594 lines
427 KiB
Plaintext
594 lines
427 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /Users/wojciechbatruszewicz/.kaggle/kaggle.json'\n",
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"Downloading home-loan-approval.zip to /Users/wojciechbatruszewicz/InformatykaStudia/SEMESTR8/IUM/ZADANIA\n",
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" 0%| | 0.00/12.6k [00:00<?, ?B/s]\n",
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"100%|██████████████████████████████████████| 12.6k/12.6k [00:00<00:00, 18.6MB/s]\n"
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]
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}
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],
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"source": [
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"!kaggle datasets download -d rishikeshkonapure/home-loan-approval"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Archive: home-loan-approval.zip\n",
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" inflating: loan_sanction_test.csv \n",
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" inflating: loan_sanction_train.csv \n"
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]
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}
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],
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"source": [
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"!unzip -o home-loan-approval.zip"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" 367 loan_sanction_test.csv\n"
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]
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}
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],
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"source": [
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"!wc -l loan_sanction_test.csv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" 614 loan_sanction_train.csv\n"
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]
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}
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],
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"source": [
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"!wc -l loan_sanction_train.csv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<bound method NDFrame.head of Loan_ID Gender Married Dependents Education Self_Employed \\\n",
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"0 LP001002 Male No 0 Graduate No \n",
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"1 LP001003 Male Yes 1 Graduate No \n",
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"2 LP001005 Male Yes 0 Graduate Yes \n",
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"3 LP001006 Male Yes 0 Not Graduate No \n",
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"4 LP001008 Male No 0 Graduate No \n",
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".. ... ... ... ... ... ... \n",
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"609 LP002978 Female No 0 Graduate No \n",
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"610 LP002979 Male Yes 3+ Graduate No \n",
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"611 LP002983 Male Yes 1 Graduate No \n",
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"612 LP002984 Male Yes 2 Graduate No \n",
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"613 LP002990 Female No 0 Graduate Yes \n",
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"\n",
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" ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term \\\n",
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"0 5849 0.0 NaN 360.0 \n",
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"1 4583 1508.0 128.0 360.0 \n",
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"2 3000 0.0 66.0 360.0 \n",
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"3 2583 2358.0 120.0 360.0 \n",
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"4 6000 0.0 141.0 360.0 \n",
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".. ... ... ... ... \n",
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"609 2900 0.0 71.0 360.0 \n",
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"610 4106 0.0 40.0 180.0 \n",
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"611 8072 240.0 253.0 360.0 \n",
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"612 7583 0.0 187.0 360.0 \n",
