Loan Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loan Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps to solve machine learning projects\n",
    "1. start\n",
    "2. data selection\n",
    "3. data description : A story of data is all abot and the features present in the data\n",
    "4. performing both statistical and graphical data analysis\n",
    "5. data tranformation and derivation of new attributes , if necessary\n",
    "6. selection of machine learning algorithms based on the patterns observed in EDA\n",
    "7. data standardization and normalization\n",
    "8. creation of train and test data sets\n",
    "9. model training using machine learning algorithms\n",
    "10. calculation of model accuracy : both training and testing accuracy\n",
    "11. hyper parameter tuning to achieve a better accuracy \n",
    "12. saving the created model file \n",
    "13. Deployement strategies for model\n",
    "14. production deployment and testing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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/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>609</th>\n",
       "      <td>LP002978</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>LP002979</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>LP002983</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>LP002984</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>LP002990</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents Education Self_Employed  \\\n",
       "609  LP002978  Female      No          0  Graduate            No   \n",
       "610  LP002979    Male     Yes         3+  Graduate            No   \n",
       "611  LP002983    Male     Yes          1  Graduate            No   \n",
       "612  LP002984    Male     Yes          2  Graduate            No   \n",
       "613  LP002990  Female      No          0  Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "609             1.0         Rural           Y  \n",
       "610             1.0         Rural           Y  \n",
       "611             1.0         Urban           Y  \n",
       "612             1.0         Urban           Y  \n",
       "613             0.0     Semiurban           N  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
      "Loan_ID              614 non-null object\n",
      "Gender               601 non-null object\n",
      "Married              611 non-null object\n",
      "Dependents           599 non-null object\n",
      "Education            614 non-null object\n",
      "Self_Employed        582 non-null object\n",
      "ApplicantIncome      614 non-null int64\n",
      "CoapplicantIncome    614 non-null float64\n",
      "LoanAmount           592 non-null float64\n",
      "Loan_Amount_Term     600 non-null float64\n",
      "Credit_History       564 non-null float64\n",
      "Property_Area        614 non-null object\n",
      "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": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()   #bydefault interger column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5849\n",
       "1      4583\n",
       "2      3000\n",
       "3      2583\n",
       "4      6000\n",
       "       ... \n",
       "609    2900\n",
       "610    4106\n",
       "611    8072\n",
       "612    7583\n",
       "613    4583\n",
       "Name: ApplicantIncome, Length: 614, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ApplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5849</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4583</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2583</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>2900</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>4106</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>8072</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>7583</td>\n",
       "      <td>187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>4583</td>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ApplicantIncome  LoanAmount\n",
       "0               5849         NaN\n",
       "1               4583       128.0\n",
       "2               3000        66.0\n",
       "3               2583       120.0\n",
       "4               6000       141.0\n",
       "..               ...         ...\n",
       "609             2900        71.0\n",
       "610             4106        40.0\n",
       "611             8072       253.0\n",
       "612             7583       187.0\n",
       "613             4583       133.0\n",
       "\n",
       "[614 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['ApplicantIncome', 'LoanAmount']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
      "Loan_ID              614 non-null object\n",
      "Gender               601 non-null object\n",
      "Married              611 non-null object\n",
      "Dependents           599 non-null object\n",
      "Education            614 non-null object\n",
      "Self_Employed        582 non-null object\n",
      "ApplicantIncome      614 non-null int64\n",
      "CoapplicantIncome    614 non-null float64\n",
      "LoanAmount           592 non-null float64\n",
      "Loan_Amount_Term     600 non-null float64\n",
      "Credit_History       564 non-null float64\n",
      "Property_Area        614 non-null object\n",
      "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# handle numerical missing data\n",
    "df['LoanAmount'] = df['LoanAmount'].fillna(df['LoanAmount'].mean())\n",
