loan-application status.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             601 non-null    object \n",
      " 2   Married            611 non-null    object \n",
      " 3   Dependents         599 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      582 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         592 non-null    float64\n",
      " 9   Loan_Amount_Term   600 non-null    float64\n",
      " 10  Credit_History     564 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tackling missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "miss=df.isnull().sum()\n",
    "print(miss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "replacing the NULL values in \"Gender\" with Male because it has more frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "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=df[\"Gender\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "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=df[\"Gender\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Gender\"].replace(np.nan, \"Male\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     345\n",
       "1     102\n",
       "2     101\n",
       "3+     51\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Dependents\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Dependents', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "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=df[\"Dependents\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "since most of the people have no dependents we would replace the NULL values as 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Dependents\"].replace(np.nan, \"0\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    398\n",
       "No     213\n",
       "Name: Married, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Married\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Married', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "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=df[\"Married\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in the married column although \"Yes\" occurs more, it would make more sense if we replace NULL values with a \"No\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Married\"].replace(np.nan, \"No\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     500\n",
       "Yes     82\n",
       "Name: Self_Employed, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Self_Employed\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Self_Employed', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "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=df[\"Self_Employed\"]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    No\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode=df[\"Self_Employed\"].mode()\n",
    "mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Self_Employed\"].replace(np.nan, mode[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      NaN\n",
       "1    128.0\n",
       "2     66.0\n",
       "3    120.0\n",
       "4    141.0\n",
       "Name: LoanAmount, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"LoanAmount\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see in the column \"Loan Amount Term\" value=360.0 is having a much higher frequency than any other value, so i would replace the NULL values withthis values only instead of the average(mean)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0    512\n",
       "180.0     44\n",
       "480.0     15\n",
       "300.0     13\n",
       "84.0       4\n",
       "240.0      4\n",
       "120.0      3\n",
       "36.0       2\n",
       "60.0       2\n",
       "12.0       1\n",
       "Name: Loan_Amount_Term, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Loan_Amount_Term\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg=df[\"Loan_Amount_Term\"].mean(axis=0)\n",
    "avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Loan_Amount_Term', ylabel='Count'>"
      ]
     },
     "execution_count": 24,
     "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.histplot(x=df[\"Loan_Amount_Term\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "mode=df[\"Loan_Amount_Term\"].mode()\n",
    "df[\"Loan_Amount_Term\"].replace(np.nan, mode[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in the column \"Credit_History\" I would replace NULL values with \"0\" rather than \"1\", even though \"1\" clearly has a higher frequency but in doing so I would be providing wrong information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    475\n",
       "0.0     89\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Credit_History\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Credit_History\"].replace(np.nan, \"0.0\", inplace=True)\n",
