WS-02 - 2118979 - Christopher Mitchell.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "565ba42f",
   "metadata": {},
   "source": [
    "<H1>Workshop 02 - Predicting loan case using Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3894c3b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae0f84b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"train_ctrUa4K.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67b798fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2d3509e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d07e57d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.sample(n=550, random_state = 79)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b29a7214",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.to_csv('ChristopherMitchell_2118979.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "62996f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('ChristopherMitchell_2118979.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a903c03c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>LP001091</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
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       "      <td>LP001487</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>149</td>\n",
       "      <td>LP001520</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4860</td>\n",
       "      <td>830.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>231</td>\n",
       "      <td>LP001768</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   Unnamed: 0   Loan_ID Gender Married Dependents Education Self_Employed  \\\n",
       "0          30  LP001091   Male     Yes          1  Graduate           NaN   \n",
       "1           0  LP001002   Male      No          0  Graduate            No   \n",
       "2         134  LP001487   Male      No          0  Graduate            No   \n",
       "3         149  LP001520   Male     Yes          0  Graduate            No   \n",
       "4         231  LP001768   Male     Yes          0  Graduate           NaN   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             4166             3369.0       201.0             360.0   \n",
       "1             5849                0.0         NaN             360.0   \n",
       "2             4895                0.0       102.0             360.0   \n",
       "3             4860              830.0       125.0             360.0   \n",
       "4             3716                0.0        42.0             180.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             NaN         Urban           N  \n",
       "1             1.0         Urban           Y  \n",
       "2             1.0     Semiurban           Y  \n",
       "3             1.0     Semiurban           Y  \n",
       "4             1.0         Rural           Y  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b1196640",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop('Unnamed: 0', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "27610a04",
   "metadata": {},
   "outputs": [
    {
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       "      <td>LP001091</td>\n",
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       "      <td>Graduate</td>\n",
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       "      <td>102.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001520</td>\n",
       "      <td>Male</td>\n",
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       "      <td>0</td>\n",
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       "      <td>360.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>LP001768</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents Education Self_Employed  \\\n",
       "0  LP001091   Male     Yes          1  Graduate           NaN   \n",
       "1  LP001002   Male      No          0  Graduate            No   \n",
       "2  LP001487   Male      No          0  Graduate            No   \n",
       "3  LP001520   Male     Yes          0  Graduate            No   \n",
       "4  LP001768   Male     Yes          0  Graduate           NaN   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             4166             3369.0       201.0             360.0   \n",
       "1             5849                0.0         NaN             360.0   \n",
       "2             4895                0.0       102.0             360.0   \n",
       "3             4860              830.0       125.0             360.0   \n",
       "4             3716                0.0        42.0             180.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             NaN         Urban           N  \n",
       "1             1.0         Urban           Y  \n",
       "2             1.0     Semiurban           Y  \n",
       "3             1.0     Semiurban           Y  \n",
       "4             1.0         Rural           Y  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "050427cb",
   "metadata": {},
   "source": [
    "<h3>Q1. Use and explain the following DataFrame functions/properties on your data.<p>describe() size ndim shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "76302a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>538.000000</td>\n",
       "      <td>502.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5273.783636</td>\n",
       "      <td>1660.239855</td>\n",
       "      <td>144.113208</td>\n",
       "      <td>342.825279</td>\n",
       "      <td>0.836653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5879.301796</td>\n",
       "      <td>3040.959688</td>\n",
       "      <td>81.909757</td>\n",
       "      <td>64.636407</td>\n",
       "      <td>0.370050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2785.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3722.000000</td>\n",
       "      <td>1211.500000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5701.000000</td>\n",
       "      <td>2332.250000</td>\n",
       "      <td>161.750000</td>\n",
       "      <td>360.000000</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>650.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       550.000000         550.000000  530.000000        538.000000   \n",
       "mean       5273.783636        1660.239855  144.113208        342.825279   \n",
       "std        5879.301796        3040.959688   81.909757         64.636407   \n",
       "min         150.000000           0.000000   17.000000         12.000000   \n",
       "25%        2785.500000           0.000000  100.000000        360.000000   \n",
       "50%        3722.000000        1211.500000  126.000000        360.000000   \n",
       "75%        5701.000000        2332.250000  161.750000        360.000000   \n",
       "max       81000.000000       41667.000000  650.000000        480.000000   \n",
       "\n",
       "       Credit_History  \n",
       "count      502.000000  \n",
       "mean         0.836653  \n",
       "std          0.370050  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c46e9ab5",
   "metadata": {},
   "source": [
    "This function returns a statistical summary of the data within the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af7fc100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7150"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd90f3b",
   "metadata": {},
   "source": [
    "This function shows the number of elements present within the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4ffa3b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d133f1",
   "metadata": {},
   "source": [
    "This function shows the dimensions of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "77e5bd08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550, 13)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3915e3a8",
   "metadata": {},
   "source": [
    "This function shows a tuple of dataset dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30053f62",
   "metadata": {},
   "source": [
    "<h3>Q2. Is there any difference between dimensions of the original dataset and the new dataset? If yes, what is the difference?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "309dc29c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550, 13)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d14b0f5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550, 13)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "300bb9be",
   "metadata": {},
   "source": [
    "Answer 2. There are no differences to the dimensions of the datasets in use."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "419458f9",
   "metadata": {},
   "source": [
    "<h3>Q3. What are the possible values 'Education' can take? Write code to display all the possible values of 'Education'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3dc889b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Graduate', 'Not Graduate'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Education'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a71ee94",
   "metadata": {},
   "source": [
    "<h6>Answer 3.</h6><p>The values can be either Graduate or Not Graduate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df9c741a",
   "metadata": {},
   "source": [
    "<H1>Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "37df17e0",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = data.columns\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "639a882b",
   "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>LP001091</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4166</td>\n",
       "      <td>3369.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2</th>\n",
       "      <td>LP001487</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001520</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4860</td>\n",
       "      <td>830.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001768</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</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  LP001091   Male     Yes          1  Graduate           NaN   \n",
       "1  LP001002   Male      No          0  Graduate            No   \n",
       "2  LP001487   Male      No          0  Graduate            No   \n",
       "3  LP001520   Male     Yes          0  Graduate            No   \n",
       "4  LP001768   Male     Yes          0  Graduate           NaN   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             4166             3369.0       201.0             360.0   \n",
       "1             5849                0.0         NaN             360.0   \n",
       "2             4895                0.0       102.0             360.0   \n",
       "3             4860              830.0       125.0             360.0   \n",
       "4             3716                0.0        42.0             180.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             NaN         Urban           N  \n",
       "1             1.0         Urban           Y  \n",
       "2             1.0     Semiurban           Y  \n",
       "3             1.0     Semiurban           Y  \n",
       "4             1.0         Rural           Y  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "53dbe933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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": [
    "data['ApplicantIncome'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dd8e782",
   "metadata": {},
   "source": [
    "<h3>Q4 Use boxplot and histogram on 'ApplicantIncome' to visualise its distribution.<p>Histogram and boxplot are used on the same feature to visualise the data distribution. Compare both the plots and report:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f39366ac",
   "metadata": {},
   "source": [
