Loan Status Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_dataset = pd.read_csv(\"loan.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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 Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_dataset = loan_dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_dataset.replace({\"Loan_Status\":{\"N\":0, \"Y\":1}}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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 Dependents     Education Self_Employed  \\\n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "5  LP001011   Male     Yes          2      Graduate           Yes   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "5             5417             4196.0       267.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area  Loan_Status  \n",
       "1             1.0         Rural            0  \n",
       "2             1.0         Urban            1  \n",
       "3             1.0         Urban            1  \n",
       "4             1.0         Urban            1  \n",
       "5             1.0         Urban            1  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     274\n",
       "2      85\n",
       "1      80\n",
       "3+     41\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_dataset['Dependents'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_dataset = loan_dataset.replace(to_replace = '3+', value = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    274\n",
       "2     85\n",
       "1     80\n",
       "4     41\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_dataset['Dependents'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ff1c6bb100>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IOLFWdUmSNq+Wewo3Ah9t0zYZmJ2ZhwKzi9dExDBgHDC8mOdHEbFLDWuTJLWjZqGQmQ8Bf2zTfDIwvZieDoytar8tM9dn5nPAMuCoWtUmSWpfV59T2C8zVwEUz/sW7QcAK6r6NRdtm4iIiRExJyLmrFmzpqbFSlJv011ONEc7bdlex8ycmplNmdk0aNCgGpclSb1LV4fCixExGKB4Xl20NwMHVvVrBFZ2cW2S1Ot1dSjcA4wvpscDd1e1j4uI/hExFDgUeLyLa5OkXq9mA+JFxK3AccA+EdEMXAJcCcyMiHOB54FPAWTm4oiYSeU7oDcAkzKzpVa1SZLaV7NQyMzTN/PWCZvpfwVwRa3qkSRtXXc50SxJ6gYMBUlSyVCQJJUMBUlSyVCQJJUMBUlSyVCQJJUMBUlSyVCQJJUMBUlSyVCQJJUMBUlSyVCQJJUMBUlSyVCQJJUMBUlSyVCQJJUMBUlSyVCQJJUMBUlSyVCQJJUMBUlSyVCQJJUMBUlSqW+9C5CkrXn+GyPqXUK3cdDFC2u6fPcUJEklQ0GSVDIUJEklQ0GSVKrLieaIWA6sA1qADZnZFBEDgduBIcBy4LTMfLke9UlSb1XPPYXjM3NUZjYVrycDszPzUGB28VqS1IW60+Gjk4HpxfR0YGwda5GkXqleoZDAf0TE3IiYWLTtl5mrAIrnfetUmyT1WvW6ee2DmbkyIvYF7o+IZzo6YxEiEwEOOuigWtUnSb1SXfYUMnNl8bwauBM4CngxIgYDFM+rNzPv1MxsysymQYMGdVXJktQrdHkoRMRfRMSerdPA3wCLgHuA8UW38cDdXV2bJPV29Th8tB9wZ0S0rv+WzLwvIp4AZkbEucDzwKfqUJsk9WpdHgqZ+Xvg8Hba1wIndHU9kqSNutMlqZKkOjMUJEklQ0GSVDIUJEklQ0GSVPLrOKVuavRXb6p3Cd3GnXvWu4Lewz0FSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklbpdKETERyNiaUQsi4jJ9a5HknqTbhUKEbEL8EPgY8Aw4PSIGFbfqiSp9+hWoQAcBSzLzN9n5n8DtwEn17kmSeo1+ta7gDYOAFZUvW4Gjq7uEBETgYnFy9ciYmkX1dbjHQz7AC/Vu45u4ZKodwWq4rZZpXO2zYM390Z3C4X2ftp824vMqcDUrimnd4mIOZnZVO86pLbcNrtOdzt81AwcWPW6EVhZp1okqdfpbqHwBHBoRAyNiF2BccA9da5JknqNbnX4KDM3RMR5wC+BXYBpmbm4zmX1Jh6WU3flttlFIjO33kuS1Ct0t8NHkqQ6MhQkSSVDYScUEftFxC0R8fuImBsRj0bEKTuwvEsj4ivbOe+QiPgf27tu7RwiIiPiu1WvvxIRl25lnrFbGpEgIs6MiAURsTgi5kfEv0