cc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120000</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</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>90000</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</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50000</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</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50000</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</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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    2          2         1   24      2      2     -1     -1   \n",
       "1   2     120000    2          2         2   26     -1      2      0      0   \n",
       "2   3      90000    2          2         2   34      0      0      0      0   \n",
       "3   4      50000    2          2         1   37      0      0      0      0   \n",
       "4   5      50000    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "   ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0  ...          0          0          0         0       689         0   \n",
       "1  ...       3272       3455       3261         0      1000      1000   \n",
       "2  ...      14331      14948      15549      1518      1500      1000   \n",
       "3  ...      28314      28959      29547      2000      2019      1200   \n",
       "4  ...      20940      19146      19131      2000     36681     10000   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "0         0         0         0                           1  \n",
       "1      1000         0      2000                           1  \n",
       "2      1000      1000      5000                           0  \n",
       "3      1100      1069      1000                           0  \n",
       "4      9000       689       679                           0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('cc_headers.xls')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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  int64\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  int64\n",
      " 13  BILL_AMT2                   30000 non-null  int64\n",
      " 14  BILL_AMT3                   30000 non-null  int64\n",
      " 15  BILL_AMT4                   30000 non-null  int64\n",
      " 16  BILL_AMT5                   30000 non-null  int64\n",
      " 17  BILL_AMT6                   30000 non-null  int64\n",
      " 18  PAY_AMT1                    30000 non-null  int64\n",
      " 19  PAY_AMT2                    30000 non-null  int64\n",
      " 20  PAY_AMT3                    30000 non-null  int64\n",
      " 21  PAY_AMT4                    30000 non-null  int64\n",
      " 22  PAY_AMT5                    30000 non-null  int64\n",
      " 23  PAY_AMT6                    30000 non-null  int64\n",
      " 24  default payment next month  30000 non-null  int64\n",
      "dtypes: int64(25)\n",
      "memory usage: 5.7 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>15000.500000</td>\n",
       "      <td>8660.398374</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7500.75</td>\n",
       "      <td>15000.5</td>\n",
       "      <td>22500.25</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>167484.322667</td>\n",
       "      <td>129747.661567</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>240000.00</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.603733</td>\n",
       "      <td>0.489129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUCATION</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.842267</td>\n",
       "      <td>0.744494</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARRIAGE</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.555467</td>\n",
       "      <td>0.518137</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>35.485500</td>\n",
       "      <td>9.217904</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.00</td>\n",
       "      <td>34.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_1</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>-0.016700</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_2</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>-0.133767</td>\n",
       "      <td>1.197186</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_3</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>1.196868</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_4</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>-0.220667</td>\n",
       "      <td>1.169139</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_5</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>-0.266200</td>\n",
       "      <td>1.133187</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_6</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>-0.291100</td>\n",
       "      <td>1.149988</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>51223.330900</td>\n",
       "      <td>73635.860576</td>\n",
       "      <td>-165580.0</td>\n",
       "      <td>3558.75</td>\n",
       "      <td>22381.5</td>\n",
       "      <td>67091.00</td>\n",
       "      <td>964511.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>49179.075167</td>\n",
       "      <td>71173.768783</td>\n",
       "      <td>-69777.0</td>\n",
       "      <td>2984.75</td>\n",
       "      <td>21200.0</td>\n",
       "      <td>64006.25</td>\n",
       "      <td>983931.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>47013.154800</td>\n",
       "      <td>69349.387427</td>\n",
       "      <td>-157264.0</td>\n",
       "      <td>2666.25</td>\n",
       "      <td>20088.5</td>\n",
       "      <td>60164.75</td>\n",
       "      <td>1664089.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>43262.948967</td>\n",
       "      <td>64332.856134</td>\n",
       "      <td>-170000.0</td>\n",
       "      <td>2326.75</td>\n",
       "      <td>19052.0</td>\n",
       "      <td>54506.00</td>\n",
       "      <td>891586.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>40311.400967</td>\n",
       "      <td>60797.155770</td>\n",