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"613 4583 0.0 133.0 360.0 \n",
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"\n",
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" Credit_History Property_Area Loan_Status \n",
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"0 1.0 Urban Y \n",
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"1 1.0 Rural N \n",
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"2 1.0 Urban Y \n",
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"3 1.0 Urban Y \n",
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"4 1.0 Urban Y \n",
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".. ... ... ... \n",
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"609 1.0 Rural Y \n",
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"610 1.0 Rural Y \n",
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"611 1.0 Urban Y \n",
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"612 1.0 Urban Y \n",
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"613 0.0 Semiurban N \n",
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"\n",
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"[614 rows x 13 columns]>"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"home_loan_train = pd.read_csv('loan_sanction_train.csv')\n",
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"home_loan_test = pd.read_csv('loan_sanction_test.csv')\n",
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"home_loan_train.head"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Loan_ID</th>\n",
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" <th>Gender</th>\n",
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" <th>Married</th>\n",
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" <th>Dependents</th>\n",
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" <th>Education</th>\n",
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" <th>Self_Employed</th>\n",
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" <th>ApplicantIncome</th>\n",
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" <th>CoapplicantIncome</th>\n",
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" <th>LoanAmount</th>\n",
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" <th>Loan_Amount_Term</th>\n",
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" <th>Credit_History</th>\n",
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" <th>Property_Area</th>\n",
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" <th>Loan_Status</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>count</th>\n",
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" <td>614</td>\n",
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" <td>601</td>\n",
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" <td>611</td>\n",
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" <td>599</td>\n",
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" <td>614</td>\n",
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" <td>582</td>\n",
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" <td>614.000000</td>\n",
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" <td>614.000000</td>\n",
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" <td>592.000000</td>\n",
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" <td>600.00000</td>\n",
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" <td>564.000000</td>\n",
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" <td>614</td>\n",
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" <td>614</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>unique</th>\n",
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" <td>614</td>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
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" <td>4</td>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>3</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>top</th>\n",
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" <td>LP001002</td>\n",
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" <td>Male</td>\n",
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" <td>Yes</td>\n",
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" <td>0</td>\n",
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" <td>Graduate</td>\n",
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" <td>No</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>Semiurban</td>\n",
