scratch_code_with_hyperparamter_tuning.ipynb

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      "source": [
        "**/kaggle/input/mortgage-based-predictions/LoanExport.csv**"
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        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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        "AIM:-To predict the mortgage backed securities prepayment risk using ml models Mortgage backed securities:- Firstly mortgage means people take loan from banks and banks issue them loans on thier credit profile,investments and certain proofs and they give those docs when loan tenure is finished.So these security papers are collected in a set and are sold to invesment banks and they are locked.now the investors gets profit from the intrests they pay.so if a customer pre pays the ammount investor might lose some profit.so we have to predict whether a customer will prepay or not."
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        "CreditScore:-\n",
        "FirstPaymentDate:-\n",
        "FirstTimeHomebuyer:- \n",
        "MaturityDate:- \n",
        "MSA(marketing service agremment)-5digit MIP-\n",
        "mortgage insurance percentage\n",
        "Units \n",
        "Occupancy \n",
        "OCLTV-ratio of all secured loans on a property to the total value of property\n",
        "DTI-debit to income ratio \n",
        "OrigUPB-orginal unpaid balance LTV \n",
        "OrigInterestRate Channel \n",
        "PPM-prepayment penalty mortgage \n",
        "ProductType \n",
        "PropertyState \n",
        "PropertyType \n",
        "PostalCode \n",
        "LoanSeqNum \n",
        "LoanPurpose \n",
        "OrigLoanTerm \n",
        "NumBorrowers \n",
        "SellerName \n",
        "ServicerName \n",
        "EverDelinquent \n",
        "MonthsDelinquent \n",
        "MonthsInRepayment"
      ],
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      "source": [
        "importing required libraries"
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        "#Import the required Libraries.\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt,seaborn as sns\n",
        "import math\n",
        "\n",
        "get_ipython().run_line_magic('matplotlib', 'inline')\n",
        "pd.set_option('display.max_columns',125)\n",
        "pd.set_option('display.max_rows',125)"
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      "source": [
        "1. Reading the Input Data from File****"
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      "source": [
        "df_train=pd.read_csv(\"/content/LoanExport.csv\",low_memory=False)\n",
        "df_train.head()"
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          "data": {
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              "   CreditScore  FirstPaymentDate FirstTimeHomebuyer  MaturityDate    MSA  MIP  \\\n",
              "0            0            199902                  N        202901  16974   25   \n",
              "1            0            199902                  N        202901  19740    0   \n",
              "2            0            199902                  N        202901  29940    0   \n",
              "3            0            199902                  N        202901  31084    0   \n",
              "4            0            199902                  N        202901  35644    0   \n",
              "\n",
              "   Units Occupancy  OCLTV  DTI  OrigUPB  LTV  OrigInterestRate Channel PPM  \\\n",
              "0      1         O     89   27   117000   89             6.750       T   N   \n",
              "1      1         O     73   17   109000   73             6.500       R   N   \n",
              "2      1         O     75   16    88000   75             6.875       T   N   \n",
              "3      1         O     76   14   160000   76             6.875       R   N   \n",
              "4      1         O     78   18   109000   78             7.125       R   N   \n",
              "\n",
              "  ProductType PropertyState PropertyType PostalCode    LoanSeqNum LoanPurpose  \\\n",
              "0       FRM              IL           SF      60400  F199Q1268030           P   \n",
              "1       FRM              CO           SF      80200  F199Q1015092           N   \n",
              "2       FRM              KS           SF      66000  F199Q1266886           N   \n",
              "3       FRM              CA           SF      90700  F199Q1178167           N   \n",
              "4       FRM              NJ           SF       7600  F199Q1178517           N   \n",
              "\n",
              "   OrigLoanTerm NumBorrowers SellerName          ServicerName  EverDelinquent  \\\n",
              "0           360            2         FL  WASHINGTONMUTUALBANK               0   \n",
              "1           360            1         FT  CHASEHOMEFINANCELLC                0   \n",
              "2           360            2         FL  WASHINGTONMUTUALBANK               0   \n",
              "3           360            2         GM  GMACMTGECORP                       0   \n",
              "4           360            2         GM  GMACMTGECORP                       0   \n",
              "\n",
              "   MonthsDelinquent  MonthsInRepayment  \n",
              "0                 0                 52  \n",
              "1                 0                144  \n",
              "2                 0                 67  \n",
              "3                 0                 35  \n",
              "4                 0                 54  "
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      "source": [
        "# Getting the Dataframe dimensions\n",
        "df_train.shape"
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      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:36.054960Z",
          "iopub.execute_input": "2023-02-26T04:18:36.055488Z",
          "iopub.status.idle": "2023-02-26T04:18:36.065831Z",
          "shell.execute_reply.started": "2023-02-26T04:18:36.055421Z",
          "shell.execute_reply": "2023-02-26T04:18:36.064262Z"
        },
        "trusted": true,
        "id": "n2U_aNprL0Us",
        "outputId": "80de3c3f-2ee5-4b12-9a84-1c7d73fb7686",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(291451, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting the columns information\n",
        "df_train.dtypes"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:36.069260Z",
          "iopub.execute_input": "2023-02-26T04:18:36.070936Z",
          "iopub.status.idle": "2023-02-26T04:18:36.085428Z",
          "shell.execute_reply.started": "2023-02-26T04:18:36.070871Z",
          "shell.execute_reply": "2023-02-26T04:18:36.083703Z"
        },
        "trusted": true,
        "id": "YkY2lnVnL0Us",
        "outputId": "d947c715-e607-4ef6-b6a2-792cc42e41bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CreditScore             int64\n",
              "FirstPaymentDate        int64\n",
              "FirstTimeHomebuyer     object\n",
              "MaturityDate            int64\n",
              "MSA                    object\n",
              "MIP                     int64\n",
              "Units                   int64\n",
              "Occupancy              object\n",
              "OCLTV                   int64\n",
              "DTI                     int64\n",
              "OrigUPB                 int64\n",
              "LTV                     int64\n",
              "OrigInterestRate      float64\n",
              "Channel                object\n",
              "PPM                    object\n",
              "ProductType            object\n",
              "PropertyState          object\n",
              "PropertyType           object\n",
              "PostalCode             object\n",
              "LoanSeqNum             object\n",
              "LoanPurpose            object\n",
              "OrigLoanTerm            int64\n",
              "NumBorrowers           object\n",
              "SellerName             object\n",
              "ServicerName           object\n",
              "EverDelinquent          int64\n",
              "MonthsDelinquent        int64\n",
              "MonthsInRepayment       int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " categoricalcolumns=[]\n",
        " numericalcolumns=[]\n",
        " for i in df_train.columns:\n",
        "   if df_train[i].dtypes==object:\n",
        "      categoricalcolumns.append(i)\n",
        "   else:\n",
        "      numericalcolumns.append(i)\n",
        "print(numericalcolumns) \n",
        "print(categoricalcolumns)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:36.087840Z",
          "iopub.execute_input": "2023-02-26T04:18:36.088546Z",
          "iopub.status.idle": "2023-02-26T04:18:36.114019Z",
          "shell.execute_reply.started": "2023-02-26T04:18:36.088471Z",
          "shell.execute_reply": "2023-02-26T04:18:36.112317Z"
        },
        "trusted": true,
        "id": "qPVJEDG8L0Us",
        "outputId": "63fe4130-7069-48d8-fb5a-a0a196cffec0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['CreditScore', 'FirstPaymentDate', 'MaturityDate', 'MIP', 'Units', 'OCLTV', 'DTI', 'OrigUPB', 'LTV', 'OrigInterestRate', 'OrigLoanTerm', 'EverDelinquent', 'MonthsDelinquent', 'MonthsInRepayment']\n",
            "['FirstTimeHomebuyer', 'MSA', 'Occupancy', 'Channel', 'PPM', 'ProductType', 'PropertyState', 'PropertyType', 'PostalCode', 'LoanSeqNum', 'LoanPurpose', 'NumBorrowers', 'SellerName', 'ServicerName']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in categoricalcolumns:\n",
        "  print(i,df_train[i].nunique())\n",
        "\n",
        "    \n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:36.115854Z",
          "iopub.execute_input": "2023-02-26T04:18:36.116890Z",
          "iopub.status.idle": "2023-02-26T04:18:36.536913Z",
          "shell.execute_reply.started": "2023-02-26T04:18:36.116818Z",
          "shell.execute_reply": "2023-02-26T04:18:36.534464Z"
        },
        "trusted": true,
        "id": "70k-lKuWL0Us",
        "outputId": "9f09a0f3-2132-438f-e95f-051b00358b88",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FirstTimeHomebuyer 3\n",
            "MSA 392\n",
            "Occupancy 3\n",
            "Channel 4\n",
            "PPM 3\n",
            "ProductType 1\n",
            "PropertyState 53\n",
            "PropertyType 7\n",
            "PostalCode 892\n",
            "LoanSeqNum 291451\n",
            "LoanPurpose 3\n",
            "NumBorrowers 3\n",
            "SellerName 20\n",
            "ServicerName 20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#columns such as MSA,propertystate,postalcode,loanseqnum,sellername,servicename might not hold that much importance because of so many unique values present in them\n"
      ],
      "metadata": {
        "id": "jDidBl0VL0Ut"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#checking whether our column has proper dtype or not\n",
        "df_train[\"EverDelinquent\"].value_counts()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:36.538815Z",
          "iopub.execute_input": "2023-02-26T04:18:36.539875Z",
          "iopub.status.idle": "2023-02-26T04:18:36.553328Z",
          "shell.execute_reply.started": "2023-02-26T04:18:36.539836Z",
          "shell.execute_reply": "2023-02-26T04:18:36.552094Z"
