Bernardia Vitri - Big Data C - Default of Credit Card Clients Datasets Mining.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "Dataset is taken from https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients.\n",
    "\n",
    "This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This data used the following 23 variables as explanatory variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "  <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",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 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",
       "\n",
       "              ...              BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  \\\n",
       "0             ...                      0          0          0         0   \n",
       "1             ...                   3272       3455       3261         0   \n",
       "\n",
       "   PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n",
       "0       689         0         0         0         0   \n",
       "1      1000      1000      1000         0      2000   \n",
       "\n",
       "   default payment next month  \n",
       "0                           1  \n",
       "1                           1  \n",
       "\n",
       "[2 rows x 25 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import itertools\n",
    "cc = pd.read_csv(\"default of credit card clients.csv\",skiprows=1)\n",
    "cc.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ID column is dropped:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc.drop('ID',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Renaming column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <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_0</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>dpnm</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <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",
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       "      <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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0      20000    2          2         1   24      2      2     -1     -1   \n",
       "1     120000    2          2         2   26     -1      2      0      0   \n",
       "\n",
       "   PAY_5  ...   BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0     -2  ...           0          0          0         0       689         0   \n",
       "1      0  ...        3272       3455       3261         0      1000      1000   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  dpnm  \n",
       "0         0         0         0     1  \n",
       "1      1000         0      2000     1  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc = cc.rename(columns={\"default payment next month\": \"dpnm\"})\n",
    "cc.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables info:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30000 entries, 0 to 29999\n",
      "Data columns (total 24 columns):\n",
      "LIMIT_BAL    30000 non-null int64\n",
      "SEX          30000 non-null int64\n",
      "EDUCATION    30000 non-null int64\n",
      "MARRIAGE     30000 non-null int64\n",
      "AGE          30000 non-null int64\n",
      "PAY_0        30000 non-null int64\n",
      "PAY_2        30000 non-null int64\n",
      "PAY_3        30000 non-null int64\n",
      "PAY_4        30000 non-null int64\n",
      "PAY_5        30000 non-null int64\n",
      "PAY_6        30000 non-null int64\n",
      "BILL_AMT1    30000 non-null int64\n",
      "BILL_AMT2    30000 non-null int64\n",
      "BILL_AMT3    30000 non-null int64\n",
      "BILL_AMT4    30000 non-null int64\n",
      "BILL_AMT5    30000 non-null int64\n",
      "BILL_AMT6    30000 non-null int64\n",
      "PAY_AMT1     30000 non-null int64\n",
      "PAY_AMT2     30000 non-null int64\n",
      "PAY_AMT3     30000 non-null int64\n",
      "PAY_AMT4     30000 non-null int64\n",
      "PAY_AMT5     30000 non-null int64\n",
      "PAY_AMT6     30000 non-null int64\n",
      "dpnm         30000 non-null int64\n",
      "dtypes: int64(24)\n",
      "memory usage: 5.5 MB\n"
     ]
    }
   ],
   "source": [
    "cc.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descriptive Statistic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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>dpnm</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>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>-0.266200</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>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>1.133187</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>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>-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>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>-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>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>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>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>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>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>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 × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            LIMIT_BAL           SEX     EDUCATION      MARRIAGE           AGE  \\\n",
       "count    30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean    167484.322667      1.603733      1.853133      1.551867     35.485500   \n",
       "std     129747.661567      0.489129      0.790349      0.521970      9.217904   \n",
       "min      10000.000000      1.000000      0.000000      0.000000     21.000000   \n",
       "25%      50000.000000      1.000000      1.000000      1.000000     28.000000   \n",
       "50%     140000.000000      2.000000      2.000000      2.000000     34.000000   \n",
       "75%     240000.000000      2.000000      2.000000      2.000000     41.000000   \n",
       "max    1000000.000000      2.000000      6.000000      3.000000     79.000000   \n",
       "\n",
       "              PAY_0         PAY_2         PAY_3         PAY_4         PAY_5  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean      -0.016700     -0.133767     -0.166200     -0.220667     -0.266200   \n",
       "std        1.123802      1.197186      1.196868      1.169139      1.133187   \n",
