notebooks/credit_risk_modeling.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "-BcYCUGzESRJ"
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      "source": [
        "# Credit Risk Segmentation Project\n",
        "\n",
        "### Problem Statement:\n",
        "\n",
        "The main objective of this project was to segment credit card users based on their risk levels. It aimed to develop a model that accurately predicts the risk level associated with a customer, which can help the bank make informed decisions regarding credit limit, interest rates, and other credit-related policies.\n",
        "\n",
        "### Project Process:\n",
        "\n",
        "The project was executed using Python, with the following steps:\n",
        "\n",
        "1. **Data Collection:** Gathered data from two sources; the bank's internal systems and national CIBIL data.\n",
        "2. **Data Preprocessing:** The data was cleaned by handling missing values and removing irrelevant columns, and merged information from internal bank records and external credit reports using the 'prospect id' to ensure a complete dataset. \n",
        "3. **Feature Engineering:** Created new features to provide the model with more insightful indicators, improving its ability to predict customer risk levels.\n",
        "4. **Feature Selection:** Used the statistical techniques, **Chi-squared test** and **ANOVA test** to reduce the number of redundant features and minimize multicollinearity, making the model simpler and more efficient.\n",
        "5. **Model Training:** Trained a multi-class classification model using **XGBoost Classifier**.\n",
        "6. **Model Evaluation:** The model achieved an overall accuracy of **78.01%** accuracy and an F1 score of 0.76.\n",
        "7. **Hyperparameter Tuning:** Used **Grid Search CV** to fine-tune the model parameters, achieving a testing accuracy of 78.01% from 77.83%.\n",
        "\n",
        "### Conclusion\n",
        "\n",
        " This project demonstrated the effectiveness of using machine learning techniques in credit risk segmentation to mitigate credit risk by categorizing customers based on their risk levels and optimize decision-making for financial strategies such as credit and lending policies in the banking sector. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now, let's dive into the project!\n",
        "\n",
        "### Importing Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "jXIg4vlUpl5C"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.stats import chi2_contingency\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import accuracy_score, classification_report, precision_recall_fscore_support\n",
        "import warnings\n",
        "import os\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "3fyyVl80qOdz"
      },
      "outputs": [],
      "source": [
        "df1 = pd.read_excel(\"/content/cibil_data.xlsx\")\n",
        "df2 = pd.read_excel(\"/content/bank_data.xlsx\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bABDHOSKqcs_",
        "outputId": "a0bd34d9-8214-48aa-e197-dec35fa9849e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(51336, 26)\n",
            "(51336, 62)\n"
          ]
        }
      ],
      "source": [
        "print(df1.shape)\n",
        "print(df2.shape)"
      ]
    },
    {
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      "execution_count": 5,
      "metadata": {
        "colab": {
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          "height": 255
        },
        "id": "acRumJ_KqzHR",
        "outputId": "5ac11380-5f41-4404-9034-151b04755abc"
      },
      "outputs": [
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              "      <th>pct_active_tl</th>\n",
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            "text/plain": [
              "   PROSPECTID  Total_TL  Tot_Closed_TL  Tot_Active_TL  Total_TL_opened_L6M  \\\n",
              "0           1         5              4              1                    0   \n",
              "1           2         1              0              1                    0   \n",
              "2           3         8              0              8                    1   \n",
              "3           4         1              0              1                    1   \n",
              "4           5         3              2              1                    0   \n",
              "\n",
              "   Tot_TL_closed_L6M  pct_tl_open_L6M  pct_tl_closed_L6M  pct_active_tl  \\\n",
              "0                  0            0.000                0.0          0.200   \n",
              "1                  0            0.000                0.0          1.000   \n",
              "2                  0            0.125                0.0          1.000   \n",
              "3                  0            1.000                0.0          1.000   \n",
              "4                  0            0.000                0.0          0.333   \n",
              "\n",
              "   pct_closed_tl  ...  CC_TL  Consumer_TL  Gold_TL  Home_TL  PL_TL  \\\n",
              "0          0.800  ...      0            0        1        0      4   \n",
              "1          0.000  ...      0            1        0        0      0   \n",
              "2          0.000  ...      0            6        1        0      0   \n",
              "3          0.000  ...      0            0        0        0      0   \n",
              "4          0.667  ...      0            0        0        0      0   \n",
              "\n",
              "   Secured_TL  Unsecured_TL  Other_TL  Age_Oldest_TL  Age_Newest_TL  \n",
              "0           1             4         0             72             18  \n",
              "1           0             1         0              7              7  \n",
              "2           2             6         0             47              2  \n",
              "3           0             1         1              5              5  \n",
              "4           3             0         2            131             32  \n",
              "\n",
              "[5 rows x 26 columns]"
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          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
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      "source": [
        "df1.head()"
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    },
    {
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      "execution_count": 6,
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        "id": "-eDh4MIGq3vJ",
        "outputId": "34e43939-55b0-4178-8e98-e9109e00811c"
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          "name": "stdout",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 51336 entries, 0 to 51335\n",
            "Data columns (total 26 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   PROSPECTID            51336 non-null  int64  \n",
            " 1   Total_TL              51336 non-null  int64  \n",
            " 2   Tot_Closed_TL         51336 non-null  int64  \n",
            " 3   Tot_Active_TL         51336 non-null  int64  \n",
            " 4   Total_TL_opened_L6M   51336 non-null  int64  \n",
            " 5   Tot_TL_closed_L6M     51336 non-null  int64  \n",
            " 6   pct_tl_open_L6M       51336 non-null  float64\n",
            " 7   pct_tl_closed_L6M     51336 non-null  float64\n",
            " 8   pct_active_tl         51336 non-null  float64\n",
            " 9   pct_closed_tl         51336 non-null  float64\n",
            " 10  Total_TL_opened_L12M  51336 non-null  int64  \n",
            " 11  Tot_TL_closed_L12M    51336 non-null  int64  \n",
            " 12  pct_tl_open_L12M      51336 non-null  float64\n",
            " 13  pct_tl_closed_L12M    51336 non-null  float64\n",
            " 14  Tot_Missed_Pmnt       51336 non-null  int64  \n",
            " 15  Auto_TL               51336 non-null  int64  \n",
            " 16  CC_TL                 51336 non-null  int64  \n",
            " 17  Consumer_TL           51336 non-null  int64  \n",
            " 18  Gold_TL               51336 non-null  int64  \n",
            " 19  Home_TL               51336 non-null  int64  \n",
            " 20  PL_TL                 51336 non-null  int64  \n",
            " 21  Secured_TL            51336 non-null  int64  \n",
            " 22  Unsecured_TL          51336 non-null  int64  \n",
            " 23  Other_TL              51336 non-null  int64  \n",
            " 24  Age_Oldest_TL         51336 non-null  int64  \n",
            " 25  Age_Newest_TL         51336 non-null  int64  \n",
            "dtypes: float64(6), int64(20)\n",
            "memory usage: 10.2 MB\n"
          ]
        }
      ],
      "source": [
        "df1.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
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          "height": 255
        },
        "id": "1RRH9IBCq7bD",
        "outputId": "d8f711e3-626a-4358-e970-a45aa4ef5057"
      },
      "outputs": [
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              "      <th>PROSPECTID</th>\n",
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              "      <th>...</th>\n",
              "      <th>pct_CC_enq_L6m_of_L12m</th>\n",
              "      <th>pct_PL_enq_L6m_of_ever</th>\n",
              "      <th>pct_CC_enq_L6m_of_ever</th>\n",
              "      <th>max_unsec_exposure_inPct</th>\n",
              "      <th>HL_Flag</th>\n",
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              "      <th>last_prod_enq2</th>\n",
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              "      <td>0</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>ConsumerLoan</td>\n",
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              "      <td>5741.667</td>\n",
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              "      <td>0</td>\n",
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              "  </tbody>\n",
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              "<p>5 rows × 62 columns</p>\n",
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              "\n",
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            ],
            "text/plain": [
              "   PROSPECTID  time_since_recent_payment  time_since_first_deliquency  \\\n",
              "0           1                        549                           35   \n",
              "1           2                         47                       -99999   \n",
              "2           3                        302                           11   \n",
              "3           4                     -99999                       -99999   \n",
              "4           5                        583                       -99999   \n",
              "\n",
              "   time_since_recent_deliquency  num_times_delinquent  max_delinquency_level  \\\n",
              "0                            15                    11                     29   \n",
              "1                        -99999                     0                 -99999   \n",
              "2                             3                     9                     25   \n",
              "3                        -99999                     0                 -99999   \n",
              "4                        -99999                     0                 -99999   \n",
              "\n",
              "   max_recent_level_of_deliq  num_deliq_6mts  num_deliq_12mts  \\\n",
              "0                         29               0                0   \n",
              "1                          0               0                0   \n",
              "2                         25               1                9   \n",
              "3                          0               0                0   \n",
              "4                          0               0                0   \n",