        },
        "trusted": true,
        "id": "nLchZ_WmL0Ut",
        "outputId": "2bc34c43-f8da-4caa-9489-6e2ab9b5fe33",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    233788\n",
              "1     57663\n",
              "Name: EverDelinquent, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "if we see there are two classes present in it,so it is a categorical column"
      ],
      "metadata": {
        "id": "jCmyxAnSL0Ut"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train[\"EverDelinquent\"]=df_train[\"EverDelinquent\"].astype(\"str\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:36.555268Z",
          "iopub.execute_input": "2023-02-26T04:18:36.555992Z",
          "iopub.status.idle": "2023-02-26T04:18:36.822479Z",
          "shell.execute_reply.started": "2023-02-26T04:18:36.555948Z",
          "shell.execute_reply": "2023-02-26T04:18:36.821036Z"
        },
        "trusted": true,
        "id": "YWaW2Pr7L0Ut"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Basic Info of the Data Frame\n",
        "df_train.info()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:36.823902Z",
          "iopub.execute_input": "2023-02-26T04:18:36.824486Z",
          "iopub.status.idle": "2023-02-26T04:18:37.054765Z",
          "shell.execute_reply.started": "2023-02-26T04:18:36.824450Z",
          "shell.execute_reply": "2023-02-26T04:18:37.053715Z"
        },
        "trusted": true,
        "id": "SwaUKh4OL0Ut",
        "outputId": "5ab31dd5-3ba0-4a09-8735-0b99af73516e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 291451 entries, 0 to 291450\n",
            "Data columns (total 28 columns):\n",
            " #   Column              Non-Null Count   Dtype  \n",
            "---  ------              --------------   -----  \n",
            " 0   CreditScore         291451 non-null  int64  \n",
            " 1   FirstPaymentDate    291451 non-null  int64  \n",
            " 2   FirstTimeHomebuyer  291451 non-null  object \n",
            " 3   MaturityDate        291451 non-null  int64  \n",
            " 4   MSA                 291451 non-null  object \n",
            " 5   MIP                 291451 non-null  int64  \n",
            " 6   Units               291451 non-null  int64  \n",
            " 7   Occupancy           291451 non-null  object \n",
            " 8   OCLTV               291451 non-null  int64  \n",
            " 9   DTI                 291451 non-null  int64  \n",
            " 10  OrigUPB             291451 non-null  int64  \n",
            " 11  LTV                 291451 non-null  int64  \n",
            " 12  OrigInterestRate    291451 non-null  float64\n",
            " 13  Channel             291451 non-null  object \n",
            " 14  PPM                 291451 non-null  object \n",
            " 15  ProductType         291451 non-null  object \n",
            " 16  PropertyState       291451 non-null  object \n",
            " 17  PropertyType        291451 non-null  object \n",
            " 18  PostalCode          291451 non-null  object \n",
            " 19  LoanSeqNum          291451 non-null  object \n",
            " 20  LoanPurpose         291451 non-null  object \n",
            " 21  OrigLoanTerm        291451 non-null  int64  \n",
            " 22  NumBorrowers        291451 non-null  object \n",
            " 23  SellerName          266457 non-null  object \n",
            " 24  ServicerName        291451 non-null  object \n",
            " 25  EverDelinquent      291451 non-null  object \n",
            " 26  MonthsDelinquent    291451 non-null  int64  \n",
            " 27  MonthsInRepayment   291451 non-null  int64  \n",
            "dtypes: float64(1), int64(12), object(15)\n",
            "memory usage: 62.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting basic statistical details of the data frame\n",
        "df_train.describe()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:37.059129Z",
          "iopub.execute_input": "2023-02-26T04:18:37.059991Z",
          "iopub.status.idle": "2023-02-26T04:18:37.453041Z",
          "shell.execute_reply.started": "2023-02-26T04:18:37.059935Z",
          "shell.execute_reply": "2023-02-26T04:18:37.451732Z"
        },
        "trusted": true,
        "id": "7ISnPy91L0Ut",
        "outputId": "5fdc9b3a-6a34-4b8c-e2f2-2d0094ec969c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         CreditScore  FirstPaymentDate   MaturityDate            MIP  \\\n",
              "count  291451.000000     291451.000000  291451.000000  291451.000000   \n",
              "mean      708.936991     199904.760553  202902.367043       9.216685   \n",
              "std        68.408791         13.821228      16.090508      12.648273   \n",
              "min         0.000000     199901.000000  202402.000000       0.000000   \n",
              "25%       675.000000     199903.000000  202902.000000       0.000000   \n",
              "50%       717.000000     199904.000000  202903.000000       0.000000   \n",
              "75%       753.000000     199905.000000  202904.000000      25.000000   \n",
              "max       849.000000     201303.000000  204302.000000      55.000000   \n",
              "\n",
              "               Units          OCLTV            DTI        OrigUPB  \\\n",
              "count  291451.000000  291451.000000  291451.000000  291451.000000   \n",
              "mean        1.026022      76.960529      30.033453  124940.387921   \n",
              "std         0.202986      15.236154      13.203974   53657.440624   \n",
              "min         0.000000       0.000000       0.000000    8000.000000   \n",
              "25%         1.000000      70.000000      22.000000   83000.000000   \n",
              "50%         1.000000      80.000000      31.000000  117000.000000   \n",
              "75%         1.000000      90.000000      39.000000  160000.000000   \n",
              "max         4.000000     110.000000      65.000000  497000.000000   \n",
              "\n",
              "                 LTV  OrigInterestRate   OrigLoanTerm  MonthsDelinquent  \\\n",
              "count  291451.000000     291451.000000  291451.000000     291451.000000   \n",
              "mean       76.937986          6.926547     359.835375          1.837729   \n",
              "std        15.225298          0.343541       2.286939          8.809392   \n",
              "min         0.000000          4.000000     301.000000          0.000000   \n",
              "25%        70.000000          6.750000     360.000000          0.000000   \n",
              "50%        80.000000          6.875000     360.000000          0.000000   \n",
              "75%        90.000000          7.125000     360.000000          0.000000   \n",
              "max       103.000000         12.350000     361.000000        180.000000   \n",
              "\n",
              "       MonthsInRepayment  \n",
              "count      291451.000000  \n",
              "mean           60.805291  \n",
              "std            46.142599  \n",
              "min             1.000000  \n",
              "25%            34.000000  \n",
              "50%            48.000000  \n",
              "75%            63.000000  \n",
              "max           212.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d402fc68-458a-47c2-a062-45bbe1ca1dfe\">\n",
              "    <div 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>CreditScore</th>\n",
              "      <th>FirstPaymentDate</th>\n",
              "      <th>MaturityDate</th>\n",
              "      <th>MIP</th>\n",
              "      <th>Units</th>\n",
              "      <th>OCLTV</th>\n",
              "      <th>DTI</th>\n",
              "      <th>OrigUPB</th>\n",
              "      <th>LTV</th>\n",
              "      <th>OrigInterestRate</th>\n",
              "      <th>OrigLoanTerm</th>\n",
              "      <th>MonthsDelinquent</th>\n",
              "      <th>MonthsInRepayment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>291451.000000</td>\n",
              "      <td>291451.000000</td>\n",
              "      <td>291451.000000</td>\n",
              "      <td>291451.000000</td>\n",
              "      <td>291451.000000</td>\n",
              "      <td>291451.000000</td>\n",
              "      <td>291451.000000</td>\n",
              "      <td>291451.000000</td>\n",
              "      <td>291451.000000</td>\n",
              "      <td>291451.000000</td>\n",
              "      <td>291451.000000</td>\n",
              "      <td>291451.000000</td>\n",
              "      <td>291451.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>708.936991</td>\n",
              "      <td>199904.760553</td>\n",
              "      <td>202902.367043</td>\n",
              "      <td>9.216685</td>\n",
              "      <td>1.026022</td>\n",
              "      <td>76.960529</td>\n",
              "      <td>30.033453</td>\n",
              "      <td>124940.387921</td>\n",
              "      <td>76.937986</td>\n",
              "      <td>6.926547</td>\n",
              "      <td>359.835375</td>\n",
              "      <td>1.837729</td>\n",
              "      <td>60.805291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>68.408791</td>\n",
              "      <td>13.821228</td>\n",
              "      <td>16.090508</td>\n",
              "      <td>12.648273</td>\n",
              "      <td>0.202986</td>\n",
              "      <td>15.236154</td>\n",
              "      <td>13.203974</td>\n",
              "      <td>53657.440624</td>\n",
              "      <td>15.225298</td>\n",
              "      <td>0.343541</td>\n",
              "      <td>2.286939</td>\n",
              "      <td>8.809392</td>\n",
              "      <td>46.142599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>199901.000000</td>\n",
              "      <td>202402.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>675.000000</td>\n",
              "      <td>199903.000000</td>\n",
              "      <td>202902.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>83000.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>6.750000</td>\n",
              "      <td>360.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>34.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>717.000000</td>\n",
              "      <td>199904.000000</td>\n",
              "      <td>202903.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>117000.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>6.875000</td>\n",
              "      <td>360.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>48.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>753.000000</td>\n",
              "      <td>199905.000000</td>\n",
              "      <td>202904.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>160000.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>7.125000</td>\n",
              "      <td>360.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>63.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>849.000000</td>\n",
              "      <td>201303.000000</td>\n",
              "      <td>204302.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>497000.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>12.350000</td>\n",
              "      <td>361.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>212.000000</td>\n",
