logestic_regression(using_sklearn).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "logestic_regression(using sklearn).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOoJzLbLZurJhxhaMxwQsV1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sarthakpant772/MLnow_2.0/blob/main/logestic_regression(using_sklearn).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGkInWhjqZJ-"
      },
      "source": [
        "#Linking drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dgP3CLNp1Os",
        "outputId": "2fab672a-78c8-4a27-9771-a0a102e9d3ca"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive') "
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBvS8Y-9qEwO",
        "outputId": "035aae07-a608-4170-e220-d808e8a99845"
      },
      "source": [
        "cd  '/content/drive/MyDrive/mashine_learning_models/loan_prediction '"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/mashine_learning_models/loan_prediction \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ne67yYlLqVG3",
        "outputId": "105b7029-89b6-4010-8081-085a1a4fa523"
      },
      "source": [
        "ls\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dataset.xlsx  Untitled0.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vj8VoJNaqcyx"
      },
      "source": [
        "#Data Cleaning\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7JqyCFM0qWCe"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWqYA7I9qrZ9"
      },
      "source": [
        "loan_data=pd.read_excel('dataset.xlsx')"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "t8NRXt3QrOBv",
        "outputId": "546d2110-f6cb-4b0d-8730-6b42df1c254c"
      },
      "source": [
        "loan_data.head()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>Male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Loan_ID Gender Married  ... Credit_History Property_Area Loan_Status\n",
              "0  LP001002   Male     Yes  ...            1.0         Urban           Y\n",
              "1  LP001003   Male     Yes  ...            1.0         Rural           N\n",
              "2  LP001005   Male     Yes  ...            1.0         Urban           Y\n",
              "3  LP001006   Male      No  ...            1.0         Urban           Y\n",
              "4  LP001008   Male     NaN  ...            1.0         Urban           Y\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h44aW7HnrfOG",
        "outputId": "8f78eb8e-14bc-4405-b33d-895157857895"
      },
      "source": [
        "for i in loan_data:\n",
        "  print(i)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loan_ID\n",
            "Gender\n",
            "Married\n",
            "Dependents\n",
            "Education\n",
            "Self_Employed\n",
            "ApplicantIncome\n",
            "CoapplicantIncome\n",
            "LoanAmount\n",
            "Loan_Amount_Term\n",
            "Credit_History\n",
            "Property_Area\n",
            "Loan_Status\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kHRW_hYkrrxu",
        "outputId": "9596d6fd-b701-472f-da83-2ffa97b364ab"
      },
      "source": [
        "print(f'Married:{loan_data.Married.unique()}')\n",
        "print(f'Dependents:{loan_data.Dependents.unique()}')\n",
        "print(f'Education:{loan_data.Education.unique()}')\n",
        "print(f'self Employd:{loan_data.Self_Employed.unique()}')"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Married:[1. 0.]\n",
            "Dependents:[1 0 2 4]\n",
            "Education:[1 0]\n",
            "self Employd:[0. 1.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Sg752UdsRLm"
      },
      "source": [
        "loan_data.fillna(0,inplace=True)"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oI4x5SlYtbM-"
      },
      "source": [
        "loan_data['Married'] = loan_data['Married'].apply({'Yes':1, 'No':0}.get)"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RqRQrPDtgK_"
      },
      "source": [
        "loan_data['Education'] = loan_data['Education'].apply({'Graduate':1, 'Not Graduate':0}.get)\n",
        "loan_data['Self_Employed'] = loan_data['Self_Employed'].apply({'No':0, 'Yes':1}.get)\n"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFlMv-X0wb1Q"
      },
      "source": [
        "loan_data['Loan_Status'] = loan_data['Loan_Status'].apply({'Y':1,'N':0}.get)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YcB_4sT132F_"
      },
      "source": [
        "loan_data['Dependents'] = loan_data['Dependents'].apply({'3+':4, 1:1,2:2,0:0}.get)"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "J585wNuqulmA",
        "outputId": "0060ce68-ba6e-4bbd-a1a0-a3452574505c"
      },
      "source": [
        "loan_data.head()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>Male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Loan_ID Gender  Married  ...  Credit_History  Property_Area  Loan_Status\n",
