credit_card_default_analysis_part1_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Card Default Analysis - Part 1 EDA\n",
    "\n",
    "#### Default Payments of Credit Card Clients in Taiwan from 2005"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import Data\n",
    "Data source from Kaggle: https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset <br>\n",
    "Original data source: http://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages and modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 25)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read file into dataframe and print out dataframe shapef\n",
    "df = pd.read_csv(\"UCI_Credit_Card.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Understand the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default.payment.next.month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19394.0</td>\n",
       "      <td>19619.0</td>\n",
       "      <td>20024.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>1815.0</td>\n",
       "      <td>657.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>542653.0</td>\n",
       "      <td>483003.0</td>\n",
       "      <td>473944.0</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>20239.0</td>\n",
       "      <td>13750.0</td>\n",
       "      <td>13770.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>221.0</td>\n",
       "      <td>-159.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>581.0</td>\n",
       "      <td>1687.0</td>\n",
       "      <td>1542.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12211.0</td>\n",
       "      <td>11793.0</td>\n",
       "      <td>3719.0</td>\n",
       "      <td>3329.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13007.0</td>\n",
       "      <td>13912.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13007.0</td>\n",
       "      <td>1122.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0   1    20000.0    2          2         1   24      2      2     -1     -1   \n",
       "1   2   120000.0    2          2         2   26     -1      2      0      0   \n",
       "2   3    90000.0    2          2         2   34      0      0      0      0   \n",
       "3   4    50000.0    2          2         1   37      0      0      0      0   \n",
       "4   5    50000.0    1          2         1   57     -1      0     -1      0   \n",
       "5   6    50000.0    1          1         2   37      0      0      0      0   \n",
       "6   7   500000.0    1          1         2   29      0      0      0      0   \n",
       "7   8   100000.0    2          2         2   23      0     -1     -1      0   \n",
       "8   9   140000.0    2          3         1   28      0      0      2      0   \n",
       "9  10    20000.0    1          3         2   35     -2     -2     -2     -2   \n",
       "\n",
       "   ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0  ...        0.0        0.0        0.0       0.0     689.0       0.0   \n",
       "1  ...     3272.0     3455.0     3261.0       0.0    1000.0    1000.0   \n",
       "2  ...    14331.0    14948.0    15549.0    1518.0    1500.0    1000.0   \n",
       "3  ...    28314.0    28959.0    29547.0    2000.0    2019.0    1200.0   \n",
       "4  ...    20940.0    19146.0    19131.0    2000.0   36681.0   10000.0   \n",
       "5  ...    19394.0    19619.0    20024.0    2500.0    1815.0     657.0   \n",
       "6  ...   542653.0   483003.0   473944.0   55000.0   40000.0   38000.0   \n",
       "7  ...      221.0     -159.0      567.0     380.0     601.0       0.0   \n",
       "8  ...    12211.0    11793.0     3719.0    3329.0       0.0     432.0   \n",
       "9  ...        0.0    13007.0    13912.0       0.0       0.0       0.0   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  default.payment.next.month  \n",
       "0       0.0       0.0       0.0                           1  \n",
       "1    1000.0       0.0    2000.0                           1  \n",
       "2    1000.0    1000.0    5000.0                           0  \n",
       "3    1100.0    1069.0    1000.0                           0  \n",
       "4    9000.0     689.0     679.0                           0  \n",
       "5    1000.0    1000.0     800.0                           0  \n",
       "6   20239.0   13750.0   13770.0                           0  \n",
       "7     581.0    1687.0    1542.0                           0  \n",
       "8    1000.0    1000.0    1000.0                           0  \n",
       "9   13007.0    1122.0       0.0                           0  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine the first 10 rows of the data\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1) Description of columns from dataset documentation\n",
    "There are 25 variables: <br>\n",
    "•   ID: ID of each client    <br>\n",
    "•   LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit  <br>\n",
    "•\tSEX: Gender (1=male, 2=female)  <br>\n",
    "•\tEDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown) <br>\n",
    "•\tMARRIAGE: Marital status (1=married, 2=single, 3=others) <br>\n",
    "•\tAGE: Age in years  <br>\n",
    "•\tPAY_0: Repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months, … 8=payment delay for eight months, 9=payment delay for nine months and above)  <br>\n",
    "•\tPAY_2: Repayment status in August, 2005 (scale same as above)  <br>\n",
    "•\tPAY_3: Repayment status in July, 2005 (scale same as above)  <br>\n",
    "•\tPAY_4: Repayment status in June, 2005 (scale same as above)  <br>\n",
    "•\tPAY_5: Repayment status in May, 2005 (scale same as above)  <br>\n",
    "•\tPAY_6: Repayment status in April, 2005 (scale same as above) <br>\n",
    "•\tBILL_AMT1: Amount of bill statement in September, 2005 (NT dollar) <br>\n",
    "•\tBILL_AMT2: Amount of bill statement in August, 2005 (NT dollar) <br>\n",
    "•\tBILL_AMT3: Amount of bill statement in July, 2005 (NT dollar) <br>\n",
    "•\tBILL_AMT4: Amount of bill statement in June, 2005 (NT dollar) <br>\n",
    "•\tBILL_AMT5: Amount of bill statement in May, 2005 (NT dollar) <br>\n",
    "•\tBILL_AMT6: Amount of bill statement in April, 2005 (NT dollar) <br>\n",
    "•\tPAY_AMT1: Amount of previous payment in September, 2005 (NT dollar) <br>\n",
    "•\tPAY_AMT2: Amount of previous payment in August, 2005 (NT dollar) <br>\n",
    "•\tPAY_AMT3: Amount of previous payment in July, 2005 (NT dollar) <br>\n",
    "•\tPAY_AMT4: Amount of previous payment in June, 2005 (NT dollar) <br>\n",
    "•\tPAY_AMT5: Amount of previous payment in May, 2005 (NT dollar) <br>\n",
    "•\tPAY_AMT6: Amount of previous payment in April, 2005 (NT dollar) <br>\n",
    "•\tdefault.payment.next.month: Default payment (1=yes, 0=no) <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0',\n",
       "       'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
       "       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n",
       "       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6',\n",
       "       'default.payment.next.month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Understand the data by printing out all column names\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                1.0\n",
       "LIMIT_BAL                     20000.0\n",
       "SEX                               2.0\n",
       "EDUCATION                         2.0\n",
       "MARRIAGE                          1.0\n",
       "AGE                              24.0\n",
       "PAY_0                             2.0\n",
       "PAY_2                             2.0\n",
       "PAY_3                            -1.0\n",
       "PAY_4                            -1.0\n",
       "PAY_5                            -2.0\n",
       "PAY_6                            -2.0\n",
       "BILL_AMT1                      3913.0\n",
       "BILL_AMT2                      3102.0\n",
       "BILL_AMT3                       689.0\n",
       "BILL_AMT4                         0.0\n",
       "BILL_AMT5                         0.0\n",
       "BILL_AMT6                         0.0\n",
       "PAY_AMT1                          0.0\n",
       "PAY_AMT2                        689.0\n",
       "PAY_AMT3                          0.0\n",
       "PAY_AMT4                          0.0\n",
       "PAY_AMT5                          0.0\n",
       "PAY_AMT6                          0.0\n",
       "default.payment.next.month        1.0\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Understand the data by checking the first row\n",
    "\n",
    "df.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2) What do 0 and -2 mean in PAY_X columns?\n",
    "\n",
    "From dataset documentation: \n",
    "PAY_0: Repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months, … 8=payment delay for eight months, 9=payment delay for nine months and above). Notice code 0 and 2 are in the PAY_X columns but are not included in the data description. Also by doing the value_counts, it shows 0 is the most frequent observation. \n",
    "\n",
    "After searching online, I realized the same question was asked by others, and the answer from the dataset contributor was:\n",
    "\n",
    "#### -2: No consumption; <br>-1: Paid in full; <br>0: The use of revolving credit, meaning the payment wasn't due, which makes sense that most customers were using the revolving credit.\n",
    "\n",
    "https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset/discussion/34608"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0    95919\n",
      "-1    34640\n",
      "-2    24415\n",
      " 2    18964\n",
      " 1     3722\n",
      " 3     1430\n",
      " 4      453\n",
      " 7      218\n",
      " 5      137\n",
      " 6       74\n",
      " 8       28\n",