              "    </tr>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Data Quality Check And Missing Values"
      ],
      "metadata": {
        "id": "L1dkHPdoL0Ut"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "3.1 Percentage of missing values for columns and rows"
      ],
      "metadata": {
        "id": "VRWpTz23L0Uu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# checking column-wise null percentages\n",
        "cols = pd.DataFrame(df_train.isnull().mean().round(4) * 100, columns =['precentage_missing_value']).sort_values(by=['precentage_missing_value'])\n",
        "print(cols)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:37.454500Z",
          "iopub.execute_input": "2023-02-26T04:18:37.454857Z",
          "iopub.status.idle": "2023-02-26T04:18:37.675819Z",
          "shell.execute_reply.started": "2023-02-26T04:18:37.454826Z",
          "shell.execute_reply": "2023-02-26T04:18:37.673845Z"
        },
        "trusted": true,
        "id": "4E-JRWL9L0Uu",
        "outputId": "6ce34fe5-2cb7-4580-e3e7-5524ff4aef6a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                    precentage_missing_value\n",
            "CreditScore                             0.00\n",
            "EverDelinquent                          0.00\n",
            "ServicerName                            0.00\n",
            "NumBorrowers                            0.00\n",
            "OrigLoanTerm                            0.00\n",
            "LoanPurpose                             0.00\n",
            "LoanSeqNum                              0.00\n",
            "PostalCode                              0.00\n",
            "PropertyType                            0.00\n",
            "PropertyState                           0.00\n",
            "ProductType                             0.00\n",
            "PPM                                     0.00\n",
            "MonthsDelinquent                        0.00\n",
            "Channel                                 0.00\n",
            "LTV                                     0.00\n",
            "OrigUPB                                 0.00\n",
            "DTI                                     0.00\n",
            "OCLTV                                   0.00\n",
            "Occupancy                               0.00\n",
            "Units                                   0.00\n",
            "MIP                                     0.00\n",
            "MSA                                     0.00\n",
            "MaturityDate                            0.00\n",
            "FirstTimeHomebuyer                      0.00\n",
            "FirstPaymentDate                        0.00\n",
            "OrigInterestRate                        0.00\n",
            "MonthsInRepayment                       0.00\n",
            "SellerName                              8.58\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#only Seller Name has missing values"
      ],
      "metadata": {
        "id": "uuS4_UkEL0Uu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#if we observe maturitydate,firstpaymentdate,the difference of these columns is origloanterm so we can exclude these columns\n",
        "##columns such as MSA,propertystate,postalcode,loanseqnum,sellername,servicename might not hold that much importance because of so many unique values present in them\n",
        "#so we can drop columns such as maturitydate,firstpaymentdate,MSA,propertystate,postalcode,loanseqnum,sellername,servicename"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:37.678720Z",
          "iopub.execute_input": "2023-02-26T04:18:37.679623Z",
          "iopub.status.idle": "2023-02-26T04:18:37.685896Z",
          "shell.execute_reply.started": "2023-02-26T04:18:37.679572Z",
          "shell.execute_reply": "2023-02-26T04:18:37.684432Z"
        },
        "trusted": true,
        "id": "9EJ_GBjCL0Uu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categoricalcolumns"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:37.687672Z",
          "iopub.execute_input": "2023-02-26T04:18:37.688301Z",
          "iopub.status.idle": "2023-02-26T04:18:37.702655Z",
          "shell.execute_reply.started": "2023-02-26T04:18:37.688261Z",
          "shell.execute_reply": "2023-02-26T04:18:37.700890Z"
        },
        "trusted": true,
        "id": "-myPVHAIL0Uu",
        "outputId": "eb173c48-8189-488f-b315-9e7b284e6f62",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['FirstTimeHomebuyer',\n",
              " 'MSA',\n",
              " 'Occupancy',\n",
              " 'Channel',\n",
              " 'PPM',\n",
              " 'ProductType',\n",
              " 'PropertyState',\n",
              " 'PropertyType',\n",
              " 'PostalCode',\n",
              " 'LoanSeqNum',\n",
              " 'LoanPurpose',\n",
              " 'NumBorrowers',\n",
              " 'SellerName',\n",
              " 'ServicerName']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train=df_train.drop(['MSA','PropertyState','PostalCode','LoanSeqNum','SellerName','ServicerName','FirstPaymentDate', 'MaturityDate'],axis=1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:37.704742Z",
          "iopub.execute_input": "2023-02-26T04:18:37.706208Z",
          "iopub.status.idle": "2023-02-26T04:18:37.765855Z",
          "shell.execute_reply.started": "2023-02-26T04:18:37.706151Z",
          "shell.execute_reply": "2023-02-26T04:18:37.764645Z"
        },
        "trusted": true,
        "id": "34n4L2UWL0Uu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categoricalcolumns=[]\n",
        "numericalcolumns=[]\n",
        "for i in df_train.columns:\n",
        "   if df_train[i].dtypes==object:\n",
        "      categoricalcolumns.append(i)\n",
        "   else:\n",
        "      numericalcolumns.append(i)\n",
        "print(numericalcolumns) \n",
        "print(categoricalcolumns)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:37.767281Z",
          "iopub.execute_input": "2023-02-26T04:18:37.768935Z",
          "iopub.status.idle": "2023-02-26T04:18:37.778625Z",
          "shell.execute_reply.started": "2023-02-26T04:18:37.768889Z",
          "shell.execute_reply": "2023-02-26T04:18:37.777065Z"
        },
        "trusted": true,
        "id": "6HGRUBCzL0Uu",
        "outputId": "08089e5b-4083-4af0-b3d6-d1f31fe0cc36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['CreditScore', 'MIP', 'Units', 'OCLTV', 'DTI', 'OrigUPB', 'LTV', 'OrigInterestRate', 'OrigLoanTerm', 'MonthsDelinquent', 'MonthsInRepayment']\n",
            "['FirstTimeHomebuyer', 'Occupancy', 'Channel', 'PPM', 'ProductType', 'PropertyType', 'LoanPurpose', 'NumBorrowers', 'EverDelinquent']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# assuming `numericalcolumns` is a list of column names\n",
        "for i, col in enumerate(numericalcolumns):\n",
        "    # create a new subplot for each column\n",
        "    plt.subplot(5, 5, i+1)\n",
        "    sns.boxplot(df_train[col])\n",
        "    plt.title(col)\n",
        "\n",
        "plt.tight_layout()  # improve spacing between subplots\n",
        "plt.show()\n",
        "    "
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:37.780004Z",
          "iopub.execute_input": "2023-02-26T04:18:37.780969Z",
          "iopub.status.idle": "2023-02-26T04:18:40.244513Z",
          "shell.execute_reply.started": "2023-02-26T04:18:37.780905Z",
          "shell.execute_reply": "2023-02-26T04:18:40.243556Z"
        },
        "trusted": true,
        "id": "MjX4nplwL0Uu",
        "outputId": "da694867-cdd3-40cb-8557-b25dca22e81a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 11 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#rechecking once again\n",
        "df_train.head()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:40.245803Z",
          "iopub.execute_input": "2023-02-26T04:18:40.246964Z",
          "iopub.status.idle": "2023-02-26T04:18:40.270329Z",
          "shell.execute_reply.started": "2023-02-26T04:18:40.246923Z",
          "shell.execute_reply": "2023-02-26T04:18:40.268775Z"
        },
        "trusted": true,
        "id": "1YOP1PcUL0Uv",
        "outputId": "c2a6da81-e136-472a-8b1c-a13b6f77331a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CreditScore FirstTimeHomebuyer  MIP  Units Occupancy  OCLTV  DTI  OrigUPB  \\\n",
              "0            0                  N   25      1         O     89   27   117000   \n",
              "1            0                  N    0      1         O     73   17   109000   \n",
              "2            0                  N    0      1         O     75   16    88000   \n",
              "3            0                  N    0      1         O     76   14   160000   \n",
              "4            0                  N    0      1         O     78   18   109000   \n",
              "\n",
              "   LTV  OrigInterestRate Channel PPM ProductType PropertyType LoanPurpose  \\\n",
              "0   89             6.750       T   N       FRM             SF           P   \n",
              "1   73             6.500       R   N       FRM             SF           N   \n",
              "2   75             6.875       T   N       FRM             SF           N   \n",
              "3   76             6.875       R   N       FRM             SF           N   \n",
              "4   78             7.125       R   N       FRM             SF           N   \n",
              "\n",
              "   OrigLoanTerm NumBorrowers EverDelinquent  MonthsDelinquent  \\\n",
              "0           360            2              0                 0   \n",
              "1           360            1              0                 0   \n",
              "2           360            2              0                 0   \n",
              "3           360            2              0                 0   \n",
              "4           360            2              0                 0   \n",
              "\n",
              "   MonthsInRepayment  \n",
              "0                 52  \n",
              "1                144  \n",
              "2                 67  \n",
              "3                 35  \n",
              "4                 54  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cd5f835e-c642-4d95-9be8-aa36da978abb\">\n",
              "    <div 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>CreditScore</th>\n",
              "      <th>FirstTimeHomebuyer</th>\n",
              "      <th>MIP</th>\n",
              "      <th>Units</th>\n",
              "      <th>Occupancy</th>\n",
              "      <th>OCLTV</th>\n",
              "      <th>DTI</th>\n",
              "      <th>OrigUPB</th>\n",
              "      <th>LTV</th>\n",
              "      <th>OrigInterestRate</th>\n",
              "      <th>Channel</th>\n",
              "      <th>PPM</th>\n",
              "      <th>ProductType</th>\n",
              "      <th>PropertyType</th>\n",
              "      <th>LoanPurpose</th>\n",
              "      <th>OrigLoanTerm</th>\n",
              "      <th>NumBorrowers</th>\n",
              "      <th>EverDelinquent</th>\n",
              "      <th>MonthsDelinquent</th>\n",
              "      <th>MonthsInRepayment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>O</td>\n",
              "      <td>89</td>\n",