       "      <td>-81334.0</td>\n",
       "      <td>1763.00</td>\n",
       "      <td>18104.5</td>\n",
       "      <td>50190.50</td>\n",
       "      <td>927171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>38871.760400</td>\n",
       "      <td>59554.107537</td>\n",
       "      <td>-339603.0</td>\n",
       "      <td>1256.00</td>\n",
       "      <td>17071.0</td>\n",
       "      <td>49198.25</td>\n",
       "      <td>961664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5663.580500</td>\n",
       "      <td>16563.280354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>5006.00</td>\n",
       "      <td>873552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5921.163500</td>\n",
       "      <td>23040.870402</td>\n",
       "      <td>0.0</td>\n",
       "      <td>833.00</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>1684259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5225.681500</td>\n",
       "      <td>17606.961470</td>\n",
       "      <td>0.0</td>\n",
       "      <td>390.00</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>4505.00</td>\n",
       "      <td>896040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>4826.076867</td>\n",
       "      <td>15666.159744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>296.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>4013.25</td>\n",
       "      <td>621000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>4799.387633</td>\n",
       "      <td>15278.305679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252.50</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>4031.50</td>\n",
       "      <td>426529.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5215.502567</td>\n",
       "      <td>17777.465775</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.75</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>528666.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEFAULT</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.221200</td>\n",
       "      <td>0.415062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count           mean            std       min       25%  \\\n",
       "ID         30000.0   15000.500000    8660.398374       1.0   7500.75   \n",
       "LIMIT_BAL  30000.0  167484.322667  129747.661567   10000.0  50000.00   \n",
       "SEX        30000.0       1.603733       0.489129       1.0      1.00   \n",
       "EDUCATION  30000.0       1.842267       0.744494       1.0      1.00   \n",
       "MARRIAGE   30000.0       1.555467       0.518137       1.0      1.00   \n",
       "AGE        30000.0      35.485500       9.217904      21.0     28.00   \n",
       "PAY_1      30000.0      -0.016700       1.123802      -2.0     -1.00   \n",
       "PAY_2      30000.0      -0.133767       1.197186      -2.0     -1.00   \n",
       "PAY_3      30000.0      -0.166200       1.196868      -2.0     -1.00   \n",
       "PAY_4      30000.0      -0.220667       1.169139      -2.0     -1.00   \n",
       "PAY_5      30000.0      -0.266200       1.133187      -2.0     -1.00   \n",
       "PAY_6      30000.0      -0.291100       1.149988      -2.0     -1.00   \n",
       "BILL_AMT1  30000.0   51223.330900   73635.860576 -165580.0   3558.75   \n",
       "BILL_AMT2  30000.0   49179.075167   71173.768783  -69777.0   2984.75   \n",
       "BILL_AMT3  30000.0   47013.154800   69349.387427 -157264.0   2666.25   \n",
       "BILL_AMT4  30000.0   43262.948967   64332.856134 -170000.0   2326.75   \n",
       "BILL_AMT5  30000.0   40311.400967   60797.155770  -81334.0   1763.00   \n",
       "BILL_AMT6  30000.0   38871.760400   59554.107537 -339603.0   1256.00   \n",
       "PAY_AMT1   30000.0    5663.580500   16563.280354       0.0   1000.00   \n",
       "PAY_AMT2   30000.0    5921.163500   23040.870402       0.0    833.00   \n",
       "PAY_AMT3   30000.0    5225.681500   17606.961470       0.0    390.00   \n",
       "PAY_AMT4   30000.0    4826.076867   15666.159744       0.0    296.00   \n",
       "PAY_AMT5   30000.0    4799.387633   15278.305679       0.0    252.50   \n",
       "PAY_AMT6   30000.0    5215.502567   17777.465775       0.0    117.75   \n",
       "DEFAULT    30000.0       0.221200       0.415062       0.0      0.00   \n",
       "\n",
       "                50%        75%        max  \n",
       "ID          15000.5   22500.25    30000.0  \n",
       "LIMIT_BAL  140000.0  240000.00  1000000.0  \n",
       "SEX             2.0       2.00        2.0  \n",
       "EDUCATION       2.0       2.00        4.0  \n",
       "MARRIAGE        2.0       2.00        3.0  \n",
       "AGE            34.0      41.00       79.0  \n",
       "PAY_1           0.0       0.00        8.0  \n",
       "PAY_2           0.0       0.00        8.0  \n",
       "PAY_3           0.0       0.00        8.0  \n",
       "PAY_4           0.0       0.00        8.0  \n",
       "PAY_5           0.0       0.00        8.0  \n",
       "PAY_6           0.0       0.00        8.0  \n",
       "BILL_AMT1   22381.5   67091.00   964511.0  \n",
       "BILL_AMT2   21200.0   64006.25   983931.0  \n",
       "BILL_AMT3   20088.5   60164.75  1664089.0  \n",
       "BILL_AMT4   19052.0   54506.00   891586.0  \n",
       "BILL_AMT5   18104.5   50190.50   927171.0  \n",
       "BILL_AMT6   17071.0   49198.25   961664.0  \n",
       "PAY_AMT1     2100.0    5006.00   873552.0  \n",
       "PAY_AMT2     2009.0    5000.00  1684259.0  \n",
       "PAY_AMT3     1800.0    4505.00   896040.0  \n",
       "PAY_AMT4     1500.0    4013.25   621000.0  \n",
       "PAY_AMT5     1500.0    4031.50   426529.0  \n",
       "PAY_AMT6     1500.0    4000.00   528666.0  \n",
       "DEFAULT         0.0       0.00        1.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                            0\n",
       "LIMIT_BAL                     0\n",
       "SEX                           0\n",
       "EDUCATION                     0\n",
       "MARRIAGE                      0\n",
       "AGE                           0\n",
       "PAY_0                         0\n",
       "PAY_2                         0\n",