      "Name: value, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Sort the repayment status codes in PAY_X columns by melting them into 1 column\n",
    "# Notice there are 2 undocumented codes: 0,-2\n",
    "\n",
    "pay = df[['PAY_0','PAY_2','PAY_3','PAY_4','PAY_5','PAY_6']]\n",
    "pay_melt = pd.melt(pay)\n",
    "print(pay_melt['value'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (3) What does 'default.payment.next.month' mean?\n",
    "Is it a predictor or actual data that was collected in the next month - Oct. 2005? According to the reply from the dataset contributor,it seems this is the actual data collected in the next month. \"Y: client's behavior; Y=0 then not default, Y=1 then default\". This information could be used to test the accuracy of the model that will be built."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.7788\n",
       "1    0.2212\n",
       "Name: default.payment.next.month, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the proportion of customers who had default payment in the next month (Oct.2005)? \n",
    "# About 22% customers had default payment next month\n",
    "\n",
    "df['default.payment.next.month'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Cleanning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1) Check missing values, column type and column names\n",
    "There is no missing value, all columns have the correct type, and all columns are named consistently since we have renamed the last inconsistent column name to 'DEF_PAY_NMO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30000 entries, 0 to 29999\n",
      "Data columns (total 25 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   ID                          30000 non-null  int64  \n",
      " 1   LIMIT_BAL                   30000 non-null  float64\n",
      " 2   SEX                         30000 non-null  int64  \n",
      " 3   EDUCATION                   30000 non-null  int64  \n",
      " 4   MARRIAGE                    30000 non-null  int64  \n",
      " 5   AGE                         30000 non-null  int64  \n",
      " 6   PAY_0                       30000 non-null  int64  \n",
      " 7   PAY_2                       30000 non-null  int64  \n",
      " 8   PAY_3                       30000 non-null  int64  \n",
      " 9   PAY_4                       30000 non-null  int64  \n",
      " 10  PAY_5                       30000 non-null  int64  \n",
      " 11  PAY_6                       30000 non-null  int64  \n",
      " 12  BILL_AMT1                   30000 non-null  float64\n",
      " 13  BILL_AMT2                   30000 non-null  float64\n",
      " 14  BILL_AMT3                   30000 non-null  float64\n",
      " 15  BILL_AMT4                   30000 non-null  float64\n",
      " 16  BILL_AMT5                   30000 non-null  float64\n",
      " 17  BILL_AMT6                   30000 non-null  float64\n",
      " 18  PAY_AMT1                    30000 non-null  float64\n",
      " 19  PAY_AMT2                    30000 non-null  float64\n",
      " 20  PAY_AMT3                    30000 non-null  float64\n",
      " 21  PAY_AMT4                    30000 non-null  float64\n",
      " 22  PAY_AMT5                    30000 non-null  float64\n",
      " 23  PAY_AMT6                    30000 non-null  float64\n",
      " 24  default.payment.next.month  30000 non-null  int64  \n",
      "dtypes: float64(13), int64(12)\n",
      "memory usage: 5.7 MB\n"
     ]
    }
   ],
   "source": [
    "# Check column names, columan data type and indentify any missing values\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_1',\n",
       "       'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
       "       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n",
       "       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6',\n",
       "       'DEF_PAY_NMO'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change inconsistent column names \"PAY_0\" to 'PAY_1', 'default.payment.next.month'to 'DEF_PAY_NMO'\n",
    "\n",
    "df.rename(columns={'PAY_0':'PAY_1','default.payment.next.month':'DEF_PAY_NMO'},inplace = True)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2) Check duplicate records\n",
    "There are no duplicate IDs or rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No duplicate IDs\n"
     ]
    }
   ],
   "source": [
    "# Check if there is any duplicate IDs\n",
    "condition = bool(df.duplicated(subset = 'ID').any())\n",
    "\n",
    "if condition:\n",
    "    print('There are duplicate IDs')\n",
    "else:\n",
    "    print('No duplicate IDs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (3) Identify outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hBGNmZkU4wZiZWRGDJhhJiyX9mZKdJW3ZioaZmVlna/TBsX+TdDIwH9gFmAKsIb3Vb2Zm1lCjM5hbgPeTXq68NC8L2E/S9JFWLGmCpLslXZeXZ0paKWm1pCsqZ0qStsrLvXl9V9U+Ts3xhyUdONI2mZnZ6GmUYB4ETsjznwK+mOcPyetG6uQB+zkHOC8iZgEbgBNz/ERgQ0S8Azgvl0PSbGAe8G7gIOBCSRNGoV1mZjYKGiWYC4D1pI+LHQr8QZ7/PLBdg+0Glc+ADgX+JS8L2I80ajPAEtL3aADm5mXy+v1z+bnA0oj4fUSsAXqB3UfSLjMzGz11E0xEzAamkS6LvQgclud/AFw9wnq/Bvw18HJe3g54NiI25eW+XDf5d21u0yZgYy7/SrzGNmZm1mYNnyKLiP8CfgV8Bzg6hz8JXDvcCiUdBqyLiLuqw7WqH2Rdo20G1rlAUo+knv7+/iG118zMhqeZoWJmAuSb7scDP46Ip0dQ517A4ZIOAbYGtiGd0UySNDGfpUwHnszl+4AZQF8e0Xlb0qW7SryiepuBfVgELALo7u6umYTMzGx0Nf2iZUS8EBFLRphciIhTI2J6RHSRbtLfFBHHADcDR+Zi83n1LGlZXiavvykiIsfn5afMZgKzgDtG0jYzMxs9zYym3CqnAEslfRG4G7goxy8CLpXUSzpzmQcQEaskXQk8AGwCToqIl1rfbDMzq6WtCSYibiG9X0NEPEaNp8Ai4nngqDrbfwn4UrkWmpnZcHksMjMzK8IJxszMinCCMTOzIpxgzMysCCcYMzMrwgnGzMyKcIIxM7MinGDMzKwIJxgzMyvCCcbMzIpwgjEzsyKcYMzMrAgnGDMzK8IJxszMinCCMTOzIpxgzMysiLH0RUuzV3QtvL5tdT9+9qFtq9tsPPEZjJmZFeEEY2ZmRTjBmJlZEU4wZmZWhBOMmZkV4QRjZmZFOMGYmVkRTjBmZlZEyxOMpBmSbpb0oKRVkk7O8SmSVkhanX8n57gknS+pV9K9knar2tf8XH61pPmt7ouZmdXXjjOYTcBnI+KPgT2BkyTNBhYCN0bELODGvAxwMDArTwuAb0JKSMDpwB7A7sDplaRkZmbt1/IEExFPRcQv8/xvgQeBacBcYEkutgQ4Is/PBS6J5HZgkqSdgAOBFRGxPiI2ACuAg1rYFTMza6Ct92AkdQG7AiuBHSPiKUhJCNghF5sGrK3arC/H6sVr1bNAUo+knv7+/tHsgpmZ1dG2BCPpLcDVwKci4jeNitaIRYP464MRiyKiOyK6p06dOvTGmpnZkLUlwUh6Aym5XBYR1+Tw0/nSF/l3XY73ATOqNp8OPNkgbmZmY0A7niITcBHwYER8tWrVMqDyJNh84Nqq+LH5abI9gY35EtoNwBxJk/PN/Tk5ZmZmY0A7vgezF/B/gfsk3ZNjfwOcDVwp6UTgCeCovG45cAjQCzwHHA8QEeslnQXcmcudGRHrW9MFMzMbTMsTTET8nNr3TwD2r1E+gJPq7GsxsHj0WmdmZqPFb/KbmVkR/mSy2QDt+lyzP9Vs443PYMzMrAgnGDMzK8IJxszMinCCMTOzIpxgzMysCCcYMzMrwgnGzMyKcIIxM7MinGDMzKwIJxgzMyvCCcbMzIpwgjEzsyKcYMzMrAgnGDMzK8IJxszMinCCMTOzIpxgzMysCCcYMzMrwgnGzMyKcIIxM7MinGDMzKwIJxgzMyui4xOMpIMkPSypV9LCdrfHzMySie1uwEhImgB8AzgA6APulLQsIh5ob8vMhq5r4fVtq/vxsw9tW902fnX6GczuQG9EPBYRLwBLgbltbpOZmdHhZzDANGBt1XIfsEeb2mLWsdp59tQOPmNrjU5PMKoRi9cVkhYAC/Lif0t6eAh1bA88M4y2dbLNsc+wefZ7c+wzOmez7PdI+/z2oW7Q6QmmD5hRtTwdeHJgoYhYBCwaTgWSeiKie3jN60ybY59h8+z35thn2Dz73Y4+d/o9mDuBWZJmStoSmAcsa3ObzMyMDj+DiYhNkj4O3ABMABZHxKo2N8vMzOjwBAMQEcuB5QWrGNaltQ63OfYZNs9+b459hs2z3y3vsyJed0/czMxsxDr9HoyZmY1RTjB1dOIQNJJmSLpZ0oOSVkk6OcenSFohaXX+nZzjknR+7uO9knar2tf8XH61pPlV8fdJui9vc74kNaqjxf2fIOluSdfl5ZmSVuY2XZEfBEHSVnm5N6/vqtrHqTn+sKQDq+I1/z7Uq6NF/Z0k6SpJD+Vj/v7N4VhL+nT++32/pMslbT3ejrWkxZLWSbq/Kta2Y9uojoYiwtOAifTAwKPALsCWwP8DZre7XU20eydgtzz/VuARYDbwj8DCHF8InJPnDwF+RHqfaE9gZY5PAR7Lv5Pz/OS87g7g/XmbHwEH53jNOlrc/88A3wOuy8tXAvPy/LeAj+b5jwHfyvPzgCvy/Ox8rLcCZua/AxMa/X2oV0eL+rsE+Ms8vyUwabwfa9LL1WuAN1b9+R833o41sA+wG3B/Vaxtx7ZeHYP2o1V/MTppyn/wN1Qtnwqc2u52DaMf15LGaXsY2CnHdgIezvPfBo6uKv9wXn808O2q+LdzbCfgoar4K+Xq1dHCvk4HbgT2A67L/yE8A0wceExJTx2+P89PzOU08DhXytX7+9Cojhb0dxvSP7QaEB/Xx5pXR++Yko/ddcCB4/FYA128NsG07djWq2OwPvgSWW21hqCZ1qa2DEu+FLArsBLYMSKeAsi/O+Ri9frZKN5XI06DOlrla8BfAy/n5e2AZyNiU16ubusr/cvrN+byQ/3zaFRHabsA/cB3lS4L/oukNzPOj3VE/CfwFeAJ4CnSsbuL8X2sK9p5bIf1b6ITTG1NDUEzVkl6C3A18KmI+E2jojViMYx4W0k6DFgXEXdVh2sUjUHWddKfx0TSJZRvRsSuwP+QLmnU00l9qyvfE5hLuqz1NuDNwME1io6nYz2YVvRlWP13gqmtqSFoxiJJbyAll8si4pocflrSTnn9TsC6HK/Xz0bx6TXijepohb2AwyU9ThpRez/SGc0kSZV3varb+kr/8vptgfUM/c/jmQZ1lNYH9EXEyrx8FSnhjPdj/efAmojoj4gXgWuADzC+j3VFO4/tsP5NdIKprSOHoMlPglwEPBgRX61atQyoPEEyn3RvphI/Nj8hsiewMZ8W3wDMkTQ5/x/jHNL15qeA30raM9d17IB91aqjuIg4NSKmR0QX6VjdFBHHADcDR9ZoU3Vbj8zlI8fn5SePZgKzSDdDa/59yNvUq6OoiPg1sFbSu3Jof+ABxvmxJl0a21PSm3K7Kv0et8e6SjuPbb06GmvVzblOm0hPTTxCeqLktHa3p8k27006bb0XuCdPh5CuH98IrM6/U3J5kT7Y9ihwH9Bdta8TgN48HV8V7wbuz9tcwKsv69asow1/Bvvy6lNku5D+0egF/g3YKse3zsu9ef0uVduflvv2MPnJmkZ/H+rV0aK+vhfoycf7B6Qnhcb9sQb+Hngot+1S0pNg4+pYA5eT7jG9SDp7OLGdx7ZRHY0mv8lvZmZF+BKZmZkV4QRjZmZFOMGYmVkRTjBmZlaEE4yZmRXhBGMdSdJbJH1NUp+k5yU9Iukjo7DfIyWFpDPy8i15eXtJb5N0hqQjBtnHGXmbIwfEu3L8uiG26TXbSTok19FVp/xxufznhljPvnm7TZL+cGBfJF2c52tNXVX7+VdJs4dSt41PTjDWcfLLYdcBJ5NesvsE6b2EP6tTfiRfbj2TNBjgb0hDk5wONEwwDfTnfZ0zwu0Oye3oqlP+Z7n8D4feRCCNKHxKjfg3835Py8u35uWjgX5JR0h6BjgGWCXpUUnbDLMNNh608gUpT55GYyK9vR3AKmCLqvgW+feWvP7rpCE+jgD+GFhBShS/Aj5dtd2HSS+1PUR6mSyAMwbsa/v8Wz0dV6d9Z+T1Rw6Id+V45UXQi/PyV4Ff5/7sQXqZbyPwhYHbkYamf007atRfKfO5vPw4aayyc0jDpNwJ/EGN7fbN220Efk8azPB1fSG9pBfAxQO2f4T08t7NwEeALwPbtPvvi6f2TT6DsU70vvy7IiIqoydTPZ/tSvo/8UdJQ15Uvo2zEviqpA9J2pE0vM7LpH/oP9ig3oH/5/6zEfaj4r2kN7dn530vBTYAp0vabkDZnwE/yfNn5XY0402kkXGvJyWIv2pQ9mpSkvlsk/uueJk01tc2pG+P/E00HmzVxjknGOtkgw1D8fGIqCSPWaRLXGcBR+X1B5A+nrQ1sDgiFgHnNdhf5R/2NRGxNCLWDLvlr/UPwIV5/o5I48j9O+lS1c7VBXOdq/PiTRGxtMk6XgY+SjpDg/qX1wCeI539LSANHdKsj+dtdyONg3WfpKlD2N7GGScY60Q9+fcASa/8Ha6ezyqjvVaGGr+BlFQq06Ia+641LHlFqXGVniWNOQXpzAHgpfw7YZTa8buIeB6ofM+k1n6rXZDbcGyzFUTETyPiXcBlwBeAd9P8GZaNQyO5+WnWLjeT7o3sCyyXdBVpKPG3UfvSz0Ok/+vfmzSA33OkYd+/T0o6zwPHS1oLfLJBvRvy766SjiZdonumQfljJHXn+VWks5LRUGnHkZLeHBHXj9J+XxERGyVdSONvzLyGpOWk4/L2qjb+brTbZp3DZzDWcSIigA8B5wN/Qrq89H+AX9Ypv4n0kapfAH9Lukz2VuC+iHiaNFJt5cmpWxrU+xjwPeCd+fePBmnqEXmfpwB/0VTnmnMZKWl+jHQpq5TzGFqCWENK0B8gPWhwDfCvo98s6xQeTdlsmCRtRUpU1Z6PiP9uR3vGCkkXk57Ce7zNTbE28xmM2fAdTXpHpXq6oK0tMhtDfAZjNkz5k7LvHhB+MiIeaEd7zMYaJxgzMyvCl8jMzKwIJxgzMyvCCcbMzIpwgjEzsyKcYMzMrIj/D0TNQ2wh80fiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use histogram to view the distribution of credit limit\n",
    "\n",
    "plt.hist(df[\"LIMIT_BAL\"])\n",
