>15549</td>\n",
              "      <td>1518</td>\n",
              "      <td>1500</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50000</td>\n",
              "      <td>46990</td>\n",
              "      <td>48233</td>\n",
              "      <td>49291</td>\n",
              "      <td>28314</td>\n",
              "      <td>28959</td>\n",
              "      <td>29547</td>\n",
              "      <td>2000</td>\n",
              "      <td>2019</td>\n",
              "      <td>1200</td>\n",
              "      <td>1100</td>\n",
              "      <td>1069</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50000</td>\n",
              "      <td>8617</td>\n",
              "      <td>5670</td>\n",
              "      <td>35835</td>\n",
              "      <td>20940</td>\n",
              "      <td>19146</td>\n",
              "      <td>19131</td>\n",
              "      <td>2000</td>\n",
              "      <td>36681</td>\n",
              "      <td>10000</td>\n",
              "      <td>9000</td>\n",
              "      <td>689</td>\n",
              "      <td>679</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Limit_bal  Sep_Bill  Aug_Bill  ...  Pay_June  Pay_May  Pay_April\n",
              "0      20000      3913      3102  ...         0        0          0\n",
              "1     120000      2682      1725  ...      1000        0       2000\n",
              "2      90000     29239     14027  ...      1000     1000       5000\n",
              "3      50000     46990     48233  ...      1100     1069       1000\n",
              "4      50000      8617      5670  ...      9000      689        679\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "Zbbxkc8qyIGG",
        "outputId": "e06db4c5-ce0a-497c-b5f3-104f1e6f0a8e"
      },
      "source": [
        "num_feature.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Limit_bal</th>\n",
              "      <th>Sep_Bill</th>\n",
              "      <th>Aug_Bill</th>\n",
              "      <th>July_Bill</th>\n",
              "      <th>June_Bill</th>\n",
              "      <th>May_Bill</th>\n",
              "      <th>Apr_Bill</th>\n",
              "      <th>Pay_Sep</th>\n",
              "      <th>Pay_Aug</th>\n",
              "      <th>Pay_July</th>\n",
              "      <th>Pay_June</th>\n",
              "      <th>Pay_May</th>\n",
              "      <th>Pay_April</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>2.996500e+04</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>2.996500e+04</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>167442.005006</td>\n",
              "      <td>51283.009778</td>\n",
              "      <td>49236.366294</td>\n",
              "      <td>4.706792e+04</td>\n",
              "      <td>43313.329885</td>\n",
              "      <td>40358.334390</td>\n",
              "      <td>38917.012281</td>\n",
              "      <td>5670.099316</td>\n",
              "      <td>5.927983e+03</td>\n",
              "      <td>5231.688837</td>\n",
              "      <td>4831.617454</td>\n",
              "      <td>4804.897047</td>\n",
              "      <td>5221.498014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>129760.135222</td>\n",
              "      <td>73658.132403</td>\n",
              "      <td>71195.567392</td>\n",
              "      <td>6.937135e+04</td>\n",
              "      <td>64353.514373</td>\n",
              "      <td>60817.130623</td>\n",
              "      <td>59574.147742</td>\n",
              "      <td>16571.849467</td>\n",
              "      <td>2.305346e+04</td>\n",
              "      <td>17616.361124</td>\n",
              "      <td>15674.464538</td>\n",
              "      <td>15286.372298</td>\n",
              "      <td>17786.976864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>-165580.000000</td>\n",
              "      <td>-69777.000000</td>\n",
              "      <td>-1.572640e+05</td>\n",
              "      <td>-170000.000000</td>\n",
              "      <td>-81334.000000</td>\n",
              "      <td>-339603.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50000.000000</td>\n",
              "      <td>3595.000000</td>\n",
              "      <td>3010.000000</td>\n",