              "\n",
              "   num_deliq_6_12mts  ...  pct_CC_enq_L6m_of_L12m  pct_PL_enq_L6m_of_ever  \\\n",
              "0                  0  ...                     0.0                     0.0   \n",
              "1                  0  ...                     0.0                     0.0   \n",
              "2                  8  ...                     0.0                     0.0   \n",
              "3                  0  ...                     0.0                     0.0   \n",
              "4                  0  ...                     0.0                     0.0   \n",
              "\n",
              "   pct_CC_enq_L6m_of_ever  max_unsec_exposure_inPct  HL_Flag  GL_Flag  \\\n",
              "0                     0.0                    13.333        1        0   \n",
              "1                     0.0                     0.860        0        0   \n",
              "2                     0.0                  5741.667        1        0   \n",
              "3                     0.0                     9.900        0        0   \n",
              "4                     0.0                -99999.000        0        0   \n",
              "\n",
              "   last_prod_enq2  first_prod_enq2  Credit_Score  Approved_Flag  \n",
              "0              PL               PL           696             P2  \n",
              "1    ConsumerLoan     ConsumerLoan           685             P2  \n",
              "2    ConsumerLoan           others           693             P2  \n",
              "3          others           others           673             P2  \n",
              "4              AL               AL           753             P1  \n",
              "\n",
              "[5 rows x 62 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwtM7HK4rBv2",
        "outputId": "224a2f91-afcb-4d4c-9c6b-4702057ec4c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 51336 entries, 0 to 51335\n",
            "Data columns (total 62 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   PROSPECTID                    51336 non-null  int64  \n",
            " 1   time_since_recent_payment     51336 non-null  int64  \n",
            " 2   time_since_first_deliquency   51336 non-null  int64  \n",
            " 3   time_since_recent_deliquency  51336 non-null  int64  \n",
            " 4   num_times_delinquent          51336 non-null  int64  \n",
            " 5   max_delinquency_level         51336 non-null  int64  \n",
            " 6   max_recent_level_of_deliq     51336 non-null  int64  \n",
            " 7   num_deliq_6mts                51336 non-null  int64  \n",
            " 8   num_deliq_12mts               51336 non-null  int64  \n",
            " 9   num_deliq_6_12mts             51336 non-null  int64  \n",
            " 10  max_deliq_6mts                51336 non-null  int64  \n",
            " 11  max_deliq_12mts               51336 non-null  int64  \n",
            " 12  num_times_30p_dpd             51336 non-null  int64  \n",
            " 13  num_times_60p_dpd             51336 non-null  int64  \n",
            " 14  num_std                       51336 non-null  int64  \n",
            " 15  num_std_6mts                  51336 non-null  int64  \n",
            " 16  num_std_12mts                 51336 non-null  int64  \n",
            " 17  num_sub                       51336 non-null  int64  \n",
            " 18  num_sub_6mts                  51336 non-null  int64  \n",
            " 19  num_sub_12mts                 51336 non-null  int64  \n",
            " 20  num_dbt                       51336 non-null  int64  \n",
            " 21  num_dbt_6mts                  51336 non-null  int64  \n",
            " 22  num_dbt_12mts                 51336 non-null  int64  \n",
            " 23  num_lss                       51336 non-null  int64  \n",
            " 24  num_lss_6mts                  51336 non-null  int64  \n",
            " 25  num_lss_12mts                 51336 non-null  int64  \n",
            " 26  recent_level_of_deliq         51336 non-null  int64  \n",
            " 27  tot_enq                       51336 non-null  int64  \n",
            " 28  CC_enq                        51336 non-null  int64  \n",
            " 29  CC_enq_L6m                    51336 non-null  int64  \n",
            " 30  CC_enq_L12m                   51336 non-null  int64  \n",
            " 31  PL_enq                        51336 non-null  int64  \n",
            " 32  PL_enq_L6m                    51336 non-null  int64  \n",
            " 33  PL_enq_L12m                   51336 non-null  int64  \n",
            " 34  time_since_recent_enq         51336 non-null  int64  \n",
            " 35  enq_L12m                      51336 non-null  int64  \n",
            " 36  enq_L6m                       51336 non-null  int64  \n",
            " 37  enq_L3m                       51336 non-null  int64  \n",
            " 38  MARITALSTATUS                 51336 non-null  object \n",
            " 39  EDUCATION                     51336 non-null  object \n",
            " 40  AGE                           51336 non-null  int64  \n",
            " 41  GENDER                        51336 non-null  object \n",
            " 42  NETMONTHLYINCOME              51336 non-null  int64  \n",
            " 43  Time_With_Curr_Empr           51336 non-null  int64  \n",
            " 44  pct_of_active_TLs_ever        51336 non-null  float64\n",
            " 45  pct_opened_TLs_L6m_of_L12m    51336 non-null  float64\n",
            " 46  pct_currentBal_all_TL         51336 non-null  float64\n",
            " 47  CC_utilization                51336 non-null  float64\n",
            " 48  CC_Flag                       51336 non-null  int64  \n",
            " 49  PL_utilization                51336 non-null  float64\n",
            " 50  PL_Flag                       51336 non-null  int64  \n",
            " 51  pct_PL_enq_L6m_of_L12m        51336 non-null  float64\n",
            " 52  pct_CC_enq_L6m_of_L12m        51336 non-null  float64\n",
            " 53  pct_PL_enq_L6m_of_ever        51336 non-null  float64\n",
            " 54  pct_CC_enq_L6m_of_ever        51336 non-null  float64\n",
            " 55  max_unsec_exposure_inPct      51336 non-null  float64\n",
            " 56  HL_Flag                       51336 non-null  int64  \n",
            " 57  GL_Flag                       51336 non-null  int64  \n",
            " 58  last_prod_enq2                51336 non-null  object \n",
            " 59  first_prod_enq2               51336 non-null  object \n",
            " 60  Credit_Score                  51336 non-null  int64  \n",
            " 61  Approved_Flag                 51336 non-null  object \n",
            "dtypes: float64(10), int64(46), object(6)\n",
            "memory usage: 24.3+ MB\n"
          ]
        }
      ],
      "source": [
        "df2.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMdWK4dWreXi"
      },
      "source": [
        "### Removing Nulls Rows and Columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "OJ3xExBgrMt8"
      },
      "outputs": [],
      "source": [
        "df1 = df1.loc[df1['Age_Oldest_TL'] != -99999]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWHrwLzPrlkO",
        "outputId": "51e6f25d-0646-41d8-bdfd-fe73b976026a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(51296, 26)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "vyOFj2tfrnA9"
      },
      "outputs": [],
      "source": [
        "# Initialize an empty list to store column names for removal\n",
        "columns_to_be_removed = []\n",
        "\n",
        "# Check each column in the DataFrame\n",
        "for i in df2.columns:\n",
        "    # If the count of occurrences of -99999 in the column is greater than 10000\n",
        "    if df2.loc[df2[i] == -99999].shape[0] > 10000:\n",
        "        # Add the column name to the list of columns to be removed\n",
        "        columns_to_be_removed.append(i)\n",
        "\n",
        "# Drop the identified columns from the DataFrame\n",
        "df2 = df2.drop(columns_to_be_removed, axis=1)\n",
        "\n",
        "# Filter out rows where any column value is equal to -99999\n",
        "for i in df2.columns:\n",
        "    df2 = df2.loc[df2[i] != -99999]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gb_56zu1sIax",
        "outputId": "f6dc2db9-067c-40f5-e4e9-ae4f552b7bb1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(42066, 54)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df2.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcRQtqAnsUuD"
      },
      "source": [
        "### Merging both the table"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WUlB1jhKsRQ5",
        "outputId": "50caf3bf-85ea-4c83-e3ab-70adf35fc62a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "PROSPECTID\n"
          ]
        }
      ],
      "source": [
        "# Checking common column names\n",
        "for i in list(df1.columns):\n",
        "    if i in list(df2.columns):\n",
        "        print (i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "xD5zqWjRsT0h"
      },
      "outputs": [],
      "source": [
        "# Merge the two dataframes, inner join so that no nulls are present\n",
        "df = pd.merge(df1, df2, how ='inner', left_on = ['PROSPECTID'], right_on = ['PROSPECTID'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "6DD22mSNsg5y",
        "outputId": "1f457217-b7f2-41a1-ab51-3e400be2eda5"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-0e390833-644f-4c98-afbc-503865a0f2a9\" class=\"colab-df-container\">\n",
              "    <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>PROSPECTID</th>\n",
              "      <th>Total_TL</th>\n",
              "      <th>Tot_Closed_TL</th>\n",
              "      <th>Tot_Active_TL</th>\n",
              "      <th>Total_TL_opened_L6M</th>\n",
              "      <th>Tot_TL_closed_L6M</th>\n",
              "      <th>pct_tl_open_L6M</th>\n",
              "      <th>pct_tl_closed_L6M</th>\n",
              "      <th>pct_active_tl</th>\n",
              "      <th>pct_closed_tl</th>\n",
              "      <th>...</th>\n",
              "      <th>pct_PL_enq_L6m_of_L12m</th>\n",
              "      <th>pct_CC_enq_L6m_of_L12m</th>\n",
              "      <th>pct_PL_enq_L6m_of_ever</th>\n",
              "      <th>pct_CC_enq_L6m_of_ever</th>\n",
              "      <th>HL_Flag</th>\n",
              "      <th>GL_Flag</th>\n",
              "      <th>last_prod_enq2</th>\n",
              "      <th>first_prod_enq2</th>\n",
              "      <th>Credit_Score</th>\n",
              "      <th>Approved_Flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <td>4</td>\n",
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              "      <td>0.000</td>\n",
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              "      <td>0.200</td>\n",
              "      <td>0.800</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PL</td>\n",
              "      <td>PL</td>\n",
              "      <td>696</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>685</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>others</td>\n",
              "      <td>693</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.667</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>AL</td>\n",
              "      <td>AL</td>\n",
              "      <td>753</td>\n",
              "      <td>P1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.167</td>\n",
              "      <td>0.833</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.429</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>PL</td>\n",
              "      <td>668</td>\n",
              "      <td>P3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>42059</th>\n",
              "      <td>51332</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>650</td>\n",
              "      <td>P4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42060</th>\n",