    "df['Loan_Amount_Term'] = df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mean())\n",
    "df['Credit_History'] = df['Credit_History'].fillna(df['Credit_History'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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            0\n",
       "Loan_Amount_Term      0\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Male'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# handle categorical missing data\n",
    "df['Gender'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gender'] = df['Gender'].fillna(df['Gender'].mode()[0])\n",
    "df['Married'] = df['Married'].fillna(df['Married'].mode()[0])\n",
    "df['Dependents'] = df['Dependents'].fillna(df['Dependents'].mode()[0])\n",
    "df['Self_Employed'] = df['Self_Employed'].fillna(df['Self_Employed'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exloratory data anlysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1729125f730>"
      ]
     },
     "execution_count": 19,
     "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": [
    "# categorical data\n",
    "import seaborn as sns\n",
    "sns.countplot(df['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17293319820>"
      ]
     },
     "execution_count": 20,
     "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(df.Dependents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1729337f790>"
      ]
     },
     "execution_count": 21,
     "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(df.Married)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1728934ad60>"
      ]
     },
     "execution_count": 23,
     "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": [
    "# numerical data\n",
    "sns.distplot(df.CoapplicantIncome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17289373bb0>"
      ]
     },
     "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.distplot(df.LoanAmount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17293595100>"
      ]
     },
     "execution_count": 25,
     "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.distplot(df.Credit_History)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>146.412162</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>128.000000</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>66.000000</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>120.000000</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>141.000000</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  146.412162             360.0   \n",
       "1             4583             1508.0  128.000000             360.0   \n",
       "2             3000                0.0   66.000000             360.0   \n",
       "3             2583             2358.0  120.000000             360.0   \n",
       "4             6000                0.0  141.000000             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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# created new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Total_income'] = df['ApplicantIncome']+df['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
       "      <th>Total_income</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>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>5849.0</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>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "      <td>6091.0</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>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>3000.0</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>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4941.0</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>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>6000.0</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  146.412162             360.0   \n",
       "1             4583             1508.0  128.000000             360.0   \n",
       "2             3000                0.0   66.000000             360.0   \n",
       "3             2583             2358.0  120.000000             360.0   \n",
       "4             6000                0.0  141.000000             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  Total_income  \n",
       "0             1.0         Urban           Y        5849.0  \n",
       "1             1.0         Rural           N        6091.0  \n",
       "2             1.0         Urban           Y        3000.0  \n",
       "3             1.0         Urban           Y        4941.0  \n",
       "4             1.0         Urban           Y        6000.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ApplicantIncomeLog'] = np.log(df['ApplicantIncome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x172935bcf10>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.ApplicantIncomeLog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jaypr\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\core\\series.py:856: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x172936e2af0>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['CoapplicantIncomeLog'] = np.log(df['CoapplicantIncome'])\n",
    "sns.distplot(df[\"ApplicantIncomeLog\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x172937e4280>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['LoanAmountLog'] = np.log(df['LoanAmount'])\n",
    "sns.distplot(df[\"LoanAmountLog\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x172938a3e20>"