              "      <td>27</td>\n",
              "      <td>117000</td>\n",
              "      <td>89</td>\n",
              "      <td>6.750</td>\n",
              "      <td>T</td>\n",
              "      <td>N</td>\n",
              "      <td>FRM</td>\n",
              "      <td>SF</td>\n",
              "      <td>P</td>\n",
              "      <td>360</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>O</td>\n",
              "      <td>73</td>\n",
              "      <td>17</td>\n",
              "      <td>109000</td>\n",
              "      <td>73</td>\n",
              "      <td>6.500</td>\n",
              "      <td>R</td>\n",
              "      <td>N</td>\n",
              "      <td>FRM</td>\n",
              "      <td>SF</td>\n",
              "      <td>N</td>\n",
              "      <td>360</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>O</td>\n",
              "      <td>75</td>\n",
              "      <td>16</td>\n",
              "      <td>88000</td>\n",
              "      <td>75</td>\n",
              "      <td>6.875</td>\n",
              "      <td>T</td>\n",
              "      <td>N</td>\n",
              "      <td>FRM</td>\n",
              "      <td>SF</td>\n",
              "      <td>N</td>\n",
              "      <td>360</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>O</td>\n",
              "      <td>76</td>\n",
              "      <td>14</td>\n",
              "      <td>160000</td>\n",
              "      <td>76</td>\n",
              "      <td>6.875</td>\n",
              "      <td>R</td>\n",
              "      <td>N</td>\n",
              "      <td>FRM</td>\n",
              "      <td>SF</td>\n",
              "      <td>N</td>\n",
              "      <td>360</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>O</td>\n",
              "      <td>78</td>\n",
              "      <td>18</td>\n",
              "      <td>109000</td>\n",
              "      <td>78</td>\n",
              "      <td>7.125</td>\n",
              "      <td>R</td>\n",
              "      <td>N</td>\n",
              "      <td>FRM</td>\n",
              "      <td>SF</td>\n",
              "      <td>N</td>\n",
              "      <td>360</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd5f835e-c642-4d95-9be8-aa36da978abb')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-cd5f835e-c642-4d95-9be8-aa36da978abb 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-cd5f835e-c642-4d95-9be8-aa36da978abb');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "for outlier analysis we will focus on\n",
        " 1)'CreditScore',\n",
        " 4)'MIP',\n",
        " 5)'Units',\n",
        " 6)'OCLTV',\n",
        " 7)'DTI',\n",
        " 8)'OrigUPB',\n",
        " 9)'LTV',\n",
        " 10)'OrigInterestRate',\n",
        " 11)'OrigLoanTerm',\n",
        " 12)'EverDelinquent',\n",
        " 13)'MonthsDelinquent',\n",
        " 14)'MonthsInRepayment'\n"
      ],
      "metadata": {
        "id": "O1WIpvtiL0Uv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#percentage of outliers present in each column\n",
        "for i in numericalcolumns:\n",
        "    \n",
        " IQR = (df_train[i]).quantile(0.75) - (df_train[i]).quantile(0.25)\n",
        " Upper_limit = (df_train[i]).quantile(0.75) + IQR*1.5\n",
        " lower_limit = (df_train[i]).quantile(0.25) - IQR*1.5\n",
        " print(i,round(100.0 * len(df_train[(df_train[i])>Upper_limit])/len(df_train), 2)+round(100.0 * len(df_train[(df_train[i])<lower_limit])/len(df_train), 2))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:40.272223Z",
          "iopub.execute_input": "2023-02-26T04:18:40.272741Z",
          "iopub.status.idle": "2023-02-26T04:18:40.564779Z",
          "shell.execute_reply.started": "2023-02-26T04:18:40.272702Z",
          "shell.execute_reply": "2023-02-26T04:18:40.563378Z"
        },
        "trusted": true,
        "id": "lQckliTuL0Uv",
        "outputId": "2c80aa2d-c61d-4a40-d3cd-8ccd05047d3a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CreditScore 0.79\n",
            "MIP 0.0\n",
            "Units 2.01\n",
            "OCLTV 3.05\n",
            "DTI 0.07\n",
            "OrigUPB 0.33\n",
            "LTV 3.05\n",
            "OrigInterestRate 3.69\n",
            "OrigLoanTerm 0.82\n",
            "MonthsDelinquent 19.78\n",
            "MonthsInRepayment 13.83\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Origintrest rate,orig loanterm,monthsdeliquent,monthsinrepayment shoudl be analyzed more so i will not replace the values with upper and lower limits in this columns"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:40.566562Z",
          "iopub.execute_input": "2023-02-26T04:18:40.567080Z",
          "iopub.status.idle": "2023-02-26T04:18:40.573307Z",
          "shell.execute_reply.started": "2023-02-26T04:18:40.567032Z",
          "shell.execute_reply": "2023-02-26T04:18:40.571668Z"
        },
        "trusted": true,
        "id": "dz7BzY9fL0Uv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "excludedcolumns=['OrigInterestRate','OrigLoanTerm','MonthsDelinquent','MonthsInRepayment']\n",
        "numericalcolumns1=[x for x in numericalcolumns if x not in excludedcolumns]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:40.575189Z",
          "iopub.execute_input": "2023-02-26T04:18:40.575856Z",
          "iopub.status.idle": "2023-02-26T04:18:40.590506Z",
          "shell.execute_reply.started": "2023-02-26T04:18:40.575818Z",
          "shell.execute_reply": "2023-02-26T04:18:40.588872Z"
        },
        "trusted": true,
        "id": "PtK6VzSsL0Uv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in numericalcolumns1:\n",
        " IQR = (df_train[i]).quantile(0.75) - (df_train[i]).quantile(0.25)\n",
        " Upper_limit = (df_train[i]).quantile(0.75) + IQR*1.5\n",
        " lower_limit = (df_train[i]).quantile(0.25) - IQR*1.5\n",
        " # Replace outliers with respective limit using np.where\n",
        " df_train[i]= np.where(df_train[i]< lower_limit, lower_limit, df_train[i])\n",
        " df_train[i]= np.where(df_train[i]> Upper_limit, Upper_limit, df_train[i])\n",
        " print(i,round(100.0 * len(df_train[(df_train[i])>Upper_limit])/len(df_train), 2)+round(100.0 * len(df_train[(df_train[i])<lower_limit])/len(df_train), 2))   "
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:40.592712Z",
          "iopub.execute_input": "2023-02-26T04:18:40.593260Z",
          "iopub.status.idle": "2023-02-26T04:18:40.860659Z",
          "shell.execute_reply.started": "2023-02-26T04:18:40.593212Z",
          "shell.execute_reply": "2023-02-26T04:18:40.858135Z"
        },
        "trusted": true,
        "id": "ok45LunzL0Uv",
        "outputId": "f5f96520-aae0-428c-e1d4-9f8937288e8c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CreditScore 0.0\n",
            "MIP 0.0\n",
            "Units 0.0\n",
            "OCLTV 0.0\n",
            "DTI 0.0\n",
            "OrigUPB 0.0\n",
            "LTV 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#SO WE HAD SUCCESFULLY TREATED OUTLIERS IN NUMERICAL COLUMNS"
      ],
      "metadata": {
        "id": "f6EoYsWDL0Uv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#now we have to see whether we can treat outliers in excluded columns\n"
      ],
      "metadata": {
        "id": "Hzy9zvMYL0Uv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in excludedcolumns:\n",
        "    fig, ax = plt.subplots()\n",
        "    sns.boxplot(df_train[i], ax=ax)\n",
        "    ax.set_title(i)\n",
        "    plt.show()  "
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:40.865326Z",
          "iopub.execute_input": "2023-02-26T04:18:40.865777Z",
          "iopub.status.idle": "2023-02-26T04:18:41.907334Z",
          "shell.execute_reply.started": "2023-02-26T04:18:40.865740Z",
          "shell.execute_reply": "2023-02-26T04:18:41.905594Z"
        },
        "trusted": true,
        "id": "f-OrSvNGL0Uv",
        "outputId": "320ef722-52c4-4914-90c5-c9e9d5504067",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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BLEmZGMCSlIkBLEmZGMCSlIkBLEmZGMCSlElX7g5IUqeIiDHdngEsSaXDDjtsTLfnJQhJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJysQAlqRMDGBJyqQrdwckqVOccMIJY7o9A1iSSmefffaYbs9LEJKUiQEsSZkYwJKUiQEsSZkYwJKUiQEsSZkYwJKUiQEsSZkYwJKUiQEsSZkYwJKUiQEsSZkYwJKUiQEsSZkYwJKUiQEsSZkYwJKUiQEsSZkYwJKUiQEsSZlESqn9mSOeAh5vY9YZwNP726kOc7DUcrDUAdbSiQ6WOmB0anlDSmlm/8ZhBXC7ImJDSmnRAV9xBgdLLQdLHWAtnehgqQPGthYvQUhSJgawJGUyWgH8pVFabw4HSy0HSx1gLZ3oYKkDxrCWUbkGLEkampcgJCkTA1iSMhl2AEfElIj4t4j494h4MCIuKtt/MyLujYi+iLgxIiaV7ZPL933l9N4DXMN+G6SWj5X9TRExo2H+iIgvlNM2RsTCfL1vNkgt10fEIxHxQET8Y0RMLNs7spZB6viHsm1jRNwcEYeX7ePu/GqY/oWIeKHh/birJSKujYifRcRPyq+3lO0deX7BoLVERFwaET+NiE0RcU5D++jUklIa1hcQwOHl64nAvcBi4Cbg1LL9auCs8vVHgavL16cCNw53m6P1NUgtvwf0ApuBGQ3zvxu4o1xuMXBv7hraqOXd5bQAbmg4Lh1ZyyB1vLZhniuAvx6v51f5fhHwVeCFhvnHXS3AtcD7K+bvyPNriFo+DPwT8Jpy2qzRrmXYI+BUqH1qTyy/EvCHwM1l+1eA95avTyrfU05/R0TEcLc7GlrVklL6cUppc8UiJwH/VC73Q+CIiJg9Rt0d1CC1/Es5LQH/Bswp5+nIWgap41dQjEaAqRTnHIzD8ysiJgD/G/irfouMu1oGWaQjzy8YtJazgItTSq+W820r5xm1WvbrGnBETIiInwDbgDuBx4DnUkp7y1m2AEeVr48Cfg5QTt8BTB9Bnw+o/rWklO4dZPZ6LaXGOrMbrJby0sOfAmvLpo6tpVUdEfFl4JfAPGBVOft4PL8+BtyWUtrab/bxWAvApeWP5ldGxOSyrWPPL2hZy28DH4yIDRFxR0S8sZx91GrZrwBOKb2SUnoLxWjqbRTfEONS/1oi4s2Zu7Tfhqjli8B3UkrfzdK5YWhVR0rpw8DrgU3AB/P1sH0VtSwBPsC+D5Bxo8Vx+RuK7//fB/4T8L/y9bB9LWqZDOxKxZ8hrwb+cbT7MaK7IFJKzwHrgLdTDMu7yklzgCfK108Avw5QTp8GbB/JdkdDQy0nDDJbvZZSY50do38tEXEBMBM4r2G2jq+l6piklF4Bvga8r2wab+fX8cBcoC8iNgOHRURfOdt4q+WElNLW8kfz3cCXKQZkMA7OLxhwjm0BvllOugU4pnw9arXsz10QMyPiiPL1VGApxYhkHfD+crY/A24tX99Wvqecfnd5PTK7FrU8PMgitwEfKn8ruhjYUfFjZBataomIM4F3AafVrm2VOrKWFnU8EhFzy7YA/oh9x2m8nV/3pZR+LaXUm1LqBV5MKc0tFxlvtTxcuxZaHpf3Ag+Ui3Tk+QWDft//M8UHJMAfAD8tX49eLWn4v0E8BvgxsJFiZ/9t2f5bFL/k6QO+Dkwu26eU7/vK6b813G2O1tcgtZxD8Wm4F/gFcE3a99vT/0Nxzft+YFHuGtqoZW/Z35+UX7X2jqylqg6KgcL3y34+AFxPeVfEeDy/+s3TeBfEuKsFuLvhuFzHvrsLOvL8GqKWI4A1ZX9/APzuaNfinyJLUib+JZwkZWIAS1ImBrAkZWIAS1ImBrAkZWIAa8QiYk5E3BoRj0bEYxFxVZRPw+s33+sj4uaqdfSbb3M0PIXuAPZzeux7atcvI+KJhvcD+iuNNm9D04iUN+DfC/x9SunL5YNmvgQ8k1L6y4b5utK+Z4UMtc7NFPdajtp/cx4RF1Lcg/vZNudvu/9SuxwBa6T+kOLv578M9T8V/p/An0fERyPitoi4G/h2RPRGxAMAEXFYRNwUEQ9FxC1RPP+25X8FXi57d/nQl29HxG+U7cvKZX8cEXdFxOvK9gujeP7x+oj4jyif7dpi3W+NiP8bEfdFxL82/HXX+oj4fERsAM4t319ZPqxlU0T8fkR8sxz5X3KA9qcOIQawRupo4L7GhlQ8OvL/AV3AQornxf5Bv+U+CjybUnoT8CngrUNsZxXwlZTSMRR/CfeFsv17FM/Y/T2KZ0Q0PuJxHsWfYb8NuCDKh9E3KttWlX18K8UDWC5tmGVSSmlRSulz5fuXU/Gwlqsp/tz+L4A3A2dERMc8uUzjQ9fQs0gjcmdK6ZmK9v8KXAWQUnogIjYOsZ63A39cvv4q8Jny9RzgxnLUOgn4WcMya1LxkJjdEbENeB3Fn5g3+h2KAL2zuJrCBKDx7/xv7Df/beW/9wMPpvKZABHxHxQPbOm4h+eocxnAGqmH2PcQJgAi4rXAb1A8h2LnKG9/FXBFSum2iDgOuLBh2u6G169Qfb4HRZC+vcX6+/e/ts5X+63/1Rbrl1ryEoRG6tsUj1T8EBQPugY+R/Ff1bw4yHLfB04pl3kTsGCI7dxD8d/0APwJUHuu8TT2PRrwz/ov1IZHgJkR8fayLxMj4uj9WI80bAawRiQVt9GcDHwgIh6leITfLmDlEIt+kSL4HgIuAR6k+B8gajZGxJby6wrgbODD5aWKPwXOLee7EPh6RNwHDPuuiZTSyxQj+Msj4t8pnhh37HDXI+0Pb0NTFuVIeWJKaVdE/DZwF/A7ZSBKhwSvWSmXw4B15V0IAXzU8NWhxhGwJGXiNWBJysQAlqRMDGBJysQAlqRMDGBJyuT/A2stNeh8q3sJAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we cannot really identify the reason for these outliers so we will leave it and see after our model results whether we can do anything"
      ],
      "metadata": {
        "id": "MVIW0eToL0Uw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in categoricalcolumns:\n",