    "<B>Q4a. What are the extreme values? Are there any outliers(s) exist in this dataset? Explain with example based on the 'ApplicantIncome'?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e079167d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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": [
    "data.boxplot(column=['ApplicantIncome'], grid = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9738c15d",
   "metadata": {},
   "source": [
    "<h6>Answer 4a</h6><p>The extreme outliers in this dataset are the values in applicant income between 60,000 and 80,000."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67876d31",
   "metadata": {},
   "source": [
    "<b>Q4b. Are the results of both the plots comparable? Are there any differences in the two plots? What are the key differences?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffeb2770",
   "metadata": {},
   "source": [
    "<h6>Answer 4b</h6>\n",
    "<p>As both plots use the same dataset, they are comparable, however it is significalty easier to see the outlying data points on the boxplot.\n",
    "<p>The histogram shows the volume of the bellcurve, being shown by the height of the bars, in a clear way that makes it easy to show the median income.\n",
    "<p>This contrasts the boxplot, which shows the box below the 10,000 but has an increased empesis on the outling datapoint in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6577c2e6",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "46157bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "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": [
    "data.boxplot(column =['LoanAmount'], grid = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cf4c7570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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": [
    "data['LoanAmount'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4a22e4ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    420\n",
       "0.0     82\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Credit_History'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6a907663",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency Table for Credit History:\n",
      "0.0     82\n",
      "1.0    420\n",
      "Name: Credit_History, dtype: int64\n",
      "\n",
      "Probability of getting loan for each Credit History class:\n",
      "                Loan_Status\n",
      "Credit_History             \n",
      "0.0                0.073171\n",
      "1.0                0.795238\n"
     ]
    }
   ],
   "source": [
    "credit_history = data['Credit_History'].value_counts(ascending=True)\n",
    "\n",
    "loan_probability = data.pivot_table(values='Loan_Status', index=['Credit_History'],\n",
    "                                   aggfunc=lambda x: x.map({'Y':1, 'N':0}).mean())\n",
    "\n",
    "print('Frequency Table for Credit History:')\n",
    "print(credit_history)\n",
    "print('\\nProbability of getting loan for each Credit History class:')\n",
    "print(loan_probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ff0a725b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    375\n",
       "N    175\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0af3cd0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550, 13)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e424bcae",
   "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>LP001091</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4166</td>\n",
       "      <td>3369.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2</th>\n",
       "      <td>LP001487</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001520</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4860</td>\n",
       "      <td>830.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001768</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</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  LP001091   Male     Yes          1  Graduate           NaN   \n",
       "1  LP001002   Male      No          0  Graduate            No   \n",
       "2  LP001487   Male      No          0  Graduate            No   \n",
       "3  LP001520   Male     Yes          0  Graduate            No   \n",
       "4  LP001768   Male     Yes          0  Graduate           NaN   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             4166             3369.0       201.0             360.0   \n",
       "1             5849                0.0         NaN             360.0   \n",
       "2             4895                0.0       102.0             360.0   \n",
       "3             4860              830.0       125.0             360.0   \n",
       "4             3716                0.0        42.0             180.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             NaN         Urban           N  \n",
       "1             1.0         Urban           Y  \n",
       "2             1.0     Semiurban           Y  \n",
       "3             1.0     Semiurban           Y  \n",
       "4             1.0         Rural           Y  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e968c3eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "fig = plt.figure(figsize=(8,4))\n",
    "axl = fig.add_subplot(121)\n",
    "axl.set_xlabel('Credit_History')\n",
    "axl.set_ylabel('Count of Applicants')\n",
    "axl.set_title(\"Applicants by Credit_History\")\n",
    "credit_history.plot(kind='bar')\n",
    "plt.show()\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.set_xlabel('Credit_History')\n",
    "ax2.set_ylabel('Probability of getting loan')\n",
    "ax2.set_title(\"Probability of getting loan by credit history\")\n",
    "loan_probability.plot(kind= 'bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7be0035c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      438\n",
       "Female    101\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "daa8f4c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               11\n",
       "Married               2\n",
       "Dependents           13\n",
       "Education             0\n",
       "Self_Employed        29\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           20\n",
       "Loan_Amount_Term     12\n",
       "Credit_History       48\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(lambda x: sum(x.isnull()), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1cbf3775",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>LP001091</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4166</td>\n",
       "      <td>3369.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2</th>\n",
       "      <td>LP001487</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001520</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4860</td>\n",
       "      <td>830.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001768</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</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  LP001091   Male     Yes          1  Graduate           NaN   \n",
       "1  LP001002   Male      No          0  Graduate            No   \n",
       "2  LP001487   Male      No          0  Graduate            No   \n",
       "3  LP001520   Male     Yes          0  Graduate            No   \n",
       "4  LP001768   Male     Yes          0  Graduate           NaN   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             4166             3369.0       201.0             360.0   \n",
       "1             5849                0.0         NaN             360.0   \n",
       "2             4895                0.0       102.0             360.0   \n",
       "3             4860              830.0       125.0             360.0   \n",
       "4             3716                0.0        42.0             180.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             NaN         Urban           N  \n",
       "1             1.0         Urban           Y  \n",
       "2             1.0     Semiurban           Y  \n",
       "3             1.0     Semiurban           Y  \n",
       "4             1.0         Rural           Y  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "efe46cff",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data['LoanAmount'].fillna(data['LoanAmount'].mean(), inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7b729141",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>LP001091</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4166</td>\n",
       "      <td>3369.0</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>144.113208</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>2</th>\n",
       "      <td>LP001487</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001520</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4860</td>\n",
       "      <td>830.0</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001768</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</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  LP001091   Male     Yes          1  Graduate           NaN   \n",
       "1  LP001002   Male      No          0  Graduate            No   \n",
       "2  LP001487   Male      No          0  Graduate            No   \n",
       "3  LP001520   Male     Yes          0  Graduate            No   \n",
       "4  LP001768   Male     Yes          0  Graduate           NaN   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             4166             3369.0  201.000000             360.0   \n",
       "1             5849                0.0  144.113208             360.0   \n",
       "2             4895                0.0  102.000000             360.0   \n",
       "3             4860              830.0  125.000000             360.0   \n",
       "4             3716                0.0   42.000000             180.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             NaN         Urban           N  \n",
       "1             1.0         Urban           Y  \n",
       "2             1.0     Semiurban           Y  \n",
       "3             1.0     Semiurban           Y  \n",
       "4             1.0         Rural           Y  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ebf9cddf",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               11\n",
       "Married               2\n",
       "Dependents           13\n",
       "Education             0\n",
       "Self_Employed        29\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            0\n",
       "Loan_Amount_Term     12\n",
       "Credit_History       48\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(lambda x: sum(x.isnull()), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f6b1a261",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550, 13)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "13c7dadd",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data.to_csv('new_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "31a5d0d1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'LoanAmount'}, xlabel='[Education, Self_Employed]'>"
      ]
     },
     "execution_count": 38,
     "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": [
    "data.boxplot(column='LoanAmount', by = ['Education', 'Self_Employed'], grid=False, rot=45, fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "13d801a5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     448\n",
       "Yes     73\n",
       "Name: Self_Employed, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Self_Employed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "848cb626",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data['Self_Employed'].fillna('No', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cae8d043",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     477\n",
       "Yes     73\n",
       "Name: Self_Employed, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Self_Employed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b99b9ad5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               11\n",