    "df[\"Credit_History\"]=df[\"Credit_History\"].astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.dropna()\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(592, 13)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "miss=df.isnull().sum()\n",
    "print(miss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='count'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEFCAYAAADuT+DpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAd/0lEQVR4nO3df1RUdeL/8ecwMKPywx9hWl9FQVNMdxY8JHY8udlqlrtu6UnMcXEVd02OrQvlJqIYRUes1OyXWKZZmCL5K+vYOaVZnkzRY5lpgympK0lKriVDOojc7x/ZfHJTxJwfyn09zunE/Ljved051/uae+feOxbDMAxERMS0QoIdQEREgktFICJicioCERGTUxGIiJicikBExORUBCIiJhfqj0HPnj3LtGnTOHDgAFarlfz8fKqqqhg/fjwdO3YEYMSIEQwaNIji4mKKiooIDQ0lPT2dfv361Tv2zp07sdvt/ogtItJoeTweEhISLviYX4pg48aNABQVFVFSUkJ+fj533HEHY8aMIS0tzfu8yspKCgsLWblyJR6PB6fTSZ8+fbDZbBcd2263061bN3/EFhFptFwu10Uf80sR9O/fn9tvvx2AI0eOEB0dze7duzlw4AAbNmygQ4cOZGdns2vXLhITE7HZbNhsNmJiYigtLcXhcPgjloiIXIBfigAgNDSUyZMn8/777/Pcc89x9OhRhg0bRo8ePSgoKODFF18kPj6eyMhI7zTh4eG43e56x/V4PPU2m4iIXB6/FQHAk08+yaRJk0hJSaGoqIg2bdoAMGDAAPLy8khKSqK6utr7/Orq6vOK4UK0a0hE5PIFfNfQmjVrOHr0KA888ABNmzbFYrHw4IMPkpOTg8PhYMuWLXTv3h2Hw8HcuXPxeDzU1NRQVlZGly5d/BFJREzizJkzlJeXc/r06WBHCYomTZrQrl07wsLCGjyNX4rgzjvvZMqUKYwcOZLa2lqys7O54YYbyMvLIywsjOjoaPLy8oiIiCA1NRWn04lhGGRmZuqIIBG5IuXl5URGRtKxY0csFkuw4wSUYRgcP36c8vJyYmNjGzyd5Vq7+qjL5dKuIRG5KJfLRXx8vOlK4GeGYVBaWvqr9WR9606dUCYijY5ZSwB+27yrCERETE5FICJicioCkzPOng12hKuG3ovGq6SkhMzMzIC93ssvv8zo0aNJS0tj7Nix7N69G4C9e/eyffv2eqddsmRJICKex6/nEcjVz2K18t36pcGOcVWI7u8MdgRpBPbv388HH3zAsmXLsFgsuFwuJk+ezNq1a3nvvfeIjo7mlltuuej0BQUF/PWvfw1gYhWBiJjU5s2bmTt3Lna7nRYtWjBjxgzCw8OZPn063377LSdOnKBv375kZGSQlZWFzWbjm2++4dixY8ycOZPu3btfcNxWrVpx5MgRVqxYQd++fenWrRsrVqzg6NGjrF69mrCwMLp3786RI0d44403vNM9++yzLF++nB9++IHc3FwcDgdff/01kyZNwuPxcPfdd/PBBx/wxhtvsGbNGkJCQujZsyeTJ0++4vdCu4ZExHQMwyAnJ4cXXniBJUuWcMstt1BQUEBFRQUJCQksXLiQZcuWsWzZMu80N954IwsXLiQ1NZXly5dfdOxWrVpRUFDAp59+yvDhw7nrrrvYuHEjbdq0YciQIYwePRqHw8HBgwd5+eWXKSwsJDY2lo8//pj09HSaN29Obm7uRcdftWoVU6dOZfny5bRv357a2torfj+0RSAipnPixAkiIiK8l7255ZZbmDNnDi1atOCLL75g69atREREUFNT453m52Pw27Zty6effnrRsQ8dOkRERAT5+fkAfPHFF4wbN47k5OTznnfdddcxefJkwsPD+frrry96iWj4qbh+lp+fz6JFi5g1axYJCQn44lQwbRGIiOm0bNkSt9vNsWPHANi2bRsdO3Zk1apVREZGMnv2bNLS0jh9+rR3RdvQ4/P37t1Lbm4uHo8HgNjYWCIjI7FarVgsFurq6qiqquK5557jmWee4YknnsBut3tf5+f/2+12KisrAdizZ493/OLiYh577DGWLFmCy+Xis88+u+L3Q1sEImIKmzdvZujQod7bDzzwAP/85z+xWCw0b96c/Px8jh8/zkMPPcSOHTto2rQpHTp08JZFQ915552UlZUxbNgwmjVrhmEYPPLII0RGRtKjRw+eeuopOnXqRM+ePRkyZAjNmjUjKirK+zqdOnVi0qRJTJ8+nWXLljFixAi6d+9OeHg4AF27duW+++6jZcuWtGnTht///vdX/N7oEhOio4bO0VFDjYPWERd+D+p7X7RFICLyG+Tm5lJWVvar+xcsWECTJk2CkOi3UxGIiPwG9R3Zc63Rl8UiIianIhARMTkVgYjIOWfr6q7q8fxF3xGIiJxjDQlhzae//gL4t7q3Z6dLPqekpIQJEybw9ttvc8MNNwAwa9Ys4uLizjvc1Z+0RSAiEmRhYWFMmTLFJ2cJ/xYqAhGRIOvduzfNmzc/7yJ0gaQiEBG5CuTm5rJ48WIOHjwY8NdWEYiIXAVatmxJdnY2WVlZ1AX4S2YVgYjIVeKOO+4gNjaW1atXB/R1ddSQiMg5Z+vqGnSkz+WMZw25vM/bU6dOZevWrT7L0BB+KYKzZ88ybdo0Dhw4gNVqJT8/H8MwyMrKwmKxcNNNN/Hoo48SEhJCcXExRUVFhIaGkp6eTr9+/fwRSUTkki53pe2L8ZKTk8/7rYKIiAg2btzo0xyX4pci+HkmioqKKCkp8RZBRkYGycnJTJ8+nQ0bNpCQkEBhYSErV67E4/HgdDrp06cPNpvNH7FEROQC/FIE/fv35/bbbwfgyJEjREdH8+GHH9KrVy8A+vbty+bNmwkJCSExMRGbzYbNZiMmJobS0lIcDoc/YomIyAX47TuC0NBQJk+ezPvvv89zzz3Hxo0bvb/wEx4eTlVVFW63m8jISO804eHhuN3uesf1eDy4XC5/xTYds1+3/X9p2br2nTlzhlOnTgU7RlCdOXPmspZlv35Z/OSTTzJp0iRSUlK8P9sGUF1dTVRUFBEREVRXV593/y+L4ULsdrtWXuI3WraufS6Xi6ZNmwY7RlCFhYVd8IdpLsYvh4+uWbOGl156CYCmTZtisVjo0aMHJSUlAGzatImkpCQcDgc7duzA4/FQVVVFWVkZXbp08UckERG5CL9sEdx5551MmTKFkSNHUltbS3Z2Np06dSInJ4c5c+YQFxfHwIEDsVqtpKam4nQ6MQyDzMxM7Ha7PyKJiFyScfYsFqv1qh3PX/xSBM2aNePZZ5/91f1Lliz51X0pKSmkpKT4I4aIyGWxWK0+/Q3vhvwO9sSJE+nRowfjxo0DftpFPnToUJ599lni4+N9lqU+OrNYRCSIcnNzWbZsGfv37wd++m51+PDhASsBUBGIiARVq1atyMnJYdq0aWzbto3Dhw8zZsyYgGZQEYiIBNnP1xjKyspi5syZ3kPtA0XXGhIRuQrce++9nD59mjZt2gT8tbVFICJictoiEBE5xzh7tkFH+lzOeKY9fFRE5Frk65X25Yz3v1chDSTtGhIRMTkVgYiIyakIRKTRMQwj2BGC5rfMu4pARBqVJk2acPz4cVOWgWEYHD9+nCZNmlzWdPqyWEQalXbt2lFeXk5lZWWwowRFkyZNaNeu3WVNoyIQkUYlLCyM2NjYYMe4pmjXkIiIyakIRERMTkUgImJyKgIREZNTEYiImJyKQETE5FQEIiImpyIQETE5FYGIiMn5/MziM2fOkJ2dzTfffENNTQ3p6em0bduW8ePH07FjRwBGjBjBoEGDKC4upqioiNDQUNLT0+nXr5+v44iIyCX4vAjWrl1LixYtePrppzlx4gRDhgxhwoQJjBkzhrS0NO/zKisrKSwsZOXKlXg8HpxOJ3369MFms/k6koiI1MPnRXDXXXcxcOBA722r1cru3bs5cOAAGzZsoEOHDmRnZ7Nr1y4SExOx2WzYbDZiYmIoLS3F4XD4OpKIiNTD50UQHh4OgNvtZuLEiWRkZFBTU8OwYcPo0aMHBQUFvPjii8THxxMZGXnedG6329dxRETkEvxy9dGKigomTJiA0+lk8ODBnDx5kqioKAAGDBhAXl4eSUlJVFdXe6eprq4+rxguxuPx4HK5/BHblLp16xbsCFcVLVtiRj4vgu+++460tDSmT5/OrbfeCsDYsWPJycnB4XCwZcsWunfvjsPhYO7cuXg8HmpqaigrK6NLly6XHN9ut2vlJX6jZUsaq/o+5Pi8CObPn8/JkyeZN28e8+bNAyArK4sZM2YQFhZGdHQ0eXl5REREkJqaitPpxDAMMjMzsdvtvo4jIiKXYDGusd9zc7lc+tTmY9+tXxrsCFeF6P7OYEcQ8Zv61p06oUxExORUBCIiJqciEBExORWBiIjJqQhERExORSAiYnIqAhERk1MRiIiYnIpARMTkVAQiIianIhARMTkVgYiIyakIRERMTkUgImJyKgIREZNTEYiImJyKQETE5FQEIiImpyIQETE5FYGIiMmpCERETE5FICJicioCERGTC/X1gGfOnCE7O5tvvvmGmpoa0tPT6dy5M1lZWVgsFm666SYeffRRQkJCKC4upqioiNDQUNLT0+nXr5+v44iIyCX4vAjWrl1LixYtePrppzlx4gRDhgwhPj6ejIwMkpOTmT59Ohs2bCAhIYHCwkJWrlyJx+PB6XTSp08fbDabryOJiEg9fF4Ed911FwMHDvTetlqt7Nmzh169egHQt29fNm/eTEhICImJidhsNmw2GzExMZSWluJwOHwdSURE6uHzIggPDwfA7XYzceJEMjIyePLJJ7FYLN7Hq6qqcLvdREZGnjed2+2+5PgejweXy+Xr2KbVrVu3YEe4qmjZEjPyeREAVFRUMGHCBJxOJ4MHD+bpp5/2PlZdXU1UVBQRERFUV1efd/8vi+Fi7Ha7Vl7iN1q2pLGq70OOz48a+u6770hLS+Pf//439913HwA333wzJSUlAGzatImkpCQcDgc7duzA4/FQVVVFWVkZXbp08XUcERG5BJ9vEcyfP5+TJ08yb9485s2bB8DUqVN54oknmDNnDnFxcQwcOBCr1UpqaipOpxPDMMjMzMRut/s6joiIXILFMAzjUk968803GTZsmPf266+/zqhRo/wa7GJcLpc2333su/VLgx3hqhDd3xnsCCJ+U9+6s94tgnfeeYcPPviAkpIStm7dCsDZs2fZt29f0IpARER8q94iuO2222jdujXff/89w4cPByAkJIT27dsHJJyIiPhfvUXQvHlzkpOTSU5O5vjx43g8HuCnrQIREWkcGvRl8WOPPcZHH33E9ddfj2EYWCwWioqK/J1NREQCoEFF8Pnnn7N+/XpCQnSNOhGRxqZBa/YOHTp4dwuJiEjj0qAtgoqKCvr160eHDh0AtGtIRKQRaVARzJ492985REQkSBpUBKtXr/7VfQ8++KDPw4iISOA1qAiio6MBMAyDL7/8krq6Or+GEhGRwGlQEdx///3n3f773//ulzAiIhJ4DSqCAwcOeP+urKykoqLCb4FERCSwGlQE06dP9/5tt9t55JFH/BZIREQCq0FFUFhYyIkTJzh8+DDt2rWjVatW/s4lIiIB0qATyt59913uv/9+5s+fz/Dhw3nrrbf8nUtERAKkQVsEixcvZtWqVd7fFf7b3/7GPffc4+9sIiISAA3aIrBYLN4fpY+IiNAviYmINCIN2iKIiYlh5syZJCUlsWPHDmJiYvydS0REAqRBWwQpKSk0b96cTz75hFWrVjFy5Eh/5xIRkQBpUBHMnDmTAQMGMH36dFasWMHMmTP9nUtERAKkQUUQGhpK586dAWjfvr1+l0BEpBFp0HcEN954I3PmzCEhIYFdu3Zx/fXX+zuXiIgESIM+2ufn59OqVSs++ugjWrVqRX5+vr9ziYhIgDRoi8ButzN69OjLGvjzzz9n1qxZFBYWsmfPHsaPH0/Hjh0BGDFiBIMGDaK4uJiioiJCQ0NJT0+nX79+l5tfRESuUIOK4HItWLCAtWvX0rRpUwC+/PJLxowZQ1pamvc5lZWVFBYWsnLlSjweD06nkz59+mCz2fwRSURELsIv3/rGxMTw/PPPe2/v3r2bDz/8kJEjR5KdnY3b7WbXrl0kJiZis9mIjIwkJiaG0tJSf8QREZF6+GWLYODAgZSXl3tvOxwOhg0bRo8ePSgoKODFF18kPj6eyMhI73N+vnzFpXg8Hlwulz9im1K3bt2CHeGqomVLzMgvRfC/BgwYQFRUlPfvvLw8kpKSqK6u9j6nurr6vGK4GLvdrpWX+I2WLWms6vuQE5ATAsaOHcuuXbsA2LJlC927d8fhcLBjxw48Hg9VVVWUlZXRpUuXQMQREZFfCMgWQW5uLnl5eYSFhREdHU1eXh4RERGkpqbidDoxDIPMzMyAXczubF0dVp0UJyICgMUwDCPYIS6Hy+Xyyeb7mk/LfJDm2ndvz058t35psGNcFaL7O4MdQcRv6lt36mOxiIjJqQhERExORSAiYnIqAhERk1MRiIiYnIpARMTkVAQiIianIhARMTkVgYiIyakIRERMTkUgImJyKgIREZNTEYiImJyKQETE5FQEIiImpyIQETE5FYGIiMmpCERETE5FICJicioCERGTUxGIiJicikBExOT8VgSff/45qampABw6dIgRI0bgdDp59NFHqaurA6C4uJihQ4eSkpLCxo0b/RVFRETq4ZciWLBgAdOmTcPj8QCQn59PRkYGS5cuxTAMNmzYQGVlJYWFhRQVFbFw4ULmzJlDTU2NP+KIiEg9/FIEMTExPP/8897be/bsoVevXgD07duXTz75hF27dpGYmIjNZiMyMpKYmBhKS0v9EUdEROrhlyIYOHAgoaGh3tuGYWCxWAAIDw+nqqoKt9tNZGSk9znh4eG43W5/xBERkXqEXvopVy4k5P/6prq6mqioKCIiIqiurj7v/l8Ww8V4PB5cLtcV5enWrdsVTS+N15UuWyLXooAUwc0330xJSQnJycls2rSJ3r1743A4mDt3Lh6Ph5qaGsrKyujSpcslx7Lb7VqRi99o2ZLGqr4POQEpgsmTJ5OTk8OcOXOIi4tj4MCBWK1WUlNTcTqdGIZBZmYmdrs9EHFEROQXLIZhGMEOcTlcLpdPPrWt+bTMB2mufff27MR365cGO8ZVIbq/M9gRRPymvnWnTigTETE5FYGIiMmpCERETE5FICJicioCERGTUxGIiJicikBExORUBCIiJqciEBExORWBiIjJqQhERExORSAiYnIqAhG5KhlnzwY7wlXD3+9FQC5DLSJyuSxWq66Me46/r4yrLQIREZNTEYiImJyKQETE5FQEIiImpyIQETE5FYGIiMmpCERETE5FICJicioCERGTC+iZxffeey+RkZEAtGvXjvHjx5OVlYXFYuGmm27i0UcfJSRE3SQiEkgBKwKPxwNAYWGh977x48eTkZFBcnIy06dPZ8OGDQwYMCBQkUREhADuGiotLeXUqVOkpaUxatQodu7cyZ49e+jVqxcAffv25ZNPPglUHBEROSdgWwRNmjRh7NixDBs2jIMHD/KPf/wDwzCwWCwAhIeHU1VVdclxPB4PLpfrirJ069btiqaXxutKly3xHf07PZ8/l82AFUFsbCwdOnTAYrEQGxtLixYt2LNnj/fx6upqoqKiLjmO3W7XAiJ+o2VLrlZXumzWVyQB2zW0YsUKZs6cCcDRo0dxu9306dOHkpISADZt2kRSUlKg4oiIyDkB2yK47777mDJlCiNGjMBisTBjxgxatmxJTk4Oc+bMIS4ujoEDBwYqjoiInBOwIrDZbMyePftX9y9ZsiRQEURE5AJ00L6IiMmpCERETE5FICJicioCERGTUxGIXEXO1tUFO4KYUEAvOici9bOGhLDm07Jgx7gq3NuzU7AjmIa2CERETE5FICJicioCERGTUxGIiJicikBExORUBCIiJqciEBExORWBiIjJqQhERExORSAiYnIqAhERk1MRiIiYnIpARMTkVAQiIianIhARMTkVgYiIyakIRERMLui/UFZXV0dubi579+7FZrPxxBNP0KFDh2DHEhExjaBvEaxfv56amhqWL1/Oww8/zMyZM4MdSUTEVIJeBDt27OC2224DICEhgd27dwc5kYiIuQR915Db7SYiIsJ722q1UltbS2johaN5PB5cLtcVv27Xplc8RKPgcrng/yUGO8ZVodIHy5UvaNn8iZbN/+OLZdPj8Vz0saAXQUREBNXV1d7bdXV1Fy0B+GmrQUREfCfou4Z69uzJpk2bANi5cyddunQJciIREXOxGIZhBDPAz0cNffXVVxiGwYwZM+jUqVMwI4mImErQi0BERIIr6LuGREQkuFQEIiImpyJopEpKSujatSvr1q077/7BgweTlZV1wWlWrVrFrFmzAhFPTKC8vJyePXuSmprq/e+FF17w6WukpqZSVlbm0zHNKOiHj4r/xMXF8c477zBo0CAA9u7dy6lTp4KcSsykc+fOFBYWBjuGXIKKoBGLj4/n4MGDnDx5kqioKNauXcvgwYOpqKhgyZIlvPfee9TW1hIZGcnzzz9/3rSFhYW88847WCwWBg0axKhRo4I0F9LYzJ49m+3bt2MYBqNHj+buu+8mNTWVrl27sm/fPpo1a0ZSUhIff/wxJ0+eZNGiRVitVqZOnUpVVRUnTpxg2LBhOJ1O75hVVVVMnTqVEydOADBt2jS6du0arFm85mjXUCM3YMAA3n//fQzDYNeuXSQmJlJXV8f333/P4sWLWbp0KbW1tXzxxRfeafbv38+6detYunQpS5cuZf369Xz99ddBnAu5Vu3fv/+8XUNr166lvLycoqIiXn/9debPn8/JkycBcDgcvPbaa9TU1NCkSRNeffVVOnfuzPbt2zl06BB/+tOfWLRoEfPnz2fx4sXnvc78+fPp3bs3hYWF5OXlkZubG/iZvYZpi6CRGzx4MLm5ubRv356kpCQAQkJCCAsL46GHHqJZs2Z8++231NbWeqf56quvOHLkCKNHjwbghx9+4D//+Q9xcXHBmAW5hv3vrqEFCxawZ88eUlNTAaitreXIkSMAdO/eHYCoqCg6d+7s/dvj8RAdHc1rr73Ge++9R0RExHnLK/y0zG7dupV3330XwFsu0jAqgkauffv2/PjjjxQWFvLQQw9x+PBh3G4369ev58033+TUqVMMHTqUX55OEhcXR+fOnXnllVewWCwsXrxYZ3yLT8TFxZGcnExeXh51dXXMmzePdu3aXXK6RYsWkZCQgNPpZOvWrXz00Ue/Gvcvf/kLgwcP5vjx47z55pv+moVGSUVgAoMGDeKtt94iNjaWw4cPY7Vaadq0KUOHDsVms9G6dWuOHTvmfX58fDy33norI0aMoKamBofDQZs2bYI4B9JY3HHHHWzbtg2n08mPP/5I//79z7vo5MX069eP3Nxc3n77bVq0aIHVaqWmpsb7+Pjx45k6dSrFxcW43W4efPBBf85Go6Mzi0VETE5fFouImJyKQETE5FQEIiImpyIQETE5FYGIiMmpCER+4fDhw0ycOJGUlBRGjRrFuHHj2Ldv328er6yszHvylMjVSucRiJxz6tQp0tPTycvLIzHxpx9N37