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" <td>Y</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>freq</th>\n",
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" <td>1</td>\n",
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" <td>489</td>\n",
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" <td>398</td>\n",
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" <td>345</td>\n",
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" <td>480</td>\n",
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" <td>500</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>233</td>\n",
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" <td>422</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>mean</th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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||
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" <td>NaN</td>\n",
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||
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" <td>NaN</td>\n",
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||
|
" <td>NaN</td>\n",
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||
|
" <td>NaN</td>\n",
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||
|
" <td>5403.459283</td>\n",
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||
|
" <td>1621.245798</td>\n",
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||
|
" <td>146.412162</td>\n",
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||
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" <td>342.00000</td>\n",
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||
|
" <td>0.842199</td>\n",
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||
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>std</th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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||
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" <td>NaN</td>\n",
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||
|
" <td>NaN</td>\n",
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||
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" <td>NaN</td>\n",
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|
" <td>6109.041673</td>\n",
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|
" <td>2926.248369</td>\n",
|
||
|
" <td>85.587325</td>\n",
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||
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" <td>65.12041</td>\n",
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||
|
" <td>0.364878</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>min</th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
|
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" <td>NaN</td>\n",
|
||
|
" <td>NaN</td>\n",
|
||
|
" <td>NaN</td>\n",
|
||
|
" <td>NaN</td>\n",
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||
|
" <td>150.000000</td>\n",
|
||
|
" <td>0.000000</td>\n",
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|
" <td>9.000000</td>\n",
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||
|
" <td>12.00000</td>\n",
|
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|
" <td>0.000000</td>\n",
|
||
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>25%</th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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|
" <td>NaN</td>\n",
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|
" <td>NaN</td>\n",
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|
" <td>2877.500000</td>\n",
|
||
|
" <td>0.000000</td>\n",
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|
" <td>100.000000</td>\n",
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|
" <td>360.00000</td>\n",
|
||
|
" <td>1.000000</td>\n",
|
||
|
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>50%</th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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|
" <td>NaN</td>\n",
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||
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" <td>NaN</td>\n",
|
||
|
" <td>NaN</td>\n",
|
||
|
" <td>NaN</td>\n",
|
||
|
" <td>3812.500000</td>\n",
|
||
|
" <td>1188.500000</td>\n",
|
||
|
" <td>128.000000</td>\n",
|
||
|
" <td>360.00000</td>\n",
|
||
|
" <td>1.000000</td>\n",
|
||
|
" <td>NaN</td>\n",
|
||
|
" <td>NaN</td>\n",
|
||
|
" </tr>\n",