       "PAY_3                         0\n",
       "PAY_4                         0\n",
       "PAY_5                         0\n",
       "PAY_6                         0\n",
       "BILL_AMT1                     0\n",
       "BILL_AMT2                     0\n",
       "BILL_AMT3                     0\n",
       "BILL_AMT4                     0\n",
       "BILL_AMT5                     0\n",
       "BILL_AMT6                     0\n",
       "PAY_AMT1                      0\n",
       "PAY_AMT2                      0\n",
       "PAY_AMT3                      0\n",
       "PAY_AMT4                      0\n",
       "PAY_AMT5                      0\n",
       "PAY_AMT6                      0\n",
       "default payment next month    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SEX [1, 2]\n"
     ]
    }
   ],
   "source": [
    "print('SEX ' + str(sorted(df['SEX'].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EDUCATION [0, 1, 2, 3, 4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "print('EDUCATION ' + str(sorted(df['EDUCATION'].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MARRIAGE [0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print('MARRIAGE ' + str(sorted(df['MARRIAGE'].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PAY_0 [-2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8]\n"
     ]
    }
   ],
   "source": [
    "print('PAY_0 ' + str(sorted(df['PAY_0'].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default.payment.next.month [0, 1]\n"
     ]
    }
   ],
   "source": [
    "print('default.payment.next.month ' \\\n",
    "      + str(sorted(df['default payment next month'].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "fill = (df.EDUCATION == 0) | (df.EDUCATION == 5) \\\n",
    "       | (df.EDUCATION == 6)\n",
    "df.loc[fill, 'EDUCATION'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "fill = (df.MARRIAGE == 0)\n",
    "df.loc[fill, 'MARRIAGE'] = 2"
   ]
  },
  {
   "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>DEFAULT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20000</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120000</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</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>90000</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</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50000</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</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50000</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</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_1  PAY_2  PAY_3  PAY_4  \\\n",
       "0   1      20000    2          2         1   24      2      2     -1     -1   \n",
       "1   2     120000    2          2         2   26     -1      2      0      0   \n",
       "2   3      90000    2          2         2   34      0      0      0      0   \n",
       "3   4      50000    2          2         1   37      0      0      0      0   \n",
       "4   5      50000    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "   ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0  ...          0          0          0         0       689         0   \n",
       "1  ...       3272       3455       3261         0      1000      1000   \n",
       "2  ...      14331      14948      15549      1518      1500      1000   \n",
       "3  ...      28314      28959      29547      2000      2019      1200   \n",
       "4  ...      20940      19146      19131      2000     36681     10000   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  DEFAULT  \n",
       "0         0         0         0        1  \n",
       "1      1000         0      2000        1  \n",
       "2      1000      1000      5000        0  \n",
       "3      1100      1069      1000        0  \n",
       "4      9000       689       679        0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(columns={'default payment next month': 'DEFAULT', \\\n",
    "                        'PAY_0': 'PAY_1'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEFAULT', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"DEFAULT\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23364\n",
       "1     6636\n",
       "Name: DEFAULT, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DEFAULT'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SEX', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"SEX\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    18112\n",
       "1    11888\n",
       "Name: SEX, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SEX'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EDUCATION', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"EDUCATION\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14030\n",
       "1    10585\n",
       "3     4917\n",
       "4      468\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EDUCATION'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MARRIAGE', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"MARRIAGE\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    16018\n",
       "1    13659\n",
       "3      323\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MARRIAGE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(15,10)})\n",
    "edu = sns.countplot(x='SEX', hue='DEFAULT', data=df)\n",
    "edu.set_xticklabels(['Male','Female'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>DEFAULT</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.758328</td>\n",
       "      <td>0.241672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.792237</td>\n",
       "      <td>0.207763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.778800</td>\n",
       "      <td>0.221200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DEFAULT         0         1\n",
       "SEX                        \n",
       "1        0.758328  0.241672\n",
       "2        0.792237  0.207763\n",
       "All      0.778800  0.221200"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.SEX,df.DEFAULT,normalize='index',margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}