    "plt.xlabel('Credit_Limit in NT$', fontweight='bold')\n",
    "plt.ylabel('# of Customers', fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use boxplot to identify any outlier in credit limit\n",
    "\n",
    "df[\"LIMIT_BAL\"].plot(kind=\"box\")\n",
    "plt.xlabel('Credit limit in NT$', fontweight='bold')\n",
    "plt.ylabel('# of Customers', fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_1</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>DEF_PAY_NMO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2197</th>\n",
       "      <td>2198</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>891586.0</td>\n",
       "      <td>927171.0</td>\n",
       "      <td>961664.0</td>\n",
       "      <td>50784.0</td>\n",
       "      <td>50723.0</td>\n",
       "      <td>896040.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>50256.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_1  PAY_2  PAY_3  \\\n",
       "2197  2198  1000000.0    2          1         1   47      0      0      0   \n",
       "\n",
       "      PAY_4  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "2197     -1  ...   891586.0   927171.0   961664.0   50784.0   50723.0   \n",
       "\n",
       "      PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  DEF_PAY_NMO  \n",
       "2197  896040.0   50000.0   50000.0   50256.0            0  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a close look at the outlier\n",
    "# The observations from other columns indicate that this was just a customer who has an excellent payment history\n",
    "# Therefore it should be considered as a valid data\n",
    "\n",
    "outliers = df.loc[df['LIMIT_BAL']>900000]\n",
    "outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (4) Check numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_1</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>DEF_PAY_NMO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>30000.00000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15000.500000</td>\n",
       "      <td>167484.322667</td>\n",
       "      <td>1.603733</td>\n",
       "      <td>1.853133</td>\n",
       "      <td>1.551867</td>\n",
       "      <td>35.485500</td>\n",
       "      <td>-0.016700</td>\n",
       "      <td>-0.133767</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>-0.220667</td>\n",
       "      <td>...</td>\n",
       "      <td>43262.948967</td>\n",
       "      <td>40311.400967</td>\n",
       "      <td>38871.760400</td>\n",
       "      <td>5663.580500</td>\n",
       "      <td>5.921163e+03</td>\n",
       "      <td>5225.68150</td>\n",
       "      <td>4826.076867</td>\n",
       "      <td>4799.387633</td>\n",
       "      <td>5215.502567</td>\n",
       "      <td>0.221200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8660.398374</td>\n",
       "      <td>129747.661567</td>\n",
       "      <td>0.489129</td>\n",
       "      <td>0.790349</td>\n",
       "      <td>0.521970</td>\n",
       "      <td>9.217904</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>1.197186</td>\n",
       "      <td>1.196868</td>\n",
       "      <td>1.169139</td>\n",
       "      <td>...</td>\n",
       "      <td>64332.856134</td>\n",
       "      <td>60797.155770</td>\n",
       "      <td>59554.107537</td>\n",
       "      <td>16563.280354</td>\n",
       "      <td>2.304087e+04</td>\n",
       "      <td>17606.96147</td>\n",
       "      <td>15666.159744</td>\n",
       "      <td>15278.305679</td>\n",
       "      <td>17777.465775</td>\n",
       "      <td>0.415062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7500.750000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2326.750000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.330000e+02</td>\n",
       "      <td>390.00000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>252.500000</td>\n",
       "      <td>117.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15000.500000</td>\n",
       "      <td>140000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19052.000000</td>\n",
       "      <td>18104.500000</td>\n",
       "      <td>17071.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>1800.00000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22500.250000</td>\n",
       "      <td>240000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>54506.000000</td>\n",
       "      <td>50190.500000</td>\n",
       "      <td>49198.250000</td>\n",
       "      <td>5006.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4505.00000</td>\n",
       "      <td>4013.250000</td>\n",
       "      <td>4031.500000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>891586.000000</td>\n",
       "      <td>927171.000000</td>\n",
       "      <td>961664.000000</td>\n",
       "      <td>873552.000000</td>\n",
       "      <td>1.684259e+06</td>\n",
       "      <td>896040.00000</td>\n",
       "      <td>621000.000000</td>\n",
       "      <td>426529.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID       LIMIT_BAL           SEX     EDUCATION      MARRIAGE  \\\n",
       "count  30000.000000    30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean   15000.500000   167484.322667      1.603733      1.853133      1.551867   \n",
       "std     8660.398374   129747.661567      0.489129      0.790349      0.521970   \n",
       "min        1.000000    10000.000000      1.000000      0.000000      0.000000   \n",
       "25%     7500.750000    50000.000000      1.000000      1.000000      1.000000   \n",
       "50%    15000.500000   140000.000000      2.000000      2.000000      2.000000   \n",
       "75%    22500.250000   240000.000000      2.000000      2.000000      2.000000   \n",
       "max    30000.000000  1000000.000000      2.000000      6.000000      3.000000   \n",
       "\n",
       "                AGE         PAY_1         PAY_2         PAY_3         PAY_4  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean      35.485500     -0.016700     -0.133767     -0.166200     -0.220667   \n",
       "std        9.217904      1.123802      1.197186      1.196868      1.169139   \n",
       "min       21.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       28.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%       34.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%       41.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max       79.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "       ...      BILL_AMT4      BILL_AMT5      BILL_AMT6       PAY_AMT1  \\\n",
       "count  ...   30000.000000   30000.000000   30000.000000   30000.000000   \n",
       "mean   ...   43262.948967   40311.400967   38871.760400    5663.580500   \n",
       "std    ...   64332.856134   60797.155770   59554.107537   16563.280354   \n",
       "min    ... -170000.000000  -81334.000000 -339603.000000       0.000000   \n",
       "25%    ...    2326.750000    1763.000000    1256.000000    1000.000000   \n",
       "50%    ...   19052.000000   18104.500000   17071.000000    2100.000000   \n",
       "75%    ...   54506.000000   50190.500000   49198.250000    5006.000000   \n",
       "max    ...  891586.000000  927171.000000  961664.000000  873552.000000   \n",
       "\n",
       "           PAY_AMT2      PAY_AMT3       PAY_AMT4       PAY_AMT5  \\\n",
       "count  3.000000e+04   30000.00000   30000.000000   30000.000000   \n",
       "mean   5.921163e+03    5225.68150    4826.076867    4799.387633   \n",
       "std    2.304087e+04   17606.96147   15666.159744   15278.305679   \n",
       "min    0.000000e+00       0.00000       0.000000       0.000000   \n",
       "25%    8.330000e+02     390.00000     296.000000     252.500000   \n",
       "50%    2.009000e+03    1800.00000    1500.000000    1500.000000   \n",
       "75%    5.000000e+03    4505.00000    4013.250000    4031.500000   \n",
       "max    1.684259e+06  896040.00000  621000.000000  426529.000000   \n",
       "\n",
       "            PAY_AMT6   DEF_PAY_NMO  \n",
       "count   30000.000000  30000.000000  \n",
       "mean     5215.502567      0.221200  \n",
       "std     17777.465775      0.415062  \n",
       "min         0.000000      0.000000  \n",
       "25%       117.750000      0.000000  \n",
       "50%      1500.000000      0.000000  \n",
       "75%      4000.000000      0.000000  \n",
       "max    528666.000000      1.000000  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the statistic summary of the columns\n",
    "# No data is extremly unresonable in these columns\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save processed csv file for part 2 - modeling\n",
    "\n",
    "df.to_csv(\"UCI_Credit_Card_Processed.csv\", index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (5) Check categorical columns\n",
    "\n",
    "Next, we will check these 3 columns: SEX',\t'EDUCATION' and\t'MARRIAGE'.\tThe 'SEX' column looks fine, while 'EDUCATION' and 'MARRIAGE' columns have some undocumented data and 'EDUCATION' column has duplicated values in different codes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    18112\n",
       "1    11888\n",
       "Name: SEX, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get SEX column's distribution. 1: male; 2: female\n",
    "# No undocumented SEX code\n",
    "\n",
    "df[\"SEX\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['F', 'M'], ordered=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change sex code to 'F' and 'M' and use category as datatype\n",
    "\n",
    "df[\"SEX\"]=df[\"SEX\"].map({1:'M',2:'F'}).astype('category')\n",