       "Married               2\n",
       "Dependents           13\n",
       "Education             0\n",
       "Self_Employed         0\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            0\n",
       "Loan_Amount_Term     12\n",
       "Credit_History       48\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(lambda x: sum(x.isnull()), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "af162d84",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>538.000000</td>\n",
       "      <td>502.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5273.783636</td>\n",
       "      <td>1660.239855</td>\n",
       "      <td>144.113208</td>\n",
       "      <td>342.825279</td>\n",
       "      <td>0.836653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5879.301796</td>\n",
       "      <td>3040.959688</td>\n",
       "      <td>80.403935</td>\n",
       "      <td>64.636407</td>\n",
       "      <td>0.370050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2785.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3722.000000</td>\n",
       "      <td>1211.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5701.000000</td>\n",
       "      <td>2332.250000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>360.000000</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>650.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       550.000000         550.000000  550.000000        538.000000   \n",
       "mean       5273.783636        1660.239855  144.113208        342.825279   \n",
       "std        5879.301796        3040.959688   80.403935         64.636407   \n",
       "min         150.000000           0.000000   17.000000         12.000000   \n",
       "25%        2785.500000           0.000000  100.000000        360.000000   \n",
       "50%        3722.000000        1211.500000  128.000000        360.000000   \n",
       "75%        5701.000000        2332.250000  160.000000        360.000000   \n",
       "max       81000.000000       41667.000000  650.000000        480.000000   \n",
       "\n",
       "       Credit_History  \n",
       "count      502.000000  \n",
       "mean         0.836653  \n",
       "std          0.370050  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ff6bdbdb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Sy7eJiA8UEZSZmZWj0aTw/4DvAt8D/JwDM7MJqtGksDEivlNoJGZmVrpGb0m9RtKnJU2XtNPgVGhkZmbWdI0eKQw+V/n0qrIA9hrfcMzMrEwNJYWI2HM8dyrps8CnyBLLvcAngW2By8guZj8G/HFErBvP/ZqZ2fAaSgqS/rRWeURcNNodStoN+AwwKyJelrQYmAvMApZGxNmSzgDOAL402vbNzGzsGj199J6q+a2Bw4CfA6NOClX73UbSK2RHCE8DC4A5qX4RsAwnBTOzplJEjH4jaUfg+xFx1Jh2Kp0KnAW8DFwfEcdLWh8RU6rWWRcRU2tsOx+YD9DZ2Vnp6empuY+BgQE6OjrGEl7h+vpGXmfGjAH6+1sr/kpldOu38s+gEY6/XI6/ON3d3X0RMbtmZUSMegK2AFaOcdupwI3AzqmdK4ETgPVD1ls3UluVSiXq6e3trVtXNhh5Wriwt6H1mjmNViv/DBrh+Mvl+IsDLI86/1cbvaZwDdlFYcgGwnsnsHhsOYrDgUcj4lep7SvIBt1bI2l6RKyWNB1YO8b2zcxsjBq9prCwan4j8HhE9I9xn08AB0valuz00WHAcuBFsltfz06vV42xfTMzG6NGb0m9SVInr19wfnisO4yI2yUtIbtQvRG4CziPbDTWxZJOIkscHxvrPszMbGwaPX30x8A/kd0RJOCbkk6PiCVj2WlEnAmcOaR4A9lRg5mZlaTR00dfBt4TEWsBJO0M/BQYU1IwM7PW1OjYR28ZTAjJs6PY1szM2kSjRwrXSfoJcGla/hPgR8WEZGZmZRnpGc1vBzoj4nRJfwS8j+yawq3AJU2Iz8zMmmikU0BfA14AiIgrIuJzEfFZsqOErxUbmpmZNdtISaErIu4ZWhgRy8lGMzUzswlkpKSw9TB124xnIGZmVr6RksKdkv5saGH6glkDw7qZmVk7Genuo9OAH0g6nteTwGxgS+APC4zLzMxKMGxSiIg1wHsldQP7peIfRsSNhUdmZmZN1+jYR71Ab8GxmJlZyfytZDMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMwsV0pSkDRF0hJJD0haKekQSTtJukHSw+l1ahmxmZlNZmUdKXwduC4i3gG8C1gJnAEsjYiZwNK0bGZmTdT0pCBpB+D9wPkAEfGbiFgPHA0sSqstAo5pdmxmZpOdIqK5O5QOAM4D7ic7SugDTgWeiogpVeuti4hNTiFJmg/MB+js7Kz09PTU3M/AwAAdHR3jHf646GtgfNkZMwbo72+t+CuV0a3fyj+DRjj+cjn+4nR3d/dFxOyalRHR1IlslNWNwO+m5a8Dfw+sH7LeupHaqlQqUU9vb2/durLByNPChb0NrdfMabRa+WfQCMdfLsdfHGB51Pm/WsY1hX6gPyJuT8tLgAOBNZKmA6TXtSXEZmY2qTU9KUTEL4EnJe2Tig4jO5V0NTAvlc0Drmp2bGZmk11DQ2cX4BTgEklbAo8AnyRLUIvTU92eAD5WUmxmZpNWKUkhIu4mu7Yw1GFNDsXMzKr4G81mZpZzUjAzs5yTgpmZ5ZwUzMwsV9bdR21PKjsCM7Px5yMFMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpWMOk0U19fa/Pm1l7cFIwM7Ock4KZmeWcFMzMLFdaUpC0maS7JF2blneSdIOkh9Pr1LJiMzObrMo8UjgVWFm1fAawNCJmAkvTspmZNVEpSUHSDOAjwPeqio8GFqX5RcAxTQ7LzGzSU0Q0f6fSEuAfgO2BL0TEkZLWR8SUqnXWRcQmp5AkzQfmA3R2dlZ6enpq7mNgYICOjo4iwgey2y2LNGPGAP39xcXfDNV9qFRKDmYMiv4dKprjL1crx9/d3d0XEbNrVkZEUyfgSODbaX4OcG2aXz9kvXUjtVWpVKKe3t7eunXjAYqdFi7sLXwfzexDOyr6d6hojr9crRw/sDzq/F8t43GchwJHSfowsDWwg6SLgTWSpkfEaknTgbUlxGZmNqk1/ZpCRCyIiBkR0QXMBW6MiBOAq4F5abV5wFXNjs3MbLJrpe8pnA18UNLDwAfTspmZNVEZp49yEbEMWJbmnwUOKzMeM7PJrpWOFMzMrGROCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWa3pSkLS7pF5JKyXdJ+nUVL6TpBskPZxepzY7NjOzya6MI4WNwOcj4p3AwcDJkmYBZwBLI2ImsDQtm5lZEzU9KUTE6oj4eZp/AVgJ7AYcDSxKqy0Cjml2bGZmk50iorydS13AzcB+wBMRMaWqbl1EbHIKSdJ8YD5AZ2dnpaenp2bbAwMDdHR0FBB1pq+vsKYBmDFjgP7+4uJvhuo+VColBzMGRf8OFc3xl6uV4+/u7u6LiNk1KyOilAnoAPqAP0rL64fUrxupjUqlEvX09vbWrRsPUOy0cGFv4ftoZh/aUdG/Q0Vz/OVq5fiB5VHn/2opdx9J2gK4HLgkIq5IxWskTU/104G1ZcRmZjaZlXH3kYDzgZURcU5V1dXAvDQ/D7iq+FjGPpmZTUSbl7DPQ4ETgXsl3Z3K/gI4G1gs6STgCeBjJcRmLejNJOESL5mZtaWmJ4WIuAWo92d+WDNjMTOzN/I3ms3MLOekYGZmOScFMzPLlXGh2SYh37Fl1h58pGBmZjknBTMzyzkpmJlZzknBJrQ38631ogc9NGtFTgpmZpZzUjAzs5yTgpmZ5fw9BbNheDA+m2x8pGBmZjknBTMzyzkpmJlZztcUzGzc+BpM+/ORgpmZ5ZwUzAryZr5NPRmfH17rG+UTvc+tqOWSgqQjJD0oaZWkM8qOx8xsMmmppCBpM+BbwB8As4DjJM0qNyqz9jKWT9qT+SilqPei6Pe/KC2VFICDgFUR8UhE/AboAY4uOSYzs0lD0UKX/CUdCxwREZ9KyycCvxsRf161znxgflrcB3iwTnPTgGcKDLdo7R4/tH8fHH+5HH9x9oiInWtVtNotqbUOit6QtSLiPOC8ERuSlkfE7PEKrNnaPX5o/z44/nI5/nK02umjfmD3quUZwNMlxWJmNum0WlK4E5gpaU9JWwJzgatLjsnMbNJoqdNHEbFR0p8DPwE2Ay6IiPvG2NyIp5haXLvHD+3fB8dfLsdfgpa60GxmZuVqtdNHZmZWIicFMzPLTcik0A5DZUi6QNJaSSuqynaSdIOkh9Pr1Kq6Bak/D0r6UDlRv07S7pJ6Ja2UdJ+kU1N5W/RB0taS7pD0ixT/36bytoh/kKTNJN0l6dq03DbxS3pM0r2S7pa0PJW1U/xTJC2R9ED6OzikneKvKyIm1ER2gfo/gL2ALYFfALPKjqtGnO8HDgRWVJX9I3BGmj8D+Gqan5X6sRWwZ+rfZiXHPx04MM1vDzyU4myLPpB9J6YjzW8B3A4c3C7xV/Xjc8C/Ade24e/QY8C0IWXtFP8i4FNpfktgSjvFX2+aiEcKbTFURkTcDDw3pPhosl800usxVeU9EbEhIh4FVpH1szQRsToifp7mXwBWArvRJn2IzEBa3CJNQZvEDyBpBvAR4HtVxW0Tfx1tEb+kHcg+2J0PEBG/iYj1tEn8w5mISWE34Mmq5f5U1g46I2I1ZP90gV1SeUv3SVIX8G6yT9tt04d06uVuYC1wQ0S0VfzA14AvAq9VlbVT/AFcL6kvDV8D7RP/XsCvgH9Np+++J2k72if+uiZiUhhxqIw21LJ9ktQBXA6cFhHPD7dqjbJS+xARr0bEAWTfnD9I0n7DrN5S8Us6ElgbEX2NblKjrOzfoUMj4kCyUZFPlvT+YdZttfg3Jzv9+52IeDfwItnponpaLf66JmJSaOehMtZImg6QXtem8pbsk6QtyBLCJRFxRSpuqz4ApMP+ZcARtE/8hwJHSXqM7BTpByRdTPvET0Q8nV7XAj8gO53SLvH3A/3p6BJgCVmSaJf465qISaGdh8q4GpiX5ucBV1WVz5W0laQ9gZnAHSXEl5MksvOpKyPinKqqtuiDpJ0lTUnz2wCHAw/QJvFHxIKImBERXWS/4zdGxAm0SfyStpO0/eA88PvACtok/oj4JfCkpH1S0WHA/bRJ/MMq+0p3ERPwYbK7Yf4D+HLZ8dSJ8VJgNfAK2aeIk4C3AkuBh9PrTlXrfzn150HgD1og/veRHf7eA9ydpg+3Sx+A/YG7UvwrgL9O5W0R/5C+zOH1u4/aIn6yc/K/SNN9g3+n7RJ/iucAYHn6HboSmNpO8debPMyFmZnlJuLpIzMzGyMnBTMzyzkpmJlZzknBzMxyTgpmZpZzUrAJS9LAyGuN6/7+UFJIekcz91sjjtMkbVtmDNa+nBTMxs9xwC1kXyYr02mAk4KNiZOCTSqSDpB0m6R7JP1gcLx7SX8m6c70fIXLBz9pS7pQ0jck/UzSI5KOrdNuB9nQEydRlRQkzZF0k6TFkh6SdLak45U9y+FeSW9L6+0haWmKa6mk367a/7FV7Q1Utbusajz/S5T5DLAr0Cupt5A30SY0JwWbbC4CvhQR+wP3Amem8isi4j0R8S6yYcBPqtpmOtk3uI8Ezq7T7jHAdRHxEPCcpAOr6t4FnAr8DnAisHdEHEQ25PUpaZ1zgYtSXJcA32igL+8mOyqYRfYN4UMj4htkY+p0R0R3A22YvYGTgk0aknYEpkTETaloEdmY+AD7Sfp3SfcCxwP7Vm16ZUS8FhH3A511mj+ObGA60utxVXV3Rvb8iQ1kwxxcn8rvBbrS/CFkD8sB+D5ZEhrJHRHRHxGvkQ0z0jX86mYj27zsAMxaxIXAMRHxC0mfIBtPaNCGqvlNhkCW9FbgA2SJJcie/heSvlhj+9eqll+j/t/g4PgzG0kf3tIghFvWievVYdoya5iPFGzSiIhfA+sk/V4qOhEYPGrYHlidhgM/fpRNH0t26mePiOiKiN2BR2ns0/6gn/H6tYjjyS5YQ/bIykqaP5rsCXEjeYGsP2aj5k8WNpFtK6m/avkcsuGMv5suJD8CfDLV/RXZk+MeJzutM5p/qsex6bWGy4GPA5c12MZngAsknU72RK/BuP4FuErSHWSjbr7YQFvnAT+WtNrXFWy0PEqqmZnlfPrIzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8v9F51mm6TYcznpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['LoanAmount'], 20, facecolor='b')\n",
    "plt.xlabel('Loan Amount')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram for loan amount')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f102f91b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "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": [
    "data.boxplot(column='LoanAmount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "01361bc2",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "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": [
    "data['LoanAmount_log'] = np.log(data['LoanAmount'])\n",
    "#data['LoanAmount_log].hist(bins = 20)\n",
    "plt.hist(data['LoanAmount_log'], 20, facecolor='b')\n",
    "plt.xlabel('Loan Amount')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram for loan amount')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "702e629b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 47,
     "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": [
    "data.boxplot(column='LoanAmount_log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0e668309",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>LoanAmount_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001091</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4166</td>\n",
       "      <td>3369.0</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>N</td>\n",
       "      <td>5.303305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>144.113208</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.970599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001487</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.624973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001520</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4860</td>\n",
       "      <td>830.0</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.828314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001768</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.737670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents Education Self_Employed  \\\n",
       "0  LP001091   Male     Yes          1  Graduate            No   \n",
       "1  LP001002   Male      No          0  Graduate            No   \n",
       "2  LP001487   Male      No          0  Graduate            No   \n",
       "3  LP001520   Male     Yes          0  Graduate            No   \n",
       "4  LP001768   Male     Yes          0  Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             4166             3369.0  201.000000             360.0   \n",
       "1             5849                0.0  144.113208             360.0   \n",
       "2             4895                0.0  102.000000             360.0   \n",
       "3             4860              830.0  125.000000             360.0   \n",
       "4             3716                0.0   42.000000             180.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  LoanAmount_log  \n",
       "0             NaN         Urban           N        5.303305  \n",
       "1             1.0         Urban           Y        4.970599  \n",
       "2             1.0     Semiurban           Y        4.624973  \n",
       "3             1.0     Semiurban           Y        4.828314  \n",
       "4             1.0         Rural           Y        3.737670  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f6c68739",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>538.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>550.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5273.783636</td>\n",
       "      <td>1660.239855</td>\n",
       "      <td>144.113208</td>\n",
       "      <td>342.825279</td>\n",
       "      <td>0.836653</td>\n",
       "      <td>4.851446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5879.301796</td>\n",
       "      <td>3040.959688</td>\n",
       "      <td>80.403935</td>\n",
       "      <td>64.636407</td>\n",
       "      <td>0.370050</td>\n",
       "      <td>0.483401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.833213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2785.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.605170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3722.000000</td>\n",
