Loan_Status_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": " Loan Status Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/reenarao00/Data-Science./blob/main/Loan_Status_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Itwi8HW-9P6"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UH1Bgx49msIs"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Gvrw17q_zY5"
      },
      "source": [
        "Data Collection and Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdl2oWTy_xs4"
      },
      "source": [
        "# loading the dataset to pandas DataFrame\n",
        "loan_dataset = pd.read_csv('/content/dataset.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cbN5oo6gASZE",
        "outputId": "65d5ad1a-92b4-4a34-b3dc-c030c452aaf9"
      },
      "source": [
        "type(loan_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "7rif01IcAVhj",
        "outputId": "ec896a41-25d5-4779-8961-ce7f71b7c01d"
      },
      "source": [
        "# printing the first 5 rows of the dataframe\n",
        "loan_dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<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>No</td>\n",
              "      <td>0</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>1</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>Yes</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>No</td>\n",
              "      <td>0</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      No  ...            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     Yes  ...            1.0         Urban           Y\n",
              "4  LP001008   Male      No  ...            1.0         Urban           Y\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cadoapvyAdp7",
        "outputId": "cfd93d61-c44b-482f-b2fe-4e9b7c3437da"
      },
      "source": [
        "# number of rows and columns\n",
        "loan_dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(614, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "oCNhpj_aBZ8S",
        "outputId": "b83a27bf-9750-43e2-acde-aa64d465d258"
      },
      "source": [
        "# statistical measures\n",
        "loan_dataset.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>614.000000</td>\n",
              "      <td>614.000000</td>\n",
              "      <td>592.000000</td>\n",
              "      <td>600.00000</td>\n",
              "      <td>564.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5403.459283</td>\n",
              "      <td>1621.245798</td>\n",
              "      <td>146.412162</td>\n",
              "      <td>342.00000</td>\n",
              "      <td>0.842199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6109.041673</td>\n",
              "      <td>2926.248369</td>\n",
              "      <td>85.587325</td>\n",
              "      <td>65.12041</td>\n",
              "      <td>0.364878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>12.00000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2877.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3812.500000</td>\n",
              "      <td>1188.500000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5795.000000</td>\n",
              "      <td>2297.250000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>81000.000000</td>\n",
              "      <td>41667.000000</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>480.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       ApplicantIncome  CoapplicantIncome  ...  Loan_Amount_Term  Credit_History\n",
              "count       614.000000         614.000000  ...         600.00000      564.000000\n",
              "mean       5403.459283        1621.245798  ...         342.00000        0.842199\n",
              "std        6109.041673        2926.248369  ...          65.12041        0.364878\n",
              "min         150.000000           0.000000  ...          12.00000        0.000000\n",
              "25%        2877.500000           0.000000  ...         360.00000        1.000000\n",
              "50%        3812.500000        1188.500000  ...         360.00000        1.000000\n",
              "75%        5795.000000        2297.250000  ...         360.00000        1.000000\n",
              "max       81000.000000       41667.000000  ...         480.00000        1.000000\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Xp06TZ3Bt6S",
        "outputId": "be7900fa-a339-444f-9d00-bfe0f8cb09a2"
      },
      "source": [
        "# number of missing values in each column\n",
        "loan_dataset.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID               0\n",
              "Gender               13\n",
              "Married               3\n",
              "Dependents           15\n",
              "Education             0\n",
              "Self_Employed        32\n",
              "ApplicantIncome       0\n",
              "CoapplicantIncome     0\n",
              "LoanAmount           22\n",
              "Loan_Amount_Term     14\n",
              "Credit_History       50\n",
              "Property_Area         0\n",
              "Loan_Status           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vhG70dhCMHv"
      },
      "source": [
        "# dropping the missing values\n",
        "loan_dataset = loan_dataset.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMZ1CK97CqhY",
        "outputId": "b384d67c-877e-448c-9e5e-0528b4b2a5fe"
      },
      "source": [
        "# number of missing values in each column\n",
        "loan_dataset.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID              0\n",
              "Gender               0\n",
              "Married              0\n",
              "Dependents           0\n",
              "Education            0\n",
              "Self_Employed        0\n",
              "ApplicantIncome      0\n",
              "CoapplicantIncome    0\n",
              "LoanAmount           0\n",
              "Loan_Amount_Term     0\n",
              "Credit_History       0\n",
              "Property_Area        0\n",
              "Loan_Status          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2j_jN_XCs6G"
      },
      "source": [
        "# label encoding\n",
        "loan_dataset.replace({\"Loan_Status\":{'N':0,'Y':1}},inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "MEbW0kjeDWLD",
        "outputId": "a8d8d4ba-c1b5-4cf5-9dc0-861287b32b5c"
      },
      "source": [
        "# printing the first 5 rows of the dataframe\n",
        "loan_dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<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>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</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>0</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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>LP001011</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.0</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",
              "1  LP001003   Male     Yes  ...            1.0         Rural           0\n",
              "2  LP001005   Male     Yes  ...            1.0         Urban           1\n",
              "3  LP001006   Male     Yes  ...            1.0         Urban           1\n",
              "4  LP001008   Male      No  ...            1.0         Urban           1\n",
              "5  LP001011   Male     Yes  ...            1.0         Urban           1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDDPZ9AcDaAL",