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" <tr>\n",
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|
" <th>75%</th>\n",
|
||
|
" <td>NaN</td>\n",
|
||
|
" <td>NaN</td>\n",
|
||
|
" <td>NaN</td>\n",
|
||
|
" <td>NaN</td>\n",
|
||
|
" <td>NaN</td>\n",
|
||
|
" <td>NaN</td>\n",
|
||
|
" <td>5795.000000</td>\n",
|
||
|
" <td>2297.250000</td>\n",
|
||
|
" <td>168.000000</td>\n",
|
||
|
" <td>360.00000</td>\n",
|
||
|
" <td>1.000000</td>\n",
|
||
|
" <td>NaN</td>\n",
|
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|
" <td>NaN</td>\n",
|
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|
" </tr>\n",
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" <tr>\n",
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" <th>max</th>\n",
|
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
|
||
|
" <td>NaN</td>\n",
|
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" <td>NaN</td>\n",
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|
" <td>81000.000000</td>\n",
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" <td>41667.000000</td>\n",
|
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" <td>700.000000</td>\n",
|
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" <td>480.00000</td>\n",
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||
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" <td>1.000000</td>\n",
|
||
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Loan_ID Gender Married Dependents Education Self_Employed \\\n",
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"count 614 601 611 599 614 582 \n",
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"unique 614 2 2 4 2 2 \n",
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"top LP001002 Male Yes 0 Graduate No \n",
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"freq 1 489 398 345 480 500 \n",
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"mean NaN NaN NaN NaN NaN NaN \n",
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"std NaN NaN NaN NaN NaN NaN \n",
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"min NaN NaN NaN NaN NaN NaN \n",
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"25% NaN NaN NaN NaN NaN NaN \n",
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"50% NaN NaN NaN NaN NaN NaN \n",
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"75% NaN NaN NaN NaN NaN NaN \n",
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"max NaN NaN NaN NaN NaN NaN \n",
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"\n",
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" ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term \\\n",
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||
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"count 614.000000 614.000000 592.000000 600.00000 \n",
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"unique NaN NaN NaN NaN \n",
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"top NaN NaN NaN NaN \n",
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"freq NaN NaN NaN NaN \n",
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"mean 5403.459283 1621.245798 146.412162 342.00000 \n",
|
||
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"std 6109.041673 2926.248369 85.587325 65.12041 \n",
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||
|
"min 150.000000 0.000000 9.000000 12.00000 \n",
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||
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"25% 2877.500000 0.000000 100.000000 360.00000 \n",
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"50% 3812.500000 1188.500000 128.000000 360.00000 \n",
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"75% 5795.000000 2297.250000 168.000000 360.00000 \n",
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"max 81000.000000 41667.000000 700.000000 480.00000 \n",
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"\n",
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" Credit_History Property_Area Loan_Status \n",
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"count 564.000000 614 614 \n",
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"unique NaN 3 2 \n",
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"top NaN Semiurban Y \n",
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"freq NaN 233 422 \n",
|
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"mean 0.842199 NaN NaN \n",
|
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"std 0.364878 NaN NaN \n",