       "      <td>1211.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.852030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5701.000000</td>\n",
       "      <td>2332.250000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.075174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.476972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       550.000000         550.000000  550.000000        538.000000   \n",
       "mean       5273.783636        1660.239855  144.113208        342.825279   \n",
       "std        5879.301796        3040.959688   80.403935         64.636407   \n",
       "min         150.000000           0.000000   17.000000         12.000000   \n",
       "25%        2785.500000           0.000000  100.000000        360.000000   \n",
       "50%        3722.000000        1211.500000  128.000000        360.000000   \n",
       "75%        5701.000000        2332.250000  160.000000        360.000000   \n",
       "max       81000.000000       41667.000000  650.000000        480.000000   \n",
       "\n",
       "       Credit_History  LoanAmount_log  \n",
       "count      502.000000      550.000000  \n",
       "mean         0.836653        4.851446  \n",
       "std          0.370050        0.483401  \n",
       "min          0.000000        2.833213  \n",
       "25%          1.000000        4.605170  \n",
       "50%          1.000000        4.852030  \n",
       "75%          1.000000        5.075174  \n",
       "max          1.000000        6.476972  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "737edf0b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data = data.drop(['LoanAmount'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebf5aebb",
   "metadata": {},
   "source": [
    "<b>Missing Values Continuous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3734301c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data['Gender'].fillna(data['Gender'].mode()[0], inplace=True)\n",
    "data['Married'].fillna(data['Married'].mode()[0], inplace=True)\n",
    "data['Dependents'].fillna(data['Dependents'].mode()[0], inplace=True)\n",
    "data['Loan_Amount_Term'].fillna(data['Loan_Amount_Term'].mode()[0], inplace=True)\n",
    "data['Credit_History'].fillna(data['Credit_History'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "98c8983b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "LoanAmount_log       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(lambda x: sum(x.isnull()), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9ef3d9e",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<h2>Q5. Use LabelEncoder, to convert categorical variables into numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a06ae024",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001091</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4166</td>\n",
       "      <td>3369.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>N</td>\n",
       "      <td>5.303305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.970599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001487</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.624973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001520</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4860</td>\n",
       "      <td>830.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.828314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001768</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.737670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents Education Self_Employed  \\\n",
       "0  LP001091   Male     Yes          1  Graduate            No   \n",
       "1  LP001002   Male      No          0  Graduate            No   \n",
       "2  LP001487   Male      No          0  Graduate            No   \n",
       "3  LP001520   Male     Yes          0  Graduate            No   \n",
       "4  LP001768   Male     Yes          0  Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  Loan_Amount_Term  Credit_History  \\\n",
       "0             4166             3369.0             360.0             1.0   \n",
       "1             5849                0.0             360.0             1.0   \n",
       "2             4895                0.0             360.0             1.0   \n",
       "3             4860              830.0             360.0             1.0   \n",
       "4             3716                0.0             180.0             1.0   \n",
       "\n",
       "  Property_Area Loan_Status  LoanAmount_log  \n",
       "0         Urban           N        5.303305  \n",
       "1         Urban           Y        4.970599  \n",
       "2     Semiurban           Y        4.624973  \n",
       "3     Semiurban           Y        4.828314  \n",
       "4         Rural           Y        3.737670  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9e99fcb8",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550, 13)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c0adec42",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d265ce43",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status', 'LoanAmount_log']\n"
     ]
    }
   ],
   "source": [
    "columns = list(data)\n",
    "print(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ab8aec3c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001091</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4166</td>\n",
       "      <td>3369.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>N</td>\n",
       "      <td>5.303305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.970599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001487</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.624973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001520</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4860</td>\n",
       "      <td>830.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.828314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001768</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.737670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents Education Self_Employed  \\\n",
       "0  LP001091   Male     Yes          1  Graduate            No   \n",
       "1  LP001002   Male      No          0  Graduate            No   \n",
       "2  LP001487   Male      No          0  Graduate            No   \n",
       "3  LP001520   Male     Yes          0  Graduate            No   \n",
       "4  LP001768   Male     Yes          0  Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  Loan_Amount_Term  Credit_History  \\\n",
       "0             4166             3369.0             360.0             1.0   \n",
       "1             5849                0.0             360.0             1.0   \n",
       "2             4895                0.0             360.0             1.0   \n",
       "3             4860              830.0             360.0             1.0   \n",
       "4             3716                0.0             180.0             1.0   \n",
       "\n",
       "  Property_Area Loan_Status  LoanAmount_log  \n",
       "0         Urban           N        5.303305  \n",
       "1         Urban           Y        4.970599  \n",
       "2     Semiurban           Y        4.624973  \n",
       "3     Semiurban           Y        4.828314  \n",
       "4         Rural           Y        3.737670  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "dc6df7e6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#columns = list(data.select_dtypes(exclude=['float64', 'int64']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5894b4ca",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "c_columns = ['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'Property_Area', 'Loan_Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7e0682f1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               object\n",
       "Gender                object\n",
       "Married               object\n",
       "Dependents            object\n",
       "Education             object\n",
       "Self_Employed         object\n",
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area         object\n",
       "Loan_Status           object\n",
       "LoanAmount_log       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ddfe2fd6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "for i in c_columns:\n",
    "    data[i] = le.fit_transform(data[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e6c107c4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001091</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4166</td>\n",
       "      <td>3369.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5.303305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.970599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001487</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.624973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001520</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4860</td>\n",
       "      <td>830.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.828314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001768</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.737670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP002582</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.416100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LP002767</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2768</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.043425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LP001883</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.905275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP002936</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3859</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.955827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LP001233</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5.743003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LP001917</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1811</td>\n",
       "      <td>1666.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.988984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LP002624</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20833</td>\n",
       "      <td>6667.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.173786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LP002847</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5116</td>\n",
       "      <td>1451.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5.105945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LP002777</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.700480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LP001046</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5955</td>\n",
       "      <td>5625.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5.752573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LP001469</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.476972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LP002403</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10416</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5.231109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LP001259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>3022.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4.700480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LP002784</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2492</td>\n",
       "      <td>2375.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.970599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LP001666</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8333</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.231109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LP001955</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>2541.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.017280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LP002243</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3010</td>\n",