XEO3ewztd2YN4JEbH/jqy/JzAUdjIREcBdwEOZeUhmjqZylVZjm35ddRHBEMBQ6PnWA6dGxD7bMM9YKsPVbCIiPgpcCHwsM4cD7wd+C+zXTt9dtr3c7TIBMBTqXYC22UeA/87Ma1sbMvMPmfmD4pPOTyLiXuA/ImKPiJgdEU9GxMKIKIcMiYivFwMPPgC8p6r9wYhoKqb3iYjlxfSQiHi4WNaTEXFMMcuVwLERMS8iLoyIXSLiqoh4ovgU+Lna/0rUBTZQuQLowrZvRMTBxXa2oHg+qNg+/ha4qtg2/qrNbF8HvpKZLwBkZktmTsvMpcUyl0fExRHxCPCpiPhssU3Nj4g7ImL3ot/QYk/5iYi4vKqm4yJiVtXrKRExoZi+uOi/KCKmRsUngSZgRlHvbhExOiJ+U+yN/zIiBnfer7Mby0wfO9ED+CLw/c28N4HKDYADi9d9gXcU0/sAy6jcNT4aWAjsDryjaP9K0e9BoKlqnuXF9O5AQzF9KDCnmD4OmFVVw0Tgn4rp/sAcYGi9f28+dni7e63YVpYDewFfAS4t3rsXGF9MnwPcVUzfCHxyM8v7I7DXFta3HPiHqtd7V03/M/D3xfQ9wNnF9CTgtWK67XY5BZhQTA+sar8Z+HgxXb3t96Oy5zKoeP1pKpfI1/3fotYP9xR2chHxw+LT0xNF0/2Z+cfWt4FvRsQC4AEqY0vtBxwL3JmZr2fmn+jYDYL9gOsiYiHwEzZzWAD4G+DsiJgHPAbsTSVEtJMrtpWbqHwwqfYB4JZi+mbgr7dluRExovh0/n8j4tNVb91eNf2+Yk91IXAGMLxo/yBwa9W6O+L4iHisWNZHqpZV7T3A+4D7i235n2hziLan6lY3r6lDFgOfaH2RmZOK47xziqb/qup7BjAIGJ2ZbxaHghpaZ93M8jew8bBiQ1X7hcCLwOHF+29sZv6g8inulx36abSz+V/Ak8ANW+jTkZufFlM5j/DrzFwIjIqIKcBuVX2qt+UbgbGZOb84DHTcVtZXvR1DsS1HRAPwIyp7BCuKk+UNm85OAIsz8wMd+Fl6FPcUdj6/Ahoi4gtVbbtvpu9ewOoiEI5n48iIDwGnFMdN9wQ+XjXPciqHlwA+2WZZqzLzLeAsKnecA6wD9qzq90vgCxHRDyAi3h0Rf7EtP6C6r2IvdCZwblXzb6lc7ACVDyKPFNNtt41q3wKujojqT9+7baYvxXJWFdvVGVXt/6fNulv9ARgWEf0jYi/ghKK9NQBeiog9ePs2Xl3vUmBQRHwAICL6RUR7exQ9jqGwk8nKAc6xwIcj4rmIeByYDvxjO91nAE0RMYfKf5hnimU8SWXXfB5wB/Bw1TxXU/mj/lsq5xRa/QgYHxG/A97Nxk9xC4ANxSGsC4F/A5YAT0bEIuB/4x5pT/Nd3r5tfBH4u+Iw5VnA+UX7bcBXI+KptieaM/PfgWuAX0TEkmJ7a6HyoaI9/5PK4cj7KbbjwvnApOLw6V5Vy19BJbwWUPl/8FTR/gpwHZVzandRGW+t1Y3AtcXhol2oBMa3I2I+lf8rx9ALOMyFJKnknoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoqNeKiJbiTtrWx+R2+rxtDJ1OWu9xVWNHERGfj4izO3Md0vby+nH1Zn/OzFF1WO9xVMYS+i1AVg1uKNWbewpSGxHx0Yh4phih89Sq9rd970QxyuaQYvrsYpTQ+RFxc9H28WKMnaci4oGofA/GEODzwIXF3smx1cuNiFER8btiWXdGxICi/cGI+HZEPB4R/xkRx3bRr0O9jKGg3my3NoePPl2MjXMdlaE/jgX+cmsLKYY/+Drwkcw8nI139D4CjMnMI6jc3fsPmbkcuJbKSLejMvPhNou7CfjHzBxJ5a7bS6re65uZRwEXtGmXOo2Hj9SbbXL4KCJGAc9l5rPF6x9TGQ58Sz4C/DQzX4JyfCCojKp5ezEO/67Ac1taSDFGzzsz8zdF03QqI9K2+lnxPJfKlxtJnc49BWlTHRlBFjYOrhabmecHwJTMHAF8jvZH49wW64vnFvxApxoxFKS3ewYYWjWA2+lV7y2nMtwzEfF+YGjRPhs4LSL2Lt4bWLTvBbxQTI+vWk67o4dm5qvAy1XnC84CftO2n1RLhoJ6s7bnFK7MzDeoHC76eXGi+Q9V/e8ABhajaH4B+E+AzFwMXAH8phhR83tF/0uBn0TEw8BLVcu5l8rQ5fPaOWE8nspXWC4ARgHf6MwfWNoaR0mVJJXcU5AklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklf4/UTZsCzvTISIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Education', hue = 'Loan_Status', data = loan_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ff1ca94c40>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Married', hue = 'Loan_Status', data = loan_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ff1cae20a0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Self_Employed', hue = 'Loan_Status', data = loan_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ff1cb429d0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M+PxmbZOBWZl5GDCrWCYiBgJjgEHFmH+KiD1qWJskqRU1C4XMfBT4y2bNpwLTivfTgNFV7Xdk5obMfB5YDoysVW2SpNZ19DWFgzJzFUDxemDRfgiwoqpfc9G2hYiYGBFzI2LumjVralqsJDWaznKhOVppy9Y6ZuaUzByemcN79epV47IkqbF0dCi8HBG9AYrX1UV7M9Cnql8TsLKDa5OkhtfRoXAfMK54Pw64t6p9TER0j4j+wGHAnA6uTZIaXs0+TyEibgeOBw6IiGbgcuAaYEZEnA+8CHwRIDMXR8QMYAmwEZiUmS21qk2S1LqahUJmjt3KqhO30v9q4Opa1SNJ2r7OcqFZktQJGAqSpJKhIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQqSpFLXeuw0Il4A1gEtwMbMHB4R+wF3Av2AF4CzMvPVetQnSY2qLqFQOCEzX6langzMysxrImJysfwP9SlNUmfy4rcH17uETqPvZQtruv3OdProVGBa8X4aMLqOtUhSQ6pXKCTw24iYFxETi7aDMnMVQPF6YJ1qk6SGVa/TR8dm5sqIOBB4KCKebevAIkQmAvTt27dW9UlSQ6rLkUJmrixeVwN3AyOBlyOiN0DxunorY6dk5vDMHN6rV6+OKlmSGkKHh0JEfDQi9tn0HvhbYBFwHzCu6DYOuLeja5OkRleP00cHAXdHxKb9/yIzH4iIJ4EZEXE+8CLwxTrUJkkNrcNDITP/BBzZSvta4MSOrkeS9L7OdEuqJKnODAVJUslQkCSVDAVJUslQkCSVDAVJUslQkCSVDAVJUqmen6cgaRuGfevWepfQady9T70raBweKUiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKnU6UIhIj4fEc9FxPKImFzveiSpkXSqUIiIPYCfAl8ABgJjI2JgfauSpMbRqUIBGAksz8w/ZeY7wB3AqXWuSZIaRtd6F7CZQ4AVVcvNwNHVHSJiIjCxWHwzIp7roNp2e4fCAcAr9a6jU7g86l2Bqvi7WaV9fjcP3dqKzhYKrX23+YGFzCnAlI4pp7FExNzMHF7vOqTN+bvZcTrb6aNmoE/VchOwsk61SFLD6Wyh8CRwWET0j4iPAGOA++pckyQ1jE51+igzN0bEBcCDwB7A1MxcXOeyGomn5dRZ+bvZQSIzt99LktQQOtvpI0lSHRkKkqSSobCbi4iMiNuqlrtGxJqImLmdccdvr4/UFhHREhHzq7761XBfL0TEAbXafiPoVBeaVRP/DzgiIvbMzLeA/wq8VOea1Fjeysyh9S5CbeORQmP4DfDfivdjgds3rYiIkRHxHxHxdPH6yc0HR8RHI2JqRDxZ9HPqEe2SiBgWEf8eEfMi4sGI6F20z46IH0XEoxGxNCJGRMSvImJZRPxj1fh7irGLi1kOWtvHORExpzg6+ZdibjVth6HQGO4AxkRED2AI8ETVumeB4zLzKOAy4DutjP9fwCOZOQI4Abg2Ij5a45q1+9iz6tTR3RHRDfgJcGZmDgOmAldX9X8nM48DbgTuBSYBRwDjI2L/os+EYuxw4OtV7QBExOHAfweOLY5SWoCza/g97jY8fdQAMnNBcR53LPBvm63eF5gWEYdRmVKkWyub+FvglIj4n8VyD6AvsLQmBWt384HTRxFxBJU/8g9FBFSeSVpV1X/TA6sLgcWZuaoY9ycqMx6spRIEpxX9+gCHFe2bnAgMA54s9rEnsLp9v63dk6HQOO4DrgOOB6r/V3UV8LvMPK0IjtmtjA3gjMx08kG1h6Dyx/4zW1m/oXh9r+r9puWuEXE88F+Az2Tm+oiYTeU/KpvvY1pmXtJuVTcITx81jqnAtzNz4Wbt+/L+hefxWxn7IPC1KP7LFRFH1aRCNYrngF4R8RmAiOgWEYN2YPy+wKtFIHwKGNVKn1nAmRFxYLGP/SJiqzOD6n2GQoPIzObM/HErq74PfDci/kDlML41V1E5rbQgIhYVy9JOKT4r5UzgexHxDDAfOGYHNvEAlSOGBVR+Fx9vZR9LgP8N/Lbo9xDQe1drbwROcyFJKnmkIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQpSKyLioIj4RUT8qZhj549VT9DuynadfVadmqEgbaZ4SO8e4NHMHFDMsTMGaKpDLc46oA5lKEhb+hsqk7LduKkhM/+cmT+JiD0i4tpixtgFEfEVKI8AZkfEXRHxbERMr3oC/PNF22PA6Zu2ubXZZyNifET8a0TcD/y2Q79zNTz/FyJtaRDw1FbWnQ+8npkjIqI78IeI2PSH+6hi7ErgD8CxETEXuIlK0CwH7qza1qbZZydExMeAORHxcLHuM8CQzPxLe35j0vYYCtJ2RMRPgb8G3gH+DAyJiDOL1ftSmaHzHWBOZjYXY+YD/YA3geczc1nR/nNg0/z/W5t9FuAhA0H1YChIW1oMnLFpITMnFR/xOBd4EfhaZj5YPaCYubN6Rs8W3v/3tbW5ZFqdfTYijqbyiXlSh/OagrSlR4AeEfH3VW17Fa8PAn9ffFAMEfGJ7Xzg0LNA/4j4q2J5bNU6Z59Vp2MoSJvJyiyRo4HPRcTzETEHmAb8A/B/gCXAU8WMsf/CNo64M/NtKqeLfl1caP5z1Wpnn1Wn4yypkqSSRwqSpJKhIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQqSpNL/B6NgAgmuMkVOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Gender', hue = 'Loan_Status', data = loan_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_dataset.replace({'Gender':{'Male':1, 'Female':0},'Married':{'Yes':1, 'No':0},\n",
    "                      'Education':{'Graduate':1, 'Not Graduate':0},\n",
    "                      'Self_Employed':{'Yes':1, 'No':0}},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "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>Rural</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>Urban</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>Urban</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>Urban</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>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender  Married Dependents  Education  Self_Employed  \\\n",