              "      <td>2.711000e+03</td>\n",
              "      <td>2360.000000</td>\n",
              "      <td>1787.000000</td>\n",
              "      <td>1262.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>8.500000e+02</td>\n",
              "      <td>390.000000</td>\n",
              "      <td>300.000000</td>\n",
              "      <td>261.000000</td>\n",
              "      <td>131.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>140000.000000</td>\n",
              "      <td>22438.000000</td>\n",
              "      <td>21295.000000</td>\n",
              "      <td>2.013500e+04</td>\n",
              "      <td>19081.000000</td>\n",
              "      <td>18130.000000</td>\n",
              "      <td>17124.000000</td>\n",
              "      <td>2102.000000</td>\n",
              "      <td>2.010000e+03</td>\n",
              "      <td>1804.000000</td>\n",
              "      <td>1500.000000</td>\n",
              "      <td>1500.000000</td>\n",
              "      <td>1500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>240000.000000</td>\n",
              "      <td>67260.000000</td>\n",
              "      <td>64109.000000</td>\n",
              "      <td>6.020100e+04</td>\n",
              "      <td>54601.000000</td>\n",
              "      <td>50247.000000</td>\n",
              "      <td>49252.000000</td>\n",
              "      <td>5008.000000</td>\n",
              "      <td>5.000000e+03</td>\n",
              "      <td>4512.000000</td>\n",
              "      <td>4016.000000</td>\n",
              "      <td>4042.000000</td>\n",
              "      <td>4000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>964511.000000</td>\n",
              "      <td>983931.000000</td>\n",
              "      <td>1.664089e+06</td>\n",
              "      <td>891586.000000</td>\n",
              "      <td>927171.000000</td>\n",
              "      <td>961664.000000</td>\n",
              "      <td>873552.000000</td>\n",
              "      <td>1.684259e+06</td>\n",
              "      <td>896040.000000</td>\n",
              "      <td>621000.000000</td>\n",
              "      <td>426529.000000</td>\n",
              "      <td>528666.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Limit_bal       Sep_Bill  ...        Pay_May      Pay_April\n",
              "count    29965.000000   29965.000000  ...   29965.000000   29965.000000\n",
              "mean    167442.005006   51283.009778  ...    4804.897047    5221.498014\n",
              "std     129760.135222   73658.132403  ...   15286.372298   17786.976864\n",
              "min      10000.000000 -165580.000000  ...       0.000000       0.000000\n",
              "25%      50000.000000    3595.000000  ...     261.000000     131.000000\n",
              "50%     140000.000000   22438.000000  ...    1500.000000    1500.000000\n",
              "75%     240000.000000   67260.000000  ...    4042.000000    4000.000000\n",
              "max    1000000.000000  964511.000000  ...  426529.000000  528666.000000\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H8JSGfX_zFii"
      },
      "source": [
        "p75=df['Limit_bal'].quantile(0.75)\n",
        "p25 = df['Limit_bal'].quantile(0.25)\n",
        "iqr = p75-p25\n",
        "ul = iqr+iqr*1.5\n",
        "ll = iqr-iqr*1.5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJXA5Plt1tfF"
      },
      "source": [
        "for i in num_feature:\n",
        "  p75=df[i].quantile(0.75)\n",
        "  p25 = df[i].quantile(0.25)\n",
        "  iqr = p75-p25\n",
        "  ul = iqr+iqr*1.5\n",
        "  ll = iqr-iqr*1.5\n",
        "  num = num_feature[(num_feature[i]<ul) & (num_feature[i]>ll)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkMuEAs8zsWB"
      },
      "source": [
        "num = num_feature[(num_feature['Pay_June']<ul) & (num_feature['Pay_June']>ll)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6alP0eOG2BEk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "e37956cc-2f31-417d-dff6-e282de050911"
      },
      "source": [
        "num_feature.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Limit_bal</th>\n",
              "      <th>Sep_Bill</th>\n",
              "      <th>Aug_Bill</th>\n",
              "      <th>July_Bill</th>\n",