      ]
     },
     "execution_count": 35,
     "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": [
    "df['Loan_Amount_Term_Log'] = np.log(df['Loan_Amount_Term'])\n",
    "sns.distplot(df[\"Loan_Amount_Term_Log\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17293946af0>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Total_Income_Log'] = np.log(df['Total_income'])\n",
    "sns.distplot(df[\"Total_Income_Log\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
       "      <th>Total_income</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>CoapplicantIncomeLog</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "      <th>Total_Income_Log</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>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>5849.0</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>-inf</td>\n",
       "      <td>4.986426</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.674026</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>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "      <td>6091.0</td>\n",
       "      <td>8.430109</td>\n",
       "      <td>7.318540</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.714568</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>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>-inf</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.006368</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>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4941.0</td>\n",
       "      <td>7.856707</td>\n",
       "      <td>7.765569</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.505323</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>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>-inf</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.699515</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  146.412162             360.0   \n",
       "1             4583             1508.0  128.000000             360.0   \n",
       "2             3000                0.0   66.000000             360.0   \n",
       "3             2583             2358.0  120.000000             360.0   \n",
       "4             6000                0.0  141.000000             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  Total_income  ApplicantIncomeLog  \\\n",
       "0             1.0         Urban           Y        5849.0            8.674026   \n",
       "1             1.0         Rural           N        6091.0            8.430109   \n",
       "2             1.0         Urban           Y        3000.0            8.006368   \n",
       "3             1.0         Urban           Y        4941.0            7.856707   \n",
       "4             1.0         Urban           Y        6000.0            8.699515   \n",
       "\n",
       "   CoapplicantIncomeLog  LoanAmountLog  Loan_Amount_Term_Log  Total_Income_Log  \n",
       "0                  -inf       4.986426              5.886104          8.674026  \n",
       "1              7.318540       4.852030              5.886104          8.714568  \n",
       "2                  -inf       4.189655              5.886104          8.006368  \n",
       "3              7.765569       4.787492              5.886104          8.505323  \n",
       "4                  -inf       4.948760              5.886104          8.699515  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['ApplicantIncome', 'CoapplicantIncome', \"LoanAmount\", \"Loan_Amount_Term\", \"Total_income\", 'Loan_ID', 'CoapplicantIncomeLog']\n",
    "df = df.drop(columns=cols, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "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>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>4.986426</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.674026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "      <td>8.430109</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.714568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.006368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>7.856707</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.505323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.699515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Married Dependents     Education Self_Employed  Credit_History  \\\n",
       "0   Male      No          0      Graduate            No             1.0   \n",
       "1   Male     Yes          1      Graduate            No             1.0   \n",
       "2   Male     Yes          0      Graduate           Yes             1.0   \n",
       "3   Male     Yes          0  Not Graduate            No             1.0   \n",
       "4   Male      No          0      Graduate            No             1.0   \n",
       "\n",
       "  Property_Area Loan_Status  ApplicantIncomeLog  LoanAmountLog  \\\n",
       "0         Urban           Y            8.674026       4.986426   \n",
       "1         Rural           N            8.430109       4.852030   \n",
       "2         Urban           Y            8.006368       4.189655   \n",
       "3         Urban           Y            7.856707       4.787492   \n",
       "4         Urban           Y            8.699515       4.948760   \n",
       "\n",
       "   Loan_Amount_Term_Log  Total_Income_Log  \n",
       "0              5.886104          8.674026  \n",
       "1              5.886104          8.714568  \n",
       "2              5.886104          8.006368  \n",