              "0  LP001002   Male      1.0  ...             1.0          Urban            1\n",
              "1  LP001003   Male      1.0  ...             1.0          Rural            0\n",
              "2  LP001005   Male      1.0  ...             1.0          Urban            1\n",
              "3  LP001006   Male      0.0  ...             1.0          Urban            1\n",
              "4  LP001008   Male      NaN  ...             1.0          Urban            1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "EGq847o1u6eg",
        "outputId": "2a3e9729-7474-4648-adb9-9e892a63c383"
      },
      "source": [
        "loan_data\n"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe 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>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>609</th>\n",
              "      <td>LP002978</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2900</td>\n",
              "      <td>0.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>610</th>\n",
              "      <td>LP002979</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3+</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4106</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>611</th>\n",
              "      <td>LP002983</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8072</td>\n",
              "      <td>240.0</td>\n",
              "      <td>253.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>612</th>\n",
              "      <td>LP002984</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7583</td>\n",
              "      <td>0.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>613</th>\n",
              "      <td>LP002990</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4583</td>\n",
              "      <td>0.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>614 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Loan_ID  Gender  Married  ... Credit_History  Property_Area  Loan_Status\n",
              "0    LP001002    Male      1.0  ...            1.0          Urban            1\n",
              "1    LP001003    Male      1.0  ...            1.0          Rural            0\n",
              "2    LP001005    Male      1.0  ...            1.0          Urban            1\n",
              "3    LP001006    Male      0.0  ...            1.0          Urban            1\n",
              "4    LP001008    Male      0.0  ...            1.0          Urban            1\n",
              "..        ...     ...      ...  ...            ...            ...          ...\n",
              "609  LP002978  Female      0.0  ...            1.0          Rural            1\n",
              "610  LP002979    Male      1.0  ...            1.0          Rural            1\n",
              "611  LP002983    Male      1.0  ...            1.0          Urban            1\n",
              "612  LP002984    Male      1.0  ...            1.0          Urban            1\n",
              "613  LP002990  Female      0.0  ...            0.0      Semiurban            0\n",
              "\n",
              "[614 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eevIJeyFvNtB"
      },
      "source": [
        "loan_dataup= loan_data[['Married','Dependents','Education','Self_Employed','ApplicantIncome','LoanAmount','Credit_History','Loan_Status']]"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "rKnk5QkZwB6w",
        "outputId": "a94f568d-c992-453b-fe50-10c6bf7c881b"
      },
      "source": [
        "loan_dataup.head()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4583</td>\n",
              "      <td>128.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3000</td>\n",
              "      <td>66.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2583</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6000</td>\n",
              "      <td>141.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Married  Dependents  Education  ...  LoanAmount  Credit_History  Loan_Status\n",
              "0      1.0           1          1  ...         0.0             1.0            1\n",
              "1      1.0           0          1  ...       128.0             1.0            0\n",
              "2      1.0           0          1  ...        66.0             1.0            1\n",
              "3      0.0           0          0  ...       120.0             1.0            1\n",
              "4      0.0           0          1  ...       141.0             1.0            1\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f4cx5heuwHW_"
      },
      "source": [
        "loan_dataup.to_csv('cleanedData.csv')"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJaMFaFcw-jE"
      },
      "source": [
        "#EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "JwSed41Qw16x",
        "outputId": "2ba577c8-65f2-41d8-dc2e-80c8f0647632"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.palplot(sns.color_palette(\"PuOr\"))\n",