              "      <th>June_Bill</th>\n",
              "      <th>May_Bill</th>\n",
              "      <th>Apr_Bill</th>\n",
              "      <th>Pay_Sep</th>\n",
              "      <th>Pay_Aug</th>\n",
              "      <th>Pay_July</th>\n",
              "      <th>Pay_June</th>\n",
              "      <th>Pay_May</th>\n",
              "      <th>Pay_April</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>2.996500e+04</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>2.996500e+04</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "      <td>29965.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>167442.005006</td>\n",
              "      <td>51283.009778</td>\n",
              "      <td>49236.366294</td>\n",
              "      <td>4.706792e+04</td>\n",
              "      <td>43313.329885</td>\n",
              "      <td>40358.334390</td>\n",
              "      <td>38917.012281</td>\n",
              "      <td>5670.099316</td>\n",
              "      <td>5.927983e+03</td>\n",
              "      <td>5231.688837</td>\n",
              "      <td>4831.617454</td>\n",
              "      <td>4804.897047</td>\n",
              "      <td>5221.498014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>129760.135222</td>\n",
              "      <td>73658.132403</td>\n",
              "      <td>71195.567392</td>\n",
              "      <td>6.937135e+04</td>\n",
              "      <td>64353.514373</td>\n",
              "      <td>60817.130623</td>\n",
              "      <td>59574.147742</td>\n",
              "      <td>16571.849467</td>\n",
              "      <td>2.305346e+04</td>\n",
              "      <td>17616.361124</td>\n",
              "      <td>15674.464538</td>\n",
              "      <td>15286.372298</td>\n",
              "      <td>17786.976864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>-165580.000000</td>\n",
              "      <td>-69777.000000</td>\n",
              "      <td>-1.572640e+05</td>\n",
              "      <td>-170000.000000</td>\n",
              "      <td>-81334.000000</td>\n",
              "      <td>-339603.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50000.000000</td>\n",
              "      <td>3595.000000</td>\n",
              "      <td>3010.000000</td>\n",
              "      <td>2.711000e+03</td>\n",
              "      <td>2360.000000</td>\n",
              "      <td>1787.000000</td>\n",
              "      <td>1262.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>8.500000e+02</td>\n",
              "      <td>390.000000</td>\n",
              "      <td>300.000000</td>\n",
              "      <td>261.000000</td>\n",
              "      <td>131.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>140000.000000</td>\n",
              "      <td>22438.000000</td>\n",
              "      <td>21295.000000</td>\n",
              "      <td>2.013500e+04</td>\n",
              "      <td>19081.000000</td>\n",
              "      <td>18130.000000</td>\n",
              "      <td>17124.000000</td>\n",
              "      <td>2102.000000</td>\n",
              "      <td>2.010000e+03</td>\n",
              "      <td>1804.000000</td>\n",
              "      <td>1500.000000</td>\n",
              "      <td>1500.000000</td>\n",
              "      <td>1500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>240000.000000</td>\n",
              "      <td>67260.000000</td>\n",
              "      <td>64109.000000</td>\n",
              "      <td>6.020100e+04</td>\n",
              "      <td>54601.000000</td>\n",
              "      <td>50247.000000</td>\n",
              "      <td>49252.000000</td>\n",
              "      <td>5008.000000</td>\n",
              "      <td>5.000000e+03</td>\n",
              "      <td>4512.000000</td>\n",
              "      <td>4016.000000</td>\n",
              "      <td>4042.000000</td>\n",
              "      <td>4000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>964511.000000</td>\n",
              "      <td>983931.000000</td>\n",
              "      <td>1.664089e+06</td>\n",
              "      <td>891586.000000</td>\n",
              "      <td>927171.000000</td>\n",
              "      <td>961664.000000</td>\n",
              "      <td>873552.000000</td>\n",