              "      <td>51333</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.500</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>others</td>\n",
              "      <td>others</td>\n",
              "      <td>702</td>\n",
              "      <td>P1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42061</th>\n",
              "      <td>51334</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.500</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>others</td>\n",
              "      <td>661</td>\n",
              "      <td>P3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42062</th>\n",
              "      <td>51335</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.500</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>others</td>\n",
              "      <td>686</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42063</th>\n",
              "      <td>51336</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>others</td>\n",
              "      <td>others</td>\n",
              "      <td>681</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>42064 rows × 79 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e390833-644f-4c98-afbc-503865a0f2a9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0e390833-644f-4c98-afbc-503865a0f2a9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0e390833-644f-4c98-afbc-503865a0f2a9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-14c348ae-5c38-4b91-b401-76b5d3bd43c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a4de13f9-7012-4da5-9d9e-fbbff0d99842\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a4de13f9-7012-4da5-9d9e-fbbff0d99842 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       PROSPECTID  Total_TL  Tot_Closed_TL  Tot_Active_TL  \\\n",
              "0               1         5              4              1   \n",
              "1               2         1              0              1   \n",
              "2               3         8              0              8   \n",
              "3               5         3              2              1   \n",
              "4               6         6              5              1   \n",
              "...           ...       ...            ...            ...   \n",
              "42059       51332         3              0              3   \n",
              "42060       51333         4              2              2   \n",
              "42061       51334         2              1              1   \n",
              "42062       51335         2              1              1   \n",
              "42063       51336         1              0              1   \n",
              "\n",
              "       Total_TL_opened_L6M  Tot_TL_closed_L6M  pct_tl_open_L6M  \\\n",
              "0                        0                  0            0.000   \n",
              "1                        0                  0            0.000   \n",
              "2                        1                  0            0.125   \n",
              "3                        0                  0            0.000   \n",
              "4                        0                  0            0.000   \n",
              "...                    ...                ...              ...   \n",
              "42059                    1                  0            0.333   \n",
              "42060                    0                  1            0.000   \n",
              "42061                    1                  1            0.500   \n",
              "42062                    0                  0            0.000   \n",
              "42063                    0                  0            0.000   \n",
              "\n",
              "       pct_tl_closed_L6M  pct_active_tl  pct_closed_tl  ...  \\\n",
              "0                   0.00          0.200          0.800  ...   \n",
              "1                   0.00          1.000          0.000  ...   \n",
              "2                   0.00          1.000          0.000  ...   \n",
              "3                   0.00          0.333          0.667  ...   \n",
              "4                   0.00          0.167          0.833  ...   \n",
              "...                  ...            ...            ...  ...   \n",
              "42059               0.00          1.000          0.000  ...   \n",
              "42060               0.25          0.500          0.500  ...   \n",
              "42061               0.50          0.500          0.500  ...   \n",
              "42062               0.00          0.500          0.500  ...   \n",
              "42063               0.00          1.000          0.000  ...   \n",
              "\n",
              "       pct_PL_enq_L6m_of_L12m  pct_CC_enq_L6m_of_L12m  pct_PL_enq_L6m_of_ever  \\\n",
              "0                         0.0                     0.0                   0.000   \n",
              "1                         0.0                     0.0                   0.000   \n",
              "2                         0.0                     0.0                   0.000   \n",
              "3                         0.0                     0.0                   0.000   \n",
              "4                         1.0                     0.0                   0.429   \n",
              "...                       ...                     ...                     ...   \n",
              "42059                     0.0                     0.0                   0.000   \n",
              "42060                     0.0                     0.0                   0.000   \n",
              "42061                     1.0                     0.0                   1.000   \n",
              "42062                     0.0                     0.0                   0.000   \n",
              "42063                     0.0                     0.0                   0.000   \n",
              "\n",
              "       pct_CC_enq_L6m_of_ever  HL_Flag  GL_Flag  last_prod_enq2  \\\n",
              "0                         0.0        1        0              PL   \n",
              "1                         0.0        0        0    ConsumerLoan   \n",
              "2                         0.0        1        0    ConsumerLoan   \n",
              "3                         0.0        0        0              AL   \n",
              "4                         0.0        1        0    ConsumerLoan   \n",
              "...                       ...      ...      ...             ...   \n",
              "42059                     0.0        0        0    ConsumerLoan   \n",
              "42060                     0.0        0        0          others   \n",
              "42061                     0.0        0        0    ConsumerLoan   \n",
              "42062                     0.0        0        0    ConsumerLoan   \n",
              "42063                     0.0        0        0          others   \n",
              "\n",
              "       first_prod_enq2  Credit_Score  Approved_Flag  \n",
              "0                   PL           696             P2  \n",
              "1         ConsumerLoan           685             P2  \n",
              "2               others           693             P2  \n",
              "3                   AL           753             P1  \n",
              "4                   PL           668             P3  \n",
              "...                ...           ...            ...  \n",
              "42059     ConsumerLoan           650             P4  \n",
              "42060           others           702             P1  \n",
              "42061           others           661             P3  \n",
              "42062           others           686             P2  \n",
              "42063           others           681             P2  \n",
              "\n",
              "[42064 rows x 79 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bwgoVAa1sjni",
        "outputId": "fe0ade4e-4a98-42d5-d0e1-d55e046f8a95"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 42064 entries, 0 to 42063\n",
            "Data columns (total 79 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   PROSPECTID                  42064 non-null  int64  \n",
            " 1   Total_TL                    42064 non-null  int64  \n",
            " 2   Tot_Closed_TL               42064 non-null  int64  \n",
            " 3   Tot_Active_TL               42064 non-null  int64  \n",
            " 4   Total_TL_opened_L6M         42064 non-null  int64  \n",
            " 5   Tot_TL_closed_L6M           42064 non-null  int64  \n",
            " 6   pct_tl_open_L6M             42064 non-null  float64\n",
            " 7   pct_tl_closed_L6M           42064 non-null  float64\n",
            " 8   pct_active_tl               42064 non-null  float64\n",
            " 9   pct_closed_tl               42064 non-null  float64\n",
            " 10  Total_TL_opened_L12M        42064 non-null  int64  \n",
            " 11  Tot_TL_closed_L12M          42064 non-null  int64  \n",
            " 12  pct_tl_open_L12M            42064 non-null  float64\n",
            " 13  pct_tl_closed_L12M          42064 non-null  float64\n",
            " 14  Tot_Missed_Pmnt             42064 non-null  int64  \n",
            " 15  Auto_TL                     42064 non-null  int64  \n",
            " 16  CC_TL                       42064 non-null  int64  \n",
            " 17  Consumer_TL                 42064 non-null  int64  \n",
            " 18  Gold_TL                     42064 non-null  int64  \n",
            " 19  Home_TL                     42064 non-null  int64  \n",
            " 20  PL_TL                       42064 non-null  int64  \n",
            " 21  Secured_TL                  42064 non-null  int64  \n",
            " 22  Unsecured_TL                42064 non-null  int64  \n",
            " 23  Other_TL                    42064 non-null  int64  \n",
            " 24  Age_Oldest_TL               42064 non-null  int64  \n",
            " 25  Age_Newest_TL               42064 non-null  int64  \n",
            " 26  time_since_recent_payment   42064 non-null  int64  \n",
            " 27  num_times_delinquent        42064 non-null  int64  \n",
            " 28  max_recent_level_of_deliq   42064 non-null  int64  \n",
            " 29  num_deliq_6mts              42064 non-null  int64  \n",
            " 30  num_deliq_12mts             42064 non-null  int64  \n",
            " 31  num_deliq_6_12mts           42064 non-null  int64  \n",
            " 32  num_times_30p_dpd           42064 non-null  int64  \n",
            " 33  num_times_60p_dpd           42064 non-null  int64  \n",
            " 34  num_std                     42064 non-null  int64  \n",
            " 35  num_std_6mts                42064 non-null  int64  \n",
            " 36  num_std_12mts               42064 non-null  int64  \n",
            " 37  num_sub                     42064 non-null  int64  \n",
            " 38  num_sub_6mts                42064 non-null  int64  \n",
            " 39  num_sub_12mts               42064 non-null  int64  \n",
            " 40  num_dbt                     42064 non-null  int64  \n",
            " 41  num_dbt_6mts                42064 non-null  int64  \n",
            " 42  num_dbt_12mts               42064 non-null  int64  \n",
            " 43  num_lss                     42064 non-null  int64  \n",
            " 44  num_lss_6mts                42064 non-null  int64  \n",
            " 45  num_lss_12mts               42064 non-null  int64  \n",
            " 46  recent_level_of_deliq       42064 non-null  int64  \n",
            " 47  tot_enq                     42064 non-null  int64  \n",
            " 48  CC_enq                      42064 non-null  int64  \n",
            " 49  CC_enq_L6m                  42064 non-null  int64  \n",
            " 50  CC_enq_L12m                 42064 non-null  int64  \n",