        "    print(i,df_train[i].value_counts())"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:41.908754Z",
          "iopub.execute_input": "2023-02-26T04:18:41.909745Z",
          "iopub.status.idle": "2023-02-26T04:18:42.065078Z",
          "shell.execute_reply.started": "2023-02-26T04:18:41.909670Z",
          "shell.execute_reply": "2023-02-26T04:18:42.063511Z"
        },
        "trusted": true,
        "id": "K81U7Q04L0Uw",
        "outputId": "5d9b20e4-5a2b-4f8f-b213-086342951cc6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FirstTimeHomebuyer N    184154\n",
            "X     78015\n",
            "Y     29282\n",
            "Name: FirstTimeHomebuyer, dtype: int64\n",
            "Occupancy O    276131\n",
            "I      8734\n",
            "S      6586\n",
            "Name: Occupancy, dtype: int64\n",
            "Channel T    167670\n",
            "R    123219\n",
            "C       407\n",
            "B       155\n",
            "Name: Channel, dtype: int64\n",
            "PPM N    282125\n",
            "X      5405\n",
            "Y      3921\n",
            "Name: PPM, dtype: int64\n",
            "ProductType FRM      291451\n",
            "Name: ProductType, dtype: int64\n",
            "PropertyType SF    244923\n",
            "PU     27506\n",
            "CO     18100\n",
            "MH       723\n",
            "LH       105\n",
            "CP        72\n",
            "X         22\n",
            "Name: PropertyType, dtype: int64\n",
            "LoanPurpose P    123404\n",
            "N    110036\n",
            "C     58011\n",
            "Name: LoanPurpose, dtype: int64\n",
            "NumBorrowers 2     187335\n",
            "1     103777\n",
            "X        339\n",
            "Name: NumBorrowers, dtype: int64\n",
            "EverDelinquent 0    233788\n",
            "1     57663\n",
            "Name: EverDelinquent, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**if we see in first time homebuyer there is another category as x,and in occupancy one category is dominating,and there aare 4 channels in which x and y are far more less compared to other one,in product type only frm is there,and in property type three are dominating,and in loan purpose only x is least(so we have to identify suitable one to replace them because they might cause data redundancy issues)**"
      ],
      "metadata": {
        "id": "Vjs8De4oL0Uw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1)in firsttime homebuyer i will replace x with n because aperson might be buying or might not be buying there are only two options so i am replacing with no\n",
        "\n",
        "2)Occupancy type is important because it indicates to a lender the lengths to which a borrower will go before they give up making payments on their mortgage.To understand this, imagine you own two properties, each with their own mortgage.\n",
        "One property is your home, where your spouse or kids live, or where you spend most of your time. This is your principal residence. The other property is a duplex that you rent out. This is your investment property.Now, imagine you faced a significant financial hardship, and you could only afford to keep making payments on one of your two mortgages. 0-own house(one-to-four unit family home) S-secondary(vaccation home) I-investmet house(most important)\n",
        "\n",
        "3)PPM-prepayment penalty mortgage Prepayment penalties are written into mortgage contracts by lenders to compensate for prepayment risk, particularly in difficult economic climates and under circumstances where the incentive for a borrower to refinance a subprime mortgage is high. These penalties don’t only kick in when a borrower pays off the entire loan. Some penalty provisions go into effect if the borrower pays a large portion of the loan balance in a single payment.(so i will also replace here with mode)"
      ],
      "metadata": {
        "id": "t7j_Tm2uL0Uw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1)first time homebuyer**"
      ],
      "metadata": {
        "id": "tk-NEWrxL0Uw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['FirstTimeHomebuyer']=df_train['FirstTimeHomebuyer'].replace(to_replace=[\"N\",\"X\",\"Y\"],value=[\"NO\",\"NO\",\"YES\"],regex=True)\n",
        "df_train['FirstTimeHomebuyer'].value_counts()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:42.066790Z",
          "iopub.execute_input": "2023-02-26T04:18:42.067289Z",
          "iopub.status.idle": "2023-02-26T04:18:43.790739Z",
          "shell.execute_reply.started": "2023-02-26T04:18:42.067248Z",
          "shell.execute_reply": "2023-02-26T04:18:43.788690Z"
        },
        "trusted": true,
        "id": "5I6ynv-7L0Uw",
        "outputId": "d078caf2-f7fb-40be-8218-d9a204a8d523",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NO     262169\n",
              "YES     29282\n",
              "Name: FirstTimeHomebuyer, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2)**channel"
      ],
      "metadata": {
        "id": "26JoZJrPL0Ux"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train[\"Channel\"]=df_train[\"Channel\"].replace(to_replace=[\"T\",\"R\",\"C\",\"B\"],value=[\"Traditional Mortgage\",\"Retail banking\",\"Retail banking\",\"Retail banking\"],regex=True)\n",
        "df_train[\"Channel\"].value_counts()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:43.792798Z",
          "iopub.execute_input": "2023-02-26T04:18:43.793297Z",
          "iopub.status.idle": "2023-02-26T04:18:45.986544Z",
          "shell.execute_reply.started": "2023-02-26T04:18:43.793258Z",
          "shell.execute_reply": "2023-02-26T04:18:45.984904Z"
        },
        "trusted": true,
        "id": "-_UAHzTTL0Ux",
        "outputId": "cff217cc-f61a-469c-ec30-0f44fb99dc06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Traditional Mortgage    167670\n",
              "Retail banking          123781\n",
              "Name: Channel, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.head()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:45.988862Z",
          "iopub.execute_input": "2023-02-26T04:18:45.989280Z",
          "iopub.status.idle": "2023-02-26T04:18:46.021338Z",
          "shell.execute_reply.started": "2023-02-26T04:18:45.989245Z",
          "shell.execute_reply": "2023-02-26T04:18:46.019773Z"
        },
        "trusted": true,
        "id": "cpkqzj12L0Ux",
        "outputId": "43888f21-4296-4cfd-8afa-86877cf83a7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CreditScore FirstTimeHomebuyer   MIP  Units Occupancy  OCLTV   DTI  \\\n",
              "0        558.0                 NO  25.0    1.0         O   89.0  27.0   \n",
              "1        558.0                 NO   0.0    1.0         O   73.0  17.0   \n",
              "2        558.0                 NO   0.0    1.0         O   75.0  16.0   \n",
              "3        558.0                 NO   0.0    1.0         O   76.0  14.0   \n",
              "4        558.0                 NO   0.0    1.0         O   78.0  18.0   \n",
              "\n",
              "    OrigUPB   LTV  OrigInterestRate               Channel PPM ProductType  \\\n",
              "0  117000.0  89.0             6.750  Traditional Mortgage   N       FRM     \n",
              "1  109000.0  73.0             6.500        Retail banking   N       FRM     \n",
              "2   88000.0  75.0             6.875  Traditional Mortgage   N       FRM     \n",
              "3  160000.0  76.0             6.875        Retail banking   N       FRM     \n",
              "4  109000.0  78.0             7.125        Retail banking   N       FRM     \n",
              "\n",
              "  PropertyType LoanPurpose  OrigLoanTerm NumBorrowers EverDelinquent  \\\n",
              "0           SF           P           360            2              0   \n",
              "1           SF           N           360            1              0   \n",
              "2           SF           N           360            2              0   \n",
              "3           SF           N           360            2              0   \n",
              "4           SF           N           360            2              0   \n",
              "\n",
              "   MonthsDelinquent  MonthsInRepayment  \n",
              "0                 0                 52  \n",
              "1                 0                144  \n",
              "2                 0                 67  \n",
              "3                 0                 35  \n",
              "4                 0                 54  "
            ],
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              "      <th>CreditScore</th>\n",
              "      <th>FirstTimeHomebuyer</th>\n",
              "      <th>MIP</th>\n",
              "      <th>Units</th>\n",
              "      <th>Occupancy</th>\n",
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              "      <td>73.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>109000.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>6.500</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*The number of months in delinquency and the number of months in repayment are related in the sense that delinquency occurs when a borrower is behind on their loan payments. The number of months in delinquency represents the length of time that a borrower has been late on their payments, while the number of months in repayment represents the length of time that a borrower has been making payments on their loan.*"
      ],
      "metadata": {
        "id": "yblKq68qL0Ux"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Analysis of our target column**"
      ],
      "metadata": {
        "id": "noYap913L0Ux"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***1 Checking imbalance in data***"
      ],
      "metadata": {
        "id": "NacLexKWL0Ux"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding % of people with deliquent and not deliquent\n",
        "\n",
        "target_0_percentage = (round (len(df_train.query('EverDelinquent==\"0\"'))/len(df_train),4)) * 100\n",
        "print (\"Target_0_percentage:\", target_0_percentage,\"%\")\n",
        "\n",
        "target_1_percentage = (round (len(df_train.query('EverDelinquent==\"1\"'))/len(df_train),4)) * 100\n",
        "print (\"Target_1_percentage:\", target_1_percentage,\"%\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:46.023162Z",
          "iopub.execute_input": "2023-02-26T04:18:46.023566Z",
          "iopub.status.idle": "2023-02-26T04:18:46.125955Z",
          "shell.execute_reply.started": "2023-02-26T04:18:46.023534Z",
          "shell.execute_reply": "2023-02-26T04:18:46.124344Z"
        },
        "trusted": true,
        "id": "Jw_dDDueL0Ux",
        "outputId": "37ba3c57-a1b7-412e-c380-c292024546a6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Target_0_percentage: 80.22 %\n",
            "Target_1_percentage: 19.78 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**As the percentage of 0 and 1 are different, there is an imbalance**"
      ],
      "metadata": {
        "id": "3lQlygO7L0Uy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Creation of two data sets - one for each Target = 1 and Target = 0*"
      ],
      "metadata": {
        "id": "vSThuecTL0Uy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # Creating Dataframe of the non-defaulters  \n",