VrF48//rgunCaNmopA5JyNGzfSu3dvbwnAT9e/ef3116moqCAnJwePx4PdbicvL4+zZ8/y8MMP07ZtWw4fPszvfvc7HnvsMY4dO8akSZMwDIPWrVt7x9q2bRvPPPMMVquV9u3b8/jjj/P222+zcuVK6urqmDhxIrfeemswZl1MTkUgck55eTkxMTHe2+np6bjdbo4dO0bbtm1JS0vjD3/4A1u2bGHWrFlkZmZy8OBBFi5cSNOmTenfvz+VlZW8+uqr/PnPfyYlJYV169axbNkyDMMgJyeHpUuXct111zF37lxWr15NaGgoUVFRFBQUBHHOxexUBCLntG3blt27d3tv/7xyTklJYefOnbz00ku88sorGIZBWFgYADExMd5LJLRu3RqPx8O+ffu45557AOjZsyfLli3jv//9L8eOHSMjIwOA06dP06dPH2JiYoiNjQ3gXIr8mopA5Jw//vGPLFiwgJ07d5KQkADAoUOH+Pbbb3E4HGRmZtKzZ0/KysrYvn07ABaL5VfjxMXF8dlnnxEfH++9vHfLli1p27Yt8+bNIzIykg0bNtCsWTMqKioICdExGxJcKgKRc8LDwykoKGD27NnMmjWL2tpaQkNDycvLIy4ujtzcXDweD6dPn2bq1KkXHedf//oXmZmZrFu3zntlzZCQEKZOncq4ceMwDIPw8HCeeuopKioqAjV7Iheli86JiJictklFRExORSAiYnIqAhERk1MRiIiYnIpARMTkVAQiIianIhARMTkVgYiIyf1/2uEmfeBIjg0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x=df['Gender'], hue=df['Loan_Status'], palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Loan_Status', ylabel='count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x=df['Loan_Status'], hue=df['Married'], palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Dependents', ylabel='count'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x=df['Dependents'], hue=df['Loan_Status'], palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Education', ylabel='count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df['Education'], hue=df['Loan_Status'], palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Credit_History', ylabel='count'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEECAYAAAAlEzNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdkUlEQVR4nO3df3AU9f3H8eflxx0xF0ggilJJSNCUSOYEC8ROKmMYMNSOU0FI5OhZCBZhUJpYCiFAiF+QBNGIqIAyWqZBCJFfRavTSqDQokZFAYELakQqgvywUJJTLoTs9w/K1RQIweTuAvt6/AO3Pz73vp29feWzu/dZi2EYBiIiYlohwS5ARESCS0EgImJyCgIREZNTEIiImJyCQETE5MKCXcDl2r59OzabLdhliIhcUbxeL7169brgvCsuCGw2G8nJycEuQ0TkiuJ2uy86T6eGRERMTkEgImJyCgIREZO74q4RXMjp06c5cOAAp06dCnYpQdGuXTtuvPFGwsPDg12KiFyBroogOHDgAFFRUXTr1g2LxRLscgLKMAy++eYbDhw4QEJCQrDLEZEr0FVxaujUqVN06tTJdCEAYLFY6NSpk2l7QyLScldFEACmDIFzzPzZRaTlrpogEBGRH0ZBICJicldtEFRWVpKbmxuw93vxxRcZNWoU2dnZjBkzhl27dgGwd+9e3n///SbXXbZsWSBKFLmiGGfOBLuENsPf2+KquGso2D777DM2btzIihUrsFgsuN1upkyZwvr16/nrX/9KbGwsffv2vej6ixYt4le/+lUAKxZp+yyhoRzbsDzYZbQJsQOdfm3fVEGwdetW5s+fj81mIzo6mjlz5hAZGUlBQQFff/01x48fp3///uTk5JCXl4fVauWrr77iyJEjFBcX07Nnzwu227FjRw4ePMiqVavo378/ycnJrFq1isOHD7N27VrCw8Pp2bMnBw8e5JVXXvGt98wzz7By5Ur+/e9/U1hYiMPh4PPPP2fSpEl4vV5+/vOfs3HjRl555RXWrVtHSEgIt912G1OmTAnUJhMRE7hqTw39L8MwmDFjBs899xzLli2jb9++LFq0iEOHDtGrVy9eeuklVqxYwYoVK3zrdOnShZdeegmXy8XKlSsv2nbHjh1ZtGgRH374IVlZWQwePJhNmzbRuXNnhgwZwqhRo3A4HHzxxRe8+OKLlJaWkpCQwD/+8Q/Gjx9Phw4dKCwsvGj7a9asYdq0aaxcuZKuXbtSX1/fmptGREzOND2C48ePY7fb6dy5MwB9+/alpKSE6OhoPv74Y959913sdjt1dXW+dc6Ncnr99dfz4YcfXrTt/fv3Y7fbKSoqAuDjjz9m7NixpKamNlquU6dOTJkyhcjISD7//POLDgkLZ4PrnKKiIl5++WWefPJJevXq1WieiEhLmaZHEBMTQ21tLUeOHAHgvffeo1u3bqxZs4aoqCieeuopsrOzOXXqlO9A29z78/fu3UthYSFerxeAhIQEoqKiCA0NxWKx0NDQQE1NDQsWLODpp59m9uzZ2Gw23/uc+9dms3H06FEAdu/e7Wu/vLycxx57jGXLluF2u/noo49aZ6OIiHCV9wi2bt3K0KFDfa8feughHnnkESwWCx06dKCoqIhvvvmGRx99lG3bthEREUF8fLwvLJrrrrvuorq6muHDh3PNNddgGAaTJ08mKiqKlJQUnnjiCbp3785tt93GkCFDuOaaa2jfvr3vfbp3786kSZMoKChgxYoVjBgxgp49exIZGQnAj3/8Y4YNG0ZMTAydO3fm1ltvbb2NJCKmZzH8cJ7hzJkzTJ8+nX379hEaGkpRURE1NTWMGzeObt26ATBixAjuvvtuysvLKSsrIywsjPHjx5Oent5k2263+7wH01xomtloG8jVSHcNndUadw01dYzwS49g06ZNAJSVlVFZWUlRUREDBgxg9OjRZGdn+5Y7evQopaWlrF69Gq/Xi9PpJC0tDavV6o+yWqywsJDq6urzpi9ZsoR27doFoSIRkZbzSxAMHDiQO++8E4CDBw8SGxvLrl272LdvHxUVFcTHx5Ofn8/OnTvp3bs3VqsVq9VKXFwcVVVVOByOi7bt9XrPe+Ta6dOn+e677/zxURq52G2bhmEE5P2bcvr06SYfRSdypVEPtzF/fr/9do0gLCyMKVOm8NZbb7FgwQIOHz7M8OHDSUlJYdGiRTz//PP06NGDqKgo3zqRkZHU1tY22e6FnlnsdruJiIjwy+e4UoSHh+uLI3IVa+n3O2jPLJ47dy5/+ctfmDFjBj/72c9ISUkBYNCgQezZswe73Y7H4/Et7/F4GgWDiIj4n1+CYN26dbzwwgsAREREYLFYePjhh9m5cycA77zzDj179sThcLBt2za8Xi81NTVUV1eTlJTkj5Iu6kxDQ5tuT0TE3/xyauiuu+5i6tSpjBw5kvr6evLz87nhhhuYNWsW4eHhxMbGMmvWLOx2Oy6XC6fTiWEY5ObmYrPZ/FHSRYWGhLDuw/MvAP9Q997W/ZLLVFZWMmHCBF577TVuuOEGAJ588kkSExMb3e4qIhIIfgmCa665hmeeeea86WVlZedNy8zMJDMz0x9ltGnh4eFMnTqVP/zhD3qwjIgElWl+WdzW3H777XTo0KHRIHQiIsGgIAiiwsJCli5dyhdffBHsUkTExBQEQRQTE0N+fj55eXk06CKziASJgiDIBgwYQEJCAmvXrg12KSJiUlf1oHPNcaahoVl3+lxOe6Ehl5ev06ZN49133221GkRELofpg+ByD9qt0V5qamqjZxXY7Xbf+EwiIoGmU0MiIianIBARMTkFgYiIySkIRERMTkEgImJypg8C48yZNt2eiIi/mf72UUtoaKs+F7U5zxadOHEiKSkpjB07Fjj7HIahQ4fyzDPP0KNHj1arRUSkOUzfIwiGwsJCVqxYwWeffQacfYBPVlaWQkBEgkJBEAQdO3ZkxowZTJ8+nffee48vv/yS0aNHB7ssETEpBUGQnBtjKC8vj+LiYj2TQESCxvTXCILp3nvv5dSpU3Tu3DnYpYiIialHICJicqbvERhnzjTrTp/Lac8SGtpq7YmI+Jvpg6C1D9qX097/jkIqIhIMfgmCM2fOMH36dPbt20doaChFRUUYhkFeXh4Wi4Wbb76ZmTNnEhISQnl5OWVlZYSFhTF+/HjS09P9UZKIiFyEX4Lg3Nj6ZWVlVFZW+oIgJyeH1NRUCgoKqKiooFevXpSWlrJ69Wq8Xi9Op5O0tDSsVqs/yhIRkQvwSxAMHDiQO++8E4CDBw8SGxvL3/72N/r16wdA//792bp1KyEhIfTu3Rur1YrVaiUuLo6qqiocDsdlv6dhGKa9BdMwjGCXICJXML9dIwgLC2PKlCm89dZbLFiwgE2bNvkO1JGRkdTU1FBbW0tUVJRvncjISGpra5ts1+v14na7G02rr6/n0KFDREdHmy4MDMPgxIkT1NfXn7ddRK5kycnJwS6hTfHn99uvF4vnzp3LpEmTyMzMxOv1+qZ7PB7at2+P3W7H4/E0mv79YLgQm8123g5y+vRpDhw4wP79+1v3A1wh2rVrR48ePQgPDw92KSLiJy0NxqaCxC9BsG7dOg4fPsxDDz1EREQEFouFlJQUKisrSU1NZcuWLdx+++04HA7mz5+P1+ulrq6O6upqkpKSLvv9wsPDSUhI8MMnERG5+vklCO666y6mTp3KyJEjqa+vJz8/n+7duzNjxgxKSkpITEwkIyOD0NBQXC4XTqcTwzDIzc3FZrP5oyQREbkIi3GFXWl0u906dyhiEq05RPyVrDV+9NrUsVNDTIiImJyCQETE5BQEIiImpyAQETE5BYGIiMkpCERETE5BICJicgoCERGTUxCIiJicgkBExOQUBCIiJqcgEBExOQWBiIjJKQhERExOQSAiYnIKAhERk1MQiIiYnIJARMTkFAQiIianIBARMbmw1m7w9OnT5Ofn89VXX1FXV8f48eO5/vrrGTduHN26dQNgxIgR3H333ZSXl1NWVkZYWBjjx48nPT29tcsREZFLaPUgWL9+PdHR0cybN4/jx48zZMgQJkyYwOjRo8nOzvYtd/ToUUpLS1m9ejVerxen00laWhpWq7W1SxIRkSa0ehAMHjyYjIwM3+vQ0FB27drFvn37qKioID4+nvz8fHbu3Env3r2xWq1YrVbi4uKoqqrC4XC0dkkiItKEVg+CyMhIAGpra5k4cSI5OTnU1dUxfPhwUlJSWLRoEc8//zw9evQgKiqq0Xq1tbWXbN/r9eJ2u1u7bBFpY5KTk4NdQpviz+NeqwcBwKFDh5gwYQJOp5N77rmHkydP0r59ewAGDRrErFmz6NOnDx6Px7eOx+NpFAwXY7PZtIOIiOm09LjXVJC0+l1Dx44dIzs7m9///vcMGzYMgDFjxrBz504A3nnnHXr27InD4WDbtm14vV5qamqorq4mKSmptcsREZFLaPUeweLFizl58iQLFy5k4cKFAOTl5TFnzhzCw8OJjY1l1qxZ2O12XC4XTqcTwzDIzc3FZrO1djkiInIJFsMwjGAXcTncbrdODYmYxLENy4NdQpsQO9DZ4jaaOnbqB2UiIianIBARMTkFgYiIySkIRERMTkEgImJyCgIREZNTEIiImJyCQETE5BQEIiImpyAQETE5BYGIiMkpCERETE5BICJicgoCERGTUxCIiJicgkBExOQUBCIiJtesIHj11Vcbvf7jH//ol2JERCTwmnxm8euvv87GjRuprKzk3XffBeDMmTN8+umnPPDAAwEpUERE/KvJILjjjju49tprOXHiBFlZWQCEhITQtWvXgBQnIiL+12QQdOjQgdTUVFJTU/nmm2/wer3A2V7BxZw+fZr8/Hy++uor6urqGD9+PDfddBN5eXlYLBZuvvlmZs6cSUhICOXl5ZSVlREWFsb48eNJT09v3U8nIiKX1GQQnPPYY4+xefNmrrvuOgzDwGKxUFZWdsFl169fT3R0NPPmzeP48eMMGTKEHj16kJOTQ2pqKgUFBVRUVNCrVy9KS0tZvXo1Xq8Xp9NJWloaVqu1VT+giIg0rVlBsGPHDjZs2EBIyKWvLQ8ePJiMjAzf69DQUHbv3k2/fv0A6N+/P1u3biUkJITevXtjtVqxWq3ExcVRVVWFw+H4gR9FRER+iGYFQXx8PF6vl4iIiEsuGxkZCUBtbS0TJ04kJyeHuXPnYrFYfPNramqora0lKiqq0Xq1tbWXbN/r9eJ2u5tTtohcwZKTk4NdQpviz+Nes4Lg0KFDpKenEx8fD9DkqaFzy0+YMAGn08k999zDvHnzfPM8Hg/t27fHbrfj8XgaTf9+MFyMzWbTDiIiptPS415TQdKsIHjqqaea/WbHjh0jOzubgoICfvrTnwJwyy23UFlZSWpqKlu2bOH222/H4XAwf/58vF4vdXV1VFdXk5SU1Oz3ERGR1tGsIFi7du150x5++OELLrt48WJOnjzJwoULWbhwIQDTpk1j9uzZlJSUkJiYSEZGBqGhobhcLpxOJ4ZhkJubi81ma8FHERGRH8JiGIZxqYXOnQYyDIM9e/bQ0NDA448/7vfiLsTtduvUkIhJHNuwPNgltAmxA50tbqOpY2ezegT3339/o9cPPvhgi4sSEZG2oVlBsG/fPt//jx49yqFDh/xWkIiIBFazgqCgoMD3f5vNxuTJk/1WkIiIBFazgqC0tJTjx4/z5ZdfcuONN9KxY0d/1yUiIgHSrGGo33zzTe6//34WL15MVlYWf/rTn/xdl4iIBEizegRLly5lzZo1vl///vrXv+aXv/ylv2sTEZEAaFaPwGKx+IaOsNvtut9fROQq0qweQVxcHMXFxfTp04dt27YRFxfn77pERCRAmtUjyMzMpEOHDrz99tusWbOGkSNH+rsuEREJkGYFQXFxMYMGDaKgoIBVq1ZRXFzs77pERCRAmhUEYWFh3HTTTQB07dq1Wc8lEBGRK0OzrhF06dKFkpISevXqxc6dO7nuuuv8XZeIiARIs/60LyoqomPHjmzevJmOHTtSVFTk77pERCRAmtUjsNlsjBo1ys+liIhIMOhkv4iIySkIRERMTkEgImJyCgIREZNTEIiImJyCQETE5BQEIiIm57cg2LFjBy6XC4Ddu3dzxx134HK5cLlcvPHGGwCUl5czdOhQMjMz2bRpk79KERGRJjTrB2WXa8mSJaxfv56IiAgA9uzZw+jRo8nOzvYtc/ToUUpLS1m9ejVerxen00laWhpWq9UfJYmIyEX4JQji4uJ49tlnfQ+537VrF/v27aOiooL4+Hjy8/PZuXMnvXv3xmq1YrVaiYuLo6qqCofD0WTbXq8Xt9vtj7JFpA1JTk4Odgltij+Pe34JgoyMDA4cOOB77XA4GD58OCkpKSxatIjnn3+eHj16EBUV5Vvm3GMwL8Vms2kHERHTaelxr6kgCcjF4kGDBpGSkuL7/549e7Db7Xg8Ht8yHo+nUTCIiEhgBCQIxowZw86dOwF455136NmzJw6Hg23btuH1eqmpqaG6upqkpKRAlCMiIt/jl1ND/6uwsJBZs2YRHh5ObGwss2bNwm6343K5cDqdGIZBbm4uNpstEOWIiMj3WAzDMIJdxOVwu926RiBiEsc2LA92CW1C7EBni9to6tipH5SJiJicgkBExOQUBCIiJqcgEBExOQWBiIjJKQhERExOQSAiYnIKAhERk1MQiIiYnIJARMTkFAQiIianIBARMTkFgYiIySkIRERMTkEgImJyCgIREZNTEIiImJyCQETE5BQEIiImpyAQETE5vwXBjh07cLlcAOzfv58RI0bgdDqZOXMmDQ0NAJSXlzN06FAyMzPZtGmTv0oREZEm+CUIlixZwvTp0/F6vQAUFRWRk5PD8uXLMQyDiooKjh49SmlpKWVlZbz00kuUlJRQV1fnj3JERKQJYf5oNC4ujmeffZbJkycDsHv3bvr16wdA//792bp1KyEhIfTu3Rur1YrVaiUuLo6qqiocDkeTbXu9Xtxutz/KFpE2JDk5OdgltCn+PO75JQgyMjI4cOCA77VhGFgsFgAiIyOpqamhtraWqKgo3zKRkZHU1tZesm2bzaYdRERMp6XHvaaCJCAXi0NC/vs2Ho+H9u3bY7fb8Xg8jaZ/PxhERCQwAhIEt9xyC5WVlQBs2bKFPn364HA42LZtG16vl5qaGqqrq0lKSgpEOSIi8j1+OTX0v6ZMmcKMGTMoKSkhMTGRjIwMQkNDcblcOJ1ODMMgNzcXm80WiHJEROR7LIZhGMEu4nK43W5dI5Cr1pmGBkJD9POec45tWB7sEtqE2IHOFrfR1LEzID2CtkZftv/StmhbQkNCWPdhdbDLaBPuva17sEswDVMGgb5s/6Uvm4joT0EREZNTEIiImJyCQETE5BQEIiImpyAQETE5BYGIiMkpCERETE5BICJicgoCERGTUxCIiJicgkBExOQUBCIiJqcgEBExOQWBiIjJKQhERExOQSAiYnIKAhERk1MQiIiYXEAfVXnvvfcSFRUFwI033si4cePIy8vDYrFw8803M3PmTEL0/FwRkYAKWBB4vV4ASktLfdPGjRtHTk4OqampFBQUUFFRwaBBgwJVkoiIEMBTQ1VVVXz33XdkZ2fzwAMPsH37dnbv3k2/fv0A6N+/P2+//XagyhERkf8IWI+gXbt2jBkzhuHDh/PFF1/wm9/8BsMwsFgsAERGRlJTU3PJdrxeL263u0W1JCcnt2j9q01Lt6e0Hu2bcjH+/J4GLAgSEhKIj4/HYrGQkJBAdHQ0u3fv9s33eDy0b9/+ku3YbDZ9WVqZtqdI29fS72lTQRKwU0OrVq2iuLgYgMOHD1NbW0taWhqVlZUAbNmyhT59+gSqHBER+Y+A9QiGDRvG1KlTGTFiBBaLhTlz5hATE8OMGTMoKSkhMTGRjIyMQJUj/2GcOYMlNDTYZbQJ2hZiVgELAqvVylNPPXXe9GXLlgWqBLkAS2goxzYsD3YZbULsQGewSxAJCt20LyJicgoCERGTUxCIiJicgkBExOQUBCIiJqcgEBExOQWBiIjJKQhERExOQSAiYnIKAhERk1MQiIiYnIJARMTkFAQiIianIBARMTkFgYiIySkIRERMTkEgImJyCgIREZNTEIiImJyCQETE5AL28PqLaWhooLCwkL1792K1Wpk9ezbx8fHBLktExDSC3iPYsGEDdXV1rFy5kt/97ncUFxcHuyQREVMJehBs27aNO+64A4BevXqxa9euIFckImIuQT81VFtbi91u970ODQ2lvr6esLALl+b1enG73S1+3x9HtLiJq4Lb7YYf9Q52GW3C0VbYr1qD9s2ztG/+V2vsm16v96Lzgh4Edrsdj8fje93Q0HDREICzvQYREWk9QT81dNttt7FlyxYAtm/fTlJSUpArEhExF4thGEYwCzh319Ann3yCYRjMmTOH7t27B7MkERFTCXoQiIhIcAX91JCIiASXgkBExOQUBCIiJqcgMIGGhgYKCgrIysrC5XKxf//+RvM3btzIfffdR1ZWFuXl5UGqUsxsx44duFyu86Zr3wyMoP+OQPzv+8N4bN++neLiYhYtWgTA6dOnKSoqYtWqVURERDBixAjS09O59tprg1y1mMWSJUtYv349ERGNf0mnfTNw1CMwgaaG8aiuriYuLo4OHTpgtVr5yU9+wgcffBCsUsWE4uLiePbZZ8+brn0zcBQEJnCxYTzOzYuKivLNi4yMpLa2NuA1inllZGRccDQB7ZuBoyAwgaaG8fjfeR6Pp9GXTyRYtG8GjoLABJoaxqN79+7s37+fEydOUFdXxwcffEDv3hroS4JP+2bg6GKxCQwaNIitW7dy//33+4bxeO211/j222/JysoiLy+PMWPGYBgG9913H507dw52yWJi2jcDT0NMiIiYnE4NiYiYnIJARMTkFAQiIianIBARMTkFgYiIySkI5Krz6aefMnbsWFwuF/fddx8LFizgcm+O83q9DBgwAIDHH3+cgwcPcuLECV577bUm1xswYECjh4RXV1f7BlPLzc2lrq7ugus1p20Rf1EQyFXl5MmTPProo+Tn51NaWkp5eTmffPIJZWVlP7jNadOm0aVLF/bu3cvGjRt/cDtPP/00Vqv1gvNa2rZIS+gHZXJVqaioIDU1lW7dugFnx1WaO3cuH330EcOHDyc8PJzMzEy6dOnC008/TWhoKF27duX//u//qKurY9KkSZw8eZK4uDhfmy6Xi8LCQhYvXkxVVRUrV64kKyvrsmsbMGAAb775Jps3b2bJkiWEhYXxox/9iCeeeKJR22lpaUybNo36+nosFgvTp0+nR48epKenk5iYSEJCAps3b+bVV18lOjqa5cuX8+233/Lggw+21mYUk1EQyFXlyJEjdO3atdG0yMhIwsPD8Xq9vPrqqxiGweDBg1m+fDmdOnVi/vz5rF27lrq6OpKSksjNzWXHjh1UVlY2amfcuHGUlZVdMgSys7MJCTnb2f7uu+/OG1759ddfZ9SoUfziF79g3bp11NbWNmp74sSJuFwuBg4ciNvtJj8/nzVr1nDo0CHWrFlDTEwM7du3589//jMjR45k/fr1PPfcc62w9cSsFARyVenSpQt79uxpNO3LL7/k/fffJyEhAYB//etfHDlyhJycHABOnTpFWloax48f9w3Xfeutt15wRMzmePnll7HZbMDZawSFhYWN5k+dOpUXXniBFStWkJiYyMCBAxvNr66upm/fvgAkJyfz9ddfAxATE0NMTAwAw4YNIzc3l759+xIbG0tsbOwPqlUEdI1ArjLp6en8/e9/55///Cdw9uEmxcXFxMTE+P5Kj4mJ4frrr2fhwoWUlpYybtw4UlNTSUxMZPv27QDs2bPHN1T3OSEhITQ0NLS4xpUrV/LII4+wbNkyAN56661GbXfv3t037r7b7fYd5M/VD2cDLyoqisWLFzNs2LAW1yTmph6BXFXsdjvFxcVMnz4dwzDweDykp6c3OriGhIQwbdo0xo4di2EYREZG8sQTT9C3b1+mTp3KiBEjSExMJDw8vFHbcXFxfPLJJyxdupRRo0b94BodDgejR48mOjqayMhI7rzzTurq6nxtT548mRkzZvDyyy9TX1/P448/fsF2MjMzmT17NvPmzfvBtYiABp0TuWK98cYbfPrpp/z2t78NdilyhVOPQOQyVVRUsHTp0vOmP/DAAwwaNCggNZSUlPDBBx+wcOHCgLyfXN3UIxARMTldLBYRMTkFgYiIySkIRERMTkEgImJyCgIREZP7f9v1PexeVWLLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x=df['Credit_History'], hue=df['Loan_Status'], palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"ApplicantIncome\"].hist(bins=40, color='darkgreen', alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Loan_Status', ylabel='count'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x=df['Loan_Status'], hue=df['Property_Area'], palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WZmamdu3aJWOMY/P2uHDhgh5++GG9++67ysrKcnTeBx98UJMnT5YkTZs2TaWlpY7Ou3//fn399deKxWJauXKlCgsLHZv3yJEj+uSTTyT9c122z+fTwYMH9eKLLyY3r3GIuro6U15ebowx5scffzSlpaVpTnTZW2+9Ze69917zyCOPGGOMWbt2rWlsbDTGGFNZWWm++OIL8/fff5t7773XRKNREwqFej9Oh48++shUV1cbY4y5ePGiWbx4saMzf/nll6aiosIYY0xjY6MpLS11dF5jjOns7DRPPfWUWbZsmTl58qSj83Z0dJgHHnjgijEn521sbDRr16413d3dpq2tzbz++uuOzvtfO3bsMIcOHUpJXsdMmzj5lvvp06dr7969vY+bm5tVWFgoSSouLlZDQ4N+/vln3XLLLZowYYImT56s6dOn67fffktL3rvvvlsbN27sfZyZmenozHfddZd27twpSTp37pxycnIcnVeSampq9Oijj+r666+X5Oyfid9++03t7e1as2aNHnvsMZ04ccLReb/99lvNnj1b69evV2lpqZYsWeLovD1++eUXnTx5UitWrEhJXseUd1tbmyZNmtT7ODMzU11dXWlMdJnX673iDlJjjFwulyQpOztbra2tamtr6/1vac94W1tbyrP2vPakSZPU1tamDRs2qKyszPGZs7KyVF5erp07d8rr9To675EjR3Tdddf1nmxIzv6ZuOaaa/TEE0/onXfe0QsvvKBNmzY5Om8wGNSvv/6q1157zYq8Pfbv36/169dLSs3Pg2PKe6i33KdTRsblwxYOh+XxeOLyh8PhK75RqRYIBPTYY4/pgQce0H333WdF5pqaGtXV1amyslLRaPSKXE7K+/HHH6uhoUElJSXy+XwqLy/XxYsXHZt3xowZuv/+++VyuTRjxgxNmTJFFy5ccGzeKVOm6I477tCECROUn58vt9ut1tZWx+aV/llR8M8//+xdiTIV/94cU9423XI/Z84cNTU1SZLq6+s1f/583Xzzzfrhhx8UjUbV2tqqU6dOpe1raGlp0Zo1a7R582YtX77c8Zk//fRT7d+/X5J07bXXyuVy6aabbnJs3vfff1+1tbU6cOCACgoKVFNTo+LiYsfm/eijj7R7925J0vnz59XW1qbbb7/dsXnnzZunY8eOyRij8+fPq729XQsXLnRsXkk6fvy4brvttt7Hqfj35pg7LJ1+y/2ZM2f0zDPP6PDhwzp9+rQqKysVi8WUn5+v6upqZWZm6vDhw/rwww9ljNHatWvl9XrTkrW6ulqff/658vPze8e2bt2q6upqR2aORCJ6/vnn1dLSoq6uLj355JOaOXOmo49xj5KSEu3YsUMZGRmOzdvZ2annn39e586dk8vl0qZNmzR16lTH5pWkl156SU1NTTLG6Omnn9a0adMcnfftt99WVlaWVq9eLUkp6QjHlDcAYPAcM20CABg8yhsALER5A4CFKG8AsBDlDQAWorwBwEKUNwBY6P+1aVdjEif+EAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"LoanAmount\"].hist(bins=50, color='black', alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x259ba603708>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 964.75x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, hue=\"Loan_Status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## making data suitable for the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Gender\"]=pd.get_dummies(df[\"Gender\"], drop_first=True)\n",
    "df[\"Married\"]=pd.get_dummies(df[\"Married\"], drop_first=True)\n",
    "df[\"Education\"]=pd.get_dummies(df[\"Education\"], drop_first=True)\n",
    "df[\"Property_Area\"]=pd.get_dummies(df[\"Property_Area\"], drop_first=True)\n",
    "df[\"Self_Employed\"]=pd.get_dummies(df[\"Self_Employed\"], drop_first=True)\n",
    "df[\"Dependents\"]=pd.get_dummies(df[\"Dependents\"], drop_first=True)\n",
    "df[\"Credit_History\"]=df[\"Credit_History\"].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "lb=LabelEncoder()\n",