       "1  LP001003       1        1          1          1              0   \n",
       "2  LP001005       1        1          0          1              1   \n",
       "3  LP001006       1        1          0          0              0   \n",
       "4  LP001008       1        0          0          1              0   \n",
       "5  LP001011       1        1          2          1              1   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "5             5417             4196.0       267.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area  Loan_Status  \n",
       "1             1.0         Rural            0  \n",
       "2             1.0         Urban            1  \n",
       "3             1.0         Urban            1  \n",
       "4             1.0         Urban            1  \n",
       "5             1.0         Urban            1  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_dataset.replace({\"Property_Area\":{\"Rural\":0, \"Semiurban\":1, \"Urban\":2}}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "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 Dependents  Education  Self_Employed  \\\n",
       "1  LP001003       1        1          1          1              0   \n",
       "2  LP001005       1        1          0          1              1   \n",
       "3  LP001006       1        1          0          0              0   \n",
       "4  LP001008       1        0          0          1              0   \n",
       "5  LP001011       1        1          2          1              1   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "5             5417             4196.0       267.0             360.0   \n",
       "\n",
       "   Credit_History  Property_Area  Loan_Status  \n",
       "1             1.0              0            0  \n",
       "2             1.0              2            1  \n",
       "3             1.0              2            1  \n",
       "4             1.0              2            1  \n",
       "5             1.0              2            1  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = loan_dataset.drop(columns = [\"Loan_ID\", \"Loan_Status\"], axis = 1)\n",
    "Y = loan_dataset[\"Loan_Status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
      "1         1        1          1          1              0             4583   \n",
      "2         1        1          0          1              1             3000   \n",
      "3         1        1          0          0              0             2583   \n",
      "4         1        0          0          1              0             6000   \n",
      "5         1        1          2          1              1             5417   \n",
      "..      ...      ...        ...        ...            ...              ...   \n",
      "609       0        0          0          1              0             2900   \n",
      "610       1        1          4          1              0             4106   \n",
      "611       1        1          1          1              0             8072   \n",