            " 51  PL_enq                      42064 non-null  int64  \n",
            " 52  PL_enq_L6m                  42064 non-null  int64  \n",
            " 53  PL_enq_L12m                 42064 non-null  int64  \n",
            " 54  time_since_recent_enq       42064 non-null  int64  \n",
            " 55  enq_L12m                    42064 non-null  int64  \n",
            " 56  enq_L6m                     42064 non-null  int64  \n",
            " 57  enq_L3m                     42064 non-null  int64  \n",
            " 58  MARITALSTATUS               42064 non-null  object \n",
            " 59  EDUCATION                   42064 non-null  object \n",
            " 60  AGE                         42064 non-null  int64  \n",
            " 61  GENDER                      42064 non-null  object \n",
            " 62  NETMONTHLYINCOME            42064 non-null  int64  \n",
            " 63  Time_With_Curr_Empr         42064 non-null  int64  \n",
            " 64  pct_of_active_TLs_ever      42064 non-null  float64\n",
            " 65  pct_opened_TLs_L6m_of_L12m  42064 non-null  float64\n",
            " 66  pct_currentBal_all_TL       42064 non-null  float64\n",
            " 67  CC_Flag                     42064 non-null  int64  \n",
            " 68  PL_Flag                     42064 non-null  int64  \n",
            " 69  pct_PL_enq_L6m_of_L12m      42064 non-null  float64\n",
            " 70  pct_CC_enq_L6m_of_L12m      42064 non-null  float64\n",
            " 71  pct_PL_enq_L6m_of_ever      42064 non-null  float64\n",
            " 72  pct_CC_enq_L6m_of_ever      42064 non-null  float64\n",
            " 73  HL_Flag                     42064 non-null  int64  \n",
            " 74  GL_Flag                     42064 non-null  int64  \n",
            " 75  last_prod_enq2              42064 non-null  object \n",
            " 76  first_prod_enq2             42064 non-null  object \n",
            " 77  Credit_Score                42064 non-null  int64  \n",
            " 78  Approved_Flag               42064 non-null  object \n",
            "dtypes: float64(13), int64(60), object(6)\n",
            "memory usage: 25.4+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8i_zVbvsnoG"
      },
      "source": [
        "We can afford the loss of 9000 rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "mEQPGNIEsk-P",
        "outputId": "1046d669-0883-4fca-8c2c-d776ec6f73a5"
      },
      "outputs": [
        {
          "data": {
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              "      <th>count</th>\n",
              "      <th>mean</th>\n",
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              "      <th>min</th>\n",
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              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PROSPECTID</th>\n",
              "      <td>42064.0</td>\n",
              "      <td>25649.827477</td>\n",
              "      <td>14844.173396</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12776.75</td>\n",
              "      <td>25706.5</td>\n",
              "      <td>38518.25</td>\n",
              "      <td>51336.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_TL</th>\n",
              "      <td>42064.0</td>\n",
              "      <td>5.262980</td>\n",
              "      <td>7.463383</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>235.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tot_Closed_TL</th>\n",
              "      <td>42064.0</td>\n",
              "      <td>2.967383</td>\n",
              "      <td>6.141098</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>216.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tot_Active_TL</th>\n",
              "      <td>42064.0</td>\n",
              "      <td>2.295597</td>\n",
              "      <td>2.404086</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_TL_opened_L6M</th>\n",
              "      <td>42064.0</td>\n",
              "      <td>0.812643</td>\n",
              "      <td>1.383559</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pct_PL_enq_L6m_of_ever</th>\n",
              "      <td>42064.0</td>\n",
              "      <td>0.195497</td>\n",
              "      <td>0.367414</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pct_CC_enq_L6m_of_ever</th>\n",
              "      <td>42064.0</td>\n",
              "      <td>0.064186</td>\n",
              "      <td>0.225989</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HL_Flag</th>\n",
              "      <td>42064.0</td>\n",
              "      <td>0.252235</td>\n",
              "      <td>0.434300</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GL_Flag</th>\n",
              "      <td>42064.0</td>\n",
              "      <td>0.056580</td>\n",
              "      <td>0.231042</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Credit_Score</th>\n",
              "      <td>42064.0</td>\n",
              "      <td>679.326336</td>\n",
              "      <td>21.133619</td>\n",
              "      <td>469.0</td>\n",
              "      <td>668.00</td>\n",
              "      <td>679.0</td>\n",
              "      <td>690.00</td>\n",
              "      <td>809.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>73 rows × 8 columns</p>\n",
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              "\n",
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              "\n",
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              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
            "text/plain": [
              "                          count          mean           std    min       25%  \\\n",
              "PROSPECTID              42064.0  25649.827477  14844.173396    1.0  12776.75   \n",
              "Total_TL                42064.0      5.262980      7.463383    1.0      1.00   \n",
              "Tot_Closed_TL           42064.0      2.967383      6.141098    0.0      0.00   \n",
              "Tot_Active_TL           42064.0      2.295597      2.404086    0.0      1.00   \n",
              "Total_TL_opened_L6M     42064.0      0.812643      1.383559    0.0      0.00   \n",
              "...                         ...           ...           ...    ...       ...   \n",
              "pct_PL_enq_L6m_of_ever  42064.0      0.195497      0.367414    0.0      0.00   \n",
              "pct_CC_enq_L6m_of_ever  42064.0      0.064186      0.225989    0.0      0.00   \n",
              "HL_Flag                 42064.0      0.252235      0.434300    0.0      0.00   \n",
              "GL_Flag                 42064.0      0.056580      0.231042    0.0      0.00   \n",
              "Credit_Score            42064.0    679.326336     21.133619  469.0    668.00   \n",
              "\n",
              "                            50%       75%      max  \n",
              "PROSPECTID              25706.5  38518.25  51336.0  \n",
              "Total_TL                    3.0      6.00    235.0  \n",
              "Tot_Closed_TL               1.0      3.00    216.0  \n",
              "Tot_Active_TL               2.0      3.00     47.0  \n",
              "Total_TL_opened_L6M         0.0      1.00     27.0  \n",
              "...                         ...       ...      ...  \n",
              "pct_PL_enq_L6m_of_ever      0.0      0.00      1.0  \n",
              "pct_CC_enq_L6m_of_ever      0.0      0.00      1.0  \n",
              "HL_Flag                     0.0      1.00      1.0  \n",
              "GL_Flag                     0.0      0.00      1.0  \n",
              "Credit_Score              679.0    690.00    809.0  \n",
              "\n",
              "[73 rows x 8 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe().T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3DxPr5V5tN50"
      },
      "source": [
        "## Feature Selection\n",
        "\n",
        "### 1. For Categorical Features\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3MvBY8zwBwP",
        "outputId": "145a7fc2-214b-445c-8769-d1493bd28dc1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MARITALSTATUS\n",
            "EDUCATION\n",
            "GENDER\n",
            "last_prod_enq2\n",
            "first_prod_enq2\n",
            "Approved_Flag\n"
          ]
        }
      ],
      "source": [
        "# check how many columns are categorical\n",
        "for i in df.columns:\n",
        "    if df[i].dtype == 'object':\n",
        "        print(i)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8IuI8-9TxI9E"
      },
      "source": [
        "### -- Chi-square test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GloYqel2s_x_",
        "outputId": "f6a476c9-8b05-4a0a-d1dd-a078ab02d705"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MARITALSTATUS --- 3.578180861038862e-233\n",
            "EDUCATION --- 2.6942265249737532e-30\n",
            "GENDER --- 1.907936100186563e-05\n",
            "last_prod_enq2 --- 0.0\n",
            "first_prod_enq2 --- 7.84997610555419e-287\n"
          ]
        }
      ],
      "source": [
        "for i in ['MARITALSTATUS', 'EDUCATION', 'GENDER', 'last_prod_enq2', 'first_prod_enq2']:\n",
        "    chi2, pval, _, _ = chi2_contingency(pd.crosstab(df[i], df['Approved_Flag']))\n",
        "    print(i, '---', pval)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0S0gzp6wKZY"
      },
      "source": [
        "Since, for all the columns the p_value <= 0.05.\n",
        "\n",
        "so, we will reject Null Hypothesis (H0), i.e., the columns are not associated.\n",
        "\n",
        "We have to take all the columns in modeling.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KkPuVK-exNBO"
      },
      "source": [
        "### 2. For Numerical Features\n",
        "\n",
        "Here, first we will check for the Multicollinearity between the input features, using VIF (Variation Inflation Factor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H5ZfMnmWxS19"
      },
      "source": [
        "### Multicolinearity\n",
        "\n",
        "Happens when two or more IV are correlated\n",
        "\n",
        "Problems with multicollinearity-\n",
        "\n",
        "1. Interpretation of IV goes wrong\n",
        "2. Coefficient of IV become misleading\n",
        "\n",
        "Needs to be removed by some method (eg- VIF)\n",
        "\n",
        "-- **Variance Inflation Factor**\n",
        "\n",
        "Used to identify multicollinearity among IVs\n",
        "\n",
        "Takes R-squared value for each IV and eliminate if crosses a threshold\n",
        "\n",
        "* VIF ranges from 1 from infinity\n",
        "* VIF = 1 : No multicollinearity\n",
        "* VIF between 1 and 5 : Low multicollinearity\n",
        "* VIF between 5 and 10 : Moderate multicollinearity\n",
        "* VIF above 10 : High multicollinearity\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "_RJA2HqrwFI_"
      },
      "outputs": [],
      "source": [
        "# VIF for numerical columns\n",
        "numeric_columns = []\n",
        "for i in df.columns:\n",
        "    if df[i].dtype != 'object' and i not in ['PROSPECTID','Approved_Flag']:\n",
        "        numeric_columns.append(i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNRuYKy6xyrh",
        "outputId": "64ff5c45-a278-456e-9604-75b3478a0e53"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "72"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(numeric_columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2aapNJRx1kE",
        "outputId": "df91e293-c2b1-469d-94f2-234c276f99a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 --- inf\n",
            "0 --- inf\n",
            "0 --- 11.320180023967996\n",
            "0 --- 8.363698035000336\n",
            "0 --- 6.520647877790928\n",
            "0 --- 5.149501618212625\n",
            "1 --- 2.611111040579735\n",
            "2 --- inf\n",
            "2 --- 1788.7926256209232\n",
            "2 --- 8.601028256477228\n",
            "2 --- 3.832800792153077\n",