       "      <td>3136.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4.970599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LP002489</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5191</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.882802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LP002798</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3887</td>\n",
       "      <td>2669.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.087596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LP001570</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4167</td>\n",
       "      <td>1447.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.062595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0   LP001091       1        1           1          0              0   \n",
       "1   LP001002       1        0           0          0              0   \n",
       "2   LP001487       1        0           0          0              0   \n",
       "3   LP001520       1        1           0          0              0   \n",
       "4   LP001768       1        1           0          0              0   \n",
       "5   LP002582       0        0           0          1              1   \n",
       "6   LP002767       1        1           0          0              0   \n",
       "7   LP001883       0        0           0          0              0   \n",
       "8   LP002936       1        1           0          0              0   \n",
       "9   LP001233       1        1           1          0              0   \n",
       "10  LP001917       0        0           0          0              0   \n",
       "11  LP002624       1        1           0          0              0   \n",
       "12  LP002847       1        1           0          0              0   \n",
       "13  LP002777       1        1           0          0              0   \n",
       "14  LP001046       1        1           1          0              0   \n",
       "15  LP001469       1        0           0          0              1   \n",
       "16  LP002403       1        0           0          0              1   \n",
       "17  LP001259       1        1           1          0              1   \n",
       "18  LP002784       1        1           1          1              0   \n",
       "19  LP001666       1        0           0          0              0   \n",
       "20  LP001955       0        0           0          0              0   \n",
       "21  LP002243       1        1           0          1              0   \n",
       "22  LP002489       0        0           1          1              0   \n",
       "23  LP002798       1        1           0          0              0   \n",
       "24  LP001570       1        1           2          0              0   \n",
       "\n",
       "    ApplicantIncome  CoapplicantIncome  Loan_Amount_Term  Credit_History  \\\n",
       "0              4166             3369.0             360.0             1.0   \n",
       "1              5849                0.0             360.0             1.0   \n",
       "2              4895                0.0             360.0             1.0   \n",
       "3              4860              830.0             360.0             1.0   \n",
       "4              3716                0.0             180.0             1.0   \n",
       "5             17263                0.0             360.0             1.0   \n",
       "6              2768             1950.0             360.0             1.0   \n",
       "7              3418                0.0             360.0             1.0   \n",
       "8              3859             3300.0             180.0             1.0   \n",
       "9             10750                0.0             360.0             1.0   \n",
       "10             1811             1666.0             360.0             1.0   \n",
       "11            20833             6667.0             360.0             1.0   \n",
       "12             5116             1451.0             360.0             0.0   \n",
       "13             2785             2016.0             360.0             1.0   \n",
       "14             5955             5625.0             360.0             1.0   \n",
       "15            20166                0.0             480.0             1.0   \n",
       "16            10416                0.0             360.0             0.0   \n",
       "17             1000             3022.0             360.0             1.0   \n",
       "18             2492             2375.0             360.0             1.0   \n",
       "19             8333             3750.0             360.0             1.0   \n",
       "20             5000             2541.0             480.0             1.0   \n",
       "21             3010             3136.0             360.0             0.0   \n",
       "22             5191                0.0             360.0             1.0   \n",
       "23             3887             2669.0             360.0             1.0   \n",
       "24             4167             1447.0             360.0             1.0   \n",
       "\n",
       "    Property_Area  Loan_Status  LoanAmount_log  \n",
       "0               2            0        5.303305  \n",
       "1               2            1        4.970599  \n",
       "2               1            1        4.624973  \n",
       "3               1            1        4.828314  \n",
       "4               0            1        3.737670  \n",
       "5               1            1        5.416100  \n",
       "6               0            1        5.043425  \n",
       "7               0            0        4.905275  \n",
       "8               0            1        4.955827  \n",
       "9               2            1        5.743003  \n",
       "10              2            1        3.988984  \n",
       "11              2            1        6.173786  \n",
       "12              2            0        5.105945  \n",
       "13              0            1        4.700480  \n",
       "14              2            1        5.752573  \n",
       "15              2            1        6.476972  \n",
       "16              2            0        5.231109  \n",
       "17              2            0        4.700480  \n",
       "18              0            1        4.970599  \n",
       "19              0            1        5.231109  \n",
       "20              0            0        5.017280  \n",
       "21              2            0        4.970599  \n",
       "22              1            1        4.882802  \n",
       "23              1            1        5.087596  \n",
       "24              0            1        5.062595  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0c29dbc5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "07983796",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001091</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>4166</td>\n",
       "      <td>3369.0</td>\n",
       "      <td>360.0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001487</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.624973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001520</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>4.828314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001768</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0  LP001091       1        1           1          0              0   \n",
       "1  LP001002       1        0           0          0              0   \n",
       "2  LP001487       1        0           0          0              0   \n",
       "3  LP001520       1        1           0          0              0   \n",
       "4  LP001768       1        1           0          0              0   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  Loan_Amount_Term  Credit_History  \\\n",
       "0             4166             3369.0             360.0             1.0   \n",
       "1             5849                0.0             360.0             1.0   \n",
       "2             4895                0.0             360.0             1.0   \n",
       "3             4860              830.0             360.0             1.0   \n",
       "4             3716                0.0             180.0             1.0   \n",
       "\n",
       "   Property_Area  Loan_Status  LoanAmount_log  \n",
       "0              2            0        5.303305  \n",
       "1              2            1        4.970599  \n",
       "2              1            1        4.624973  \n",
       "3              1            1        4.828314  \n",
       "4              0            1        3.737670  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_data = data.copy()\n",
    "original_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "21c69782",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Dependents</th>\n",
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       "      <th>Self_Employed</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3716</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "    Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0  LP001091       1        1           1          0              0   \n",
       "1  LP001002       1        0           0          0              0   \n",
       "2  LP001487       1        0           0          0              0   \n",
       "3  LP001520       1        1           0          0              0   \n",
       "4  LP001768       1        1           0          0              0   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  Loan_Amount_Term  Credit_History  \\\n",
       "0             4166             3369.0             360.0             1.0   \n",
       "1             5849                0.0             360.0             1.0   \n",
       "2             4895                0.0             360.0             1.0   \n",
       "3             4860              830.0             360.0             1.0   \n",
       "4             3716                0.0             180.0             1.0   \n",
       "\n",
       "   Property_Area  Loan_Status  LoanAmount_log  \n",
       "0              2            0        5.303305  \n",
       "1              2            1        4.970599  \n",
       "2              1            1        4.624973  \n",
       "3              1            1        4.828314  \n",
       "4              0            1        3.737670  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_data[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "96f16be8",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>3.737670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0  LP001091       1        1           1          0              0   \n",
       "1  LP001002       1        0           0          0              0   \n",
       "2  LP001487       1        0           0          0              0   \n",
       "3  LP001520       1        1           0          0              0   \n",
       "4  LP001768       1        1           0          0              0   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  Loan_Amount_Term  Credit_History  \\\n",
       "0             4166             3369.0             360.0             1.0   \n",
       "1             5849                0.0             360.0             1.0   \n",
       "2             4895                0.0             360.0             1.0   \n",
       "3             4860              830.0             360.0             1.0   \n",
       "4             3716                0.0             180.0             1.0   \n",
       "\n",
       "   Property_Area  Loan_Status  LoanAmount_log  \n",
       "0              2            0        5.303305  \n",