        "pal = sns.color_palette('PuOr', 6)\n",
        "pal.as_hex()\n",
        "plt.style.use(['seaborn'])\n",
        "sns_colors = [\"#c6690c\", \"#664697\"]\n",
        "sns.set_palette(sns_colors)\n",
        "sns.palplot(sns.color_palette(sns_colors))"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 144x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lnWzXyDF2sKC",
        "outputId": "a53cb8b9-5543-4721-9732-cd7fefa77297"
      },
      "source": [
        "print(loan_dataup.columns)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Married', 'Dependents', 'Education', 'Self_Employed',\n",
            "       'ApplicantIncome', 'LoanAmount', 'Credit_History', 'Loan_Status'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cmlzkEYO22Ok",
        "outputId": "0f620ace-4d90-4f69-bf49-66ea860af6c2"
      },
      "source": [
        "sns.pairplot(loan_dataup,\n",
        "              x_vars=['Married', 'Dependents', 'Education', 'Self_Employed','ApplicantIncome', 'LoanAmount', 'Credit_History', 'Loan_Status'],\n",
        "              y_vars=['Married', 'Dependents', 'Education', 'Self_Employed','ApplicantIncome', 'LoanAmount', 'Credit_History'],\n",
        "              diag_kind='kde')"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f6b4d261290>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1260 with 63 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FsyIJnRB5R1Q"
      },
      "source": [
        "#Analysis form the data.\n",
        "\n",
        "### Q1) Do applicant income matter?\n",
        "```Yes it do, as we can see that the success rate of getting a loan is greater beterrn 20K to 60K and married at same time ```\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxWPIksh5Oul"
      },
      "source": [
        "#Data spliting \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVqILtK73Ztg"
      },
      "source": [
        "trainData=loan_dataup.sample(frac=0.8 , random_state=0)\n",
        "testData=loan_dataup.drop(trainData.index)"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulKE8Oh97R8t",
        "outputId": "ef49778e-a194-42a3-8d94-bc9213c24e8a"
      },
      "source": [
        "print(trainData.head())\n",
        "print(testData.head())"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Married  Dependents  Education  ...  LoanAmount  Credit_History  Loan_Status\n",
            "454      0.0           0          1  ...        84.0             1.0            1\n",
            "52       0.0           0          1  ...       112.0             1.0            0\n",
            "536      1.0           0          1  ...       324.0             1.0            1\n",
            "469      1.0           0          1  ...       110.0             1.0            0\n",
            "55       1.0           2          1  ...        97.0             1.0            1\n",
            "\n",
            "[5 rows x 8 columns]\n",
            "    Married  Dependents  Education  ...  LoanAmount  Credit_History  Loan_Status\n",
            "3       0.0           0          0  ...       120.0             1.0            1\n",
            "9       1.0           1          1  ...       349.0             1.0            0\n",
            "11      1.0           2          1  ...       109.0             1.0            1\n",
            "19      1.0           0          1  ...       115.0             1.0            1\n",
            "23      1.0           2          0  ...       112.0             0.0            0\n",
            "\n",
            "[5 rows x 8 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dgJRup5P7alL",
        "outputId": "1185238c-e5da-4094-b66f-e996f2f1a92a"
      },
      "source": [
        "x_train=trainData.copy()\n",
        "x_test=testData.copy()\n",
        "\n",
        "print(x_train)\n",
        "print(x_test)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Married  Dependents  Education  ...  LoanAmount  Credit_History  Loan_Status\n",
            "454      0.0           0          1  ...        84.0             1.0            1\n",
            "52       0.0           0          1  ...       112.0             1.0            0\n",
            "536      1.0           0          1  ...       324.0             1.0            1\n",
            "469      1.0           0          1  ...       110.0             1.0            0\n",
            "55       1.0           2          1  ...        97.0             1.0            1\n",
            "..       ...         ...        ...  ...         ...             ...          ...\n",
            "451      1.0           2          0  ...        60.0             0.0            1\n",
            "260      1.0           1          1  ...       330.0             0.0            1\n",
            "352      1.0           4          1  ...        95.0             1.0            1\n",