       "3              5.886104          8.505323  \n",
       "4              5.886104          8.699515  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Loan_Status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 12 columns):\n",
      "Gender                  614 non-null object\n",
      "Married                 614 non-null object\n",
      "Dependents              614 non-null object\n",
      "Education               614 non-null object\n",
      "Self_Employed           614 non-null object\n",
      "Credit_History          614 non-null float64\n",
      "Property_Area           614 non-null object\n",
      "Loan_Status             614 non-null object\n",
      "ApplicantIncomeLog      614 non-null float64\n",
      "LoanAmountLog           614 non-null float64\n",
      "Loan_Amount_Term_Log    614 non-null float64\n",
      "Total_Income_Log        614 non-null float64\n",
      "dtypes: float64(5), object(7)\n",
      "memory usage: 57.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Graduate        480\n",
       "Not Graduate    134\n",
       "Name: Education, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Education.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### handling categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 12 columns):\n",
      "Gender                  614 non-null object\n",
      "Married                 614 non-null object\n",
      "Dependents              614 non-null object\n",
      "Education               614 non-null object\n",
      "Self_Employed           614 non-null object\n",
      "Credit_History          614 non-null float64\n",
      "Property_Area           614 non-null object\n",
      "Loan_Status             614 non-null object\n",
      "ApplicantIncomeLog      614 non-null float64\n",
      "LoanAmountLog           614 non-null float64\n",
      "Loan_Amount_Term_Log    614 non-null float64\n",
      "Total_Income_Log        614 non-null float64\n",
      "dtypes: float64(5), object(7)\n",
      "memory usage: 57.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "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>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>4.986426</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.674026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "      <td>8.430109</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.714568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.006368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>7.856707</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.505323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.699515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Married Dependents     Education Self_Employed  Credit_History  \\\n",
       "0   Male      No          0      Graduate            No             1.0   \n",
       "1   Male     Yes          1      Graduate            No             1.0   \n",
       "2   Male     Yes          0      Graduate           Yes             1.0   \n",
       "3   Male     Yes          0  Not Graduate            No             1.0   \n",
       "4   Male      No          0      Graduate            No             1.0   \n",
       "\n",
       "  Property_Area Loan_Status  ApplicantIncomeLog  LoanAmountLog  \\\n",
       "0         Urban           Y            8.674026       4.986426   \n",
       "1         Rural           N            8.430109       4.852030   \n",
       "2         Urban           Y            8.006368       4.189655   \n",
       "3         Urban           Y            7.856707       4.787492   \n",
       "4         Urban           Y            8.699515       4.948760   \n",
       "\n",
       "   Loan_Amount_Term_Log  Total_Income_Log  \n",
       "0              5.886104          8.674026  \n",
       "1              5.886104          8.714568  \n",
       "2              5.886104          8.006368  \n",
       "3              5.886104          8.505323  \n",
       "4              5.886104          8.699515  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = pd.get_dummies(df['Gender'], drop_first= True)\n",
    "d2 = pd.get_dummies(df['Married'], drop_first= True)\n",
    "d3 = pd.get_dummies(df['Dependents'], drop_first= True)\n",
    "d4 = pd.get_dummies(df['Education'], drop_first= True)\n",
    "d5 = pd.get_dummies(df['Self_Employed'], drop_first= True)\n",
    "d6 = pd.get_dummies(df['Property_Area'], drop_first= True)\n",
    "\n",
    "\n",
    "\n",
    "df1 = pd.concat([df, d1, d2, d3, d4, d5, d6], axis = 1)\n",
    "df=df1\n",
    "\n",
    "cols = ['Gender', 'Married', \"Dependents\", \"Education\", \"Self_Employed\", 'Property_Area']\n",
    "df = df.drop(columns=cols, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cols = ['Gender',\"Married\",\"Education\",'Self_Employed',\"Property_Area\",\"Loan_Status\",\"Dependents\"]\n",
    "# for col in cols:\n",
    "#     df[col] = pd.get_dummies(df[col], drop_first= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "      <th>Male</th>\n",
       "      <th>Yes</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3+</th>\n",
       "      <th>Not Graduate</th>\n",
       "      <th>Yes</th>\n",
       "      <th>Semiurban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>4.986426</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>8.430109</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.714568</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>7.856707</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.505323</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit_History Loan_Status  ApplicantIncomeLog  LoanAmountLog  \\\n",