      "612       1        1          2          1              0             7583   \n",
      "613       0        0          0          1              1             4583   \n",
      "\n",
      "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
      "1               1508.0       128.0             360.0             1.0   \n",
      "2                  0.0        66.0             360.0             1.0   \n",
      "3               2358.0       120.0             360.0             1.0   \n",
      "4                  0.0       141.0             360.0             1.0   \n",
      "5               4196.0       267.0             360.0             1.0   \n",
      "..                 ...         ...               ...             ...   \n",
      "609                0.0        71.0             360.0             1.0   \n",
      "610                0.0        40.0             180.0             1.0   \n",
      "611              240.0       253.0             360.0             1.0   \n",
      "612                0.0       187.0             360.0             1.0   \n",
      "613                0.0       133.0             360.0             0.0   \n",
      "\n",
      "     Property_Area  \n",
      "1                0  \n",
      "2                2  \n",
      "3                2  \n",
      "4                2  \n",
      "5                2  \n",
      "..             ...  \n",
      "609              0  \n",
      "610              0  \n",
      "611              2  \n",
      "612              2  \n",
      "613              1  \n",
      "\n",
      "[480 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.1, stratify = Y, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(480, 11) (432, 11) (48, 11)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "support_vector_machine = svm.SVC(kernel = \"linear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "support_vector_machine.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_prediction = support_vector_machine.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Training Data:  0.7800925925925926\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy on Training Data: \", training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_prediction = support_vector_machine.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test Data:  0.7916666666666666\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy on Test Data: \", test_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "This Person isn't eligible for Loan.\n"
     ]
    }
   ],
   "source": [
    "Input_data = (1,1,4,1,0,3036,2504.0,158.0,360.0,0.0,1)\n",
    "\n",
    "Input_data_as_numpy_array = np.asarray(Input_data)\n",
    "\n",
    "input_reshaping = Input_data_as_numpy_array.reshape(1,-1)\n",
    "\n",
    "prediction = support_vector_machine.predict(input_reshaping)\n",
    "\n",
    "print(prediction)\n",
    "\n",
    "if(prediction[0]=='1'):\n",
    "    print(\"This Person is eligible for Loan.\")\n",
    "else:\n",
    "    print(\"This Person isn't eligible for Loan.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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