            "3 --- 6.099653381646723\n",
            "3 --- 5.581352009642766\n",
            "4 --- 1.985584353098778\n",
            "5 --- inf\n",
            "5 --- 4.80953830281934\n",
            "6 --- 23.270628983464636\n",
            "6 --- 30.595522588100053\n",
            "6 --- 4.384346405965583\n",
            "7 --- 3.0646584155234238\n",
            "8 --- 2.898639771299251\n",
            "9 --- 4.377876915347324\n",
            "10 --- 2.207853583695844\n",
            "11 --- 4.916914200506864\n",
            "12 --- 5.214702030064725\n",
            "13 --- 3.3861625024231476\n",
            "14 --- 7.840583309478997\n",
            "14 --- 5.255034641721434\n",
            "15 --- inf\n",
            "15 --- 7.380634506427238\n",
            "15 --- 1.4210050015175733\n",
            "16 --- 8.083255010190316\n",
            "16 --- 1.6241227524040114\n",
            "17 --- 7.257811920140003\n",
            "17 --- 15.59624383268298\n",
            "17 --- 1.825857047132431\n",
            "18 --- 1.5080839450032664\n",
            "19 --- 2.172088834824578\n",
            "20 --- 2.6233975535272274\n",
            "21 --- 2.2959970812106176\n",
            "22 --- 7.360578319196446\n",
            "22 --- 2.1602387773102567\n",
            "23 --- 2.8686288267891467\n",
            "24 --- 6.458218003637272\n",
            "24 --- 2.8474118865638247\n",
            "25 --- 4.753198156284083\n",
            "26 --- 16.22735475594825\n",
            "26 --- 6.424377256363877\n",
            "26 --- 8.887080381808678\n",
            "26 --- 2.3804746142952653\n",
            "27 --- 8.60951347651454\n",
            "27 --- 13.06755093547673\n",
            "27 --- 3.500040056654653\n",
            "28 --- 1.9087955874813773\n",
            "29 --- 17.006562234161628\n",
            "29 --- 10.730485153719197\n",
            "29 --- 2.3538497522950275\n",
            "30 --- 22.10485591513649\n",
            "30 --- 2.7971639638512924\n",
            "31 --- 3.424171203217696\n",
            "32 --- 10.175021454450922\n",
            "32 --- 6.408710354561292\n",
            "32 --- 1.001151196262563\n",
            "33 --- 3.069197305397273\n",
            "34 --- 2.8091261600643724\n",
            "35 --- 20.249538381980678\n",
            "35 --- 15.864576541593774\n",
            "35 --- 1.833164974053215\n",
            "36 --- 1.5680839909542046\n",
            "37 --- 1.9307572353811682\n",
            "38 --- 4.331265056645244\n",
            "39 --- 9.390334396150173\n"
          ]
        }
      ],
      "source": [
        "vif_data = df[numeric_columns]\n",
        "total_columns = vif_data.shape[1]\n",
        "columns_to_be_kept = []\n",
        "column_index = 0\n",
        "\n",
        "\n",
        "# Loop for VIF Sequential Check\n",
        "for i in range (0,total_columns):\n",
        "\n",
        "    vif_value = variance_inflation_factor(vif_data, column_index)\n",
        "    print (column_index,'---',vif_value)\n",
        "\n",
        "\n",
        "    if vif_value <= 6:\n",
        "        columns_to_be_kept.append( numeric_columns[i] )\n",
        "        column_index = column_index+1\n",
        "\n",
        "    else:\n",
        "        vif_data = vif_data.drop([ numeric_columns[i] ] , axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e4w6BXHfyLKT",
        "outputId": "8dfe10d4-4150-4c88-b3d8-d5a109c8196f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "39"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(columns_to_be_kept)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8xCC1ezyd27"
      },
      "source": [
        "See we have reduced our columns based on Multicolinearity."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "35EKckG7ynoZ"
      },
      "source": [
        "### -- ANOVA for columns_to_be_kept\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "4zj-VW_7ysgP"
      },
      "outputs": [],
      "source": [
        "from scipy.stats import f_oneway"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "vuZPDyuPychg"
      },
      "outputs": [],
      "source": [
        "columns_to_be_kept_numerical = []\n",
        "\n",
        "for i in columns_to_be_kept:\n",
        "    a = list(df[i])\n",
        "    b = list(df['Approved_Flag'])\n",
        "\n",
        "    group_P1 = [value for value, group in zip(a, b) if group == 'P1']\n",
        "    group_P2 = [value for value, group in zip(a, b) if group == 'P2']\n",
        "    group_P3 = [value for value, group in zip(a, b) if group == 'P3']\n",
        "    group_P4 = [value for value, group in zip(a, b) if group == 'P4']\n",
        "\n",
        "\n",
        "    f_statistic, p_value = f_oneway(group_P1, group_P2, group_P3, group_P4)\n",
        "\n",
        "    if p_value <= 0.05:\n",
        "        columns_to_be_kept_numerical.append(i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "6WlcfPBiyuek"
      },
      "outputs": [],
      "source": [
        "# Listing all the Final Features\n",
        "features = columns_to_be_kept_numerical + ['MARITALSTATUS', 'EDUCATION', 'GENDER', 'last_prod_enq2', 'first_prod_enq2']\n",
        "data = df[features + ['Approved_Flag']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sFmXhaJ0Poa",
        "outputId": "fb5df4e5-b7e8-4369-a146-f6dca5b6ba47"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(42064, 43)"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        },
        "id": "Wgj8gGhX0RNd",
        "outputId": "8be280a4-2055-451f-9e95-4a00a77c2959"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-a11f8b42-d84b-4b70-ba4d-068b77ae210f\" class=\"colab-df-container\">\n",
              "    <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>pct_tl_open_L6M</th>\n",
              "      <th>pct_tl_closed_L6M</th>\n",
              "      <th>Tot_TL_closed_L12M</th>\n",
              "      <th>pct_tl_closed_L12M</th>\n",
              "      <th>Tot_Missed_Pmnt</th>\n",
              "      <th>CC_TL</th>\n",
              "      <th>Home_TL</th>\n",
              "      <th>PL_TL</th>\n",
              "      <th>Secured_TL</th>\n",
              "      <th>Unsecured_TL</th>\n",
              "      <th>...</th>\n",
              "      <th>pct_PL_enq_L6m_of_ever</th>\n",
              "      <th>pct_CC_enq_L6m_of_ever</th>\n",
              "      <th>HL_Flag</th>\n",
              "      <th>GL_Flag</th>\n",
              "      <th>MARITALSTATUS</th>\n",
              "      <th>EDUCATION</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>last_prod_enq2</th>\n",
              "      <th>first_prod_enq2</th>\n",
              "      <th>Approved_Flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>40200</th>\n",
              "      <td>0.095</td>\n",
              "      <td>0.048</td>\n",
              "      <td>1</td>\n",
              "      <td>0.048</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>0.714</td>\n",
              "      <td>0.500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>GRADUATE</td>\n",
              "      <td>M</td>\n",
              "      <td>PL</td>\n",
              "      <td>CC</td>\n",
              "      <td>P4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33184</th>\n",
              "      <td>0.400</td>\n",
              "      <td>0.200</td>\n",
              "      <td>5</td>\n",
              "      <td>0.333</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Single</td>\n",
              "      <td>12TH</td>\n",
              "      <td>M</td>\n",
              "      <td>others</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12172</th>\n",
              "      <td>0.500</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>UNDER GRADUATE</td>\n",
              "      <td>F</td>\n",
              "      <td>PL</td>\n",
              "      <td>others</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35745</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.500</td>\n",
              "      <td>1</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>SSC</td>\n",
              "      <td>F</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>AL</td>\n",
              "      <td>P4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11552</th>\n",
              "      <td>0.104</td>\n",
              "      <td>0.083</td>\n",
              "      <td>5</td>\n",
              "      <td>0.104</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>30</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.167</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>GRADUATE</td>\n",
              "      <td>M</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>others</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33927</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.222</td>\n",
              "      <td>3</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>SSC</td>\n",
              "      <td>M</td>\n",
              "      <td>PL</td>\n",
              "      <td>PL</td>\n",
              "      <td>P3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11557</th>\n",
              "      <td>0.500</td>\n",
              "      <td>0.500</td>\n",
              "      <td>1</td>\n",
              "      <td>0.500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>12TH</td>\n",
              "      <td>M</td>\n",
              "      <td>PL</td>\n",
              "      <td>PL</td>\n",
              "      <td>P3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3436</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>GRADUATE</td>\n",
              "      <td>M</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>others</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18085</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.333</td>\n",
              "      <td>1</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>SSC</td>\n",
              "      <td>M</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15800</th>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>12TH</td>\n",
              "      <td>M</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>PL</td>\n",
              "      <td>P4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11878</th>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>GRADUATE</td>\n",
              "      <td>M</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14311</th>\n",
              "      <td>0.333</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0.333</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>SSC</td>\n",
              "      <td>M</td>\n",
              "      <td>AL</td>\n",
              "      <td>others</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3518</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>GRADUATE</td>\n",
              "      <td>M</td>\n",
              "      <td>PL</td>\n",
              "      <td>others</td>\n",
              "      <td>P1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21072</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Single</td>\n",
              "      <td>GRADUATE</td>\n",
              "      <td>M</td>\n",
              "      <td>CC</td>\n",
              "      <td>CC</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23758</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.048</td>\n",
              "      <td>6</td>\n",
              "      <td>0.143</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>GRADUATE</td>\n",
              "      <td>M</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>P1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1169</th>\n",
              "      <td>0.333</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Single</td>\n",
              "      <td>GRADUATE</td>\n",
              "      <td>M</td>\n",
              "      <td>PL</td>\n",
              "      <td>PL</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6003</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Married</td>\n",