       "0             1.0           Y            8.674026       4.986426   \n",
       "1             1.0           N            8.430109       4.852030   \n",
       "2             1.0           Y            8.006368       4.189655   \n",
       "3             1.0           Y            7.856707       4.787492   \n",
       "4             1.0           Y            8.699515       4.948760   \n",
       "\n",
       "   Loan_Amount_Term_Log  Total_Income_Log  Male  Yes  1  2  3+  Not Graduate  \\\n",
       "0              5.886104          8.674026     1    0  0  0   0             0   \n",
       "1              5.886104          8.714568     1    1  1  0   0             0   \n",
       "2              5.886104          8.006368     1    1  0  0   0             0   \n",
       "3              5.886104          8.505323     1    1  0  0   0             1   \n",
       "4              5.886104          8.699515     1    0  0  0   0             0   \n",
       "\n",
       "   Yes  Semiurban  Urban  \n",
       "0    0          0      1  \n",
       "1    0          0      0  \n",
       "2    1          0      1  \n",
       "3    0          0      1  \n",
       "4    0          0      1  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 15 columns):\n",
      "Credit_History          614 non-null float64\n",
      "Loan_Status             614 non-null object\n",
      "ApplicantIncomeLog      614 non-null float64\n",
      "LoanAmountLog           614 non-null float64\n",
      "Loan_Amount_Term_Log    614 non-null float64\n",
      "Total_Income_Log        614 non-null float64\n",
      "Male                    614 non-null uint8\n",
      "Yes                     614 non-null uint8\n",
      "1                       614 non-null uint8\n",
      "2                       614 non-null uint8\n",
      "3+                      614 non-null uint8\n",
      "Not Graduate            614 non-null uint8\n",
      "Yes                     614 non-null uint8\n",
      "Semiurban               614 non-null uint8\n",
      "Urban                   614 non-null uint8\n",
      "dtypes: float64(5), object(1), uint8(9)\n",
      "memory usage: 34.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Credit_History</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "      <th>Male</th>\n",
       "      <th>Yes</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3+</th>\n",
       "      <th>Not Graduate</th>\n",
       "      <th>Yes</th>\n",
       "      <th>Semiurban</th>\n",
       "      <th>Urban</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>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.842199</td>\n",
       "      <td>8.341213</td>\n",
       "      <td>4.862066</td>\n",
       "      <td>5.802065</td>\n",
       "      <td>8.669414</td>\n",
       "      <td>0.817590</td>\n",
       "      <td>0.653094</td>\n",
       "      <td>0.166124</td>\n",
       "      <td>0.164495</td>\n",
       "      <td>0.083062</td>\n",
       "      <td>0.218241</td>\n",
       "      <td>0.133550</td>\n",
       "      <td>0.379479</td>\n",
       "      <td>0.328990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.349681</td>\n",
       "      <td>0.645263</td>\n",
       "      <td>0.496575</td>\n",
       "      <td>0.312482</td>\n",
       "      <td>0.545102</td>\n",
       "      <td>0.386497</td>\n",
       "      <td>0.476373</td>\n",
       "      <td>0.372495</td>\n",
       "      <td>0.371027</td>\n",
       "      <td>0.276201</td>\n",
       "      <td>0.413389</td>\n",
       "      <td>0.340446</td>\n",
       "      <td>0.485653</td>\n",
       "      <td>0.470229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.010635</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>2.484907</td>\n",
       "      <td>7.273786</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.964677</td>\n",
       "      <td>4.607658</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.334712</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.246040</td>\n",
       "      <td>4.859812</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.597205</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.664750</td>\n",
       "      <td>5.104426</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.925549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.302204</td>\n",
       "      <td>6.551080</td>\n",
       "      <td>6.173786</td>\n",
       "      <td>11.302204</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Credit_History  ApplicantIncomeLog  LoanAmountLog  \\\n",
       "count      614.000000          614.000000     614.000000   \n",
       "mean         0.842199            8.341213       4.862066   \n",
       "std          0.349681            0.645263       0.496575   \n",
       "min          0.000000            5.010635       2.197225   \n",
       "25%          1.000000            7.964677       4.607658   \n",
       "50%          1.000000            8.246040       4.859812   \n",
       "75%          1.000000            8.664750       5.104426   \n",
       "max          1.000000           11.302204       6.551080   \n",
       "\n",
       "       Loan_Amount_Term_Log  Total_Income_Log        Male         Yes  \\\n",
       "count            614.000000        614.000000  614.000000  614.000000   \n",