    "df[\"SEX\"].dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'EDUCATION' column: notice 5 and 6 are both recorded as 'unknown' and there is 0 which isn't explained in the dataset description. Since the amounts are so small, let's combine 0,4,5,6 to 0 which means\"other'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 3, 5, 4, 6, 0], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)\n",
    "\n",
    "df[\"EDUCATION\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14030\n",
       "1    10585\n",
       "3     4917\n",
       "5      280\n",
       "4      123\n",
       "6       51\n",
       "0       14\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the total counts of each EDUCATION level\n",
    "\n",
    "df[\"EDUCATION\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14030\n",
       "1    10585\n",
       "3     4917\n",
       "0      468\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change values 4, 5, 6 to 0 and define 0 as 'others'\n",
    "# 1=graduate school, 2=university, 3=high school, 0=others\n",
    "\n",
    "df[\"EDUCATION\"] = df[\"EDUCATION\"].replace({4:0,5:0,6:0})\n",
    "df[\"EDUCATION\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'MARRIAGE' column: what does 0 mean in 'MARRIAGE'?\n",
    "Since there are only 0.18% (or 54) observations of 0, we will combine 0 and 3 in one value as 'others'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 0], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From dataset description: MARRIAGE: Marital status (1=married, 2=single, 3=others), but there is also 0\n",
    "\n",
    "df[\"MARRIAGE\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.532133\n",
       "1    0.455300\n",
       "3    0.010767\n",
       "0    0.001800\n",
       "Name: MARRIAGE, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many customers had \"MARRIAGE\" status as 0?\n",
    "\n",
    "df[\"MARRIAGE\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.532133\n",
       "1    0.455300\n",
       "3    0.012567\n",
       "Name: MARRIAGE, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine 0 and 3 by changing the value 0 into 3\n",
    "\n",
    "df[\"MARRIAGE\"] = df[\"MARRIAGE\"].replace({0:3})\n",
    "df[\"MARRIAGE\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Trends, Hypotheses and Findings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_1</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>DEF_PAY_NMO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120000.0</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90000.0</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    LIMIT_BAL SEX  EDUCATION  MARRIAGE  AGE  PAY_1  PAY_2  PAY_3  PAY_4  \\\n",
       "ID                                                                        \n",
       "1     20000.0   F          2         1   24      2      2     -1     -1   \n",
       "2    120000.0   F          2         2   26     -1      2      0      0   \n",
       "3     90000.0   F          2         2   34      0      0      0      0   \n",
       "4     50000.0   F          2         1   37      0      0      0      0   \n",
       "5     50000.0   M          2         1   57     -1      0     -1      0   \n",
       "\n",
       "    PAY_5  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "ID         ...                                                                  \n",
       "1      -2  ...        0.0        0.0        0.0       0.0     689.0       0.0   \n",
       "2       0  ...     3272.0     3455.0     3261.0       0.0    1000.0    1000.0   \n",
       "3       0  ...    14331.0    14948.0    15549.0    1518.0    1500.0    1000.0   \n",
       "4       0  ...    28314.0    28959.0    29547.0    2000.0    2019.0    1200.0   \n",
       "5       0  ...    20940.0    19146.0    19131.0    2000.0   36681.0   10000.0   \n",
       "\n",
       "    PAY_AMT4  PAY_AMT5  PAY_AMT6  DEF_PAY_NMO  \n",
       "ID                                             \n",
       "1        0.0       0.0       0.0            1  \n",
       "2     1000.0       0.0    2000.0            1  \n",
       "3     1000.0    1000.0    5000.0            0  \n",
       "4     1100.0    1069.0    1000.0            0  \n",
       "5     9000.0     689.0     679.0            0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set ID as dataframe index\n",
    "\n",
    "df.set_index('ID', inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column \"HAS_DEF\" to indicate customers who have at least 1 deafult payment from PAY_1 to Pay_6\n",
    "# 0 : no default ; 1: has default\n",
    "\n",
    "def_condition = (df.PAY_1 >1) | (df.PAY_2 >1) | (df.PAY_3 >1) | (df.PAY_4 >1) | (df.PAY_5 >1) | (df.PAY_6 >1)\n",
    "df.loc[def_condition, \"HAS_DEF\"] = 1\n",
    "df.loc[df.HAS_DEF.isna(), \"HAS_DEF\"] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (1) On average, which sex group tends to have more delayed payments? Since there are more female than male in the dataset, we will use percentage within each sex group.\n",
    "\n",
    "30% male have default payment while 26% female have default payment, the difference is not significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to plot stacked bar chart since there will be multiple stacked bars charts to plot\n",
    "\n",
    "def create_stacked_bar(column_name):\n",
    "\n",
    "    # Get the percentage of default by each group\n",
    "    default_by_group = pd.crosstab(index=df['HAS_DEF'],columns = df[column_name], normalize = 'columns')\n",
    "    \n",
    "    # Round up to 2 decimal\n",
    "    default_by_group = default_by_group.apply(lambda x: round(x,2))\n",
    "    \n",
    "    labels = default_by_group.columns\n",
    "    list1 = default_by_group.iloc[0].to_list()\n",
    "    list2 = default_by_group.iloc[1].to_list()\n",
    "    \n",
    "    list1_name = \"No default\"\n",
    "    list2_name = \"Has default\"\n",
    "    title = f\"Default by {column_name}\"\n",
    "    xlabel = column_name\n",
    "    ylabel = \"Default percentage\"\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(10, 5))\n",
    "    bar_width = 0.5\n",
    "    \n",
    "    ax1 = ax.bar(labels,list1, bar_width, label = list1_name)\n",
    "    ax2 = ax.bar(labels,list2, bar_width, bottom = list1, label = list2_name)\n",
    "\n",
    "    ax.set_title(title, fontweight = \"bold\")\n",
    "    ax.set_xlabel(xlabel, fontweight = \"bold\")\n",
    "    ax.set_ylabel(ylabel, fontweight = \"bold\")\n",
    "    ax.legend(loc=\"best\")\n",
    "    \n",
    "    plt.xticks(list(range(len(labels))), labels,rotation=90)\n",
    "    plt.yticks(fontsize=9)\n",
    "\n",
    "    for r1, r2 in zip(ax1, ax2):\n",
    "        h1 = r1.get_height()\n",
    "        h2 = r2.get_height()\n",
    "        plt.text(r1.get_x() + r1.get_width() / 2., h1 / 2., f\"{h1:.0%}\", ha=\"center\", va=\"center\", color=\"white\", fontsize=9, fontweight=\"bold\")\n",
    "        plt.text(r2.get_x() + r2.get_width() / 2., h1 + h2 / 2., f\"{h2:.0%}\", ha=\"center\", va=\"center\", color=\"white\", fontsize=9, fontweight=\"bold\")\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a stacked bar chart to visualize default percentage by female and male\n",
    "\n",
    "create_stacked_bar('SEX')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (2) Did customers with higher education have less delayed payment?\n",
    "\n",
    "The data indicates customers with lower education levels default more. Customers with high school and university educational level had higher default percentages than customers with grad school education did. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a stacked bar chart to show default by education level\n",
    "\n",
    "create_stacked_bar('EDUCATION')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (3) Did customers with a high education level get higher credit limits?\n",
    "From the boxplot, we can see that customers with grad school education have the highest 25% percentile,highest median,highest 75th percentile and highest maximum numbers, which proves that customers with higher education levels did get higher credit limits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "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": [
    "# Use boxplot to visualize credit limit grouped by education level\n",
    "\n",
    "data = []\n",
    "for i in [0,1,2,3]:\n",
    "    temp = df.loc[df.EDUCATION == i, \"LIMIT_BAL\"]\n",
    "    data.append(temp)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(data)\n",
    "ax.set_xticklabels([\"others\",\"grad school\", \"university\", \"high school\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (4) Does marital status have anything to do with default risk? Note the credit limit includes the family's total credit.\n",
    "There is no difference of default risk in terms of marital status, although the 'other'marital status group had high default percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a stacked bar chart to visualize default and non-default distribution by marital status \n",
    "\n",
    "create_stacked_bar('MARRIAGE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (5) Do younger people tend to miss the payment deadline?\n",
    "\n",
    "Customers aged between 30-50 had the lowest delayed payment rate, while younger groups (20-30) and older groups (50-70) all had higher delayed payment rates. However, the delayed rate dropped slightly again in customers older than 70 years.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Segment the 'AGE' column to 6 groups\n",
    "\n",
    "bins= [21,30,40,50,60,70,80]\n",
    "labels = ['20-30','30-40','40-50','50-60','60-70','70-80']\n",
    "df['AGE'] = pd.cut(df['AGE'],bins=bins, labels=labels,right=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a stacked bar chart to visualize default and non-default by age\n",
    "\n",