              "      <td>1.684259e+06</td>\n",
              "      <td>896040.000000</td>\n",
              "      <td>621000.000000</td>\n",
              "      <td>426529.000000</td>\n",
              "      <td>528666.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Limit_bal       Sep_Bill  ...        Pay_May      Pay_April\n",
              "count    29965.000000   29965.000000  ...   29965.000000   29965.000000\n",
              "mean    167442.005006   51283.009778  ...    4804.897047    5221.498014\n",
              "std     129760.135222   73658.132403  ...   15286.372298   17786.976864\n",
              "min      10000.000000 -165580.000000  ...       0.000000       0.000000\n",
              "25%      50000.000000    3595.000000  ...     261.000000     131.000000\n",
              "50%     140000.000000   22438.000000  ...    1500.000000    1500.000000\n",
              "75%     240000.000000   67260.000000  ...    4042.000000    4000.000000\n",
              "max    1000000.000000  964511.000000  ...  426529.000000  528666.000000\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "abDW9FsD1O-_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "0cf8d0ba-c75e-4d45-a6ce-c276a41140a6"
      },
      "source": [
        "num.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Limit_bal</th>\n",
              "      <th>Sep_Bill</th>\n",
              "      <th>Aug_Bill</th>\n",
              "      <th>July_Bill</th>\n",
              "      <th>June_Bill</th>\n",
              "      <th>May_Bill</th>\n",
              "      <th>Apr_Bill</th>\n",
              "      <th>Pay_Sep</th>\n",
              "      <th>Pay_Aug</th>\n",
              "      <th>Pay_July</th>\n",
              "      <th>Pay_June</th>\n",
              "      <th>Pay_May</th>\n",
              "      <th>Pay_April</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>26991.000000</td>\n",
              "      <td>26991.000000</td>\n",
              "      <td>26991.000000</td>\n",
              "      <td>26991.000000</td>\n",
              "      <td>26991.000000</td>\n",
              "      <td>26991.000000</td>\n",
              "      <td>26991.000000</td>\n",
              "      <td>26991.000000</td>\n",
              "      <td>2.699100e+04</td>\n",
              "      <td>26991.000000</td>\n",
              "      <td>26991.000000</td>\n",
              "      <td>26991.000000</td>\n",
              "      <td>26991.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>155743.384091</td>\n",
              "      <td>46103.224149</td>\n",
              "      <td>44241.509651</td>\n",
              "      <td>42064.041792</td>\n",
              "      <td>38520.454263</td>\n",
              "      <td>34160.970694</td>\n",
              "      <td>33508.067986</td>\n",
              "      <td>4706.621578</td>\n",
              "      <td>4.755770e+03</td>\n",
              "      <td>4108.257308</td>\n",
              "      <td>1964.678115</td>\n",
              "      <td>3857.144789</td>\n",
              "      <td>4197.893001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>122122.704561</td>\n",
              "      <td>62772.004827</td>\n",
              "      <td>60163.927508</td>\n",
              "      <td>57692.497915</td>\n",
              "      <td>52916.045768</td>\n",
              "      <td>48658.725913</td>\n",
              "      <td>47943.662135</td>\n",
              "      <td>13517.607026</td>\n",
              "      <td>1.728985e+04</td>\n",
              "      <td>13240.578080</td>\n",
              "      <td>2172.473322</td>\n",
              "      <td>13158.437628</td>\n",
              "      <td>15458.663057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>-154973.000000</td>\n",
              "      <td>-69777.000000</td>\n",
              "      <td>-157264.000000</td>\n",
              "      <td>-50616.000000</td>\n",
              "      <td>-53007.000000</td>\n",
              "      <td>-339603.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50000.000000</td>\n",
              "      <td>3213.000000</td>\n",
              "      <td>2644.500000</td>\n",
              "      <td>2360.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>1200.000000</td>\n",