       "mean               5.802065          8.669414    0.817590    0.653094   \n",
       "std                0.312482          0.545102    0.386497    0.476373   \n",
       "min                2.484907          7.273786    0.000000    0.000000   \n",
       "25%                5.886104          8.334712    1.000000    0.000000   \n",
       "50%                5.886104          8.597205    1.000000    1.000000   \n",
       "75%                5.886104          8.925549    1.000000    1.000000   \n",
       "max                6.173786         11.302204    1.000000    1.000000   \n",
       "\n",
       "                1           2          3+  Not Graduate         Yes  \\\n",
       "count  614.000000  614.000000  614.000000    614.000000  614.000000   \n",
       "mean     0.166124    0.164495    0.083062      0.218241    0.133550   \n",
       "std      0.372495    0.371027    0.276201      0.413389    0.340446   \n",
       "min      0.000000    0.000000    0.000000      0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000      0.000000    0.000000   \n",
       "50%      0.000000    0.000000    0.000000      0.000000    0.000000   \n",
       "75%      0.000000    0.000000    0.000000      0.000000    0.000000   \n",
       "max      1.000000    1.000000    1.000000      1.000000    1.000000   \n",
       "\n",
       "        Semiurban       Urban  \n",
       "count  614.000000  614.000000  \n",
       "mean     0.379479    0.328990  \n",
       "std      0.485653    0.470229  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    0.000000  \n",
       "50%      0.000000    0.000000  \n",
       "75%      1.000000    1.000000  \n",
       "max      1.000000    1.000000  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv(\"test.csv\")\n",
    "# filling numerical missing data\n",
    "test['LoanAmount']=test['LoanAmount'].fillna(test['LoanAmount'].mean())\n",
    "test['Loan_Amount_Term']=test['Loan_Amount_Term'].fillna(test['Loan_Amount_Term'].mean())\n",
    "test['Credit_History']=test['Credit_History'].fillna(test['Credit_History'].mean())\n",
    "\n",
    "# filling categorical missing data\n",
    "test['Gender']=test['Gender'].fillna(test['Gender'].mode()[0])\n",
    "test['Married']=test['Married'].fillna(test['Married'].mode()[0])\n",
    "test['Dependents']=test['Dependents'].fillna(test['Dependents'].mode()[0])\n",
    "test['Self_Employed']=test['Self_Employed'].fillna(test['Self_Employed'].mode()[0])\n",
    "\n",
    "test['Total_income'] = test['ApplicantIncome']+test['CoapplicantIncome']\n",
    "\n",
    "# apply log transformation to the attribute\n",
    "test['ApplicantIncomeLog'] = np.log(test['ApplicantIncome'])\n",
    "\n",
    "test['CoapplicantIncomeLog'] = np.log(test['CoapplicantIncome'])\n",
    "\n",
    "test['LoanAmountLog'] = np.log(test['LoanAmount'])\n",
    "\n",
    "test['Loan_Amount_Term_Log'] = np.log(test['Loan_Amount_Term'])\n",
    "\n",
    "test['Total_Income_Log'] = np.log(test['Total_income'])\n",
    "\n",
    "cols = ['ApplicantIncome', 'CoapplicantIncome', \"LoanAmount\", \"Loan_Amount_Term\", \"Total_income\", 'Loan_ID', 'CoapplicantIncomeLog']\n",
    "test = test.drop(columns=cols, axis=1)\n",
    "\n",
    "t1 = pd.get_dummies(test['Gender'], drop_first= True)\n",
    "t2 = pd.get_dummies(test['Married'], drop_first= True)\n",
    "t3 = pd.get_dummies(test['Dependents'], drop_first= True)\n",
    "t4 = pd.get_dummies(test['Education'], drop_first= True)\n",
    "t5 = pd.get_dummies(test['Self_Employed'], drop_first= True)\n",
    "t6 = pd.get_dummies(test['Property_Area'], drop_first= True)\n",
    "\n",
    "\n",
    "\n",
    "df1 = pd.concat([test, t1, t2, t3, t4, t5, t6], axis = 1)\n",
    "test=df1\n",
    "\n",
    "cols = ['Gender', 'Married', \"Dependents\", \"Education\", \"Self_Employed\", 'Property_Area']\n",
    "test = test.drop(columns=cols, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Credit_History</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "      <th>Male</th>\n",
       "      <th>Yes</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3+</th>\n",
       "      <th>Not Graduate</th>\n",
       "      <th>Yes</th>\n",
       "      <th>Semiurban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.651724</td>\n",
       "      <td>4.700480</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.651724</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.031385</td>\n",
       "      <td>4.836282</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.428581</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.517193</td>\n",
       "      <td>5.337538</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.824678</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.825444</td>\n",
       "      <td>7.757906</td>\n",
       "      <td>4.605170</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.494129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.094378</td>\n",
       "      <td>4.356709</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.094378</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit_History  ApplicantIncomeLog  LoanAmountLog  Loan_Amount_Term_Log  \\\n",
       "0        1.000000            8.651724       4.700480              5.886104   \n",
       "1        1.000000            8.031385       4.836282              5.886104   \n",
       "2        1.000000            8.517193       5.337538              5.886104   \n",