              "      <td>GRADUATE</td>\n",
              "      <td>M</td>\n",
              "      <td>PL</td>\n",
              "      <td>HL</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22539</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>12TH</td>\n",
              "      <td>M</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>AL</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29595</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>POST-GRADUATE</td>\n",
              "      <td>M</td>\n",
              "      <td>others</td>\n",
              "      <td>others</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34416</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Single</td>\n",
              "      <td>OTHERS</td>\n",
              "      <td>M</td>\n",
              "      <td>others</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1476</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.333</td>\n",
              "      <td>1</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>SSC</td>\n",
              "      <td>F</td>\n",
              "      <td>others</td>\n",
              "      <td>HL</td>\n",
              "      <td>P1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34409</th>\n",
              "      <td>0.500</td>\n",
              "      <td>0.167</td>\n",
              "      <td>1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Single</td>\n",
              "      <td>GRADUATE</td>\n",
              "      <td>M</td>\n",
              "      <td>others</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21022</th>\n",
              "      <td>0.500</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Single</td>\n",
              "      <td>GRADUATE</td>\n",
              "      <td>M</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>others</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22856</th>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>SSC</td>\n",
              "      <td>M</td>\n",
              "      <td>others</td>\n",
              "      <td>others</td>\n",
              "      <td>P2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17367</th>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Married</td>\n",
              "      <td>GRADUATE</td>\n",
              "      <td>M</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>ConsumerLoan</td>\n",
              "      <td>P4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>25 rows × 43 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a11f8b42-d84b-4b70-ba4d-068b77ae210f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a11f8b42-d84b-4b70-ba4d-068b77ae210f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a11f8b42-d84b-4b70-ba4d-068b77ae210f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7271920d-15f4-4423-81a0-568d9aa83331 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       pct_tl_open_L6M  pct_tl_closed_L6M  Tot_TL_closed_L12M  \\\n",
              "40200            0.095              0.048                   1   \n",
              "33184            0.400              0.200                   5   \n",
              "12172            0.500              0.000                   0   \n",
              "35745            0.000              0.500                   1   \n",
              "11552            0.104              0.083                   5   \n",
              "33927            0.000              0.222                   3   \n",
              "11557            0.500              0.500                   1   \n",
              "3436             0.000              0.000                   0   \n",
              "18085            0.000              0.333                   1   \n",
              "15800            1.000              0.000                   0   \n",
              "11878            0.000              1.000                   1   \n",
              "14311            0.333              0.000                   1   \n",
              "3518             0.000              0.000                   0   \n",
              "21072            0.000              0.000                   0   \n",
              "23758            0.000              0.048                   6   \n",
              "1169             0.333              0.000                   0   \n",
              "6003             0.000              0.000                   0   \n",
              "22539            0.000              0.000                   0   \n",
              "29595            0.000              0.000                   0   \n",
              "34416            0.000              0.000                   0   \n",
              "1476             0.000              0.333                   1   \n",
              "34409            0.500              0.167                   1   \n",
              "21022            0.500              0.000                   0   \n",
              "22856            0.000              1.000                   1   \n",
              "17367            1.000              0.000                   0   \n",
              "\n",
              "       pct_tl_closed_L12M  Tot_Missed_Pmnt  CC_TL  Home_TL  PL_TL  Secured_TL  \\\n",
              "40200               0.048                1      2        0      2          12   \n",
              "33184               0.333                4      0        0      0          15   \n",
              "12172               0.000                1      0        0      1           3   \n",
              "35745               0.500                0      0        0      1           1   \n",
              "11552               0.104                2      2        0      2          18   \n",
              "33927               0.333                0      0        0      0           9   \n",
              "11557               0.500                1      0        0      2           0   \n",
              "3436                0.000                2      0        0      0          10   \n",
              "18085               0.333                0      0        0      0           1   \n",
              "15800               0.000                0      0        0      1           0   \n",
              "11878               1.000                0      0        0      0           0   \n",
              "14311               0.333                1      0        0      0           3   \n",
              "3518                0.000                0      0        0      0           2   \n",
              "21072               0.000                0      1        0      0           0   \n",
              "23758               0.143                1      0        0      0          37   \n",
              "1169                0.000                1      2        0      1           0   \n",
              "6003                0.000                0      0        1      0           3   \n",
              "22539               0.000                0      0        0      0           1   \n",
              "29595               0.000                0      0        0      0           1   \n",
              "34416               0.000                0      0        0      0           0   \n",
              "1476                0.333                0      1        0      1           0   \n",
              "34409               0.167                2      0        0      2           0   \n",
              "21022               0.000                0      0        0      0           1   \n",
              "22856               1.000                0      0        0      0           1   \n",
              "17367               0.000                0      0        0      0           0   \n",
              "\n",
              "       Unsecured_TL  ...  pct_PL_enq_L6m_of_ever  pct_CC_enq_L6m_of_ever  \\\n",
              "40200             9  ...                   0.714                   0.500   \n",
              "33184             0  ...                   0.000                   0.000   \n",
              "12172             1  ...                   1.000                   0.000   \n",
              "35745             1  ...                   0.000                   0.000   \n",
              "11552            30  ...                   0.000                   0.167   \n",
              "33927             0  ...                   0.333                   0.000   \n",
              "11557             2  ...                   0.600                   0.000   \n",
              "3436              0  ...                   0.000                   0.000   \n",
              "18085             2  ...                   0.000                   0.000   \n",
              "15800             1  ...                   1.000                   0.000   \n",
              "11878             1  ...                   0.000                   0.000   \n",
              "14311             0  ...                   0.000                   0.000   \n",
              "3518              1  ...                   0.000                   0.000   \n",
              "21072             1  ...                   0.000                   0.000   \n",
              "23758             5  ...                   0.000                   0.000   \n",
              "1169              3  ...                   0.000                   0.000   \n",
              "6003              0  ...                   0.000                   0.000   \n",
              "22539             0  ...                   0.000                   0.000   \n",
              "29595             0  ...                   0.000                   0.000   \n",
              "34416             1  ...                   0.000                   0.000   \n",
              "1476              3  ...                   0.000                   0.000   \n",
              "34409             6  ...                   0.500                   0.000   \n",
              "21022             1  ...                   0.000                   0.000   \n",
              "22856             0  ...                   0.000                   0.000   \n",
              "17367             1  ...                   1.000                   0.000   \n",
              "\n",
              "       HL_Flag  GL_Flag  MARITALSTATUS       EDUCATION  GENDER  \\\n",
              "40200        1        0        Married        GRADUATE       M   \n",
              "33184        1        0         Single            12TH       M   \n",
              "12172        1        0        Married  UNDER GRADUATE       F   \n",
              "35745        0        0        Married             SSC       F   \n",
              "11552        1        0        Married        GRADUATE       M   \n",
              "33927        1        0        Married             SSC       M   \n",
              "11557        0        0        Married            12TH       M   \n",
              "3436         1        0        Married        GRADUATE       M   \n",
              "18085        0        0        Married             SSC       M   \n",
              "15800        0        0        Married            12TH       M   \n",
              "11878        0        0        Married        GRADUATE       M   \n",
              "14311        0        0        Married             SSC       M   \n",
              "3518         0        0        Married        GRADUATE       M   \n",
              "21072        0        0         Single        GRADUATE       M   \n",
              "23758        1        0        Married        GRADUATE       M   \n",
              "1169         0        0         Single        GRADUATE       M   \n",
              "6003         1        1        Married        GRADUATE       M   \n",
              "22539        0        0        Married            12TH       M   \n",
              "29595        0        0        Married   POST-GRADUATE       M   \n",
              "34416        0        0         Single          OTHERS       M   \n",