              "      <td>938.000000</td>\n",
              "      <td>780.000000</td>\n",
              "      <td>6.640000e+02</td>\n",
              "      <td>335.000000</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>130000.000000</td>\n",
              "      <td>21027.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>19560.000000</td>\n",
              "      <td>18670.000000</td>\n",
              "      <td>15877.000000</td>\n",
              "      <td>15515.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2.000000e+03</td>\n",
              "      <td>1600.000000</td>\n",
              "      <td>1132.000000</td>\n",
              "      <td>1320.000000</td>\n",
              "      <td>1250.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>220000.000000</td>\n",
              "      <td>61935.500000</td>\n",
              "      <td>59834.000000</td>\n",
              "      <td>56277.000000</td>\n",
              "      <td>50496.000000</td>\n",
              "      <td>46676.000000</td>\n",
              "      <td>45997.500000</td>\n",
              "      <td>4573.500000</td>\n",
              "      <td>4.294500e+03</td>\n",
              "      <td>3799.500000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3400.000000</td>\n",
              "      <td>3386.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>800000.000000</td>\n",
              "      <td>746814.000000</td>\n",
              "      <td>581775.000000</td>\n",
              "      <td>855086.000000</td>\n",
              "      <td>572805.000000</td>\n",
              "      <td>823540.000000</td>\n",
              "      <td>501370.000000</td>\n",
              "      <td>493358.000000</td>\n",
              "      <td>1.227082e+06</td>\n",
              "      <td>417588.000000</td>\n",
              "      <td>9665.000000</td>\n",
              "      <td>388071.000000</td>\n",
              "      <td>528666.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Limit_bal       Sep_Bill  ...        Pay_May      Pay_April\n",
              "count   26991.000000   26991.000000  ...   26991.000000   26991.000000\n",
              "mean   155743.384091   46103.224149  ...    3857.144789    4197.893001\n",
              "std    122122.704561   62772.004827  ...   13158.437628   15458.663057\n",
              "min     10000.000000 -154973.000000  ...       0.000000       0.000000\n",
              "25%     50000.000000    3213.000000  ...     144.000000       0.000000\n",
              "50%    130000.000000   21027.000000  ...    1320.000000    1250.000000\n",
              "75%    220000.000000   61935.500000  ...    3400.000000    3386.500000\n",
              "max    800000.000000  746814.000000  ...  388071.000000  528666.000000\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5M3C1mSxrwcW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "999c3d67-64c8-47c1-fc82-7a92e1f86ae6"
      },
      "source": [
        "for i in num.columns:\n",
        "  sns.boxplot(num[i])\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N7KbUev9qZj91uYbiz+2vkfSDOP9HZvb7ZTwWYFEIYHj7ipKfwW+WtFXJozjzjkn62ZA8+esPJT0W619Q8tBtmdn1kppDCIfnaeMOM3tVydX1eklfK+sRAFeIAIarEMJrkjJKrn6HixZfLek5M3td0p9K+nCsPyfpU/Epcp+R9KXLNPNsCOEmST+u5JkIf1CWzgNLRACjGryg5KHazxTVPyvplRDCRyT9kpIHqSiEMC3pgJLnwP6qpC8vpJGQPPjka5J+rjzdBpam8fKrABX3V0p+u8CYmW1L1a/W+x/K3V20zeeVhOm/hBB+sIi2Pq7kqVaAO66A4S6EMBFC2Fti0eckPW5m31TRxUJIflvGu5K+uIAm7ohfQ3tNya98+uxS+wyUA4+jRE0ysw9K+oakG0IIFy+zOlCVuAJGzYk/PHFQycOxCV/ULK6AURfM7NOSdhaV/y2E8Dse/QEWggAGACfcggAAJwQwADghgAHACQEMAE7+H+ldp0zsx2oJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K6z/Sy7chngohnIzB+UMlV8lSciX76FXU+8kQwpsk3STpq2a2qY59BuqCK2C4MbO1Sv5ywhvj823blTzs5pfxa6H86/9WczUbQjhnZr+X9HYlV9NA0+AKGJ4+Lun7IYRNIYTeEMJGJY9mfI+kt8U/tNgm6RNKPqSrmpldp+QvVzxfr04D9cIVMDxtV/IHUQs9Kunzkn6n5HGMWUm/lvRYlXXvMbM5Jc+UfSSEcKjGvgJ1x+Mo0XTin7P5agjhDu++AGniFgQAOOEKGC3LzB5T8udpCn09hPCER3+AahHAAOCEWxAA4IQABgAnBDAAOCGAAcDJ/wFkPZa3m3BRnAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NpP+Ja6j52JWpreZjFx9La/x4gaIweOdM9zeXNU+jtr2S/iRl3Kr6/RBvu1bS3+vV0K75uKXWWakAjYt4l6TvJJZvlHRjBfsb1eTQfkpSW+Ib7qn48Zclvb+4naT3S/pyYv2X43Vtkn6cWF/QrkxNGRUGY8XrKdXHNGobUHrwFMybpO/Ec5s6v/E3zXFJTcVfB7nnxo+b4nZT/cbyz5Iur6exS6mtrsZOUoukH0j63Znuby5rnkZte5Ue2lWdU0nnSXpE0jpJD8xmHio9brmPSp/TXiXpSGL5Z/G6SgmS/sXMDpnZR+J1K0IIP48fPy9pxRS1lVv/s5T1M1WNekr1MR1/bmaHzeyrZrZ0lrUtk/SrEMLplNryz4m3vxi3T2VmGUlvU/TKrK7Grqg2qQ7GzswazewxRae+HlL0Cm+m+5vLmkvWFkLIjdtn43H7nJktLK5tmjWc7Zx+XtIOSWfi5dnMQ0XGrdh8uxD57hDC2yV1S/q4mb0nuTFEP85CTSpLUY16ZtjHFyW9QdLFkn4u6a8qVdd0mFmrpH+UdE0I4aXktlqPXUptdTF2IYSJEMLFil45/o6kN9WijjTFtZnZWxS94nyTpHcoOuVxQ4VrmDSnZnaFpBdCCIcq2fdcqXRoH1V04SbnvHhdRYQQjsafX5B0r6Iv2l+YWZskxZ9fmKK2cuvPS1k/U9Wop1QfZYUQfhF/Y52R9DeKxm82tY1Jep2ZNaXUln9OvH1J3L6AmS1QFIp3hxD+aYrjqurYpdVWT2MX1/MrRRdM3zWL/c1lzeVqWx9C+HmI/J+kv9Psx+1s5vT3JF1pZqOS7lF0imR3mWOqybjlTXX+5Gw+FJ3v+amik/K5E/BvrlBfiyW9JvH4PxVdsb1dhRchbosfb1DhhY5H4/WvV3Q1e2n88Yyk18fbii909EyjrowKzxtXvJ5SfUyjtrbE4+2S7okfv1mFF1h+qujiSsn5lbRPhRdYPhY//rgKL+J8K6Uuk3SXpM8Xra/52JWpreZjJ2m5pNfFj5slfU/SFTPd31zWPI3a2hLj+nlJu2r1/RBvv1SvXois+bil1liJAC0ahB5FV9h/Iqmvgv2sjQcj95aivnj9MkUXGJ6W9HBigk3SX8d1PSGpK7GvqxW9BScr6UOJ9V2Snoyf8wVNfQHtHxT9qnxK0fmqLdWop1Qf06jt63HfhyXdp8Ig6ov7eUqJd82Umt94Ph6Na94naWG8flG8nI23r02p7d2KfoU9rMRb6Oph7MrUVvOxk3SRoresHY6P7ebZ7m+uap5GbQfjcXtS0jf06jtMqvr9kNjHpXo1tGs+bmkf/Bk7ADgy3y5EAsC8RmgDgCOENgA4QmgDgCOENgA4QmgDgCOENmrCzCbM7DEze9LM9plZyxztd8DMgpm1J9ZdE6+r2//iDUwXoY1aeSWEcHEI4S2STkr6sznc9xOK/lIt5ypFf3AFuEdoox58T1K7mf2RmX3fzH5oZg+b2QozazCzp81suSTFy9nccgnflrQxbv8GRXdhO57baGZfNLMRM/uRmQ3G69aZ2bcTbS43s3srcKzAWSG0UVPxzXK6Fb06/ndJ7wwhvE3RjXt2hOgGTN+Q9IH4KZdJejyEcKzMbl+SdCS+i9z7FP1HkKS+EEKXoj+t/gMzu0jRDYzelPhh8CFF/90HqCuENmqlOb638oik/5X0FUV3OfuOmT0h6S8U3YBHisJzc/z4akV3g5vKPYoC+48V3fEx6U/N7AeK7oXxZkX/RSQoun/IJjN7naK74w3P8tiAimmauglQEa+E6N7KeWY2JOmOEMJ9Znapov8GoxDCETP7hZmtU3Trzg8U7yzFA4ru7jYSQnjJzHJ9XCDpeknvCCGcMLO9im4AJEU/DO6X9BtJ+8KrN6cH6gahjXqyRK/eT7i3aNvfKjpN8vUQwsRUOwohvGxmNyi6s1rSayX9WtKLZrZC0amZf4uf85yZPSepX9FpGKDucHoE9WRA0j4zO6TEhcPYfZJaNb1TI5KkEMI9IYQfFK17XNFpkR8r+ieu/1H0tLslHQkh/PfMSgeqg1uzwoX4PdafCyH8foX7+YKkH4YQvlLJfoDZ4vQI6p6ZfVLSRzW9c9ln088hRadOrqtkP8DZ4JU2XDKzPkV/NJO0L4Tw2VrUA1QLoQ0AjnAhEgAcIbQBwBFCGwAcIbQBwJH/BzqK2mAwtCANAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "tvIwgmLIrwZz",
        "outputId": "0f328e7e-a659-4f8e-d20d-d40718468602"
      },
      "source": [
        "sns.pointplot(df['Repayment_April'],df['Defaulter'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe489521ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCbqSLourwXX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FdAYZZORrwU1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tr982QnOrwSM"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xG3cc-kyrwP7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHN2pBcNrwMq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "arh38JE76Rs4"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXYchiH1ZVXr"
      },
      "source": [
        "X =new_df.drop(columns=['Defaulter'])\n",
        "y =new_df['Defaulter']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwsa9t3M5u-B"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLS08y17WYuV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c94def9-a4dd-41fc-a306-7b34e37d20c6"
      },
      "source": [
        "#importing SMote to make our dataset balanced\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "smote = SMOTE()\n",
        "\n",
        "# fit predictor and target variable\n",
        "X_smote, y_smote = smote.fit_resample(X,y)\n",
        "\n",
        "print('Original dataset shape', len(df))\n",
        "print('Resampled dataset shape', len(y_smote))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/externals/six.py:31: FutureWarning: 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/).\", FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original dataset shape 29965\n",
            "Resampled dataset shape 46670\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWt-5Ov4y9qC"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ycJRfKj0wmS4"
      },
      "source": [
        "#Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.fit_transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wc4J7clRwmKx"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "# from sklearn.tree import DecisionTreeClassifier\n",
        "# from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,GradientBoostingClassifier\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "def fit_and_score(x_train,x_test,y_train,y_test,X,y):\n",