    "df[\"Loan_Status\"]=lb.fit_transform(df[\"Loan_Status\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>0</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0  LP001003       1        1           1          0              0   \n",
       "1  LP001005       1        1           0          0              1   \n",
       "2  LP001006       1        1           0          1              0   \n",
       "3  LP001008       1        0           0          0              0   \n",
       "4  LP001011       1        1           0          0              1   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             4583             1508.0       128.0             360.0   \n",
       "1             3000                0.0        66.0             360.0   \n",
       "2             2583             2358.0       120.0             360.0   \n",
       "3             6000                0.0       141.0             360.0   \n",
       "4             5417             4196.0       267.0             360.0   \n",
       "\n",
       "   Credit_History  Property_Area  Loan_Status  \n",
       "0               1              0            0  \n",
       "1               1              0            1  \n",
       "2               1              0            1  \n",
       "3               1              0            1  \n",
       "4               1              0            1  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=df.corr()\n",
    "top_corr_feat=corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "s=sns.heatmap(df[top_corr_feat].corr(), annot=True, cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0       1        1           1          0              0             4583   \n",
       "1       1        1           0          0              1             3000   \n",
       "2       1        1           0          1              0             2583   \n",
       "3       1        0           0          0              0             6000   \n",
       "4       1        1           0          0              1             5417   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0             1508.0       128.0             360.0               1   \n",
       "1                0.0        66.0             360.0               1   \n",
       "2             2358.0       120.0             360.0               1   \n",
       "3                0.0       141.0             360.0               1   \n",
       "4             4196.0       267.0             360.0               1   \n",
       "\n",
       "   Property_Area  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df.iloc[:,1:]\n",
    "X.drop(columns=[\"Loan_Status\"], axis=1, inplace=True)\n",
    "y=df.iloc[:,-1]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model=ExtraTreesRegressor()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.02923884 0.01990489 0.03065658 0.02825868 0.02903658 0.20675122\n",
      " 0.14789315 0.23212056 0.0632419  0.18453711 0.02836047]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importance=pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importance.nlargest(10).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>0</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0  LP001003       1        1           1          0              0   \n",
       "1  LP001005       1        1           0          0              1   \n",
       "2  LP001006       1        1           0          1              0   \n",
       "3  LP001008       1        0           0          0              0   \n",
       "4  LP001011       1        1           0          0              1   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             4583             1508.0       128.0             360.0   \n",
       "1             3000                0.0        66.0             360.0   \n",
       "2             2583             2358.0       120.0             360.0   \n",
       "3             6000                0.0       141.0             360.0   \n",
       "4             5417             4196.0       267.0             360.0   \n",
       "\n",
       "   Credit_History  Property_Area  Loan_Status  \n",
       "0               1              0            0  \n",
       "1               1              0            1  \n",
       "2               1              0            1  \n",
       "3               1              0            1  \n",
       "4               1              0            1  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>Self_Employed</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4583</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2583</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6000</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>360.0</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5417</td>\n",