        "deliquent_0_df = df_train.query('EverDelinquent==\"0\"')\n",
        "deliquent_1_df = df_train.query('EverDelinquent==\"1\"')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:46.136425Z",
          "iopub.execute_input": "2023-02-26T04:18:46.136863Z",
          "iopub.status.idle": "2023-02-26T04:18:46.226822Z",
          "shell.execute_reply.started": "2023-02-26T04:18:46.136832Z",
          "shell.execute_reply": "2023-02-26T04:18:46.225475Z"
        },
        "trusted": true,
        "id": "vP50F-hPL0Uy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(deliquent_0_df.shape)\n",
        "print(deliquent_1_df.shape)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:46.228373Z",
          "iopub.execute_input": "2023-02-26T04:18:46.229003Z",
          "iopub.status.idle": "2023-02-26T04:18:46.236399Z",
          "shell.execute_reply.started": "2023-02-26T04:18:46.228953Z",
          "shell.execute_reply": "2023-02-26T04:18:46.235159Z"
        },
        "trusted": true,
        "id": "AWsiuP9gL0Uy",
        "outputId": "2f400533-7111-46fa-994b-695cb0ff0519",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(233788, 20)\n",
            "(57663, 20)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " categoricalcolumns=[]\n",
        " numericalcolumns=[]\n",
        " for i in df_train.columns:\n",
        "   if df_train[i].dtypes==object:\n",
        "      categoricalcolumns.append(i)\n",
        "   else:\n",
        "      numericalcolumns.append(i)\n",
        "print(numericalcolumns) \n",
        "print(categoricalcolumns)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:46.238346Z",
          "iopub.execute_input": "2023-02-26T04:18:46.238875Z",
          "iopub.status.idle": "2023-02-26T04:18:46.261303Z",
          "shell.execute_reply.started": "2023-02-26T04:18:46.238826Z",
          "shell.execute_reply": "2023-02-26T04:18:46.259401Z"
        },
        "trusted": true,
        "id": "3E0xlu2IL0Uy",
        "outputId": "467d49fd-e20d-4892-b8d9-a588e94ef503",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['CreditScore', 'MIP', 'Units', 'OCLTV', 'DTI', 'OrigUPB', 'LTV', 'OrigInterestRate', 'OrigLoanTerm', 'MonthsDelinquent', 'MonthsInRepayment']\n",
            "['FirstTimeHomebuyer', 'Occupancy', 'Channel', 'PPM', 'ProductType', 'PropertyType', 'LoanPurpose', 'NumBorrowers', 'EverDelinquent']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categoricalcolumns.remove('EverDelinquent')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:46.263969Z",
          "iopub.execute_input": "2023-02-26T04:18:46.264564Z",
          "iopub.status.idle": "2023-02-26T04:18:46.279372Z",
          "shell.execute_reply.started": "2023-02-26T04:18:46.264522Z",
          "shell.execute_reply": "2023-02-26T04:18:46.277776Z"
        },
        "trusted": true,
        "id": "R2pyn9a1L0Uy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "*** Univariate Analysis for categorical variable***"
      ],
      "metadata": {
        "id": "sOPo5cxyL0Uy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Under univariate analysis, we will look at percentage distribution of values of categorial variable*"
      ],
      "metadata": {
        "id": "FuuUU325L0Uy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#loop for performing univariate analysis\n",
        "for i in categoricalcolumns:\n",
        "    plt.figure(figsize=(5,5))\n",
        "    plt.subplot(1,2,1)\n",
        "    deliquent_0_df[i].value_counts(normalize=True).plot.bar()\n",
        "    plt.title(i+ '- deliquent = 0')\n",
        "    plt.subplot(1,2,2)\n",
        "    deliquent_1_df[i].value_counts(normalize=True).plot.bar()\n",
        "    plt.title(i+ '- deliquent = 1')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:46.281114Z",
          "iopub.execute_input": "2023-02-26T04:18:46.281609Z",
          "iopub.status.idle": "2023-02-26T04:18:49.226098Z",
          "shell.execute_reply.started": "2023-02-26T04:18:46.281571Z",
          "shell.execute_reply": "2023-02-26T04:18:49.224811Z"
        },
        "trusted": true,
        "id": "wMJ7qtUbL0Uy",
        "outputId": "7fffbf65-8782-486f-bcd4-534384cb7f44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#highlights from the plot \n",
        "1)**num of borrowers are more for target 1 deliquent which means persons who are deliquate has borrowed from one source**\n",
        "2**even in loan purpose the category c are deliqueating more in case of target 1**\n",
        "3)**even in channel  the category traditional mortgage are deliqueating more in case of target 1**"
      ],
      "metadata": {
        "id": "IdiKyWrTL0Uy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#lets plot heatmaps"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:49.228677Z",
          "iopub.execute_input": "2023-02-26T04:18:49.229063Z",
          "iopub.status.idle": "2023-02-26T04:18:49.234041Z",
          "shell.execute_reply.started": "2023-02-26T04:18:49.229031Z",
          "shell.execute_reply": "2023-02-26T04:18:49.232823Z"
        },
        "trusted": true,
        "id": "pSri9leeL0Uy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(df_train.corr(),annot=True)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:49.235846Z",
          "iopub.execute_input": "2023-02-26T04:18:49.236240Z",
          "iopub.status.idle": "2023-02-26T04:18:50.332720Z",
          "shell.execute_reply.started": "2023-02-26T04:18:49.236204Z",
          "shell.execute_reply": "2023-02-26T04:18:50.331512Z"
        },
        "trusted": true,
        "id": "NDw6r5t-L0Uz",
        "outputId": "8065657a-1295-43f3-bf9e-879be62d9d93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 705
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,8))\n",
        "plt.subplot(1,2,1)\n",
        "sns.heatmap(deliquent_0_df[numericalcolumns].corr(),annot=True)\n",
        "b, t = plt.ylim() # discover the values for bottom and top\n",
        "b += 0.5 # Add 0.5 to the bottom\n",
        "t -= 0.5 # Subtract 0.5 from the top\n",
        "plt.ylim(b, t) # update the ylim(bottom, top) values\n",
        "plt.yticks(rotation = 0)\n",
        "plt.title('heatmap- Target = 0')\n",
        "plt.subplot(1,2,2)\n",
        "sns.heatmap(deliquent_0_df[numericalcolumns].corr(),annot=True,)\n",
        "plt.title('heatmap- Target = 1')\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:50.334324Z",
          "iopub.execute_input": "2023-02-26T04:18:50.336170Z",
          "iopub.status.idle": "2023-02-26T04:18:52.555227Z",
          "shell.execute_reply.started": "2023-02-26T04:18:50.336018Z",
          "shell.execute_reply": "2023-02-26T04:18:52.553955Z"
        },
        "trusted": true,
        "id": "eRbMGA4hL0Uz",
        "outputId": "026faa31-bd54-4106-a077-4a78673cbccb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'heatmap- Target = 1')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Through the heatmap we can see same set of columns seem to have a high correlation across all three data sets. Top correlate colums are:\n",
        "ltv vs mip \n",
        "ltv,ocltv vs credit score\n",
        "ltv vs ocltv\n"
      ],
      "metadata": {
        "id": "XEpRib_hL0Uz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#now we have to select best feautures for model building but we will first try to build model using all the features\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:18:52.556798Z",
          "iopub.execute_input": "2023-02-26T04:18:52.557172Z",
          "iopub.status.idle": "2023-02-26T04:18:52.562946Z",
          "shell.execute_reply.started": "2023-02-26T04:18:52.557139Z",
          "shell.execute_reply": "2023-02-26T04:18:52.561536Z"
        },
        "trusted": true,
        "id": "asikoWyrL0Uz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in categoricalcolumns:\n",
        "    print(i,df_train[i].nunique())"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:20:33.720941Z",
          "iopub.execute_input": "2023-02-26T04:20:33.721422Z",
          "iopub.status.idle": "2023-02-26T04:20:33.895996Z",
          "shell.execute_reply.started": "2023-02-26T04:20:33.721388Z",
          "shell.execute_reply": "2023-02-26T04:20:33.894358Z"
        },
        "trusted": true,
        "id": "GFB6XE_KL0Uz",
        "outputId": "c229cae2-7ab8-45b3-a64f-7c6810793932",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FirstTimeHomebuyer 2\n",
            "Occupancy 3\n",
            "Channel 2\n",
            "PPM 3\n",
            "ProductType 1\n",
            "PropertyType 7\n",
            "LoanPurpose 3\n",
            "NumBorrowers 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categoricalcolumnsle=[\"FirstTimeHomebuyer\",\"Channel\",\"ProductType\",]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:23:45.506863Z",
          "iopub.execute_input": "2023-02-26T04:23:45.507769Z",
          "iopub.status.idle": "2023-02-26T04:23:45.516682Z",
          "shell.execute_reply.started": "2023-02-26T04:23:45.507692Z",
          "shell.execute_reply": "2023-02-26T04:23:45.514531Z"
        },
        "trusted": true,
        "id": "7zl3CijwL0Uz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#function for labelencoder:\n",
        "def label_encode(column):\n",
        "  label={}\n",
        "  encodedcolumn=[]\n",
        "  for i in column:\n",
        "    if i not in label:\n",
        "      label[i]=len(label)\n",
        "      encodedcolumn.append(label[i])\n",
        "  return label,encodedcolumn    "
      ],
      "metadata": {
        "id": "o6bjVO9DcW9A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(label_encode(df_train[\"FirstTimeHomebuyer\"]))\n",
        "print(label_encode(df_train[\"Channel\"]))\n",
        "print(label_encode(df_train[\"ProductType\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VO0e3Fo3nMfo",
        "outputId": "e8139dfa-eac7-4dfe-8377-32685cbe1c9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "({'NO': 0, 'YES': 1}, [0, 1])\n",
            "({'Traditional Mortgage': 0, 'Retail banking': 1}, [0, 1])\n",
            "({'FRM  ': 0}, [0])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "for i in categoricalcolumnsle:\n",
        "    df_train[i] = le.fit_transform(df_train[i])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:23:50.715977Z",
          "iopub.execute_input": "2023-02-26T04:23:50.716465Z",
          "iopub.status.idle": "2023-02-26T04:23:51.074244Z",
          "shell.execute_reply.started": "2023-02-26T04:23:50.716406Z",
          "shell.execute_reply": "2023-02-26T04:23:51.072892Z"
        },
        "trusted": true,
        "id": "NRmNlyR0L0Uz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train= pd.get_dummies(df_train, columns=['Occupancy',\n",
        " 'PPM',\n",
        " 'PropertyType',\n",
        " 'LoanPurpose',\n",
        " 'NumBorrowers'],drop_first=True)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:24:50.429044Z",
          "iopub.execute_input": "2023-02-26T04:24:50.430073Z",
          "iopub.status.idle": "2023-02-26T04:24:50.629788Z",
          "shell.execute_reply.started": "2023-02-26T04:24:50.430035Z",
          "shell.execute_reply": "2023-02-26T04:24:50.628467Z"
        },
        "trusted": true,
        "id": "ttZlwGZFL0U0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "get dummies mean we will get different for train and test data,so one hot encoding is simpler."