        "outputId": "08a9a03e-55ca-4e11-f2c4-160df7cc71e7"
      },
      "source": [
        "# Dependent column values\n",
        "loan_dataset['Dependents'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     274\n",
              "2      85\n",
              "1      80\n",
              "3+     41\n",
              "Name: Dependents, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WkE_kBJDEBbA"
      },
      "source": [
        "# replacing the value of 3+ to 4\n",
        "loan_dataset = loan_dataset.replace(to_replace='3+', value=4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VWxrpy59Eg5m",
        "outputId": "3e9ea59c-fc00-4e84-faa6-4ba0987a95f9"
      },
      "source": [
        "# dependent values\n",
        "loan_dataset['Dependents'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    274\n",
              "2     85\n",
              "1     80\n",
              "4     41\n",
              "Name: Dependents, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYsIbm7aExy5"
      },
      "source": [
        "Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "dnujp9ffErKo",
        "outputId": "5465d9c8-7951-4acb-b3df-0e140705686d"
      },
      "source": [
        "# education & Loan Status\n",
        "sns.countplot(x='Education',hue='Loan_Status',data=loan_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60020a91d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "89aJ1gxoFUCF",
        "outputId": "4723b902-9c3d-4989-9433-05e1b448bb09"
      },
      "source": [
        "# marital status & Loan Status\n",
        "sns.countplot(x='Married',hue='Loan_Status',data=loan_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6000f5a650>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Flk0RHzEGWXw"
      },
      "source": [
        "# convert categorical columns to numerical values\n",
        "loan_dataset.replace({'Married':{'No':0,'Yes':1},'Gender':{'Male':1,'Female':0},'Self_Employed':{'No':0,'Yes':1},\n",
        "                      'Property_Area':{'Rural':0,'Semiurban':1,'Urban':2},'Education':{'Graduate':1,'Not Graduate':0}},inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "id": "CqrryB06IQ3R",
        "outputId": "69f211e4-b094-4cac-ffcf-b7f939b49f15"
      },
      "source": [
        "loan_dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>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>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>LP001011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</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",
              "1  LP001003       1        1  ...            1.0              0            0\n",
              "2  LP001005       1        1  ...            1.0              2            1\n",
              "3  LP001006       1        1  ...            1.0              2            1\n",
              "4  LP001008       1        0  ...            1.0              2            1\n",
              "5  LP001011       1        1  ...            1.0              2            1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jF5D9a3aIUn5"
      },
      "source": [
        "# separating the data and label\n",
        "X = loan_dataset.drop(columns=['Loan_ID','Loan_Status'],axis=1)\n",
        "Y = loan_dataset['Loan_Status']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-jNlYt-JIbs",
        "outputId": "ea89fb2b-038a-451c-f644-4b4ecd573606"
      },
      "source": [
        "print(X)\n",
        "print(Y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Gender  Married  ... Credit_History  Property_Area\n",
            "1         1        1  ...            1.0              0\n",
            "2         1        1  ...            1.0              2\n",
            "3         1        1  ...            1.0              2\n",
            "4         1        0  ...            1.0              2\n",
            "5         1        1  ...            1.0              2\n",
            "..      ...      ...  ...            ...            ...\n",
            "609       0        0  ...            1.0              0\n",
            "610       1        1  ...            1.0              0\n",
            "611       1        1  ...            1.0              2\n",
            "612       1        1  ...            1.0              2\n",
            "613       0        0  ...            0.0              1\n",
            "\n",
            "[480 rows x 11 columns]\n",
            "1      0\n",
            "2      1\n",
            "3      1\n",
            "4      1\n",
            "5      1\n",
            "      ..\n",
            "609    1\n",
            "610    1\n",
            "611    1\n",
            "612    1\n",
            "613    0\n",
            "Name: Loan_Status, Length: 480, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iOjT0xv2JVfO"
      },
      "source": [
        "Train Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p84EJrhcJKQM"
      },
      "source": [
        "X_train, X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.1,stratify=Y,random_state=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x7pYOaRCKKG3",
        "outputId": "86ecde19-f526-41a6-b2d1-5e17f6fae14f"
      },
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(480, 11) (432, 11) (48, 11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fOobHS7iKZ4z"
      },
      "source": [
        "Training the model:\n",
        "\n",
        "Support Vector Machine Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5O54v0U-KPy6"
      },
      "source": [
        "classifier = svm.SVC(kernel='linear')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3HfJbRgKtId",
        "outputId": "ea7363e0-6bd6-47ee-8016-47272739b205"
      },
      "source": [
        "#training the support Vector Macine model\n",
        "classifier.fit(X_train,Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7CuMq2KKLdXs"
      },
      "source": [
        "Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZneuWAIK4Qu"
      },
      "source": [
        "# accuracy score on training data\n",
        "X_train_prediction = classifier.predict(X_train)\n",
        "training_data_accuray = accuracy_score(X_train_prediction,Y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "av8yolB9MCtG",
        "outputId": "739ddd52-2fd3-4dfe-b5ca-d6fa06ed0609"
      },
      "source": [
        "print('Accuracy on training data : ', training_data_accuray)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training data :  0.7986111111111112\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVGYbc2uMLYn"
      },
      "source": [
        "# accuracy score on training data\n",
        "X_test_prediction = classifier.predict(X_test)\n",
        "test_data_accuray = accuracy_score(X_test_prediction,Y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aou4ZalSMiyn",
        "outputId": "bc0d89ce-242e-4df6-8de0-75c30391dad2"
      },
      "source": [
        "print('Accuracy on test data : ', test_data_accuray)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on test data :  0.8333333333333334\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8qYe7OsNRlk"
      },
      "source": [
        "Making a predictive system"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cjoWNhtGMniu"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}