    "create_stacked_bar('AGE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (6) Has the repayment status changed in the 6 month from April 2005 (PAY_6) to September 2005(PAY_0)? \n",
    "There was a huge jump from May,2005 (PAY_5) to July, 2005 (PAY_3) when delayed payment increased significantly, then it peaked at August, 2005 (PAY_2), things started to get better in September, 2005 (PAY_1). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lzMx1fDpvI/vSMunQsDrX9GrGRZ0b2QisRyFpZyrDJi3m51Xb6N6iFi9e2ikikq0lC2PMn2RmZfPdsmRGz1zHb39sJ7p8Ofp3PI6rezWna9PYiGxzL0lUlU/mJvH0l0vJyMrm733bcf2pLUp0E54lC2PMISl70xg/Zz3j5qxn8+6DNIqtzBU9mnL5yU2oU7V03C9QkiTvOcgjny3mu2Vb6dIklpcGdaJN/Wp+h5UnSxbGlHGqytx1Oxk9cx1fL9lMRpZyWus6XNOrGWcfX4+o8sf+9DmTP1Vl8sJNDJ+cwP60LO4+pw1Dz2hJhRL2727DfRhTxr0xI5GXv11JtUpRXNWzGVf1bEarulX9DqvMEBEGdGnEqa3r8MQXCbw0bQVTF2/mpUGdw/IUwHCwMwtjSrmlm/Zw0b9/oW+H+rx8WWdiou03ot++XryZx75IYFdqOref1Zo7z2pdJM8WP1YFnVn4H50xJmwysrK5f+JCYmOieXZgR0sUJUS/jg2Yfs8ZXNS5ISO+X8Vf/vULCzfs8jusAlmyMKYUe/vHP0jYtIdnBpbOIbUjWc0q0bxyeRdGXRfH7gMZXPyfX3nu62UczMjyO7Q8WbIwppRasWUvr3+/igs6NeD8E8vOUNqR5uzj6/PtvWdw+clNePvH1fR//Wfi15a8J0dbsjCmFMp0zU/VKlXgqYs6+B2OCaJ6pQo8d0knxt7Yg/SsbC57eybDJyeQmp7pd2iHWLIwphR65+c1LErazZMXdaC23TsRMU5rU4dp/3cG1/Rsxvu/reW8137it8RtfocFhDlZiMg9IpIgIktE5CMRqSQiLURktoisEpGPRSTa1a3ophPd/OYB6xnmyleIyHnhjNmYSJe4dR+vfreS8zrU58JO1vwUaapUjOLJAScy4ZZelBfhindn8/Bni9l7MMPXuMKWLESkEfA3IE5VTwTKA4OBF4BXVbUNsBO40S1yI7BTVVsDr7p6iEh7t1wH4HzgPyJig9UYk4esbOX+iQuJiS7P0wNPtCE7Ilj3FrX4+u4zGHpGS8bPWU/fV39ixoqtvsUT7maoKKCyiEQBMcBm4Gxgopv/ATDQfR7gpnHz+4j3lz4AGK+qaaq6BkgEuoc5bmMi0nu/rmH++l0M/0sH6lWL3IfwGE/l6PI83P8EPr3tFKpWjOL6937nvgkL2ZWaXuyxhC1ZqOpG4GVgPV6S2A3MBXapak6vTRLQyH1uBGxwy2a6+rUDy/NY5hARGSoi8SISn5KSUvQ7ZEwJt2bbfl6atoJzTqjHgC4N/Q7HFKGTmtbky7+dxl1nt+bzBRs599WfmJawpVhjCGczVE28s4IWQEOgCtAvj6o5t5Dndb6sBZQfWaA6UlXjVDWubt26Rxe0MREqO1t5cOIiKkaV49mLO1rzUylUMao89/Vtxxd3nEqdqhW5Zcxc7hw3j+370opl++FshjoHWKOqKaqaAUwCTgFiXbMUQGNgk/ucBDQBcPNrADsCy/NYxhgDjJ65ljlrd/DYhe2pX92an0qzExvVYPKdp3LfuW2ZlrCFc1/9iSkLNxHuoZvCmSzWAz1FJMb1PfQBlgIzgEGuzrXAF+7zZDeNm/8/9fZ+MjDYXS3VAmgDzAlj3MZElPXbU3nhmxX0bleXQd0a+x2OKQYVypfjrj5t+Opvp9OkZmXu+mg+t4yZy9Y9B8O2zUIlCxGpIiLHhVJXVWfjdVTPAxa7bY0EHgTuFZFEvD6J/7pF/gvUduX3Ag+59SQAE/ASzTfAHapaMu+HN6aYZWcrD366iPLlhH9Y81OZ07Z+NT697RQe7n88P65M4ZxXfmTi3KSwnGUEHXVWREYD24E38DqoqwIPq+oLRR5NEbFRZ01ZMXbWOh79fAnPXdKRId2b+h2O8dHqlH08+OkiosqV48ObehzVc7+P6eFHIpIMPAVUB27G60Noqaol9nzXkoUpC5J2pnLeqz9xUtOajLmxu51VGLKzlb0HM6kRU+Golj/WIcqrA2nAScDHeE1JdY4qEmNMkVBVhk1ajALPXWLNT8ZTrpwcdaIIJpTB7ROBfwKV8Tqe2wDJYYnGGBOSCfEb+HnVNp4e0IEmtWL8DseUAaGcWdwO/ILXZ/El3p3Y74QzKGNM/jbvPsAzXy6jZ8taXNmjmd/hmDIi6JmFqv4M/BxQ9HT4wjHGFERVeXjSYjKzlRcu7XRUnZjGHI2gZxYiMlBElonIARFJd6/iuWXQGHOESfM2MmNFCvef145mtav4HY4pQ0LpsxgJxALrgJLzJA5jypitew7y5JQE4prV5LpTmvsdjiljQkkWO4BnVHVEuIMxxuRNVXn4syWkZWbz4iBrfjLFL99kISIXuY9fAjeLyG68508AqKpOCXdwxhjP5IWb+G5ZMg/3P56Wdav6HY4pgwo6s/icI0eEHRXwWfEeZmSMCbOUvWk8MTmBLk1iufG0ln6HY8qogpLFP8hjKHBjTPF6/IslpKZn8fJlnShvzU/GJ/kmC1V9FEBErgB+VtUNbroW0Lp4wjOmbPtq0Wa+XrKFB85vR+t61fwOx5RhodyUNwboGTDdF5gZnnCMMTm270vj8S+W0KlxDYaebs1Pxl8FdXBfCFyE10dxi4ic62Z1A8I3aLoxBoDhU5ay52AG4wb1JKp8OB89Y0xwBfVZdANuwuu3ONu9cowPZ1DGlHXTErYwZeEm7j23Le2Os+Yn47+CksW7wDTgN+Ax4Du8xLFTVVcVQ2zGlEm7UtN55LMltG9Qndt6t/I7HGOAgju4NwIbRaQCkA3Uw10uKyINVdWeg21MGDw1ZSm7UtP54IaTqWDNT6aECOUv8RZgD7AJ2OBe68MZlDFl1f+WJzNp/kZu792KDg1r+B2OMYeEMtzHM3hjQv0PGxvKmLDZfSCDYZMW065+Ne48u43f4RhzhFCSxXrgbVV9M9zBGFOWPfvVUrbtS+eda+KIjrLmJ1OyhJIs5gGPiEg9jhwb6l8FLSQi7fAew5qjJfA43gi2NwMprvxhVZ3qlhkG3AhkAX9T1Wmu/Hzgdbw+k3dV9fkQ4jYmYvy4MoUJ8Unc1rsVnRrH+h2OMX8iqgWP6CEi2XkUq6qGPDaUiJQHNgI9gOuBfar6cq467YGPgO5AQ7yrr9q62SuBc4Ek4HdgiKouzW97cXFxGh8fH2p4xvhq78EMznv1J2IqRvHlXadRqYINu2b8ISJzVTUur3mhnFncXAQx9AH+UNV1BTxYfgAwXlXTgDUikoiXOAASVXU1gIiMd3XzTRbGRJLnvl7Olj0HmXjbKZYoTIkVymNV/ysiNTh84J6tqnsKuZ3BeGcNOe4UkWuAeOA+Vd0JNAJmBdRJcmXgXYEVWN4j9wZEZCgwFKBp06aFDM8Yf/yWuI1xs9dz8+kt6Nq0pt/hGJOvUB6r2gtYBXzjXqtE5E8H6wKWj8YbNuQTV/Qm0AroAmwG/plTNY/FtYDyIwtUR6pqnKrG1a1bN9TwjPHN/rRMHvh0ES3qVOG+vu38DseYAoVyycWreDflvQS87D6/Woht9APmqWoygKomq2qWqmYD73D4jCUJaBKwXGO8ezvyKzcmor34zXI27jrAi4M6WfOTKfFC6bPogNdUNBJARP7g8NlAKIYQ0AQlIg1UdbObvBhY4j5PBsaJyCt4HdxtgDl4ZxZtRKQFXif5YOCKQmzfmBJn1urtfDBzHdef2pyTm9fyOxxjggolWWwGrhSR5W76ClcWlIjE4F3FdEtA8Ysi0gWvKWltzjxVTRCRCXgd15nAHaqa5dZzJ944VeWBUaqaEMr2jSmJDqRn8eCni2haK4b7z7PmJxMZQkkWrwD/AWa4aQFuDWXlqpoK1M5VdnUB9Z8Fns2jfCowNZRtGlPSvTRtBeu2p/LRzT2JiQ7lK2iM/0K5GuotEVmN1/cA8JWqfhfesIwpneLX7uC939Zwdc9m9GpVO/gCxpQQof6smYXXX5Az6mwnVV0UtqiMKYUOZmTxwMRFNKxRmYf6He93OMYUStBkISIPAU/mUdcu3zCmEF6dvpLV2/Yz9sYeVKlozU8msoTyF/sQkAz8gjdmkzGmkOav38k7P69mSPcmnNamjt/hGFNooSSLVcBYVX093MEYUxqlZXrNT/WrV2JY/xP8DseYoxJKsrgDmCEif8F7CBJ4AwleGr6wjCk9Rny/ilVb9/H+9SdTvVIFv8Mx5qiEkizeBSoDZweUFTxUrTEGgMVJu3nrx9UM6taY3u3q+R2OMUctlGRRDxgBvAVkhDccY0qP9Mxs7p+4kNpVonnsgvZ+h2PMMQklWYwFOgJ1gd3hDceY0uONGYks37KXd6+Jo0aMNT+ZyBZKsrgXr9npnFzldumsMflYumkPb8xIZGCXhpzTvr7f4RhzzEJJFuOwPgpjQpaR5TU/xcZE88RfOvgdjjFFIpThPq4qjkCMKS3e/vEPEjbt4a2rulKzSrTf4RhTJEJ5noUxJkQrk/cy4vtELujUgPNPbOB3OMYUGUsWxhSRzKxs7v9kIVUrRfHURdb8ZEoXG6DGmCLy7i9rWJi0m39fcRK1q1b0OxxjilS+yUJEGha0oKrao02NcRK37uOV6Ss5v8NxXNDRmp9M6VPQmUUS+V8FpUGWNabMyMpW7p+4kJjo8jw98ERExO+QjClyBR3wv8VLCtWAnhx+HvbJwMzwh2ZMZHjv1zXMX7+L1y7vQt1q1vxkSqd8O7hV9XxV7QekAA+p6imq2gsYBmwvrgCNKcnWbNvPS9NWcM4J9RjQpcCWW2MiWihNSb2BNBFpzOEzizPDGZQxkSA7W3lw4iIqRpXj2Ys7WvOTKdVCuXR2KvBXYB2wFhgEfBVsIRFpJyILAl57ROT/RKSWiEwXkVXuvaarLyIyQkQSRWSRiHQNWNe1rv4qEbn2qPbUmCI2euZa5qzdwWMXtqd+9Up+h2NMWIVyZnEDMI/DZxMzgP8EW0hVVwBdAESkPN4zvD/De/Le96r6vHtk60PAg0A/oI179QDeBHqISC3gCSAOrw9lrohMVtWdoe6kMUVt/fZUXvhmBb3b1WVQt8Z+h2NM2IUy3EeaiLwOTAPWq+qeYMvkoQ/wh6quE5EBeE1bAB8AP+AliwHAaFVVYJaIxIpIA1d3uqruABCR6cD5wEdHEYcxxyw7W3nw00WULyf8w5qfTBkRtBlKRDoDicACoLuILBGRNwu5ncEcPrjXV9XNAO4954kwjYANAcskubL8ynPHOVRE4kUkPiUlpZDhGRO6cXPWM3P1dh654AQaxlb2OxxjikUofRZvAOl4ndvZwHi8X/YhEZFo4CLgk2BV8yjTAsqPLFAdqapxqhpXt27dUMMzplCSdqby3NRlnNa6DoNPbuJ3OMYUm1CSRWdgVMD0RqAwA/T3A+aparKbTnbNS7j3ra48CQj89jUGNhVQbkyxUlWGTVqMAs9dYs1PpmwJJVlsAk53nzsAQ/GuigrVEI7sX5gM5FzRdC3wRUD5Ne6qqJ7AbtdMNQ3oKyI13ZVTfV2ZMcVqQvwGfl61jWH9jqdJrRi/wzGmWIVyNdRLwEj3+TW8ZqEbQ1m5iMQA5wK3BBQ/D0wQkRuB9cBlrnwq0B+vfyQVuB5AVXeIyNPA767eUzmd3cYUl827D/DMl8vo2bIWV/Zo5nc4xhS7UK6GeldEVgMX4iWKL1X1+1BWrqqpQO1cZdvxro7KXVeBO/JZzyiObAozptioKg9PWkxmtvLCpZ0oV86an0zZEzRZiMho4ANVvbcY4jGmxJk0byMzVqTw+IXtaVa7it/hGOOLUPosrgS+FZENIvIPETkh3EEZU1Js3XOQJ6ckcHLzmlx3SnO/wzHGN6EkiwZ4zUPLgL8DS0RkjruvwYYpN6WWqvLI50tIy8zmxUGdrfnJlGmh9FlsFZGReJfMVsAb9qObe/UDLg5rhMb4ZPLCTUxfmswj/U+gRR1rfjJlWyh3cL+Id6/D53iXzr4EtAbuxLt6yZhSJ2VvGsMnJ3BS01huOK2F3+EY47tQmpH+jvewoweACaqaDiAiXwI2gL8plZ6YvIT96Vm8NKgT5a35yZiQkkUXVV2Uu1BVNwCPFX1Ixvjrq0Wbmbp4Cw+c347W9ar5HY4xJUIoySJRRO4HOgI5g/arql4evs4F1kMAABk5SURBVLCM8cf2fWk8/sUSOjWuwdDTW/odjjElRijJ4h28ITsCB/X700B+xpQGw6csZc/BDMYN6klU+VAuFjSmbAjl29CXww87ugKYBDwatoiM8cm0hC1MWbiJu85uQ7vjrPnJmEChJIuqeM+yEKAKXmf3beEMypjitis1nUc+W0L7BtW5rXcrv8MxpsQJpRlqI16SWAO8hZdgksIZlDHF7akpS9mVms4HN5xMBWt+MuZPQvlW3A7MwRuafCEwH++53MaUCv9bnsyk+Ru5vXcrOjSs4Xc4xpRIodzB/W3AZFwYYzGm2O0+kMGwSYtpV78ad57dxu9wjCmx8k0WIlLQMyNUVWsXMN+YiPDsV0vZti+dd66JIzrKmp+MyU9BZxap2CWyphT7cWUKE+KTuK13Kzo1jvU7HGNKtHyThao2Ls5AjClOew9mMOzTRbSuV5W7+1jzkzHBhHTe7YYj/0REuorI/SLSO8xxGRNWz329nC17DvLioE5UqlDe73CMKfFCGXX2GbxLZi8BYvGG/XgqzHEZEzYzVmxl3Oz13HR6S7o2rel3OMZEhFDOLK7HG/Ijxy94CcOYiLN2237u/mg+xx9XjXvPbet3OMZEjFCSRWWOvAnvOCAzlJWLSKyITBSR5SKyTER6ichwEdkoIgvcq39A/WEikigiK0TkvIDy811Zoog8FOrOGRNof1omQ8fEU66c8M41cdb8ZEwhhHIH94/Ave7zC0Bn4IsQ1/868I2qDhKRaCAGOA94VVVfDqwoIu2BwXgPWGoIfCciOT/93gDOxUtav4vIZFVdGmIMxqCq/P2ThSRu3cfoG3rQpFaM3yEZE1FCObO4C1iCNzZUN2AWcE+whUSkOnAG8F8AVU1X1V0FLDIAGK+qaaq6BkgEurtXoqqudg9eGu/qGhOy//zwB18v2cKwfidwWps6fodjTMQJmixUNUlVT8fr3I5V1TNUNZSxoVoCKcB7IjJfRN4VkZwHGd8pIotEZJSI5PQwNgI2BCyf5MryKz+Cu2IrXkTiU1JSQgjPlBUzlm/l5W9XMKBLQ2463R6RaszRKOgO7pH5lIN3B/ctIay7K3CXqs4WkdeBh4B/A0/j3fD3NPBPvLGm8np2pZJ3QvvTzYKqOhIYCRAXF2c3ExoA1mzbz9/Gz+eE46rz/CWdcv5+jTGFVFCfxU0BnwMffJQzHSxZJAFJqjrbTU8EHlLV5JwKIvIO8GVA/SYByzcGNrnP+ZUbk699aZkMHR1PVDnh7au7UTnaOrSNOVoFNUP1cq9XgN+A84H+wK94ZwMFUtUtwAYRaeeK+gBLRaRBQLWL8fpDACYDg0Wkooi0ANrgjXb7O9BGRFq4TvLBrq4x+crOVu6bsIDV2/bzxhVdrUPbmGNU0HAfswFEZBzwcs7os+5APgx4IIT13wV86A7yq/Hu2RghIl3wzk7W4s5QVDVBRCYAS/Euzb1DVbPcNu8EpgHlgVGqmlD4XTVlyRszEpmWkMxjF7bnlNbWoW3MsRLVgpv3RWQtUB3vcaqCdzawS1VL7NPs4+LiND4+3u8wjE++X5bMTaPjGdilEa/8tbP1UxgTIhGZq6p5PooilEtnbwYy8DqhrwfS8R6EZEyJ80fKPv5v/AI6NKzOc5d0tERhTBEJ5eFH00WkKd7NcgAJqpoW3rCMKby9BzMYOjqe6KhyvH213aFtTFEK5Q5uXHKYF+ZYjDlq2dnKPR8vZO32VMbe2INGsZX9DsmYUsUeDWZKhRH/W8V3y5J59IIT6NXKHuJoTFGzZGEi3vSlybz23Sou7dqY605p7nc4xpRKlixMREvcuo97Pl5Ap8Y1ePbiE61D25gwKWi4jx0FLKeqauf6xld7XId2pQrleOuqbtahbUwYFdTBnUoeYzAZUxJkZyv3jF/A+h2pfHhTDxpah7YxYVXQHdyNizMQYwrjte9W8v3yrTw1oAM9WtpJrjHhFtKlsyJyMt6jVCu5IlXVN8MWlTEF+GbJFkb8L5HLujXm6p7N/A7HmDIhaLIQkUeAp/KYZcnCFLtVyXu5b8ICOjeJ5emB1qFtTHEJ5WqoW4Bv3ecXgQS8kWiNKVa7D2QwdMxcKkdH8bZ1aBtTrEJJFvU5PCR4PN7Di84LW0TG5CErW7l7/Hw27Ejlzau6clyNSsEXMsYUmVD6LLYD2cBuvDOLSoBdemKK1SvTV/DDihSeGXgiJzev5Xc4xpQ5oZxZjAD2AM8AzfHONJ4LY0zGHOHrxZt5Y8YfDD65CVf2aOp3OMaUSaGcWfwELFfVHe4xqMLhq6KMCasVW/Zy3ycLOalpLE8O6GAd2sb4JJQzi5/xHomKqu4F+gGbwxmUMQC7UzMYOiaeKhWjeOuqblSMsg5tY/xS0HAfpwFn4J1JXCIibdys3ngPQzImbLKylbvGz2fTrgOMH9qT+tXtZNYYPxXUDNUHeAJvyI/L3SvH/8IZlDEvf7uCn1am8NwlHenWzDq0jfFbQcliIrACGIfXyT0TL3HsBH4Mf2imrPpy0Sbe/OEPrujRlCHdrUPbmJIg3z4LVU1Q1fFAG+AlYAdeopitqumhrFxEYkVkoogsF5FlItJLRGqJyHQRWeXea7q6IiIjRCRRRBaJSNeA9Vzr6q8SkWuPaY9NibZs8x7u/2QR3ZrVZPhfOgRfwBhTLELp4K4HzAe+ca9VItIjxPW/DnyjqscDnYFlwEPA96raBvjeTYPXcd7GvYbihhMRkVp4zWE9gO7AEzkJxpQuu1LTGTomnuqVo3jzyq5ER9njVowpKUL5Nr6Kd1PeS8DL7vOrwRYSkep4HeT/BVDVdFXdBQwAPnDVPgAGus8DgNHqmQXEikgDvLvFp6vqDlXdCUwHzg9x/0yEyMzK5q6P5pO8O423rupGPevQNqZECeU+iw7Afao6EkBE/gD+GcJyLYEU4D0R6QzMBe4G6qvqZgBV3Swi9Vz9RsCGgOWTXFl+5UcQkaF4ZyQ0bWrt3JHmpWkr+HnVNl64tCMnNbUTR2NKmlDOLDYDV4rIGSJyBnAFod1nEQV0Bd5U1ZOA/RxucspLXndbaQHlRxaojlTVOFWNq1u3bgjhmZJi8sJNvP3Taq7u2YzLT7ZEb0xJFEqyeAU4HZjhXmfgNUcFkwQkqepsNz0RL3kku+Yl3PvWgPpNApZvDGwqoNyUAgmbdvPAxIWc3Lwmj13Y3u9wjDH5CJosVPUtvD6CEe7VN6dJKshyW4ANItLOFfUBluKNYJtzRdO1wBfu82TgGndVVE9gt2uumgb0FZGarmO7ryszEW7n/nRuGTOX2MrR/OfKbtahbUwJVtAd3KuBu1T1K1X9lsPPtCiMu4APRSQaWA1cj5egJojIjcB64DJXdyrQH0jEe/739QBuTKqngd9dvadUdcdRxGJKkMysbO78aB5b96bxyS29qFutot8hGWMKUFAHd3OgyrGsXFUXAHF5zOqTR10F7shnPaOAUccSiylZnv96Ob8mbuelQZ3o3CTW73CMMUEEuxrqTBHJ8xpGVR0dhnhMGfDFgo28+8saru3VjMvimgRfwBjju2DJ4lb3CiR4VyNZsjCFtmTjbh6YuIjuLWrxqHVoGxMxgiWLccCC4gjElH7b96Vxy5i51K4SzX+u7EqF8tahbUykCJYspqjqhGKJxJRqmVnZ3DluPin70ph4ay/qVLUObWMiSUE/7dbh3UhnzDH7x9TlzFy9necu7kinxtahbUykyffMQlVbFGcgpvSaNC+JUb+u4fpTm3Npt8Z+h2OMOQrWaGzCanHSboZNWkzPlrV4uP8JfodjjDlKlixM2Gzbl8YtY+KpU7Uib1xhHdrGRLJQRp01ptAysrK5/cN5bN+fzqe3nUJt69A2JqJZsjBh8exXy5izZgevXd6FExvV8DscY8wxsnYBU+Q+id/A+7+t5abTWjDwpD89esQYE4EsWZgitXDDLh75fAmntq7NQ/2O9zscY0wRsWRhikzKXu8O7bpVK/KvIV2Jsg5tY0oN67MwRSI9M5vbP5zLrgNeh3atKtF+h2SMKUKWLEyRePrLpfy+dicjhpxEh4bWoW1MaWPtBOaYffz7esbMWsfQM1pyUeeGfodjjAkDSxbmmMxbv5PHPk/g9DZ1eOC8dsEXMMZEJEsW5qht3XuQ28bOpX6NivxryEnWoW1MKWZ9FuaopGdmc9vYeew5kMmk208hNsY6tI0pzSxZmKMyfEoCc9ft5N9XnMQJDar7HY4xJszC2m4gImtFZLGILBCReFc2XEQ2urIFItI/oP4wEUkUkRUicl5A+fmuLFFEHgpnzCa4j+asZ9zs9dx6Zisu7GQd2saUBcVxZnGWqm7LVfaqqr4cWCAi7YHBQAegIfCdiLR1s98AzgWSgN9FZLKqLg1z3CYPc9ft4PEvlnBG27rcbx3axpQZJakZagAwXlXTgDUikgh0d/MSVXU1gIiMd3UtWRSz5D0HuXXsPBrGVmbE4C6ULyd+h2SMKSbhvnxFgW9FZK6IDA0ov1NEFonIKBGp6coaARsC6iS5svzKjyAiQ0UkXkTiU1JSinYvDGmZWdw6di770zIZeXWcdWgbU8aEO1mcqqpdgX7AHSJyBvAm0AroAmwG/unq5vUzVQsoP7JAdaSqxqlqXN26dYskeONRVZ74IoH563fx8mWdaXdcNb9DMsYUs7AmC1Xd5N63Ap8B3VU1WVWzVDUbeIfDTU1JQJOAxRsDmwooN8Xkw9nrGf/7Bu44qxX9OzbwOxxjjA/ClixEpIqIVMv5DPQFlohI4NHmYmCJ+zwZGCwiFUWkBdAGmAP8DrQRkRYiEo3XCT45XHGbI8Wv3cGTUxLo3a4u955rHdrGlFXh7OCuD3wmIjnbGaeq34jIGBHpgteUtBa4BUBVE0RkAl7HdSZwh6pmAYjIncA0oDwwSlUTwhi3cbbs9jq0G8VW5vXBJ1mHtjFlmKj+qfk/4sXFxWl8fLzfYUS0gxlZXD5yFonJe/nsjlNpW9/6KYwp7URkrqrG5TWvJF06a0oIVeXxL5awcMMu3rqqmyUKY4wNJGj+bOysdUyIT+Kus1tz/onH+R2OMaYEsGRhjjB79XaenLKUPsfX455z2gZfwBhTJliyMIds2nWAO8bNo2mtGF4d3IVy1qFtjHGsz8IAXof2rWPncjAjm/FDu1G9UgW/QzLGlCCWLAyqyiOfLWFR0m5GXt2N1vWsQ9sYcyRrhjJ88NtaPp2XxN192tC3g3VoG2P+zM4sSglVJTU9i10HMtidmsGuA+nsOZDBrtQMdh/I8MoD5u0OmLf3YCbnnFCfu/u08Xs3jDEllCWLEiY9M9s7qB/IYHeug3rOe85rV2o6uw5kHEoKmdn532BZobxQo3KFQ6961SrRtl41qleuwHE1KnFVz2bWoW2MyZclizDIzlb2pmXm+mV/+MD/5/JMdrsDf2p6VoHrrlYpitgY74AfWzmaBjUqUyOmArEuCeTMq1E5+ojpmOjyuKFXjDGm0CxZFOBgRlauX/bpuX7Z527iOTy/gB/5VIwqd8QBv1FsZTo0rO6mK1AjZ15M9OGyyhWoXrmCjc9kjPGFJYsA2/alccU7sw4lgrTM7HzrlhMON+u4g3qzWjG5ft3nOui78koVyhfjXhljzLGzZBGgSnQULetUPXRgrx5wgI8NaNapXrkC1SpGWRu/MabMsGQRoHJ0ed66upvfYRhjTIlj91kYY4wJypKFMcaYoCxZGGOMCcqShTHGmKAsWRhjjAnKkoUxxpigLFkYY4wJypKFMcaYoES1gEGMIpSIpADrjmEVdYBtRRROpChr+1zW9hdsn8uKY9nnZqpaN68ZpTJZHCsRiVfVOL/jKE5lbZ/L2v6C7XNZEa59tmYoY4wxQVmyMMYYE5Qli7yN9DsAH5S1fS5r+wu2z2VFWPbZ+iyMMcYEZWcWxhhjgrJkYYwxJqgymyxE5GIRURE53u9Ywsnt45iA6SgRSRGRL/2Mq7iIyD6/Y/BLsH0XkR9EJKIvKy0r3+NAIvKIiCSIyCIRWSAiPYpju2U2WQBDgF+AwYVZSEQi7QHa+4ETRaSymz4X2OhjPMYUpaP6HkcqEekFXAh0VdVOwDnAhuLYdplMFiJSFTgVuBH3RyYivUXkJxH5TESWishbIlLOzdsnIk+JyGygl3+RH7WvgQvc5yHARzkzRKS7iPwmIvPdeztX/rOIdAmo96uIdCrWqIuI+7/9MmD63yJynfu8VkSeFJF5IrK4tP1CLWjfI10B3+P8/q/7i8hyEflFREZE6Nl1A2CbqqYBqOo2Vd0kIt1E5EcRmSsi00SkARw6e3zNfbeXiEj3o91wmUwWwEDgG1VdCewQka6uvDtwH9ARaAVc4sqrAEtUtYeq/lLs0R678cBgEakEdAJmB8xbDpyhqicBjwP/cOXvAtcBiEhboKKqLiq2iIvXNlXtCrwJ/N3vYEzI8vse/4n7238b6KeqpwF5DmkRAb4FmojIShH5j4icKSIVgH8Bg1S1GzAKeDZgmSqqegpwu5t3VMpqshiCdwDFvQ9xn+eo6mpVzcL79X2aK88CPi3eEIuOO8g3x9vPqblm1wA+EZElwKtAB1f+CXCh+0O8AXi/WIL1xyT3Phfv38lEhvy+x3k5Hlitqmvc9EcF1C2xVHUf0A0YCqQAHwO3ACcC00VkAfAo0DhgsY/csj8B1UUk9mi2HXUMcUckEakNnI3Xjq9AeUDxDqK5bzrJmT7oEkgkmwy8DPQGageUPw3MUNWLRaQ58AOAqqaKyHRgAPBXIJI7QjM58odRpVzz09x7FqXvOxFs3yNSAd/jyeS9v1K8EYaPOxb9APwgIouBO4AEVc2viTy/41qhlMUzi0HAaFVtpqrNVbUJsAbvLKK7iLRwfRWX43WclRajgKdUdXGu8hoc7vC+Lte8d4ERwO+quiO84YXVOqC9iFQUkRpAH78DKkaldd/z+x5D3vu7HGjpfhCB9/2OOCLSTkTaBBR1AZYBdV3nNyJSQUQ6BNS53JWfBuxW1d1Hs+3S9isqFEOA53OVfQrcBsx08zoCPwGfFW9o4aOqScDrecx6EfhARO4F/pdrmbkisgd4rxhCLHIiEgWkqeoGEZkALAJWAfP9jSz8ysC+5/c9vgL40/6q6gERuR34RkS2AXOKMdaiVBX4l2tKygQS8ZqkRgIjXIKMAl4DEtwyO0XkN6A6XpPyUbHhPhwR6Q38XVUv9DuWkkJEGuKd7h6vqtk+h1NoItIZeEdVj/oKkEhVlvc9PyJSVVX3iYgAbwCrVPVVv+MKJxH5Ae+4Fn+s6yqLzVAmBCJyDd5VU49EaKK4Fa9j71G/YyluZXnfg7jZdQAn4DW/vu1zPBHFziyMMcYEZWcWxhhjgrJkYYwxJihLFsYYY4KyZGEinog0dyOPqog8GlA+Kqf8GNffXkSGuyvmcsred+s+qpsVA2Iu1PhEIlJLRKaKSLKIpIrITBHpFjD/VPFGI01z4111DZg3UEQSReSgGzOoRa5YAl+vHc1+mdLLkoUpba4XTxXgsiJaZ3vgCby734tKCt69Ai8UcrnqQCO8ewxeAHoAE+HQ+EefAtWAe4D6wEQRKS8ix+ENibEHuB9vyIgPcq37LRfTkDzmmbJOVe1lr4h+4Y3npMAf7v0svJuPDgJJ3p+5gjfkwyN4dzXvBWYAHdy84W7ZfwMr8Q7mlwWsO/DVG2+sLMUbsG093jDRp7t1DcS7ISwN2AT8s4CYv3TTOet7ycV8aH25losCygVMz3XLxQAXu8/3u3lPuek+eMlDgcvcvNFuulVALDcCMX7/f9qrZL7szMKUJsvw7g25wb0+B3YFzL8eeAbv7t5HgJOBL9xgiTnOwbthqwber/cUvLthwfvVPgRYGlD/LLy7ZxvjJRyAJ4HKeKN8voz3TJFQnYr3Cz9wfYeoaqa6+15EpBneAHlzVTUVaOGq5QzfkuTeWwaZl+MdYL94D9bpWYiYTRlgycKUNqPwzghO5c/DlPR37/eq6gjgC7xf1m0D6ryiqq8Dq4Hmqrof+NXNW6Kq41V1a0D94ar6DN5ZRHNXtgqohfeLvhxeMglVXuv7E9esNNXVuza/au49rz6bwHn78ZrZBuIN0d4W+LAQMZsywJKFKW3G440emwRMz6dOQR3eOQMmBo7WGmr9nKcoXonXpLMZr39gVsEhB13fEQKGYWkA9FXVnDGAcgbSyxmeulFAeb7zVDVFVZ9S1cmq+k+8M6+Wrg/EGKBsDiRoSjFV3SMiNwB7VTXbGwbokK+AS4FX3PDrF+H1c6wMstqd7v10ERmMd0ZSkOfw+kUWAn2BtiISpaqZhdubPxPv6XA/Aq3x+jdai0hrYAreExG3AreJyF68hLUWL7EsxWtWe1BE6uP1b/yiqn+IyM14TXKz8ZqrugALVfXgscZrSg9LFqbUUdWP85n1Pt4v6pvxnoXwO3CnqmbkSiq5/QJ8D5zhlmsSJIRoYBheU9Qmt41jThROHbxEAd5ZS44WqrpWRC7D63N5HW8MpJvVe/7BZhEZgpdgXsZLDNe7ZVfiJZYheGc03wD/V0TxmlLCxoYyxhgTlPVZGGOMCcqShTHGmKAsWRhjjAnKkoUxxpigLFkYY4wJypKFMcaYoCxZGGOMCer/AV3XaK37fV/6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Subset a dataframe with the records that have default\n",
    "\n",
    "has_default = df[df['HAS_DEF']== 1]\n",
    "default_trend = has_default[['PAY_6','PAY_5','PAY_4','PAY_3','PAY_2','PAY_1']].sum(axis=0)\n",
    "\n",
    "# Draw a line chart to show the trend. The lower the number, the shorter delayed payment\n",
    "fig,ax = plt.subplots()\n",
    "ax.plot(default_trend)\n",
    "plt.xticks(['PAY_6','PAY_5','PAY_4','PAY_3','PAY_2','PAY_1'],['Apr','May','Jun','Jul','Aug','Sep'])\n",
    "\n",
    "plt.xlabel('Months in 2005',fontweight='bold')\n",
    "plt.ylabel('Total delayed months',fontweight='bold')\n",
    "plt.title('Delayed payment trend',fontweight='bold')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (7) Is there any correlation between bill amount and the delayed time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There isn't an obvious correlation between bill amount and the total months of delay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make 6 boxplots to visualize bill amounts and the total months of delayed payment\n",
    "\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "# Make \"PAY_1\" to\"PAY_6\" , 'BILL_AMT1\" to'BILL_AMT6\" into a list respectively\n",
    "pay_list = [ f\"PAY_{i}\" for i in range(1, 7) ]\n",
    "bill_amt_list = [ f\"BILL_AMT{i}\" for i in range(1, 7) ]\n",
    "\n",
    "fig, ax = plt.subplots(3,2, figsize=(10, 10))\n",
    "\n",
    "for pay, bill_amt, myax in zip(pay_list, bill_amt_list, ax.flatten()):\n",
    "    \n",
    "    # Prepare data for boxploting\n",
    "    data = []\n",
    "    for i in sorted(has_default[pay].unique()):\n",
    "        temp = has_default.loc[has_default[pay] == i, bill_amt]\n",
    "        data.append(temp)\n",
    "        \n",
    "    # Make boxplot for each PAY variable\n",
    "    myax.boxplot(data, showfliers=False,) \n",
    "    myax.set_xticklabels(sorted(has_default[pay].unique()))\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (8) Is there any correlation between credit limit and the default payment next month?\n",
    "\n",
    "Unsurprisingly, customers who had higher credit limits had lower delayed payment rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "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": [
    "# Make a boxplot to visualize credit limit and default payment next month\n",
    "# 1: default next month; 0: no default next month\n",
    "\n",
    "def0 = df.loc[df['DEF_PAY_NMO'] == 0,'LIMIT_BAL']\n",
    "def1 = df.loc[df['DEF_PAY_NMO'] == 1,'LIMIT_BAL']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot([def0, def1],  showfliers=False)\n",
    "\n",
    "ax.set_xticklabels(['No_default',\"Default\"],fontweight ='bold')\n",
    "ax.set_ylabel('Credit limit',fontweight ='bold')\n",
    "ax.set_title('Credit limit & default next month',fontweight ='bold')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customers with high credit limits tend to have higher 'no-default'rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### (9) Why are there negative bills?\n",
    "\n",
    "Interestingly enough, the minimals of those 6 bill columns are negative numbers. In general, there are 599-688 bills with negative amounts each month, which is less than 2% of total 30,000 records monthly. Also, the average negative amount in USD is $38 - $102 which is a small amount. Therefore, an informed guess would be these are refunds from the last billing cycle. This will be investigated in data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51223.330900</td>\n",
       "      <td>49179.075167</td>\n",
       "      <td>4.701315e+04</td>\n",
       "      <td>43262.948967</td>\n",
       "      <td>40311.400967</td>\n",
       "      <td>38871.760400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>73635.860576</td>\n",
       "      <td>71173.768783</td>\n",
       "      <td>6.934939e+04</td>\n",
       "      <td>64332.856134</td>\n",
       "      <td>60797.155770</td>\n",
       "      <td>59554.107537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-165580.000000</td>\n",
       "      <td>-69777.000000</td>\n",
       "      <td>-1.572640e+05</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3558.750000</td>\n",
       "      <td>2984.750000</td>\n",
       "      <td>2.666250e+03</td>\n",
       "      <td>2326.750000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22381.500000</td>\n",