            "270      0.0           0          1  ...        30.0             1.0            1\n",
            "317      1.0           0          1  ...        88.0             0.0            1\n",
            "\n",
            "[491 rows x 8 columns]\n",
            "     Married  Dependents  Education  ...  LoanAmount  Credit_History  Loan_Status\n",
            "3        0.0           0          0  ...       120.0             1.0            1\n",
            "9        1.0           1          1  ...       349.0             1.0            0\n",
            "11       1.0           2          1  ...       109.0             1.0            1\n",
            "19       1.0           0          1  ...       115.0             1.0            1\n",
            "23       1.0           2          0  ...       112.0             0.0            0\n",
            "..       ...         ...        ...  ...         ...             ...          ...\n",
            "586      1.0           0          1  ...       104.0             1.0            1\n",
            "594      1.0           0          1  ...       260.0             1.0            1\n",
            "599      1.0           2          1  ...       192.0             1.0            1\n",
            "600      0.0           4          1  ...       350.0             0.0            0\n",
            "608      1.0           0          1  ...       108.0             1.0            1\n",
            "\n",
            "[123 rows x 8 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bmaNH1xE8a9o",
        "outputId": "77d091f9-013e-4419-b1cb-65408cee1d53"
      },
      "source": [
        "y_test=x_test.pop('Loan_Status')\n",
        "y_train=x_train.pop('Loan_Status')\n",
        "\n",
        "print(y_train)\n",
        "print(y_test)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "454    1\n",
            "52     0\n",
            "536    1\n",
            "469    0\n",
            "55     1\n",
            "      ..\n",
            "451    1\n",
            "260    1\n",
            "352    1\n",
            "270    1\n",
            "317    1\n",
            "Name: Loan_Status, Length: 491, dtype: int64\n",
            "3      1\n",
            "9      0\n",
            "11     1\n",
            "19     1\n",
            "23     0\n",
            "      ..\n",
            "586    1\n",
            "594    1\n",
            "599    1\n",
            "600    0\n",
            "608    1\n",
            "Name: Loan_Status, Length: 123, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SP8EloUO81pl"
      },
      "source": [
        "# Problem statement \n",
        "\n",
        "\n",
        "create a classification model to pridict loan will get passed or not"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tqqd2P0a8wwN"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJGgefEw-5QX"
      },
      "source": [
        "classifier = LogisticRegression(random_state=0,solver='liblinear')\n",
        "classifier = classifier.fit(x_train,y_train)"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VjWfj8j-_N0b"
      },
      "source": [
        "y_train_pred= classifier.predict(x_train)"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8QeyTlc-_cdI"
      },
      "source": [
        "y_test_pred= classifier.predict(x_test)"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_pv3PFJ_h3L"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm_train = confusion_matrix(y_train,y_train_pred)\n",
        "cm_test = confusion_matrix(y_test,y_test_pred)"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zU9iKaJt_mnI"
      },
      "source": [
        ""
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kfzsCyj3__4U"
      },
      "source": [
        "def predPlot(labels, preds):\n",
        "  plt.scatter(labels, preds)\n",
        "  plt.xlabel('True-Values/Labels')\n",
        "  plt.ylabel('Predicted-Values')\n",
        "  plt.axis('equal')\n",
        "  plt.axis('square')\n",
        "  #plt.xlim([0,plt.xlim()[1]])\n",
        "  #plt.ylim([0,plt.ylim()[1]])\n",
        "  #plt.plot([-100,100],[-100,100])\n",
        "  plt.show()"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "T1OCgXHYC0ZM",
        "outputId": "9859add3-bb07-473b-d917-15cdb66490b4"
      },
      "source": [
        "predPlot(y_test,y_test_pred)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D85Trly-DJWj"
      },
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JALvYd9tD9X9"
      },
      "source": [
        "#accuracy of our model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JC0wTcdPDs53",
        "outputId": "9a3ef23a-406e-4521-f856-f5140d194c06"
      },
      "source": [
        " accuracy_score(y_test, y_test_pred)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7317073170731707"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNLtFg47D0wD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}