      ],
      "metadata": {
        "id": "D30pgk8Jb_oe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Normalizing and Scaling( Numerical Variables)\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "#from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:26:54.068927Z",
          "iopub.execute_input": "2023-02-26T04:26:54.069410Z",
          "iopub.status.idle": "2023-02-26T04:26:54.075581Z",
          "shell.execute_reply.started": "2023-02-26T04:26:54.069377Z",
          "shell.execute_reply": "2023-02-26T04:26:54.074295Z"
        },
        "trusted": true,
        "id": "6O8H2TBKL0U0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "std_scale=StandardScaler()\n",
        "std_scale\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:30:54.171787Z",
          "iopub.execute_input": "2023-02-26T04:30:54.172804Z",
          "iopub.status.idle": "2023-02-26T04:30:54.181485Z",
          "shell.execute_reply.started": "2023-02-26T04:30:54.172756Z",
          "shell.execute_reply": "2023-02-26T04:30:54.180159Z"
        },
        "trusted": true,
        "id": "ddWTuGS1L0U0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "6b4b670f-74c5-47db-c41d-d2d20841991d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train[\"EverDelinquent\"]=df_train[\"EverDelinquent\"].astype(\"int\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:30:56.391773Z",
          "iopub.execute_input": "2023-02-26T04:30:56.392190Z",
          "iopub.status.idle": "2023-02-26T04:30:56.400032Z",
          "shell.execute_reply.started": "2023-02-26T04:30:56.392156Z",
          "shell.execute_reply": "2023-02-26T04:30:56.398547Z"
        },
        "trusted": true,
        "id": "bXd_KBavL0U0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in numericalcolumns:\n",
        "    df_train[i]=std_scale.fit_transform(df_train[[i]])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:27:03.282825Z",
          "iopub.execute_input": "2023-02-26T04:27:03.283999Z",
          "iopub.status.idle": "2023-02-26T04:27:03.437782Z",
          "shell.execute_reply.started": "2023-02-26T04:27:03.283939Z",
          "shell.execute_reply": "2023-02-26T04:27:03.436594Z"
        },
        "trusted": true,
        "id": "bwoTBFAML0U0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X= df_train.drop([\"EverDelinquent\"],axis=1)\n",
        "y= df_train[\"EverDelinquent\"]\n",
        "print(X.shape)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:27:07.192070Z",
          "iopub.execute_input": "2023-02-26T04:27:07.192610Z",
          "iopub.status.idle": "2023-02-26T04:27:07.223651Z",
          "shell.execute_reply.started": "2023-02-26T04:27:07.192568Z",
          "shell.execute_reply": "2023-02-26T04:27:07.222244Z"
        },
        "trusted": true,
        "id": "qQ3-QSi4L0U0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7c4ad42-56f0-4efd-a948-e25e5596a28c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(291451, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42,stratify=y)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:31:02.743632Z",
          "iopub.execute_input": "2023-02-26T04:31:02.744109Z",
          "iopub.status.idle": "2023-02-26T04:31:02.920603Z",
          "shell.execute_reply.started": "2023-02-26T04:31:02.744069Z",
          "shell.execute_reply": "2023-02-26T04:31:02.919330Z"
        },
        "trusted": true,
        "id": "UtV7msXvL0U0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:31:04.681208Z",
          "iopub.execute_input": "2023-02-26T04:31:04.681658Z",
          "iopub.status.idle": "2023-02-26T04:31:04.688836Z",
          "shell.execute_reply.started": "2023-02-26T04:31:04.681625Z",
          "shell.execute_reply": "2023-02-26T04:31:04.686999Z"
        },
        "trusted": true,
        "id": "Sr-IINgJL0U0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6eedfb46-5ade-44b7-84f0-60f22c65a864"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(195272, 28)\n",
            "(96179, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ddd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "hhMeFHwres-t",
        "outputId": "419ee8e2-da48-4e03-afef-f32e936b08a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-52-fb145a0fef0c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mddd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'ddd' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the perceptron class\n",
        "class Perceptron:\n",
        "    def __init__(self, learning_rate=0.1):\n",
        "        self.learning_rate = learning_rate\n",
        "        self.weights = None\n",
        "        self.bias = None\n",
        "\n",
        "    def fit(self, X, y, epochs=100):\n",
        "        # Initialize the weights and bias\n",
        "        self.weights = np.zeros(X.shape[1])\n",
        "        self.bias = 0\n",
        "\n",
        "        for epoch in range(epochs):\n",
        "            for i in range(X.shape[0]):\n",
        "                # Calculate the predicted output\n",
        "                y_pred = self.predict(X[i])\n",
        "\n",
        "                # Update the weights and bias\n",
        "                self.weights += self.learning_rate * (y[i] - y_pred) * X[i]\n",
        "                self.bias += self.learning_rate * (y[i] - y_pred)\n",
        "\n",
        "    def predict(self, X):\n",
        "        # Calculate the dot product of the input and weights, add the bias, and pass through the activation function\n",
        "        linear_output = np.dot(X, self.weights) + self.bias\n",
        "        activation_output = self.activation(linear_output)\n",
        "        \n",
        "        # Return the predicted output\n",
        "        return activation_output\n",
        "\n",
        "    def activation(self, x):\n",
        "        # Use the sigmoid activation function\n",
        "        return 1 / (1 + np.exp(-x))\n",
        "\n",
        "# Create an instance of the perceptron and fit the data\n",
        "perceptron1 = Perceptron()\n",
        "perceptron1.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the training and test data\n",
        "trainpreds = perceptron1.predict(X_train) > 0.5\n",
        "testpreds = perceptron1.predict(X_test) > 0.5\n",
        "\n",
        "# Print the accuracy scores\n",
        "print(\"train-\", accuracy_score(y_train, trainpreds), \"test-\", accuracy_score(y_test, testpreds))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "UJ4DTA5rgIKA",
        "outputId": "f47cdbf0-c441-4fa7-b1d4-aeb69a67c1b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3628\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3629\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3630\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 0",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-55-8211dab7b173>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;31m# Create an instance of the perceptron and fit the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0mperceptron1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPerceptron\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m \u001b[0mperceptron1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;31m# Make predictions on the training and test data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-55-8211dab7b173>\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, epochs)\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m                 \u001b[0;31m# Calculate the predicted output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m                 \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m                 \u001b[0;31m# Update the weights and bias\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3503\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3504\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3505\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3506\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3507\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3629\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3630\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3631\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3632\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3633\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 0"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#building a simple ann model for classification\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Normalization,BatchNormalization\n",
        "from keras import regularizers\n",
        "from keras.utils.metrics_utils import ConfusionMatrix\n",
        "from sklearn.metrics import precision_score, recall_score,f1_score\n",
        "from sklearn.metrics import accuracy_score,recall_score,confusion_matrix"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:31:06.708941Z",
          "iopub.execute_input": "2023-02-26T04:31:06.709408Z",
          "iopub.status.idle": "2023-02-26T04:31:06.715221Z",
          "shell.execute_reply.started": "2023-02-26T04:31:06.709372Z",
          "shell.execute_reply": "2023-02-26T04:31:06.713974Z"
        },
        "trusted": true,
        "id": "trllNvcpL0U1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#simple ann neural network\n",
        "thresholds=[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
        "for i in thresholds:\n",
        " perceptron1 = Sequential()\n",
        " perceptron1.add(Dense(1,activation='sigmoid'))\n",
        " perceptron1.compile(optimizer='rmsprop',loss=keras.losses.BinaryCrossentropy())#Adam\n",
        " perceptron1.fit(X_train,y_train,epochs=5)\n",
        " trainpreds= perceptron1.predict(X_train)>i\n",
        " testpreds= perceptron1.predict(X_test)>i\n",
        " print(i,\"train-\",accuracy_score(y_train,trainpreds),\"test-\",accuracy_score(y_test,testpreds))\n",
        " \n",
        " "
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:55:42.192776Z",
          "iopub.execute_input": "2023-02-26T04:55:42.193382Z",
          "iopub.status.idle": "2023-02-26T05:04:55.316668Z",
          "shell.execute_reply.started": "2023-02-26T04:55:42.193324Z",
          "shell.execute_reply": "2023-02-26T05:04:55.315318Z"
        },
        "trusted": true,
        "id": "KyRU0T1NL0U1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**from this we can clearly see threshold of 0.4 is having good results so we will use it for confusion matrix analysis**"
      ],
      "metadata": {
        "id": "6KmmT9qAL0U1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "perceptron1 = Sequential()\n",
        "perceptron1.add(Dense(1,activation='sigmoid'))\n",
        "perceptron1.compile(optimizer='rmsprop',loss=keras.losses.BinaryCrossentropy())#Adam\n",
        "perceptron1.fit(X_train,y_train,epochs=5)\n",
        "trainpreds= perceptron1.predict(X_train)>0.4\n",
        "testpreds= perceptron1.predict(X_test)>0.4\n",
        "print(i,\"train-\",accuracy_score(y_train,trainpreds),\"test-\",accuracy_score(y_test,testpreds))\n",
        "print(confusion_matrix(y_train,trainpreds))\n",
        "print(confusion_matrix(y_test,testpreds))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T05:08:04.765921Z",
          "iopub.execute_input": "2023-02-26T05:08:04.766360Z",
          "iopub.status.idle": "2023-02-26T05:09:42.417093Z",
          "shell.execute_reply.started": "2023-02-26T05:08:04.766328Z",
          "shell.execute_reply": "2023-02-26T05:09:42.415613Z"
        },
        "trusted": true,
        "id": "CSvJUsXAL0U1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score, recall_score,f1_score\n",
        "precision_score=precision_score(y_train,trainpreds)\n",
        "recall_score=recall_score(y_train,trainpreds)\n",
        "f1_score=f1_score(y_train,trainpreds)\n",
        "print(\"scores for train\",precision_score, recall_score,f1_score)\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T05:16:35.417614Z",
          "iopub.execute_input": "2023-02-26T05:16:35.418045Z",
          "iopub.status.idle": "2023-02-26T05:16:35.635635Z",
          "shell.execute_reply.started": "2023-02-26T05:16:35.418015Z",
          "shell.execute_reply": "2023-02-26T05:16:35.634293Z"
        },
        "trusted": true,
        "id": "C8ScWy5RL0U1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8SdbEkg1L0U1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***there are large ammount of false positives and false negatives in the prediction given by the model so we should go for another model with different parameters. ***"