       "1              2            1        4.970599  \n",
       "2              1            1        4.624973  \n",
       "3              1            1        4.828314  \n",
       "4              0            1        3.737670  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8127c662",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001091</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4166</td>\n",
       "      <td>3369.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5.303305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.970599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001487</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.624973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001520</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4860</td>\n",
       "      <td>830.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.828314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001768</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.737670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0  LP001091       1        1           1          0              0   \n",
       "1  LP001002       1        0           0          0              0   \n",
       "2  LP001487       1        0           0          0              0   \n",
       "3  LP001520       1        1           0          0              0   \n",
       "4  LP001768       1        1           0          0              0   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  Loan_Amount_Term  Credit_History  \\\n",
       "0             4166             3369.0             360.0             1.0   \n",
       "1             5849                0.0             360.0             1.0   \n",
       "2             4895                0.0             360.0             1.0   \n",
       "3             4860              830.0             360.0             1.0   \n",
       "4             3716                0.0             180.0             1.0   \n",
       "\n",
       "   Property_Area  Loan_Status  LoanAmount_log  \n",
       "0              2            0        5.303305  \n",
       "1              2            1        4.970599  \n",
       "2              1            1        4.624973  \n",
       "3              1            1        4.828314  \n",
       "4              0            1        3.737670  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e9c1e297",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data_for_norm = data.drop(['Loan_ID','Loan_Status'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e44dbc20",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "normalized_data = normalize(data_for_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2e77f091",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.86224734e-04 1.86224734e-04 1.86224734e-04 0.00000000e+00\n",
      "  0.00000000e+00 7.75812242e-01 6.27391129e-01 6.70409043e-02\n",
      "  1.86224734e-04 3.72449468e-04 9.87606546e-04]\n",
      " [1.70646398e-04 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 9.98110780e-01 0.00000000e+00 6.14327032e-02\n",
      "  1.70646398e-04 3.41292795e-04 8.48214840e-04]\n",
      " [2.03739739e-04 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 9.97306025e-01 0.00000000e+00 7.33463062e-02\n",
      "  2.03739739e-04 2.03739739e-04 9.42290756e-04]\n",
      " [2.02286099e-04 2.02286099e-04 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 9.83110439e-01 1.67897462e-01 7.28229955e-02\n",
      "  2.02286099e-04 2.02286099e-04 9.76700749e-04]\n",
      " [2.68791246e-04 2.68791246e-04 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 9.98828271e-01 0.00000000e+00 4.83824243e-02\n",
      "  2.68791246e-04 0.00000000e+00 1.00465287e-03]]\n"
     ]
    }
   ],
   "source": [
    "print(normalized_data[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b73e95fc",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550, 11)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4b6ca83d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550, 13)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e6307f74",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "normalized_data = pd.DataFrame(normalized_data, columns=data_for_norm.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "5b32d793",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000186</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000171</td>\n",
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       "      <td>0.000341</td>\n",
       "      <td>0.000848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000204</td>\n",
       "      <td>0.000942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.072823</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.998828</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048382</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender   Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0  0.000186  0.000186    0.000186        0.0            0.0         0.775812   \n",
       "1  0.000171  0.000000    0.000000        0.0            0.0         0.998111   \n",
       "2  0.000204  0.000000    0.000000        0.0            0.0         0.997306   \n",
       "3  0.000202  0.000202    0.000000        0.0            0.0         0.983110   \n",
       "4  0.000269  0.000269    0.000000        0.0            0.0         0.998828   \n",
       "\n",
       "   CoapplicantIncome  Loan_Amount_Term  Credit_History  Property_Area  \\\n",
       "0           0.627391          0.067041        0.000186       0.000372   \n",
       "1           0.000000          0.061433        0.000171       0.000341   \n",
       "2           0.000000          0.073346        0.000204       0.000204   \n",
       "3           0.167897          0.072823        0.000202       0.000202   \n",
       "4           0.000000          0.048382        0.000269       0.000000   \n",
       "\n",
       "   LoanAmount_log  \n",
       "0        0.000988  \n",
       "1        0.000848  \n",
       "2        0.000942  \n",
       "3        0.000977  \n",
       "4        0.001005  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d68ee8c8",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "normalized_data['Loan_ID'] = data['Loan_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "200e231c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
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       "      <th>Loan_ID</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000171</td>\n",
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       "      <td>0.000848</td>\n",
       "      <td>LP001002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000204</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000942</td>\n",
       "      <td>LP001487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000202</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>LP001520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>LP001768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender   Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0  0.000186  0.000186    0.000186        0.0            0.0         0.775812   \n",
       "1  0.000171  0.000000    0.000000        0.0            0.0         0.998111   \n",
       "2  0.000204  0.000000    0.000000        0.0            0.0         0.997306   \n",
       "3  0.000202  0.000202    0.000000        0.0            0.0         0.983110   \n",
       "4  0.000269  0.000269    0.000000        0.0            0.0         0.998828   \n",
       "\n",
       "   CoapplicantIncome  Loan_Amount_Term  Credit_History  Property_Area  \\\n",
       "0           0.627391          0.067041        0.000186       0.000372   \n",
       "1           0.000000          0.061433        0.000171       0.000341   \n",
       "2           0.000000          0.073346        0.000204       0.000204   \n",
       "3           0.167897          0.072823        0.000202       0.000202   \n",
       "4           0.000000          0.048382        0.000269       0.000000   \n",
       "\n",
       "   LoanAmount_log   Loan_ID  \n",
       "0        0.000988  LP001091  \n",
       "1        0.000848  LP001002  \n",
       "2        0.000942  LP001487  \n",
       "3        0.000977  LP001520  \n",
       "4        0.001005  LP001768  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "55c07961",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "normalized_data['Loan_Status'] = data['Loan_Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "73b735be",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
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       "      <th>CoapplicantIncome</th>\n",
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       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Loan_Status</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.000186</td>\n",
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       "      <td>0.000988</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.061433</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.000341</td>\n",
       "      <td>0.000848</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.073346</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000204</td>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.998828</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048382</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>LP001768</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.999783</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020849</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>LP002582</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.812925</td>\n",
       "      <td>0.572689</td>\n",
       "      <td>0.105727</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001481</td>\n",
       "      <td>LP002767</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\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>0.994498</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.104745</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001427</td>\n",
       "      <td>LP001883</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.759529</td>\n",
       "      <td>0.649507</td>\n",
       "      <td>0.035428</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000975</td>\n",
       "      <td>LP002936</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.033470</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.000534</td>\n",
       "      <td>LP001233</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "     Gender   Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0  0.000186  0.000186    0.000186   0.000000       0.000000         0.775812   \n",
       "1  0.000171  0.000000    0.000000   0.000000       0.000000         0.998111   \n",
       "2  0.000204  0.000000    0.000000   0.000000       0.000000         0.997306   \n",
       "3  0.000202  0.000202    0.000000   0.000000       0.000000         0.983110   \n",
       "4  0.000269  0.000269    0.000000   0.000000       0.000000         0.998828   \n",
       "5  0.000000  0.000000    0.000000   0.000058       0.000058         0.999783   \n",
       "6  0.000294  0.000294    0.000000   0.000000       0.000000         0.812925   \n",
       "7  0.000000  0.000000    0.000000   0.000000       0.000000         0.994498   \n",
       "8  0.000197  0.000197    0.000000   0.000000       0.000000         0.759529   \n",
       "9  0.000093  0.000093    0.000093   0.000000       0.000000         0.999440   \n",
       "\n",
       "   CoapplicantIncome  Loan_Amount_Term  Credit_History  Property_Area  \\\n",
       "0           0.627391          0.067041        0.000186       0.000372   \n",
       "1           0.000000          0.061433        0.000171       0.000341   \n",