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"min 0.000000 NaN NaN \n",
|
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|
"25% 1.000000 NaN NaN \n",
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||
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"50% 1.000000 NaN NaN \n",
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|
"75% 1.000000 NaN NaN \n",
|
||
|
"max 1.000000 NaN NaN "
|
||
|
]
|
||
|
},
|
||
|
"execution_count": 9,
|
||
|
"metadata": {},
|
||
|
"output_type": "execute_result"
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"home_loan_train.describe(include = \"all\")"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 12,
|
||
|
"metadata": {},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"data": {
|
||
|
"text/plain": [
|
||
|
"Y 422\n",
|
||
|
"N 192\n",
|
||
|
"Name: Loan_Status, dtype: int64"
|
||
|
]
|
||
|
},
|
||
|
"execution_count": 12,
|
||
|
"metadata": {},
|
||
|
"output_type": "execute_result"
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"home_loan_train[\"Loan_Status\"].value_counts()"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 13,
|
||
|
"metadata": {},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"data": {
|
||
|
"text/plain": [
|
||
|
"<Axes: >"
|
||
|
]
|
||
|
},
|
||
|
"execution_count": 13,
|
||
|
"metadata": {},
|
||
|
"output_type": "execute_result"
|
||
|
},
|
||
|
{
|
||
|
"data": {
|
||
|
"image/png": "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
|
||
|
"text/plain": [
|
||
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
]
|
||
|
},
|
||
|
"metadata": {},
|
||
|
"output_type": "display_data"
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"home_loan_train[\"Loan_Status\"].value_counts().plot(kind=\"bar\")"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 14,
|
||
|
"metadata": {},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"data": {
|
||
|
"text/plain": [
|
||
|
"<Axes: xlabel='Loan_Status'>"
|
||
|
]
|
||
|
},
|
||
|
"execution_count": 14,
|
||
|
"metadata": {},
|
||
|
"output_type": "execute_result"
|
||
|
},
|
||
|
{
|
||
|
"data": {
|
||
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjEAAAGtCAYAAADnIyVRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAvG0lEQVR4nO3de1jUdd7/8dfISVAYBYVxksIKDcPTT1vE29LybIStdqtZpLuuVpbGreZhzdQt0dw7Dy2/7WferrhmS/fdRttmsZqb7G0esyhNtJPHBDF3HFARFL+/P7r8biOewAN84Pm4rrku+c57Zj5frmZ57ne+M+OwLMsSAACAYepV9wIAAACqgogBAABGImIAAICRiBgAAGAkIgYAABiJiAEAAEYiYgAAgJGIGAAAYCT/6l7A9XL27FkdOnRIoaGhcjgc1b0cAABwBSzLUnFxsdxut+rVu/SxllobMYcOHVJ0dHR1LwMAAFTBgQMH1Lx580vO1NqICQ0NlfTjLyEsLKyaVwMAAK5EUVGRoqOj7b/jl1JrI+bcS0hhYWFEDAAAhrmSU0E4sRcAABiJiAEAAEYiYgAAgJFq7TkxAIB/sSxLZ86cUXl5eXUvBXWcn5+f/P39r8nHnxAxAFDLlZWVKT8/XydPnqzupQCSpJCQEDVr1kyBgYFXdT9EDADUYmfPntWePXvk5+cnt9utwMBAPgAU1cayLJWVlenIkSPas2ePYmNjL/uBdpdCxABALVZWVqazZ88qOjpaISEh1b0cQMHBwQoICNC+fftUVlam+vXrV/m+OLEXAOqAq/l/u8C1dq3+e+S/agAAYCQiBgAAGImIAQDUWTNnzlT79u3tn0eMGKEHH3yw2taDyuHEXgCog2KmrLqhj7d37v1Vut2GDRt09913q1evXsrOzr7Gq6po0aJFsizruj/OT3Xv3l3t27fXwoUL7W179+5VixYt9Nlnn/lEFnxxJAYAUGP94Q9/0NixY7V+/Xrt37//uj+e0+lUo0aNrvvj4NogYgAANdKJEyf03//933ryySeVlJSkjIwM+7p169bJ4XBo1apVateunerXr6+EhARt377dnsnIyFCjRo30zjvvqGXLlqpfv7569eqlAwcOXPQxz3856ezZs3rppZd0++23KygoSDfffLNmz55tXz958mS1bNlSISEhuvXWWzV9+nSdPn3avv7cy1UrVqxQTEyMnE6nhg4dquLiYvvxcnJytGjRIjkcDjkcDu3du7fCus7t79q1a9WpUyeFhISoS5cu2r17t8/cu+++q06dOql+/fpq0qSJBg4caF/n8Xj02GOPqXHjxgoJCVG/fv309ddfV/h9vffee2rVqpVCQkL00EMP6cSJE1q+fLliYmLUuHFjjR071ueTn8vKyjRp0iTddNNNatCggRISErRu3bqL/o6vJV5OqoVu9GFiVK+qHqYHaro333xTrVq1UqtWrfToo49q7Nixmj59us+H9T377LNatGiRXC6Xfv3rXys5OVlfffWVAgICJEknT57U7NmztXz5cgUGBmrMmDEaOnSoPv744ytaw9SpU7VkyRItWLBAXbt2VX5+vnbt2mVfHxoaqoyMDLndbm3fvl2jRo1SaGioJk2aZM98++23euedd/Tee+/J4/Fo8ODBmjt3rmbPnq1Fixbpq6++Unx8vH7zm99Ikpo2bXrR0Jo2bZpefvllNW3aVE888YR++ctf2vuyatUqDRw4UNOmTdOKFStUVlamVav+9fdgxIgR+vrrr/Xuu+8qLCxMkydPVv/+/bVz506f39crr7yizMxMFRcXa+DAgRo4cKAaNWqk999/X999950GDRqkrl27asiQIZKkX/ziF9q7d68yMzPldruVlZWlvn37avv27YqNjb2i33NVETEAgBpp6dKlevTRRyVJffv21fHjx7V27Vr17NnTnpkxY4Z69eolSVq+fLmaN2+urKwsDR48WJJ0+vRppaenKyEhwZ6Ji4vTli1b9LOf/eySj19cXKxFixYpPT1dw4cPlyTddttt6tq1qz3z3HPP2f+OiYnRhAkT9Oabb/pEzNmzZ5WRkaHQ0FBJUkpKitauXavZs2fL6XQqMDBQISEhcrlcl/2dzJ49W926dZMkTZkyRffff79OnTql+vXra/bs2Ro6dKhmzZplz7dr106S7Hj5+OOP1aVLF0nSypUrFR0drXfeeUf//u//bv++Xn31Vd12222SpIceekgrVqzQ4cOH1bBhQ7Vu3Vr33nuvPvroIw0ZMkTffvut/vSnP+ngwYNyu92SpIkTJyo7O1vLli1TWlraZffpahAxAIAaZ/fu3dqyZYvefvttSZK/v7+GDBmiP/zhDz4Rk5iYaP87PDxcrVq1Ul5enr3N399fnTp1sn++44471KhRI+Xl5V02YvLy8lRaWqoePXpcdOatt97SwoUL9c033+j48eM6c+aMwsLCfGZiYmLsgJGkZs2aqbCw8DK/gQtr27atz/1IUmFhoW6++Wbl5uZq1KhRF90Xf39/O+YkKSIiosLvKyQkxA4YSYqKilJMTIwaNmzos+3c+j/99FNZlqWWLVv6PF5paakiIiKqtI+VQcQAAGqcpUuX6syZM7rpppvsbZZlKSAgQB6P55K3Pf+7oS70XVFX8v1RwcHBl7x+06ZN9pGPPn36yOl0KjMzUy+//LLP3LmXan762GfPnr3s41/IT+/r3D6cu69Lrfdi77iyLMvnd3GhtV5q/WfPnpWfn5+2bdsmPz8/n7mfhs/1QsQAgEEqe87bTaF+mnlvpMqCi+TwP3WdVnV5Xxw8dsWzZ86c0R8ylmvC9BeVeM+9PtdNeHy45v3fJbq9VWtJ0pur1qrPAz+XJBUdO6Zdu79ScNNofXHwmA7886TOnDmjP636SG06dJQk7f32ax07dkx+jW/SFweP6XDRKZ06XW6vz3OiTMUlp/XFwWMqDW6q+vWDtfytv2rgw49VWOf/rPpQzW6K1gPDn5IklUj6bOfXOmtZ9v2df/+SdOhYiU6Xn7W3lZ6tp8Pekz4z3+cXXfHv65y2bdtq7dq1+sUvflHhutatW+vMmTPavHmz/XLS0aNH9dVXXykuLq7Sj3VOhw4dVF5ersLCQt19991Vvp+qImIAADXKPz78m4q8x/TzoY8qNMzpc12v/snKynxdz8748VyLxQvnqVHjcIU3aar0eS+qcXi47uvzr5Pd/QMCNPf5yZr8m7kK8A/QnOnPqu3/ucuOmksJql9fvxjzjBbMnqGAgEC175Qgzz9/0Ddf7dLAoSm6OeZWFRw6qA/+8mfFt/s/+sffV+vv2e9Ven9vir5Z2z/bpu8P7FdIgwZyNmpc6fuQfjw/qEePHrrttts0dOhQnTlzRh988IEmTZqk2NhYDRgwQKNGjdLixYsVGhqqKVOm6KabbtKAAQOq9HiS1LJlSz3yyCN67LHH9PLLL6tDhw764Ycf9Pe//11t2rRR//79q3zfV4KIAYA66N2n/626l3BRWW+uUOeu3SoEjCT17J+s/0qfr7ztn0uSnpk6Qy/NmKL9e79Ty7h4LfrDGwoIDLTng4OD9Ysxz2jq06N0uOCQOtzVWbP+83dXvJbRzzwrPz8//f7lNBUeLlDTyCj9+6M/Hum4t09/PfqrJzV3+iSVlZXp7vt6afQzz+r/LZhbqf197PGnNf0/xmjgfZ116lSJ3t/weaVuf0737t31P//zP3rhhRc0d+5chYWF6Z577rGvX7ZsmZ555hklJSWprKxM99xzj95///0KLxdV1rJly/Tiiy9qwoQJ+v777xUREaHExMTrHjCS5LBu9EcT3iBFRUVyOp3yer0VTrKq7XiLdd3CW6zrlqq+nBTpbi6Hf+Dlb2CIrRvX61eDH9D/7tirMGfF2JGkv/z3G/rtrKla/+W+G7y6a6dt80bVvYTr4tSpU9qzZ49atGih+vXr+1xXmb/ffNgdAAAwEhEDAACMRMQAAIxzV2JXfX7Ac9GXkiRpwOBhRr+UhMsjYgAAgJGIGACoxc5akmRJtfM9HDDUtXpPEREDALXYsVNndbrcknWmrLqXAthOnjwpqeInBFc
|
||
|
"text/plain": [
|
||
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
]
|
||
|
},
|
||
|
"metadata": {},
|
||
|
"output_type": "display_data"
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"home_loan_train[[\"Loan_Status\", \"ApplicantIncome\"]].groupby(\"Loan_Status\").mean().plot(kind=\"bar\")"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 15,
|
||
|
"metadata": {},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"data": {
|
||
|
"text/plain": [
|
||
|
"<seaborn.axisgrid.FacetGrid at 0x13920f1c0>"
|
||
|
]
|
||
|
},
|
||
|
"execution_count": 15,
|
||
|
"metadata": {},
|
||
|
"output_type": "execute_result"
|
||
|
},
|
||
|
{
|
||
|
"data": {
|
||
|
"image/png": "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
|
||
|
"text/plain": [
|
||
|
"<Figure size 605.847x500 with 1 Axes>"
|
||
|
]
|
||
|
},
|
||
|
"metadata": {},
|
||
|
"output_type": "display_data"
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"import seaborn as sns\n",
|
||
|
"sns.set_theme()\n",
|
||
|
"sns.relplot(data=home_loan_train, x=\"LoanAmount\", y=\"ApplicantIncome\", hue=\"Loan_Status\")\n"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 16,
|
||
|
"metadata": {},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"data": {
|
||
|
"text/plain": [
|
||
|
"<seaborn.axisgrid.PairGrid at 0x11f6bdbd0>"
|
||
|
]
|
||
|
},
|
||
|
"execution_count": 16,
|
||
|
"metadata": {},
|
||
|
"output_type": "execute_result"
|
||
|
},
|
||
|
{
|
||
|
"data": {
|
||
|
"image/png": "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
|
||
|
"text/plain": [
|
||
|
"<Figure size 1355.85x1250 with 30 Axes>"
|
||
|
]
|
||
|
},
|
||
|
"metadata": {},
|
||
|
"output_type": "display_data"
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"sns.pairplot(data=home_loan_train.drop(columns=[\"Loan_ID\"]), hue=\"Loan_Status\")"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 17,
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": [
|
||
|
"from sklearn.model_selection import train_test_split\n"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": null,
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": []
|
||
|
}
|
||
|
],
|
||
|
"metadata": {
|
||
|
"kernelspec": {
|
||
|
"display_name": "IUMEnv",
|
||
|
"language": "python",
|
||
|
"name": "python3"
|
||
|
},
|
||
|
"language_info": {
|
||
|
"codemirror_mode": {
|
||
|
"name": "ipython",
|
||
|
"version": 3
|
||
|
},
|
||
|
"file_extension": ".py",
|
||
|
"mimetype": "text/x-python",
|
||
|
"name": "python",
|
||
|
"nbconvert_exporter": "python",
|
||
|
"pygments_lexer": "ipython3",
|
||
|
"version": "3.10.8"
|
||
|
},
|
||
|
"orig_nbformat": 4
|
||
|
},
|
||
|
"nbformat": 4,
|
||
|
"nbformat_minor": 2
|
||
|
}
|