      ],
      "metadata": {
        "id": "QH6VUEilL0U1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#model 2 to reduce fp and fn\n",
        "from keras.layers import Dropout,BatchNormalization,Normalization\n",
        "model2 = Sequential()\n",
        "model2.add(Dense(8,activation='relu')) #0 -- max\n",
        "model2.add(Dense(16, activation='relu'))\n",
        "model2.add(Dense(1, activation='sigmoid'))# binary ; multiclass softmax\n",
        "model2.compile(optimizer='Adam',loss=keras.losses.BinaryCrossentropy())\n",
        "model2.fit(X_train,y_train,epochs=20)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:32:46.915086Z",
          "iopub.execute_input": "2023-02-26T04:32:46.916070Z",
          "iopub.status.idle": "2023-02-26T04:36:31.613206Z",
          "shell.execute_reply.started": "2023-02-26T04:32:46.916030Z",
          "shell.execute_reply": "2023-02-26T04:36:31.612174Z"
        },
        "trusted": true,
        "id": "C-wHH_cUL0U1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainpreds= model2.predict(X_train)>0.3\n",
        "print(trainpreds[0:10])\n",
        "testpreds= model2.predict(X_test)>0.3\n",
        "print(testpreds[0:10])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:36:31.615927Z",
          "iopub.execute_input": "2023-02-26T04:36:31.616450Z",
          "iopub.status.idle": "2023-02-26T04:36:46.078833Z",
          "shell.execute_reply.started": "2023-02-26T04:36:31.616385Z",
          "shell.execute_reply": "2023-02-26T04:36:46.077402Z"
        },
        "trusted": true,
        "id": "LUIGceLyL0U1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils.metrics_utils import ConfusionMatrix\n",
        "from sklearn.metrics import accuracy_score,recall_score,confusion_matrix\n",
        "print(\"a.s for train\",accuracy_score(y_train,trainpreds))\n",
        "print(\"a.s for test\",accuracy_score(y_test,testpreds))\n",
        "\n",
        "print(\"confusion_matrix for train\",confusion_matrix(y_train,trainpreds))\n",
        "print(\"confusion_matrix for test\",confusion_matrix(y_test,testpreds))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T04:38:56.626145Z",
          "iopub.execute_input": "2023-02-26T04:38:56.626592Z",
          "iopub.status.idle": "2023-02-26T04:38:56.712481Z",
          "shell.execute_reply.started": "2023-02-26T04:38:56.626557Z",
          "shell.execute_reply": "2023-02-26T04:38:56.710243Z"
        },
        "trusted": true,
        "id": "3xAj94OSL0U1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "without keras module"
      ],
      "metadata": {
        "id": "CYV4iGzBb5BT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert y_train to a Numpy array and flatten it\n",
        "y_train_flat = y_train.values.reshape(-1, 1)\n",
        "# Define activation functions\n",
        "def relu(x):\n",
        "    return np.maximum(0, x)\n",
        "\n",
        "def relu_gradient(x):\n",
        "    return np.where(x > 0, 1, 0)\n",
        "\n",
        "\n",
        "def sigmoid(x):\n",
        "    return 1 / (1 + np.exp(-x))\n",
        "\n",
        "# Define loss function\n",
        "def binary_cross_entropy(y_true, y_pred):\n",
        "    epsilon = 1e-15\n",
        "    y_pred = np.clip(y_pred, epsilon, 1 - epsilon)\n",
        "    y_true = y_true.ravel()\n",
        "    return -(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred)).mean()\n",
        "input_size = X_train.shape[1]\n",
        "hidden_size = 32\n",
        "output_size = 1    \n",
        "# Initialize the weight matrix with random values\n",
        "W1 = np.random.randn(input_size, hidden_size)\n",
        "b1 = np.zeros((1, hidden_size))\n",
        "\n",
        "W2 = np.random.randn(hidden_size, output_size)\n",
        "b2 = np.zeros((1, output_size))\n",
        "\n",
        "\n",
        "# Train Model 2\n",
        "learning_rate = 0.01\n",
        "num_epochs = 20\n",
        "batch_size = 32\n",
        "\n",
        "num_batches = len(X_train) // batch_size\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    epoch_loss = 0.0\n",
        "    for batch in range(num_batches):\n",
        "        # Forward pass\n",
        "        start = batch * batch_size\n",
        "        end = (batch + 1) * batch_size\n",
        "        X_batch = X_train[start:end]\n",
        "        y_batch = y_train_flat[start:end]\n",
        "\n",
        "        Z1 = np.dot(X_batch, W1) + b1\n",
        "        A1 = relu(Z1)\n",
        "\n",
        "        Z2 = np.dot(A1, W2) + b2\n",
        "        A2 = sigmoid(Z2)\n",
        "\n",
        "        # Backward pass\n",
        "        dZ2 = A2 - y_batch\n",
        "        dW2 = np.dot(A1.T, dZ2) / batch_size\n",
        "        db2 = np.sum(dZ2, axis=0, keepdims=True) / batch_size\n",
        "\n",
        "        dA1 = np.dot(dZ2, W2.T)\n",
        "        dZ1 = dA1 * relu_gradient(Z1)\n",
        "        dW1 = np.dot(X_batch.T, dZ1) / batch_size\n",
        "        db1 = np.sum(dZ1, axis=0, keepdims=True) / batch_size\n",
        "\n",
        "        # Update parameters\n",
        "        W2 -= learning_rate * dW2\n",
        "        b2 -= learning_rate * db2\n",
        "        W1 -= learning_rate * dW1\n",
        "        b1 -= learning_rate * db1\n",
        "\n",
        "        batch_loss = binary_cross_entropy(y_batch, A2)\n",
        "        epoch_loss += batch_loss\n",
        "\n",
        "    # Compute validation loss\n",
        "    val_Z1 = np.dot(X_test, W1) + b1\n",
        "    val_A1 = relu(val_Z1)\n",
        "\n",
        "    val_Z2 = np.dot(val_A1, W2) + b2\n",
        "    val_A2 = sigmoid(val_Z2)\n",
        "\n",
        "    val_loss = binary_cross_entropy(y_test, val_A2)\n",
        "\n",
        "    # Print progress\n",
        "    print(\"Epoch {}/{} - Loss: {:.4f} - Val Loss: {:.4f}\".format(epoch + 1, num_epochs, epoch_loss / num_batches, val_loss))\n"
      ],
      "metadata": {
        "id": "Q8C91DS9lyOx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from itertools import product\n",
        "\n",
        "# Split the data into training and validation sets\n",
        "split_idx = int(0.8 * len(X))\n",
        "X_train, y_train = X[:split_idx], y[:split_idx]\n",
        "X_val, y_val = X[split_idx:], y[split_idx:]\n",
        "\n",
        "# Define a function to create and train the model\n",
        "def create_model(hidden_layer_sizes, learning_rate_init):\n",
        "    model = MLPClassifier(hidden_layer_sizes=hidden_layer_sizes, learning_rate_init=learning_rate_init)\n",
        "    model.fit(X_train, y_train)\n",
        "    return model\n",
        "\n",
        "# Define a range of hyperparameters to explore\n",
        "hidden_layer_sizes_list = [(10,), (50,), (100,)]\n",
        "learning_rate_init_list = [0.001, 0.01, 0.1]\n",
        "\n",
        "# Perform the grid search\n",
        "best_score = 0\n",
        "for hidden_layer_sizes, learning_rate_init in product(hidden_layer_sizes_list, learning_rate_init_list):\n",
        "    model = create_model(hidden_layer_sizes, learning_rate_init)\n",
        "    y_pred = model.predict(X_val)\n",
        "    score = accuracy_score(y_val, y_pred)\n",
        "    if score > best_score:\n",
        "        best_score = score\n",
        "        best_params = {'hidden_layer_sizes': hidden_layer_sizes, 'learning_rate_init': learning_rate_init}\n",
        "\n",
        "# Print the best hyperparameters and accuracy score\n",
        "print(\"Best hyperparameters: \", best_params)\n",
        "print(\"Best accuracy score: \", best_score)\n",
        "\n",
        "# Train the final model using the selected hyperparameters\n",
        "best_model = create_model(best_params['hidden_layer_sizes'], best_params['learning_rate_init'])\n",
        "\n",
        "# Evaluate the performance of the final model on a separate test set\n",
        "X_test, y_test = digits.data[-100:], digits.target[-100:]\n",
        "y_pred = best_model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Test accuracy score: \", accuracy)\n"
      ],
      "metadata": {
        "id": "fUXG-HTqezZR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now lets try with different models\n",
        "1)decision tree classifier\n",
        "2)random forest classifer\n",
        "3)knn classifer\n",
        "4)logistic regression"
      ],
      "metadata": {
        "id": "_6tJqlG2L0U2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#model-logiticregression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression()\n",
        "\n",
        "# Train the classifier on the training data\n",
        "lr.fit(X_train, y_train)\n",
        "# Make predictions on the training data\n",
        "train_preds = lr.predict(X_train)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "test_preds = lr.predict(X_test)\n",
        "\n",
        "# Evaluate the performance of the classifier using accuracy, precision, recall, and f1-score\n",
        "print(\"trainaccuracy\",accuracy_score(y_train,train_preds))\n",
        "print(\"confusionaccuracy\",confusion_matrix(y_train,train_preds))\n",
        "print(\"testaccuracy\",accuracy_score(y_test,test_preds))\n",
        "print(\"confusionaccuracy\",confusion_matrix(y_test,test_preds))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T05:39:26.848774Z",
          "iopub.execute_input": "2023-02-26T05:39:26.849281Z",
          "iopub.status.idle": "2023-02-26T05:39:30.918898Z",
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          "shell.execute_reply": "2023-02-26T05:39:30.917666Z"
        },
        "trusted": true,
        "id": "OSDajeTEL0U2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model2-decisiontreeclassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "lr = DecisionTreeClassifier(max_depth=3)\n",
        "\n",
        "# Train the classifier on the training data\n",
        "lr.fit(X_train, y_train)\n",
        "# Make predictions on the training data\n",
        "train_preds = lr.predict(X_train)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "test_preds = lr.predict(X_test)\n",
        "\n",
        "# Evaluate the performance of the classifier using accuracy, precision, recall, and f1-score\n",
        "print(\"trainaccuracy\",accuracy_score(y_train,train_preds))\n",
        "print(\"confusionaccuracy\",confusion_matrix(y_train,train_preds))\n",
        "print(\"testaccuracy\",accuracy_score(y_test,test_preds))\n",
        "print(\"confusionaccuracy\",confusion_matrix(y_test,test_preds))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T05:43:06.141481Z",
          "iopub.execute_input": "2023-02-26T05:43:06.141951Z",
          "iopub.status.idle": "2023-02-26T05:43:06.575299Z",
          "shell.execute_reply.started": "2023-02-26T05:43:06.141916Z",
          "shell.execute_reply": "2023-02-26T05:43:06.573617Z"
        },
        "trusted": true,
        "id": "N3YYTT4aL0U2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Randomforestclassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "lr = RandomForestClassifier(n_estimators=100)\n",
        "\n",
        "# Train the classifier on the training data\n",
        "lr.fit(X_train, y_train)\n",
        "# Make predictions on the training data\n",
        "train_preds = lr.predict(X_train)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "test_preds = lr.predict(X_test)\n",
        "\n",
        "# Evaluate the performance of the classifier using accuracy, precision, recall, and f1-score\n",
        "print(\"trainaccuracy\",accuracy_score(y_train,train_preds))\n",
        "print(\"confusionaccuracy\",confusion_matrix(y_train,train_preds))\n",
        "print(\"testaccuracy\",accuracy_score(y_test,test_preds))\n",
        "print(\"confusionaccuracy\",confusion_matrix(y_test,test_preds))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-26T05:59:53.801201Z",
          "iopub.execute_input": "2023-02-26T05:59:53.801644Z",
          "iopub.status.idle": "2023-02-26T06:00:10.294002Z",
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          "shell.execute_reply": "2023-02-26T06:00:10.292891Z"
        },
        "trusted": true,
        "id": "Mz5UEGOjL0U2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BhEucRfwbmNE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}