       "2           0.000000          0.073346        0.000204       0.000204   \n",
       "3           0.167897          0.072823        0.000202       0.000202   \n",
       "4           0.000000          0.048382        0.000269       0.000000   \n",
       "5           0.000000          0.020849        0.000058       0.000058   \n",
       "6           0.572689          0.105727        0.000294       0.000000   \n",
       "7           0.000000          0.104745        0.000291       0.000000   \n",
       "8           0.649507          0.035428        0.000197       0.000000   \n",
       "9           0.000000          0.033470        0.000093       0.000186   \n",
       "\n",
       "   LoanAmount_log   Loan_ID  Loan_Status  \n",
       "0        0.000988  LP001091            0  \n",
       "1        0.000848  LP001002            1  \n",
       "2        0.000942  LP001487            1  \n",
       "3        0.000977  LP001520            1  \n",
       "4        0.001005  LP001768            1  \n",
       "5        0.000314  LP002582            1  \n",
       "6        0.001481  LP002767            1  \n",
       "7        0.001427  LP001883            0  \n",
       "8        0.000975  LP002936            1  \n",
       "9        0.000534  LP001233            1  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9d68c6c7",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
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       "      <th>LoanAmount_log</th>\n",
       "      <th>Loan_Status</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000183</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.872064</td>\n",
       "      <td>0.312089</td>\n",
       "      <td>0.079209</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.001086</td>\n",
       "      <td>0.681818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.178991</td>\n",
       "      <td>0.320177</td>\n",
       "      <td>0.039053</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.000430</td>\n",
       "      <td>0.466194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>0.009983</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002207</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.794058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049806</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000798</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.963920</td>\n",
       "      <td>0.258816</td>\n",
       "      <td>0.078249</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.001098</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.997069</td>\n",
       "      <td>0.603572</td>\n",
       "      <td>0.104359</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.000372</td>\n",
       "      <td>0.001359</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.000673</td>\n",
       "      <td>0.000589</td>\n",
       "      <td>0.001609</td>\n",
       "      <td>0.000673</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.999941</td>\n",
       "      <td>0.242218</td>\n",
       "      <td>0.000673</td>\n",
       "      <td>0.001346</td>\n",
       "      <td>0.002665</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gender     Married  Dependents   Education  Self_Employed  \\\n",
       "count  550.000000  550.000000  550.000000  550.000000     550.000000   \n",
       "mean     0.000183    0.000145    0.000162    0.000061       0.000023   \n",
       "std      0.000125    0.000133    0.000251    0.000122       0.000067   \n",
       "min      0.000000    0.000000    0.000000    0.000000       0.000000   \n",
       "25%      0.000090    0.000000    0.000000    0.000000       0.000000   \n",
       "50%      0.000194    0.000148    0.000000    0.000000       0.000000   \n",
       "75%      0.000272    0.000250    0.000281    0.000000       0.000000   \n",
       "max      0.000673    0.000589    0.001609    0.000673       0.000413   \n",
       "\n",
       "       ApplicantIncome  CoapplicantIncome  Loan_Amount_Term  Credit_History  \\\n",
       "count       550.000000         550.000000        550.000000      550.000000   \n",
       "mean          0.872064           0.312089          0.079209        0.000194   \n",
       "std           0.178991           0.320177          0.039053        0.000124   \n",
       "min           0.009983           0.000000          0.002207        0.000000   \n",
       "25%           0.794058           0.000000          0.049806        0.000105   \n",
       "50%           0.963920           0.258816          0.078249        0.000206   \n",
       "75%           0.997069           0.603572          0.104359        0.000281   \n",
       "max           0.999996           0.999941          0.242218        0.000673   \n",
       "\n",
       "       Property_Area  LoanAmount_log  Loan_Status  \n",
       "count     550.000000      550.000000   550.000000  \n",
       "mean        0.000246        0.001086     0.681818  \n",
       "std         0.000236        0.000430     0.466194  \n",
       "min         0.000000        0.000073     0.000000  \n",
       "25%         0.000000        0.000798     0.000000  \n",
       "50%         0.000218        0.001098     1.000000  \n",
       "75%         0.000372        0.001359     1.000000  \n",
       "max         0.001346        0.002665     1.000000  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa34dfb",
   "metadata": {},
   "source": [
    "<b>Building a Decision Tree Classifier using sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "42800823",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import tree\n",
    "from sklearn.tree import  DecisionTreeClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "import pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8c92f300",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Gender',\n",
       " 'Married',\n",
       " 'Dependents',\n",
       " 'Education',\n",
       " 'Self_Employed',\n",
       " 'ApplicantIncome',\n",
       " 'CoapplicantIncome',\n",
       " 'Loan_Amount_Term',\n",
       " 'Credit_History',\n",
       " 'Property_Area',\n",
       " 'LoanAmount_log',\n",
       " 'Loan_ID',\n",
       " 'Loan_Status']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = list(normalized_data.columns)\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8c8e28c1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Loan_Status</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.000204</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000942</td>\n",
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       "    <tr>\n",
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       "      <td>0.000202</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.998828</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048382</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>LP001768</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender   Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0  0.000186  0.000186    0.000186        0.0            0.0         0.775812   \n",
       "1  0.000171  0.000000    0.000000        0.0            0.0         0.998111   \n",
       "2  0.000204  0.000000    0.000000        0.0            0.0         0.997306   \n",
       "3  0.000202  0.000202    0.000000        0.0            0.0         0.983110   \n",
       "4  0.000269  0.000269    0.000000        0.0            0.0         0.998828   \n",
       "\n",
       "   CoapplicantIncome  Loan_Amount_Term  Credit_History  Property_Area  \\\n",
       "0           0.627391          0.067041        0.000186       0.000372   \n",
       "1           0.000000          0.061433        0.000171       0.000341   \n",
       "2           0.000000          0.073346        0.000204       0.000204   \n",
       "3           0.167897          0.072823        0.000202       0.000202   \n",
       "4           0.000000          0.048382        0.000269       0.000000   \n",
       "\n",
       "   LoanAmount_log   Loan_ID  Loan_Status  \n",
       "0        0.000988  LP001091            0  \n",
       "1        0.000848  LP001002            1  \n",
       "2        0.000942  LP001487            1  \n",
       "3        0.000977  LP001520            1  \n",
       "4        0.001005  LP001768            1  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7bbf1962",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "features = normalized_data.drop(['Loan_ID', 'Loan_Status'], axis=1)\n",
    "\n",
    "classes = pd.DataFrame(normalized_data['Loan_Status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "68de648d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features:\n",
      "     Gender   Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
      "0  0.000186  0.000186    0.000186        0.0            0.0         0.775812   \n",
      "1  0.000171  0.000000    0.000000        0.0            0.0         0.998111   \n",
      "2  0.000204  0.000000    0.000000        0.0            0.0         0.997306   \n",
      "3  0.000202  0.000202    0.000000        0.0            0.0         0.983110   \n",
      "4  0.000269  0.000269    0.000000        0.0            0.0         0.998828   \n",
      "\n",
      "   CoapplicantIncome  Loan_Amount_Term  Credit_History  Property_Area  \\\n",
      "0           0.627391          0.067041        0.000186       0.000372   \n",
      "1           0.000000          0.061433        0.000171       0.000341   \n",
      "2           0.000000          0.073346        0.000204       0.000204   \n",
      "3           0.167897          0.072823        0.000202       0.000202   \n",
      "4           0.000000          0.048382        0.000269       0.000000   \n",
      "\n",
      "   LoanAmount_log  \n",
      "0        0.000988  \n",
      "1        0.000848  \n",
      "2        0.000942  \n",
      "3        0.000977  \n",
      "4        0.001005  \n",
      "Classes:\n",
      "   Loan_Status\n",
      "0            0\n",
      "1            1\n",
      "2            1\n",
      "3            1\n",
      "4            1\n"
     ]
    }
   ],
   "source": [
    "print('Features:')\n",
    "print(features.head())\n",
    "\n",
    "print('Classes:')\n",
    "print(classes.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "83807074",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000204</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.073346</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000942</td>\n",
       "      <td>LP001487</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.167897</td>\n",
       "      <td>0.072823</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>LP001520</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.998828</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048382</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>LP001768</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender   Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0  0.000186  0.000186    0.000186        0.0            0.0         0.775812   \n",
       "1  0.000171  0.000000    0.000000        0.0            0.0         0.998111   \n",
       "2  0.000204  0.000000    0.000000        0.0            0.0         0.997306   \n",
       "3  0.000202  0.000202    0.000000        0.0            0.0         0.983110   \n",
       "4  0.000269  0.000269    0.000000        0.0            0.0         0.998828   \n",
       "\n",
       "   CoapplicantIncome  Loan_Amount_Term  Credit_History  Property_Area  \\\n",
       "0           0.627391          0.067041        0.000186       0.000372   \n",
       "1           0.000000          0.061433        0.000171       0.000341   \n",
       "2           0.000000          0.073346        0.000204       0.000204   \n",
       "3           0.167897          0.072823        0.000202       0.000202   \n",