        "  log = LogisticRegression(random_state=0)\n",
        "  knn =KNeighborsClassifier(n_neighbors = 5,metric='minkowski',p=2)\n",
        "  svc = SVC(kernel='linear',random_state=0)\n",
        "  svc_rbf = SVC(kernel='rbf',random_state=0)\n",
        "  gauss = GaussianNB()\n",
        "  # tree = DecisionTreeClassifier(max_depth=10)\n",
        "  # forest = RandomForestClassifier(n_estimators=500,random_state=0)\n",
        "  # ada_boost = AdaBoostClassifier(base_estimator=forest)\n",
        "  # gradient =GradientBoostingClassifier()\n",
        "  xgboost = XGBClassifier()\n",
        "  model = [log,knn,svc,svc_rbf,gauss,xgboost]\n",
        "  train_score = []\n",
        "  test_score = []\n",
        "  Precision_score = []\n",
        "  Recall_score = []\n",
        "  F1_score = []\n",
        "\n",
        "\n",
        "  for i in model:\n",
        "    i.fit(x_train,y_train)\n",
        "    train_score.append(i.score(x_train,y_train))\n",
        "    pred = i.predict(x_test)\n",
        "    test_score.append(accuracy_score(y_test,pred))\n",
        "    Precision_score.append(precision_score(y_test,pred),\n",
        "    Recall_score.append(recall_score(y_test,pred)),\n",
        "    F1_score.append(f1_score(y_test,pred)),\n",
        "\n",
        "  # cv_score=[]\n",
        "  # for i in model:\n",
        "  #   cv =np.mean(cross_val_score(i,X,y,cv=10,scoring='accuracy'))\n",
        "  #   cv_score.append(cv)\n",
        "    \n",
        "  \n",
        "\n",
        "  scores= pd.DataFrame(list(zip(train_score,test_score)),index=['Logistic','KNeighbors','SVC Linear','SVC rbf','GaussianNB','Xg boost'],columns=['Train_score','Test_score'])\n",
        "  return scores\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K2rMqdnjwmHo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "5e753be0-11a2-469a-b27f-ddbd7d7dd6ab"
      },
      "source": [
        "fit_and_score(X_train,X_test,y_train,y_test,X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Train_score</th>\n",
              "      <th>Test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Logistic</th>\n",
              "      <td>0.818705</td>\n",
              "      <td>0.820290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighbors</th>\n",
              "      <td>0.841941</td>\n",
              "      <td>0.786918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVC Linear</th>\n",
              "      <td>0.818246</td>\n",
              "      <td>0.822126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVC rbf</th>\n",
              "      <td>0.827632</td>\n",
              "      <td>0.820290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.762056</td>\n",
              "      <td>0.757384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Xg boost</th>\n",
              "      <td>0.825213</td>\n",
              "      <td>0.820457</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Train_score  Test_score\n",
              "Logistic       0.818705    0.820290\n",
              "KNeighbors     0.841941    0.786918\n",
              "SVC Linear     0.818246    0.822126\n",
              "SVC rbf        0.827632    0.820290\n",
              "GaussianNB     0.762056    0.757384\n",
              "Xg boost       0.825213    0.820457"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrAKw-Brbg_-"
      },
      "source": [
        "lr=LogisticRegression().fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwMmSE3mwoIE"
      },
      "source": [
        "pr = lr.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WY_Ozb-7wsbL"
      },
      "source": [
        "accuracy_score(y_test,pr)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VGGVzWXrwxfb"
      },
      "source": [
        "round(precision_score(y_test,pr),4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOpkUBp902SN"
      },
      "source": [
        "a=[]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3jZcsWX9w0ij"
      },
      "source": [
        "a.append(recall_score(y_test,pr))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hkDr1MbF0xX5"
      },
      "source": [
        "a"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_wx0-OO070A"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}