       "      <td>267.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Self_Employed  Loan_Amount_Term  CoapplicantIncome  Credit_History  \\\n",
       "0              0             360.0             1508.0               1   \n",
       "1              1             360.0                0.0               1   \n",
       "2              0             360.0             2358.0               1   \n",
       "3              0             360.0                0.0               1   \n",
       "4              1             360.0             4196.0               1   \n",
       "\n",
       "   ApplicantIncome  LoanAmount  \n",
       "0             4583       128.0  \n",
       "1             3000        66.0  \n",
       "2             2583       120.0  \n",
       "3             6000       141.0  \n",
       "4             5417       267.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes=[\"Self_Employed\",\"Loan_Amount_Term\", \"CoapplicantIncome\", \"Credit_History\", \"ApplicantIncome\", \"LoanAmount\"]\n",
    "df1=df[attributes]\n",
    "output=df[\"Loan_Status\"]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(473, 6)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(df1,output,test_size=0.2, random_state=42)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log=LogisticRegression(random_state=0)\n",
    "log.fit(X_train,Y_train)\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree=DecisionTreeClassifier()\n",
    "tree.fit(X_train,Y_train)\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest=RandomForestClassifier()\n",
    "forest.fit(X_train,Y_train)\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "svc=SVC()\n",
    "svc.fit(X_train,Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy report log 0.7815126050420168\n",
      "\n",
      " mean squared error for log 0.2184873949579832\n",
      "\n",
      " accuracy report tree 0.7310924369747899\n",
      "\n",
      " mean squared error for tree  0.2689075630252101\n",
      "\n",
      " accuracy report forest 0.7310924369747899\n",
      "\n",
      " mean squared error for forest 0.2689075630252101\n",
      "\n",
      " accuracy report SVC 0.6638655462184874\n",
      "\n",
      " mean squared error for SVC 0.33613445378151263\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "print(\"accuracy report log\",accuracy_score(Y_test, log.predict(X_test)))\n",
    "print(\"\\n mean squared error for log\",mean_squared_error(Y_test, log.predict(X_test)))\n",
    " \n",
    "print(\"\\n accuracy report tree\",accuracy_score(Y_test, tree.predict(X_test)))\n",
    "print(\"\\n mean squared error for tree \",mean_squared_error(Y_test, tree.predict(X_test)))\n",
    "\n",
    "print(\"\\n accuracy report forest\",accuracy_score(Y_test, forest.predict(X_test)))\n",
    "print(\"\\n mean squared error for forest\",mean_squared_error(Y_test, forest.predict(X_test)))\n",
    "\n",
    "print(\"\\n accuracy report SVC\",accuracy_score(Y_test, svc.predict(X_test)))\n",
    "print(\"\\n mean squared error for SVC\",mean_squared_error(Y_test, svc.predict(X_test)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy for training data: 0.7758985200845666\n"
     ]
    }
   ],
   "source": [
    "yhat=log.predict(X_train)\n",
    "print(\"accuracy for training data:\",accuracy_score(yhat,Y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report of Logistic Regression :-\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.55      0.63        40\n",
      "           1       0.80      0.90      0.85        79\n",
      "\n",
      "    accuracy                           0.78       119\n",
      "   macro avg       0.77      0.72      0.74       119\n",
      "weighted avg       0.78      0.78      0.77       119\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(\"Classification report of Logistic Regression :-\\n\")\n",
    "cla_rep=classification_report(Y_test, log.predict(X_test))\n",
    "print(cla_rep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values are-\n",
      " [0 1 1 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0\n",
      " 1 0 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 0 0 1 1 1 0 1 1 0 0 1 0 1 1 1 1 1 1\n",
      " 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1\n",
      " 1 1 1 0 1 1 1 1]\n",
      "------\n",
      "True values are-\n",
      " [0 1 1 0 0 0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 0 0 1 1 0\n",
      " 1 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 0 0 1 0 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1\n",
      " 0 1 0 1 0 0 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 0 1\n",
      " 1 0 1 0 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "pred=log.predict(X_test)\n",
    "print(\"Predicted values are-\\n\",pred)\n",
    "print(\"------\")\n",
    "print(\"True values are-\\n\",(Y_test).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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