       "3        0.825444            7.757906       4.605170              5.886104   \n",
       "4        1.000000            8.094378       4.356709              5.886104   \n",
       "\n",
       "   Total_Income_Log  Male  Yes  1  2  3+  Not Graduate  Yes  Semiurban  Urban  \n",
       "0          8.651724     1    1  0  0   0             0    0          0      1  \n",
       "1          8.428581     1    1  1  0   0             0    0          0      1  \n",
       "2          8.824678     1    1  0  1   0             0    0          0      1  \n",
       "3          8.494129     1    1  0  1   0             0    0          0      1  \n",
       "4          8.094378     1    0  0  0   0             1    0          0      1  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### split datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "      <th>Male</th>\n",
       "      <th>Yes</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3+</th>\n",
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       "      <th>Semiurban</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
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       "      <td>4.189655</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.006368</td>\n",
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       "      <th>3</th>\n",
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       "      <td>4.787492</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.505323</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit_History Loan_Status  ApplicantIncomeLog  LoanAmountLog  \\\n",
       "0             1.0           Y            8.674026       4.986426   \n",
       "1             1.0           N            8.430109       4.852030   \n",
       "2             1.0           Y            8.006368       4.189655   \n",
       "3             1.0           Y            7.856707       4.787492   \n",
       "4             1.0           Y            8.699515       4.948760   \n",
       "\n",
       "   Loan_Amount_Term_Log  Total_Income_Log  Male  Yes  1  2  3+  Not Graduate  \\\n",
       "0              5.886104          8.674026     1    0  0  0   0             0   \n",
       "1              5.886104          8.714568     1    1  1  0   0             0   \n",
       "2              5.886104          8.006368     1    1  0  0   0             0   \n",
       "3              5.886104          8.505323     1    1  0  0   0             1   \n",
       "4              5.886104          8.699515     1    0  0  0   0             0   \n",
       "\n",
       "   Yes  Semiurban  Urban  \n",
       "0    0          0      1  \n",
       "1    0          0      0  \n",
       "2    1          0      1  \n",
       "3    0          0      1  \n",
       "4    0          0      1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify input and output attributes\n",
    "x = df.drop(columns=['Loan_Status'], axis=1)\n",
    "y = df['Loan_Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "      <th>Total_Income_Log</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.699515</td>\n",
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       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.972466</td>\n",
       "      <td>4.262680</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>7.972466</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.320205</td>\n",
       "      <td>3.688879</td>\n",
       "      <td>5.192957</td>\n",
       "      <td>8.320205</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.996157</td>\n",
       "      <td>5.533389</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>9.025456</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.933664</td>\n",
       "      <td>5.231109</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.933664</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.430109</td>\n",
       "      <td>4.890349</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.430109</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Credit_History  ApplicantIncomeLog  LoanAmountLog  Loan_Amount_Term_Log  \\\n",
       "0               1.0            8.674026       4.986426              5.886104   \n",
       "1               1.0            8.430109       4.852030              5.886104   \n",
       "2               1.0            8.006368       4.189655              5.886104   \n",
       "3               1.0            7.856707       4.787492              5.886104   \n",
       "4               1.0            8.699515       4.948760              5.886104   \n",
       "..              ...                 ...            ...                   ...   \n",
       "609             1.0            7.972466       4.262680              5.886104   \n",
       "610             1.0            8.320205       3.688879              5.192957   \n",
       "611             1.0            8.996157       5.533389              5.886104   \n",
       "612             1.0            8.933664       5.231109              5.886104   \n",
       "613             0.0            8.430109       4.890349              5.886104   \n",
       "\n",
       "     Total_Income_Log  Male  Yes  1  2  3+  Not Graduate  Yes  Semiurban  \\\n",
       "0            8.674026     1    0  0  0   0             0    0          0   \n",
       "1            8.714568     1    1  1  0   0             0    0          0   \n",
       "2            8.006368     1    1  0  0   0             0    1          0   \n",