              "1476         0        0        Married             SSC       F   \n",
              "34409        0        0         Single        GRADUATE       M   \n",
              "21022        0        0         Single        GRADUATE       M   \n",
              "22856        0        0        Married             SSC       M   \n",
              "17367        0        0        Married        GRADUATE       M   \n",
              "\n",
              "       last_prod_enq2  first_prod_enq2  Approved_Flag  \n",
              "40200              PL               CC             P4  \n",
              "33184          others     ConsumerLoan             P2  \n",
              "12172              PL           others             P2  \n",
              "35745    ConsumerLoan               AL             P4  \n",
              "11552    ConsumerLoan           others             P2  \n",
              "33927              PL               PL             P3  \n",
              "11557              PL               PL             P3  \n",
              "3436     ConsumerLoan           others             P2  \n",
              "18085    ConsumerLoan     ConsumerLoan             P2  \n",
              "15800    ConsumerLoan               PL             P4  \n",
              "11878    ConsumerLoan     ConsumerLoan             P2  \n",
              "14311              AL           others             P2  \n",
              "3518               PL           others             P1  \n",
              "21072              CC               CC             P2  \n",
              "23758    ConsumerLoan     ConsumerLoan             P1  \n",
              "1169               PL               PL             P2  \n",
              "6003               PL               HL             P2  \n",
              "22539    ConsumerLoan               AL             P2  \n",
              "29595          others           others             P2  \n",
              "34416          others     ConsumerLoan             P2  \n",
              "1476           others               HL             P1  \n",
              "34409          others     ConsumerLoan             P2  \n",
              "21022    ConsumerLoan           others             P2  \n",
              "22856          others           others             P2  \n",
              "17367    ConsumerLoan     ConsumerLoan             P4  \n",
              "\n",
              "[25 rows x 43 columns]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.sample(25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aMwRiaNK0VjG",
        "outputId": "ce6e29c4-e7cd-4017-b474-55f7b34884da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MARITALSTATUS:  ['Married' 'Single']\n",
            "EDUCATION:  ['12TH' 'GRADUATE' 'SSC' 'POST-GRADUATE' 'UNDER GRADUATE' 'OTHERS'\n",
            " 'PROFESSIONAL']\n",
            "GENDER:  ['M' 'F']\n",
            "last_prod_enq2:  ['PL' 'ConsumerLoan' 'AL' 'CC' 'others' 'HL']\n",
            "first_prod_enq2:  ['PL' 'ConsumerLoan' 'others' 'AL' 'HL' 'CC']\n"
          ]
        }
      ],
      "source": [
        "# Label encoding for the categorical features\n",
        "print(\"MARITALSTATUS: \", df['MARITALSTATUS'].unique())\n",
        "print(\"EDUCATION: \", df['EDUCATION'].unique())\n",
        "print(\"GENDER: \", df['GENDER'].unique())\n",
        "print(\"last_prod_enq2: \", df['last_prod_enq2'].unique())\n",
        "print(\"first_prod_enq2: \", df['first_prod_enq2'].unique())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "awfgZpkf3I7z"
      },
      "source": [
        "### Ordinal feature -- EDUCATION\n",
        "- SSC            : 1\n",
        "- 12TH           : 2\n",
        "- GRADUATE       : 3\n",
        "- UNDER GRADUATE : 3\n",
        "- POST-GRADUATE  : 4\n",
        "- OTHERS         : 1\n",
        "- PROFESSIONAL   : 3\n",
        "\n",
        "##### Others has to be verified by the business end user"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3VphT9680y-C",
        "outputId": "4603968b-50e3-42f6-f49f-888c139456df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 42064 entries, 0 to 42063\n",
            "Data columns (total 43 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   pct_tl_open_L6M            42064 non-null  float64\n",
            " 1   pct_tl_closed_L6M          42064 non-null  float64\n",
            " 2   Tot_TL_closed_L12M         42064 non-null  int64  \n",
            " 3   pct_tl_closed_L12M         42064 non-null  float64\n",
            " 4   Tot_Missed_Pmnt            42064 non-null  int64  \n",
            " 5   CC_TL                      42064 non-null  int64  \n",
            " 6   Home_TL                    42064 non-null  int64  \n",
            " 7   PL_TL                      42064 non-null  int64  \n",
            " 8   Secured_TL                 42064 non-null  int64  \n",
            " 9   Unsecured_TL               42064 non-null  int64  \n",
            " 10  Other_TL                   42064 non-null  int64  \n",
            " 11  Age_Oldest_TL              42064 non-null  int64  \n",
            " 12  Age_Newest_TL              42064 non-null  int64  \n",
            " 13  time_since_recent_payment  42064 non-null  int64  \n",
            " 14  max_recent_level_of_deliq  42064 non-null  int64  \n",
            " 15  num_deliq_6_12mts          42064 non-null  int64  \n",
            " 16  num_times_60p_dpd          42064 non-null  int64  \n",
            " 17  num_std_12mts              42064 non-null  int64  \n",
            " 18  num_sub                    42064 non-null  int64  \n",
            " 19  num_sub_6mts               42064 non-null  int64  \n",
            " 20  num_sub_12mts              42064 non-null  int64  \n",
            " 21  num_dbt                    42064 non-null  int64  \n",
            " 22  num_dbt_12mts              42064 non-null  int64  \n",
            " 23  num_lss                    42064 non-null  int64  \n",
            " 24  recent_level_of_deliq      42064 non-null  int64  \n",
            " 25  CC_enq_L12m                42064 non-null  int64  \n",
            " 26  PL_enq_L12m                42064 non-null  int64  \n",
            " 27  time_since_recent_enq      42064 non-null  int64  \n",
            " 28  enq_L3m                    42064 non-null  int64  \n",
            " 29  NETMONTHLYINCOME           42064 non-null  int64  \n",
            " 30  Time_With_Curr_Empr        42064 non-null  int64  \n",
            " 31  CC_Flag                    42064 non-null  int64  \n",
            " 32  PL_Flag                    42064 non-null  int64  \n",
            " 33  pct_PL_enq_L6m_of_ever     42064 non-null  float64\n",
            " 34  pct_CC_enq_L6m_of_ever     42064 non-null  float64\n",
            " 35  HL_Flag                    42064 non-null  int64  \n",
            " 36  GL_Flag                    42064 non-null  int64  \n",
            " 37  MARITALSTATUS              42064 non-null  object \n",
            " 38  EDUCATION                  42064 non-null  int64  \n",
            " 39  GENDER                     42064 non-null  object \n",
            " 40  last_prod_enq2             42064 non-null  object \n",
            " 41  first_prod_enq2            42064 non-null  object \n",
            " 42  Approved_Flag              42064 non-null  object \n",
            "dtypes: float64(5), int64(33), object(5)\n",
            "memory usage: 13.8+ MB\n"
          ]
        }
      ],
      "source": [
        "data.loc[data['EDUCATION'] == 'SSC',['EDUCATION']]              = 1\n",
        "data.loc[data['EDUCATION'] == '12TH',['EDUCATION']]             = 2\n",
        "data.loc[data['EDUCATION'] == 'GRADUATE',['EDUCATION']]         = 3\n",
        "data.loc[data['EDUCATION'] == 'UNDER GRADUATE',['EDUCATION']]   = 3\n",
        "data.loc[data['EDUCATION'] == 'POST-GRADUATE',['EDUCATION']]    = 4\n",
        "data.loc[data['EDUCATION'] == 'OTHERS',['EDUCATION']]           = 1\n",
        "data.loc[data['EDUCATION'] == 'PROFESSIONAL',['EDUCATION']]     = 3\n",
        "\n",
        "data['EDUCATION'].value_counts()\n",
        "data['EDUCATION'] = data['EDUCATION'].astype(int)\n",
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m0esBUf0BDqc"
      },
      "source": [
        "### One-Hot Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HKv25Yrw4DWC",
        "outputId": "f2547a49-049a-4f9f-912c-155bf1f01ec2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 42064 entries, 0 to 42063\n",
            "Data columns (total 55 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   pct_tl_open_L6M               42064 non-null  float64\n",
            " 1   pct_tl_closed_L6M             42064 non-null  float64\n",
            " 2   Tot_TL_closed_L12M            42064 non-null  int64  \n",
            " 3   pct_tl_closed_L12M            42064 non-null  float64\n",
            " 4   Tot_Missed_Pmnt               42064 non-null  int64  \n",
            " 5   CC_TL                         42064 non-null  int64  \n",
            " 6   Home_TL                       42064 non-null  int64  \n",
            " 7   PL_TL                         42064 non-null  int64  \n",
            " 8   Secured_TL                    42064 non-null  int64  \n",
            " 9   Unsecured_TL                  42064 non-null  int64  \n",
            " 10  Other_TL                      42064 non-null  int64  \n",
            " 11  Age_Oldest_TL                 42064 non-null  int64  \n",
            " 12  Age_Newest_TL                 42064 non-null  int64  \n",
            " 13  time_since_recent_payment     42064 non-null  int64  \n",
            " 14  max_recent_level_of_deliq     42064 non-null  int64  \n",
            " 15  num_deliq_6_12mts             42064 non-null  int64  \n",
            " 16  num_times_60p_dpd             42064 non-null  int64  \n",
            " 17  num_std_12mts                 42064 non-null  int64  \n",
            " 18  num_sub                       42064 non-null  int64  \n",
            " 19  num_sub_6mts                  42064 non-null  int64  \n",
            " 20  num_sub_12mts                 42064 non-null  int64  \n",
            " 21  num_dbt                       42064 non-null  int64  \n",
            " 22  num_dbt_12mts                 42064 non-null  int64  \n",
            " 23  num_lss                       42064 non-null  int64  \n",
            " 24  recent_level_of_deliq         42064 non-null  int64  \n",
            " 25  CC_enq_L12m                   42064 non-null  int64  \n",
            " 26  PL_enq_L12m                   42064 non-null  int64  \n",
            " 27  time_since_recent_enq         42064 non-null  int64  \n",
            " 28  enq_L3m                       42064 non-null  int64  \n",
            " 29  NETMONTHLYINCOME              42064 non-null  int64  \n",
            " 30  Time_With_Curr_Empr           42064 non-null  int64  \n",
            " 31  CC_Flag                       42064 non-null  int64  \n",
            " 32  PL_Flag                       42064 non-null  int64  \n",
            " 33  pct_PL_enq_L6m_of_ever        42064 non-null  float64\n",
            " 34  pct_CC_enq_L6m_of_ever        42064 non-null  float64\n",
            " 35  HL_Flag                       42064 non-null  int64  \n",
            " 36  GL_Flag                       42064 non-null  int64  \n",
            " 37  EDUCATION                     42064 non-null  int64  \n",
            " 38  Approved_Flag                 42064 non-null  object \n",
            " 39  MARITALSTATUS_Married         42064 non-null  bool   \n",
            " 40  MARITALSTATUS_Single          42064 non-null  bool   \n",
            " 41  GENDER_F                      42064 non-null  bool   \n",
            " 42  GENDER_M                      42064 non-null  bool   \n",
            " 43  last_prod_enq2_AL             42064 non-null  bool   \n",
            " 44  last_prod_enq2_CC             42064 non-null  bool   \n",