       "      <td>21200.000000</td>\n",
       "      <td>2.008850e+04</td>\n",
       "      <td>19052.000000</td>\n",
       "      <td>18104.500000</td>\n",
       "      <td>17071.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>67091.000000</td>\n",
       "      <td>64006.250000</td>\n",
       "      <td>6.016475e+04</td>\n",
       "      <td>54506.000000</td>\n",
       "      <td>50190.500000</td>\n",
       "      <td>49198.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>964511.000000</td>\n",
       "      <td>983931.000000</td>\n",
       "      <td>1.664089e+06</td>\n",
       "      <td>891586.000000</td>\n",
       "      <td>927171.000000</td>\n",
       "      <td>961664.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           BILL_AMT1      BILL_AMT2     BILL_AMT3      BILL_AMT4  \\\n",
       "count   30000.000000   30000.000000  3.000000e+04   30000.000000   \n",
       "mean    51223.330900   49179.075167  4.701315e+04   43262.948967   \n",
       "std     73635.860576   71173.768783  6.934939e+04   64332.856134   \n",
       "min   -165580.000000  -69777.000000 -1.572640e+05 -170000.000000   \n",
       "25%      3558.750000    2984.750000  2.666250e+03    2326.750000   \n",
       "50%     22381.500000   21200.000000  2.008850e+04   19052.000000   \n",
       "75%     67091.000000   64006.250000  6.016475e+04   54506.000000   \n",
       "max    964511.000000  983931.000000  1.664089e+06  891586.000000   \n",
       "\n",
       "           BILL_AMT5      BILL_AMT6  \n",
       "count   30000.000000   30000.000000  \n",
       "mean    40311.400967   38871.760400  \n",
       "std     60797.155770   59554.107537  \n",
       "min    -81334.000000 -339603.000000  \n",
       "25%      1763.000000    1256.000000  \n",
       "50%     18104.500000   17071.000000  \n",
       "75%     50190.500000   49198.250000  \n",
       "max    927171.000000  961664.000000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get statistic summary of bill statement columns\n",
    "# The min numbers are negative\n",
    "\n",
    "bill = df[['BILL_AMT1','BILL_AMT2','BILL_AMT3','BILL_AMT4','BILL_AMT5','BILL_AMT6']]\n",
    "bill.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bill_cycle</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <td>590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <td>655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <td>655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            amount\n",
       "bill_cycle        \n",
       "BILL_AMT1      590\n",
       "BILL_AMT2      669\n",
       "BILL_AMT3      655\n",
       "BILL_AMT4      675\n",
       "BILL_AMT5      655\n",
       "BILL_AMT6      688"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many bills have negative amount?\n",
    "# There are 3932 bills with negative amounts, each month has 599-688 records \n",
    "\n",
    "bill_melt = pd.melt(bill, var_name = 'bill_cycle',value_name = 'amount')\n",
    "neg_bill = bill_melt[bill_melt['amount']<0]\n",
    "neg_bill.groupby('bill_cycle').count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average negative amounts in NTD are: \n",
      "\n",
      "bill_cycle\n",
      "BILL_AMT1   -1154.796610\n",
      "BILL_AMT2   -1230.621824\n",
      "BILL_AMT3   -1466.291603\n",
      "BILL_AMT4   -1631.242963\n",
      "BILL_AMT5   -1634.708397\n",
      "BILL_AMT6   -3074.492733\n",
      "Name: amount, dtype: float64\n",
      "\n",
      "Average negative amounts in USD are: \n",
      "\n",
      "bill_cycle\n",
      "BILL_AMT1    -38.493220\n",
      "BILL_AMT2    -41.020727\n",
      "BILL_AMT3    -48.876387\n",
      "BILL_AMT4    -54.374765\n",
      "BILL_AMT5    -54.490280\n",
      "BILL_AMT6   -102.483091\n",
      "Name: amount, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Get the average amount of negative bill each month\n",
    "# Use USD/NTD = 30 to get the amount in USD. The average negative amount is $38-$102\n",
    "\n",
    "neg_bill_mean_ndt = neg_bill.groupby('bill_cycle')['amount'].mean()\n",
    "\n",
    "print('Average negative amounts in NTD are: \\n')\n",
    "print(neg_bill_mean_ndt)\n",
    "print('\\nAverage negative amounts in USD are: \\n')\n",
    "print(neg_bill_mean_ndt/30)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (10) Why are some bill statement amounts greater than credit limit? Could it be late payment interest?\n",
    "\n",
    "The common sense is that the bill statement amount shouldn't exceed credit limit, however, there are 3931 customers whose bill amounts are greater than credit limit. Could the difference be late payment interest assuming these customers had delayed payment? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3931, 25)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Is there any bill amount that is greater than credit limit?\n",
    "\n",
    "condition1 = df['BILL_AMT1'] > df['LIMIT_BAL'] \n",
    "condition2 = df['BILL_AMT2'] > df['LIMIT_BAL'] \n",
    "condition3 = df['BILL_AMT3'] > df['LIMIT_BAL'] \n",
    "condition4 = df['BILL_AMT4'] > df['LIMIT_BAL'] \n",
    "condition5 = df['BILL_AMT5'] > df['LIMIT_BAL'] \n",
    "condition6 = df['BILL_AMT6'] > df['LIMIT_BAL'] \n",
    "\n",
    "large_bill = df[condition1 | condition2 |condition3 | condition4 | condition5 | condition6]\n",
    "large_bill.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    2184\n",
       "1.0    1747\n",
       "Name: HAS_DEF, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_bill[\"HAS_DEF\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (11) Why did customers who had no consumption in 6 months then default in the next month?\n",
    "\n",
    "First explore if there is any customer who never had consumption and the data shows there are 870 customers who were inactive in 6 months. Then check against the 'default.payment.next.month' column to see if these customers all had no default. The common sense should be no default payment next month if the previous bill was 0. However, we see there are 317 \"1\"s which means default in the next month. We will investigate this in the data analysis process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29909</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29910</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29974</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29980</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29986</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>870 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       BILL_AMT1  BILL_AMT2  BILL_AMT3  BILL_AMT4  BILL_AMT5  BILL_AMT6\n",
       "ID                                                                     \n",
       "19           0.0        0.0        0.0        0.0        0.0        0.0\n",
       "20           0.0        0.0        0.0        0.0        0.0        0.0\n",
       "46           0.0        0.0        0.0        0.0        0.0        0.0\n",
       "80           0.0        0.0        0.0        0.0        0.0        0.0\n",
       "122          0.0        0.0        0.0        0.0        0.0        0.0\n",
       "...          ...        ...        ...        ...        ...        ...\n",
       "29909        0.0        0.0        0.0        0.0        0.0        0.0\n",
       "29910        0.0        0.0        0.0        0.0        0.0        0.0\n",
       "29974        0.0        0.0        0.0        0.0        0.0        0.0\n",
       "29980        0.0        0.0        0.0        0.0        0.0        0.0\n",
       "29986        0.0        0.0        0.0        0.0        0.0        0.0\n",
       "\n",
       "[870 rows x 6 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Is there any customer who never had any transaction, meaning bill amount in 6 months remained as 0\n",
    "# There are 870 customers whose bill amount was 0 in 6 months\n",
    "\n",
    "bill_amt = df[['BILL_AMT1','BILL_AMT2','BILL_AMT3','BILL_AMT4','BILL_AMT5','BILL_AMT6']]\n",
    "no_transaction = bill_amt[bill_amt.sum(axis=1) ==0]\n",
    "no_transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEF_PAY_NMO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29909</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29910</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29974</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29980</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29986</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>870 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       DEF_PAY_NMO\n",
       "ID                \n",
       "19               0\n",
       "20               0\n",
       "46               1\n",
       "80               1\n",
       "122              1\n",
       "...            ...\n",
       "29909            1\n",
       "29910            0\n",
       "29974            1\n",
       "29980            0\n",
       "29986            0\n",
       "\n",
       "[870 rows x 1 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if those 870 customers defaulted in the next month (Oct.2005)\n",
    "# The assumption is since they did not have any consumption, they shouldn't default, so there shouldn't be any '1'\n",
    "\n",
    "no_transaction_de = df.loc[no_transaction.index,['DEF_PAY_NMO']]\n",
    "no_transaction_de"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    553\n",
       "1    317\n",
       "Name: DEF_PAY_NMO, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many inactive customers had default payment next month\n",
    "# 317 customers had default payment next month which is against common sense\n",
    "\n",
    "no_transaction_de['DEF_PAY_NMO'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Statistical Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Should this section be embeded to previous EDA step?\n",