       "3            8.505323     1    1  0  0   0             1    0          0   \n",
       "4            8.699515     1    0  0  0   0             0    0          0   \n",
       "..                ...   ...  ... .. ..  ..           ...  ...        ...   \n",
       "609          7.972466     0    0  0  0   0             0    0          0   \n",
       "610          8.320205     1    1  0  0   1             0    0          0   \n",
       "611          9.025456     1    1  1  0   0             0    0          0   \n",
       "612          8.933664     1    1  0  1   0             0    0          0   \n",
       "613          8.430109     0    0  0  0   0             0    1          1   \n",
       "\n",
       "     Urban  \n",
       "0        1  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  \n",
       "..     ...  \n",
       "609      0  \n",
       "610      0  \n",
       "611      1  \n",
       "612      1  \n",
       "613      0  \n",
       "\n",
       "[614 rows x 14 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Y\n",
       "1      N\n",
       "2      Y\n",
       "3      Y\n",
       "4      Y\n",
       "      ..\n",
       "609    Y\n",
       "610    Y\n",
       "611    Y\n",
       "612    Y\n",
       "613    N\n",
       "Name: Loan_Status, Length: 614, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Credit_History</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "      <th>Male</th>\n",
       "      <th>Yes</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3+</th>\n",
       "      <th>Not Graduate</th>\n",
       "      <th>Yes</th>\n",
       "      <th>Semiurban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.093462</td>\n",
       "      <td>4.394449</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.535622</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.294050</td>\n",
       "      <td>4.941642</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.779557</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.867850</td>\n",
       "      <td>4.828314</td>\n",
       "      <td>4.094345</td>\n",
       "      <td>8.867850</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.507143</td>\n",
       "      <td>4.828314</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.507143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.140316</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.451053</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Credit_History  ApplicantIncomeLog  LoanAmountLog  Loan_Amount_Term_Log  \\\n",
       "92              1.0            8.093462       4.394449              5.886104   \n",
       "304             1.0            8.294050       4.941642              5.886104   \n",
       "68              1.0            8.867850       4.828314              4.094345   \n",
       "15              1.0            8.507143       4.828314              5.886104   \n",
       "211             0.0            8.140316       4.852030              5.886104   \n",
       "\n",
       "     Total_Income_Log  Male  Yes  1  2  3+  Not Graduate  Yes  Semiurban  \\\n",
       "92           8.535622     1    1  0  1   0             1    0          0   \n",
       "304          8.779557     1    0  0  0   0             0    0          0   \n",
       "68           8.867850     1    1  0  0   1             1    1          0   \n",
       "15           8.507143     1    0  0  0   0             0    0          0   \n",
       "211          8.451053     1    1  0  0   1             0    0          1   \n",
       "\n",
       "     Urban  \n",
       "92       1  \n",
       "304      0  \n",
       "68       1  \n",
       "15       1  \n",
       "211      0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "350    Y\n",
       "377    Y\n",
       "163    Y\n",
       "609    Y\n",
       "132    Y\n",
       "Name: Loan_Status, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# randomforest classifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier()\n",
    "\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 79.22077922077922\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy is\", model.score(x_test, y_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 70.77922077922078\n"
     ]
    }
   ],
   "source": [
    "# decision tree classifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model2 = DecisionTreeClassifier()\n",
    "model2.fit(x_train, y_train)\n",
    "print(\"Accuracy is\", model2.score(x_test, y_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 77.27272727272727\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jaypr\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:938: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# logistic regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model3 = LogisticRegression()\n",
    "model3.fit(x_train, y_train)\n",
    "print(\"Accuracy is\", model3.score(x_test, y_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# confusion matrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24, 30],\n",
       "       [ 2, 98]], dtype=int64)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# random forest classifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = model.predict(x_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "file=open(\"model.pkl\", 'wb')\n",
    "pickle.dump(model, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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