            " 45  last_prod_enq2_ConsumerLoan   42064 non-null  bool   \n",
            " 46  last_prod_enq2_HL             42064 non-null  bool   \n",
            " 47  last_prod_enq2_PL             42064 non-null  bool   \n",
            " 48  last_prod_enq2_others         42064 non-null  bool   \n",
            " 49  first_prod_enq2_AL            42064 non-null  bool   \n",
            " 50  first_prod_enq2_CC            42064 non-null  bool   \n",
            " 51  first_prod_enq2_ConsumerLoan  42064 non-null  bool   \n",
            " 52  first_prod_enq2_HL            42064 non-null  bool   \n",
            " 53  first_prod_enq2_PL            42064 non-null  bool   \n",
            " 54  first_prod_enq2_others        42064 non-null  bool   \n",
            "dtypes: bool(16), float64(5), int64(33), object(1)\n",
            "memory usage: 13.2+ MB\n"
          ]
        }
      ],
      "source": [
        "df_encoded = pd.get_dummies(data, columns=['MARITALSTATUS','GENDER', 'last_prod_enq2' ,'first_prod_enq2'])\n",
        "\n",
        "df_encoded.info()\n",
        "k = df_encoded.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9G5XpdxK_OF1"
      },
      "source": [
        "### Machine Learing Model Building"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83w0CTIt9Ur5",
        "outputId": "5258ccfb-316f-4ad9-e26a-85f7ac39c2b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 0.7636990372043266\n",
            "\n",
            "Overall F1 Score: 0.74\n",
            "\n",
            "Class p1:\n",
            "Precision: 0.8370457209847597\n",
            "Recall: 0.7041420118343196\n",
            "F1 Score: 0.7648634172469203\n",
            "\n",
            "Class p2:\n",
            "Precision: 0.7957519116397621\n",
            "Recall: 0.9282457879088206\n",
            "F1 Score: 0.8569075937785909\n",
            "\n",
            "Class p3:\n",
            "Precision: 0.4423380726698262\n",
            "Recall: 0.21132075471698114\n",
            "F1 Score: 0.28600612870275793\n",
            "\n",
            "Class p4:\n",
            "Precision: 0.7178502879078695\n",
            "Recall: 0.7269193391642371\n",
            "F1 Score: 0.7223563495895703\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Data processing\n",
        "# 1. Random Forest\n",
        "\n",
        "y = df_encoded['Approved_Flag']\n",
        "x = df_encoded. drop ( ['Approved_Flag'], axis = 1 )\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "rf_classifier = RandomForestClassifier(n_estimators = 200, random_state=42)\n",
        "\n",
        "rf_classifier.fit(x_train, y_train)\n",
        "\n",
        "y_pred = rf_classifier.predict(x_test)\n",
        "\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print ()\n",
        "print(f'Accuracy: {accuracy}')\n",
        "print ()\n",
        "overall_f1_score = precision_recall_fscore_support(y_test, y_pred, average='weighted')[2]\n",
        "print(f\"Overall F1 Score: {overall_f1_score:.2f}\")\n",
        "\n",
        "print ()\n",
        "precision, recall, f1_score, _ = precision_recall_fscore_support(y_test, y_pred)\n",
        "\n",
        "\n",
        "for i, v in enumerate(['p1', 'p2', 'p3', 'p4']):\n",
        "    print(f\"Class {v}:\")\n",
        "    print(f\"Precision: {precision[i]}\")\n",
        "    print(f\"Recall: {recall[i]}\")\n",
        "    print(f\"F1 Score: {f1_score[i]}\")\n",
        "    print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0wUqDHjc_Htj",
        "outputId": "71a9b50e-29d4-4260-fda8-aa91b6860f49"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 0.78\n",
            "Overall F1 Score: 0.76\n",
            "\n",
            "Class p1:\n",
            "Precision: 0.823906083244397\n",
            "Recall: 0.7613412228796844\n",
            "F1 Score: 0.7913890312660173\n",
            "\n",
            "Class p2:\n",
            "Precision: 0.8255418233924413\n",
            "Recall: 0.913577799801784\n",
            "F1 Score: 0.8673315769665036\n",
            "\n",
            "Class p3:\n",
            "Precision: 0.4756380510440835\n",
            "Recall: 0.30943396226415093\n",
            "F1 Score: 0.3749428440786465\n",
            "\n",
            "Class p4:\n",
            "Precision: 0.7342386032977691\n",
            "Recall: 0.7356656948493683\n",
            "F1 Score: 0.7349514563106796\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 2. xgboost\n",
        "\n",
        "import xgboost as xgb\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "xgb_classifier = xgb.XGBClassifier(objective='multi:softmax',  num_class=4)\n",
        "\n",
        "\n",
        "y = df_encoded['Approved_Flag']\n",
        "x = df_encoded. drop ( ['Approved_Flag'], axis = 1 )\n",
        "\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "y_encoded = label_encoder.fit_transform(y)\n",
        "\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y_encoded, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "xgb_classifier.fit(x_train, y_train)\n",
        "y_pred = xgb_classifier.predict(x_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print ()\n",
        "print(f'Accuracy: {accuracy:.2f}')\n",
        "\n",
        "overall_f1_score = precision_recall_fscore_support(y_test, y_pred, average='weighted')[2]\n",
        "print(f\"Overall F1 Score: {overall_f1_score:.2f}\")\n",
        "\n",
        "print ()\n",
        "\n",
        "precision, recall, f1_score, _ = precision_recall_fscore_support(y_test, y_pred)\n",
        "\n",
        "for i, v in enumerate(['p1', 'p2', 'p3', 'p4']):\n",
        "    print(f\"Class {v}:\")\n",
        "    print(f\"Precision: {precision[i]}\")\n",
        "    print(f\"Recall: {recall[i]}\")\n",
        "    print(f\"F1 Score: {f1_score[i]}\")\n",
        "    print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yk-i6VLu_Hqt",
        "outputId": "ffb57a0a-a15f-49cf-ff21-18a086b83b96"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 0.71\n",
            "\n",
            "Overall F1 Score: 0.71\n",
            "\n",
            "Class p1:\n",
            "Precision: 0.7204724409448819\n",
            "Recall: 0.7218934911242604\n",
            "F1 Score: 0.7211822660098522\n",
            "\n",
            "Class p2:\n",
            "Precision: 0.8084320963668156\n",
            "Recall: 0.824777006937562\n",
            "F1 Score: 0.8165227629513344\n",
            "\n",
            "Class p3:\n",
            "Precision: 0.3415213946117274\n",
            "Recall: 0.32528301886792454\n",
            "F1 Score: 0.3332044839582528\n",
            "\n",
            "Class p4:\n",
            "Precision: 0.6487854251012146\n",
            "Recall: 0.6229348882410107\n",
            "F1 Score: 0.6355974219137334\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 3. Decision Tree\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "\n",
        "y = df_encoded['Approved_Flag']\n",
        "x = df_encoded. drop ( ['Approved_Flag'], axis = 1 )\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "dt_model = DecisionTreeClassifier(max_depth=20, min_samples_split=10)\n",
        "dt_model.fit(x_train, y_train)\n",
        "y_pred = dt_model.predict(x_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print ()\n",
        "print(f\"Accuracy: {accuracy:.2f}\")\n",
        "print ()\n",
        "overall_f1_score = precision_recall_fscore_support(y_test, y_pred, average='weighted')[2]\n",
        "print(f\"Overall F1 Score: {overall_f1_score:.2f}\")\n",
        "\n",
        "print ()\n",
        "\n",
        "precision, recall, f1_score, _ = precision_recall_fscore_support(y_test, y_pred)\n",
        "\n",
        "for i, v in enumerate(['p1', 'p2', 'p3', 'p4']):\n",
        "    print(f\"Class {v}:\")\n",
        "    print(f\"Precision: {precision[i]}\")\n",
        "    print(f\"Recall: {recall[i]}\")\n",
        "    print(f\"F1 Score: {f1_score[i]}\")\n",
        "    print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVRuDJJXDqiD"
      },
      "source": [
        "Since, among all of them XGBoost showing higher F1-Score as 76. We will use XGBoost algorithm for development and final pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NuyRoaib_eni"
      },
      "outputs": [],
      "source": [
        "data.to_csv('credit_data.csv', index = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kog5frfRftuh"
      },
      "source": [
        "### Hyperparameter-tuning on XGBoost Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "5DeZJda6AzWT"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import cross_val_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zeq9i_2Kf4r6",
        "outputId": "acf2d7a8-5915-4466-aa54-8069a707017c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7747467738698969"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Now use the encoded labels for cross-validation\n",
        "cross_val_score(xgb_classifier, x_train, y_train, cv=5, scoring='accuracy').mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJLVfN-KgVxH",
        "outputId": "76439d5b-c705-457d-b82b-2430cbf67eb8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7783192677998336"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_pred = xgb_classifier.predict(x_test)\n",
        "\n",
        "accuracy_score(y_test,test_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JBYAYEh4hQzp",
        "outputId": "c2a37548-1c53-4608-8498-718d43202717"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Overall F1 Score: 0.76\n"
          ]
        }
      ],
      "source": [
        "overall_f1_score = precision_recall_fscore_support(y_test, y_pred, average='weighted')[2]\n",
        "print(f\"Overall F1 Score: {overall_f1_score:.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10iCitx2iZX6"
      },
      "source": [
        "### Find optimal tuning parameters for the pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "QwECNm2ZhoC_"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7OrLGgOQiVmL",
        "outputId": "ac44cec6-3a76-442e-e54a-bf44cd898ab6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best parameters: {'eta': 0.15, 'gamma': 0.2, 'max_depth': 5}\n",
            "Best score: 0.7801552374710344\n",
            "Overall F1 Score: 0.76\n"
          ]
        }
      ],
      "source": [
        "param_grid = {\n",
        "    'eta': [0.01, 0.1, 0.15],\n",
        "    'gamma': [0.1, 0.2, 0.3],\n",
        "    'max_depth': [5, 6, 7, 8, 9, 10],\n",
        "}\n",
        "\n",
        "grid_search = GridSearchCV(xgb_classifier, param_grid, cv=5, scoring='accuracy')\n",
        "grid_search.fit(x_train, y_train)\n",
        "\n",
        "print(f\"Best parameters: {grid_search.best_params_}\")\n",
        "print(f\"Best score: {grid_search.best_score_}\")\n",
        "\n",
        "best_model = grid_search.best_estimator_\n",
        "\n",
        "test_pred = best_model.predict(x_test)\n",
        "accuracy_score(y_test,test_pred)\n",
        "overall_f1_score = precision_recall_fscore_support(y_test, y_pred, average='weighted')[2]\n",
        "print(f\"Overall F1 Score: {overall_f1_score:.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4_SiX1-qC7q"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "Using `Grid Search CV`, I have fine-tuned the model parameters, achieving a testing accuracy of 78.01% from 77.83%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xriq9di3sQPk"
      },
      "source": [
        "The hyperparameter tuning further enhanced the model's performance, showcasing the effectiveness of employing advanced methodologies in credit risk assessment.\n",
        "\n",
        "This project underscores the importance of leveraging data-driven approaches to mitigate credit risk and optimize financial strategies in the banking sector."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GBGVXRL_iXgM"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}