    "- Correlation between credit limit and bill amount: Pearson R \n",
    "- Correlation between credit limit and payment default: t-test\n",
    "- Correlation between education and  payment default: chi-squared test\n",
    "- Correlation between age and payment default: chi-squared test\n",
    "- Correlation between sex and payment default: permutation test\n",
    "- Correlation between sex and credit limit: t-test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation between credit limit and bill amount.\n",
    "\n",
    "We assume customers who have a high credit limit will have a higher bill amount. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>BILL_SUM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.302044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_SUM</th>\n",
       "      <td>0.302044</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LIMIT_BAL  BILL_SUM\n",
       "LIMIT_BAL   1.000000  0.302044\n",
       "BILL_SUM    0.302044  1.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BILL_SUM'] = df[['BILL_AMT1','BILL_AMT2','BILL_AMT3','BILL_AMT4','BILL_AMT5','BILL_AMT6']].sum(axis=1)\n",
    "credit_bill = df[['LIMIT_BAL','BILL_SUM']]\n",
    "credit_bill.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3020437970261578, 0.0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "credit_limit = df['LIMIT_BAL']\n",
    "bill_amount = df[['BILL_AMT1','BILL_AMT2','BILL_AMT3','BILL_AMT4','BILL_AMT5','BILL_AMT6']].sum(axis=1)\n",
    "\n",
    "stats.pearsonr(credit_limit, bill_amount)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Pearson R shows there is a positive correlation between these 2 variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation between credit limit and payment default.\n",
    "Ideally, we should have customers' income data, but since this data is not available, we can assume higher income customers have higher credit limits. Therefore, we will verify if there is a correlation between credit limit and default payment using t-test.\n",
    "\n",
    "- Null hypothesis: credit limit does not affect default likelihood.\n",
    "- Alternative hypothesis: credit limit impact default likelihood.\n",
    "- Set significance level $\\alpha$ to 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_def_limit = df.loc[df['HAS_DEF']==0,'LIMIT_BAL']\n",
    "def_limit = df.loc[df['HAS_DEF']==1,'LIMIT_BAL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=46.75876568055524, pvalue=0.0)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "from scipy.stats import ttest_ind_from_stats\n",
    "\n",
    "ttest_ind_from_stats(mean1=np.mean(no_def_limit),std1=np.std(no_def_limit),nobs1=len(no_def_limit),\n",
    "                     mean2=np.mean(def_limit),std2=np.std(def_limit),nobs2=len(def_limit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a p value as 0, therefore we need to reject the null hypothesis and accept the alternative hypothesis. Credit limit has an impact on payment default."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation between education and payment default.\n",
    "\n",
    "Previous visualization indicates education impacts default likelihood. We will use a Chi-squared test to verify this finding.\n",
    "- Null hypothesis: education does not affect default likelihood.\n",
    "- Alternative hypothesis: education impacts default likelihood.\n",
    "- Set significance level $\\alpha$ to 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the contigence table of education and default data\n",
    "\n",
    "edu_def_contigence = pd.crosstab(df['HAS_DEF'], df['EDUCATION'], margins=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.997508691343157e-76"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Chi-squared test to test if education affects default likelihood.\n",
    "\n",
    "from scipy.stats import chisquare\n",
    "from scipy.stats import chi2_contingency\n",
    "stat, p, dof, expected = chi2_contingency(edu_def_contigence)\n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p value is close to 0, we will reject the null hypothesis and accept the alternative hypothesis. Because education has a strong correlation with default probability, we should keep this variable in the machine learning model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation between age and payment default.\n",
    "\n",
    "Previous visualization indicates age impacts default likelihood. We will use a chi-squared test to verify this finding.\n",
    "- Null hypothesis: age does not affect default likelihood.\n",
    "- Alternative hypothesis: age impacts default likelihood.\n",
    "- Set significance level $\\alpha$ to 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the contigence table of age and default data\n",
    "\n",
    "age_def_contigence = pd.crosstab(df['HAS_DEF'], df['AGE'], margins=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.185243361338736e-26"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use chi-squared test to test if age affects default likelihood.\n",
    "\n",
    "from scipy.stats import chisquare\n",
    "from scipy.stats import chi2_contingency\n",
    "stat, p, dof, expected = chi2_contingency(age_def_contigence)\n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p value is smaller than significance level $\\alpha$, we will reject the null hypothesis and accept the alternative hypothesis, which is age has impact on default probability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation between sex and payment default. \n",
    "\n",
    "In previous data visualization, it appears males tend to default more than females. Does sex have any correlations with default or was this observation due to chance event? Let's find out with a permutation test on each group's default proportions and mean respectively.\n",
    "- Null hypothesis: sex has no impact on default probability. \n",
    "- Alternative hypothesis: sex has impact on default probability. \n",
    "- Set significance level $\\alpha$ to 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03849496062187008"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the true difference of 'HAS_DEF' proportions of two sex groups\n",
    "\n",
    "male = df.loc[df['SEX']=='M','HAS_DEF']\n",
    "female = df.loc[df['SEX']=='F','HAS_DEF']\n",
    "true_diff_prop = male.sum()/len(male) - female.sum()/len(female)\n",
    "true_diff_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define functions to do permutation test\n",
    "\n",
    "def permutation_sample(data1, data2):\n",
    "    data = np.concatenate((data1,data2))\n",
    "    permuted_data = np.random.permutation(data)\n",
    "    perm_sample_1 = permuted_data[:len(data1)]\n",
    "    perm_sample_2 = permuted_data[len(data1):]\n",
    "\n",
    "    return perm_sample_1, perm_sample_2\n",
    "\n",
    "def diff_of_prop(data1,data2):\n",
    "    diff = data1.sum()/len(data1) - data2.sum()/len(data2)\n",
    "\n",
    "    return diff\n",
    "\n",
    "def draw_perm_reps(data1, data2, func, size=1):\n",
    "    perm_replicates = np.empty(size)\n",
    "\n",
    "    for i in range(size):\n",
    "        perm_sample_1, perm_sample_2 = permutation_sample(data1,data2)\n",
    "        perm_replicates[i] = func(perm_sample_1,perm_sample_2)\n",
    "\n",
    "    return perm_replicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw 10000 permutation tests\n",
    "\n",
    "perm_replicates = draw_perm_reps(male,female,diff_of_prop,size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate p value - the probability of getting a mean difference equal or greater than the observed mean difference\n",
    "\n",
    "p = np.sum(perm_replicates >= true_diff_prop) / len(perm_replicates)\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test conclusion: since the p value is 0, we should reject the null hypothsis and accept the alternative hypothsis, which is sex has impact on default likelihood. To be more concrete in this case, male customers tend to default more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation between credit limit and sex.\n",
    "\n",
    "Since we know the credit limit is strongly correlated with default probability. Let's see if sex plays any role in credit limit and default likelihood. \n",
    "- Null hypothesis: sex has no impact on credit limit. \n",
    "- Alternative hypothesis: sex has impact on credit limit. \n",
    "- Set significance level $\\alpha$ to 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "163519.8250336474 170086.46201413427\n"
     ]
    }
   ],
   "source": [
    "# Calculate the true mean difference of credit limit\n",
    "\n",
    "male_limit= df.loc[df['SEX']=='M','LIMIT_BAL']\n",
    "female_limit = df.loc[df['SEX']=='F','LIMIT_BAL']\n",
    "print(np.mean(male_limit),np.mean(female_limit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-4.289049447941284, pvalue=1.800001301383108e-05)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use t-test to verify if different sex groups have the same mean of credit limit.\n",
    "\n",
    "from scipy import stats\n",
    "from scipy.stats import ttest_ind_from_stats\n",
    "\n",
    "ttest_ind_from_stats(mean1=np.mean(male_limit),std1=np.std(male_limit),nobs1=len(male_limit),\n",
    "                     mean2=np.mean(female_limit),std2=np.std(female_limit),nobs2=len(female_limit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p value is much lower than  𝛼 , so we will reject null hypothesis and accept alternative hypothesis. We have verified that sex plays a role in credit limit and it is not due to chance."
   ]
  }
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