       "4           0.000000          0.048382        0.000269       0.000000   \n",
       "\n",
       "   LoanAmount_log   Loan_ID  Loan_Status  \n",
       "0        0.000988  LP001091            0  \n",
       "1        0.000848  LP001002            1  \n",
       "2        0.000942  LP001487            1  \n",
       "3        0.000977  LP001520            1  \n",
       "4        0.001005  LP001768            1  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "921c56ed",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "normalized_data = normalized_data.sample(n=550, random_state=8979)\n",
    "normalized_data.to_csv('CM_Train.csv')\n",
    "normalized_data.to_csv('CM_Test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "da332d7d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "datatrain = pd.read_csv('CM_Train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5700d55c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6f98e032",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(368, 11) (182, 11)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(features, classes, test_size=.33, random_state=1)\n",
    "print(x_train.shape, x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bd150f84",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(criterion='entropy')\n"
     ]
    }
   ],
   "source": [
    "decisionTree = DecisionTreeClassifier(criterion='entropy')\n",
    "print(decisionTree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7de38f22",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "dtc_model = decisionTree.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "5554fd55",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature:0, Score: 0.10463\n",
      "Feature:1, Score: 0.06480\n",
      "Feature:2, Score: 0.08161\n",
      "Feature:3, Score: 0.00824\n",
      "Feature:4, Score: 0.02504\n",
      "Feature:5, Score: 0.08407\n",
      "Feature:6, Score: 0.06998\n",
      "Feature:7, Score: 0.09196\n",
      "Feature:8, Score: 0.24071\n",
      "Feature:9, Score: 0.08755\n",
      "Feature:10, Score: 0.14141\n"
     ]
    },
    {
     "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": [
    "importance = dtc_model.feature_importances_\n",
    "\n",
    "for i,v in enumerate(importance):\n",
    "    print('Feature:%0d, Score: %.5f' %(i,v))\n",
    "\n",
    "pyplot.bar([x for x in range(len(importance))], importance)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0516d901",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "prediction = dtc_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "fe139a62",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_true = le.inverse_transform(y_test['Loan_Status'])\n",
    "y_pred = le.inverse_transform(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7babad9c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27f31683d00>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_true, y_pred)\n",
    "labels = ['N','Y']\n",
    "ConfusionMatrixDisplay(cm, display_labels=labels).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "59e0ea62",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           N       0.54      0.55      0.55        56\n",
      "           Y       0.80      0.79      0.80       126\n",
      "\n",
      "    accuracy                           0.72       182\n",
      "   macro avg       0.67      0.67      0.67       182\n",
      "weighted avg       0.72      0.72      0.72       182\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a139f8be",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "graphviz_path = 'C:\\Program Files\\Graphviz\\bin'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "adf439e6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"PATH\"] += os.pathsep + graphviz_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "5e1be0d4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from graphviz import Source\n",
    "from sklearn import tree\n",
    "graph = Source( tree.export_graphviz(dtc_model, out_file=None, feature_names=features.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "568bfe48",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "no library called \"cairo-2\" was found\nno library called \"cairo\" was found\nno library called \"libcairo-2\" was found\ncannot load library 'libcairo.so.2': error 0x7e\ncannot load library 'libcairo.2.dylib': error 0x7e\ncannot load library 'libcairo-2.dll': error 0x7e",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-100-aa22caa5dde8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mcairosvg\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msvg2png\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0msvg2png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytestring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpipe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'svg'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mwrite_to\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'output.png'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"output.png\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\cairosvg\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[1;31m# VERSION is used in the \"url\" module imported by \"surface\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msurface\u001b[0m  \u001b[1;31m# noqa isort:skip\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\cairosvg\\surface.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mcairocffi\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcairo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mcolors\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnegate_color\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\cairocffi\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m cairo = dlopen(\n\u001b[0m\u001b[0;32m     49\u001b[0m     \u001b[0mffi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'cairo-2'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'cairo'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'libcairo-2'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     ('libcairo.so.2', 'libcairo.2.dylib', 'libcairo-2.dll'))\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\cairocffi\\__init__.py\u001b[0m in \u001b[0;36mdlopen\u001b[1;34m(ffi, library_names, filenames)\u001b[0m\n\u001b[0;32m     43\u001b[0m     error_message = '\\n'.join(  # pragma: no cover\n\u001b[0;32m     44\u001b[0m         str(exception) for exception in exceptions)\n\u001b[1;32m---> 45\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merror_message\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pragma: no cover\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOSError\u001b[0m: no library called \"cairo-2\" was found\nno library called \"cairo\" was found\nno library called \"libcairo-2\" was found\ncannot load library 'libcairo.so.2': error 0x7e\ncannot load library 'libcairo.2.dylib': error 0x7e\ncannot load library 'libcairo-2.dll': error 0x7e"
     ]
    }
   ],
   "source": [
    "from cairosvg import svg2png\n",
    "from IPython.display import Image\n",
    "\n",
    "svg2png(bytestring=graph.pipe(format='svg'),write_to='output.png')\n",
    "Image(\"output.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c68fb074",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<h2>Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c557ded1",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<h3>Q6. Based on the feature importance, select a different set of features to build another dicision tree model. You should aim to improve the result of the baseline model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "4d38009f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(308, 11) (242, 11)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(features, classes, test_size= .44,\n",
    "                                                    random_state=1)\n",
    "print(x_train.shape, x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "85fe8235",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(criterion='entropy')\n"
     ]
    }
   ],
   "source": [
    "decisionTree = DecisionTreeClassifier(criterion='entropy')\n",
    "print(decisionTree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "05ad0368",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 0, Score: 0.07072\n",
      "Feature: 1, Score: 0.05728\n",
      "Feature: 2, Score: 0.06387\n",
      "Feature: 3, Score: 0.03163\n",
      "Feature: 4, Score: 0.00756\n",
      "Feature: 5, Score: 0.15480\n",
      "Feature: 6, Score: 0.02154\n",
      "Feature: 7, Score: 0.11081\n",
      "Feature: 8, Score: 0.25973\n",
      "Feature: 9, Score: 0.06708\n",
      "Feature: 10, Score: 0.15498\n"
     ]
    },
    {
     "data": {
      "image/png": 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TPAx8h4VPiX2Xd8GjAlYqydeA3wSuSDIH/DlwL/APSe5g4T+431/1unwEgiS17WK9dCNJFw2DXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXu/wC/mBne0idJIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtc_model = decisionTree.fit(x_train, y_train)\n",
    "\n",
    "importance = dtc_model.feature_importances_\n",
    "\n",
    "for i,v in enumerate(importance):\n",
    "    print('Feature: %0d, Score: %.5f' % (i,v))\n",
    "\n",
    "pyplot.bar([x for x in range(len(importance))], importance)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "6481f66a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "prediction = dtc_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "4295eac2",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_true = le.inverse_transform(y_test['Loan_Status'])\n",
    "y_pred = le.inverse_transform(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "4fe06785",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method ConfusionMatrixDisplay.plot of <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x0000027F2BB457C0>>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm1 = confusion_matrix(y_true, y_pred)\n",
    "labels = ['N','Y']\n",
    "ConfusionMatrixDisplay(cm1, display_labels=labels).plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c546ae07",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           N       0.48      0.60      0.53        72\n",
      "           Y       0.81      0.72      0.76       170\n",
      "\n",
      "    accuracy                           0.69       242\n",
      "   macro avg       0.64      0.66      0.65       242\n",
      "weighted avg       0.71      0.69      0.69       242\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6705f26b",
   "metadata": {},
   "source": [
    "<b>Answer 6.</b>\n",
    "<p>I used a test size of .44 in an attempt to increase accuracy by having a larger volume of data for the model to use.</p>\n",
    "<p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca8db25",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<h3>Q7. Write a summary to compare both the models. The summary should include: idea behind selecting those particular features and comparative analysis of the results of both the models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af151e63",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<h6>Answer 7.</h6><p>\n",
    "The actual results, as shown in the classification report above, are only slightly more accurate when looking at the weighted average. This has increased to 0.72 from 0.71. This was not the results I anticipated. One reason this could be the case is that the decision tree implemented in this example is not scaling as I expected it to with the implemented change, and that maybe a change to the criterion, or dataset would be more effective in this scenario.\n",
    "The feature importance of the second model is notably different from the original model."
   ]
  }
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