       "min       -2.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       -1.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        8.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "           ...           BILL_AMT4      BILL_AMT5      BILL_AMT6  \\\n",
       "count      ...        30000.000000   30000.000000   30000.000000   \n",
       "mean       ...        43262.948967   40311.400967   38871.760400   \n",
       "std        ...        64332.856134   60797.155770   59554.107537   \n",
       "min        ...      -170000.000000  -81334.000000 -339603.000000   \n",
       "25%        ...         2326.750000    1763.000000    1256.000000   \n",
       "50%        ...        19052.000000   18104.500000   17071.000000   \n",
       "75%        ...        54506.000000   50190.500000   49198.250000   \n",
       "max        ...       891586.000000  927171.000000  961664.000000   \n",
       "\n",
       "            PAY_AMT1      PAY_AMT2      PAY_AMT3       PAY_AMT4  \\\n",
       "count   30000.000000  3.000000e+04   30000.00000   30000.000000   \n",
       "mean     5663.580500  5.921163e+03    5225.68150    4826.076867   \n",
       "std     16563.280354  2.304087e+04   17606.96147   15666.159744   \n",
       "min         0.000000  0.000000e+00       0.00000       0.000000   \n",
       "25%      1000.000000  8.330000e+02     390.00000     296.000000   \n",
       "50%      2100.000000  2.009000e+03    1800.00000    1500.000000   \n",
       "75%      5006.000000  5.000000e+03    4505.00000    4013.250000   \n",
       "max    873552.000000  1.684259e+06  896040.00000  621000.000000   \n",
       "\n",
       "            PAY_AMT5       PAY_AMT6          dpnm  \n",
       "count   30000.000000   30000.000000  30000.000000  \n",
       "mean     4799.387633    5215.502567      0.221200  \n",
       "std     15278.305679   17777.465775      0.415062  \n",
       "min         0.000000       0.000000      0.000000  \n",
       "25%       252.500000     117.750000      0.000000  \n",
       "50%      1500.000000    1500.000000      0.000000  \n",
       "75%      4031.500000    4000.000000      0.000000  \n",
       "max    426529.000000  528666.000000      1.000000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23364\n",
       "1     6636\n",
       "Name: dpnm, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc['dpnm'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "23364 person out of 30000 are cardholders without default payment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.7788\n",
       "1    0.2212\n",
       "Name: dpnm, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc['dpnm'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "22.12% person are cardholders with default payment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantitative - Quantitative | Correlation matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6)) \n",
    "corr_matrix = cc.corr()\n",
    "sns.heatmap(corr_matrix);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantitative - Categorical | Variables vs Default Payment boxplots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe7db051c50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x1080 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,15))\n",
    "plt.subplot(4,3,1)\n",
    "sns.boxplot(x='dpnm',y='LIMIT_BAL',data=cc)\n",
    "plt.subplot(4,3,2)\n",
    "sns.boxplot(x='dpnm',y='SEX',data=cc)\n",
    "plt.subplot(4,3,3)\n",
    "sns.boxplot(x='dpnm',y='EDUCATION',data=cc)\n",
    "plt.subplot(4,3,4)\n",
    "sns.boxplot(x='dpnm',y='MARRIAGE',data=cc)\n",
    "plt.subplot(4,3,5)\n",
    "sns.boxplot(x='dpnm',y='AGE',data=cc)\n",
    "plt.subplot(4,3,6)\n",
    "sns.boxplot(x='dpnm',y='PAY_0',data=cc)\n",
    "plt.subplot(4,3,7)\n",
    "sns.boxplot(x='dpnm',y='PAY_2',data=cc)\n",
    "plt.subplot(4,3,8)\n",
    "sns.boxplot(x='dpnm',y='PAY_3',data=cc)\n",
    "plt.subplot(4,3,9)\n",
    "sns.boxplot(x='dpnm',y='PAY_4',data=cc)\n",
    "plt.subplot(4,3,10)\n",
    "sns.boxplot(x='dpnm',y='PAY_5',data=cc)\n",
    "plt.subplot(4,3,11)\n",
    "sns.boxplot(x='dpnm',y='PAY_6',data=cc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe7dc0666a0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x1800 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,25))\n",
    "plt.subplot(6,2,1)\n",
    "sns.boxplot(x='dpnm',y='BILL_AMT1',data=cc)\n",
    "plt.subplot(6,2,2)\n",
    "sns.boxplot(x='dpnm',y='BILL_AMT2',data=cc)\n",
    "plt.subplot(6,2,3)\n",
    "sns.boxplot(x='dpnm',y='BILL_AMT3',data=cc)\n",
    "plt.subplot(6,2,4)\n",
    "sns.boxplot(x='dpnm',y='BILL_AMT4',data=cc)\n",
    "plt.subplot(6,2,5)\n",
    "sns.boxplot(x='dpnm',y='BILL_AMT5',data=cc)\n",
    "plt.subplot(6,2,6)\n",
    "sns.boxplot(x='dpnm',y='PAY_AMT1',data=cc)\n",
    "plt.subplot(6,2,7)\n",
    "sns.boxplot(x='dpnm',y='PAY_AMT2',data=cc)\n",
    "plt.subplot(6,2,8)\n",
    "sns.boxplot(x='dpnm',y='PAY_AMT3',data=cc)\n",
    "plt.subplot(6,2,9)\n",
    "sns.boxplot(x='dpnm',y='PAY_AMT4',data=cc)\n",
    "plt.subplot(6,2,10)\n",
    "sns.boxplot(x='dpnm',y='PAY_AMT5',data=cc)\n",
    "plt.subplot(6,2,11)\n",
    "sns.boxplot(x='dpnm',y='PAY_AMT6',data=cc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical - Categorical | Education vs Default Payment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe7dc0ed048>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3))\n",
    "sns.countplot(x='EDUCATION', hue='dpnm', data=cc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing alll the necessary packages to use the various classification algorithms\n",
    "from sklearn.linear_model import LogisticRegression  # for Logistic Regression algorithm\n",
    "from sklearn.model_selection import train_test_split #to split the dataset for training and testing\n",
    "from sklearn.neighbors import KNeighborsClassifier  # for K nearest neighbours\n",
    "from sklearn import svm  #for Support Vector Machine (SVM) Algorithm\n",
    "from sklearn import metrics #for checking the model accuracy\n",
    "from sklearn.tree import DecisionTreeClassifier #for using Decision Tree Algorithm\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 24)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_a,test_a=train_test_split(cc,test_size=0.3,random_state=0) \n",
    "train_b_a=train_a[['LIMIT_BAL','SEX','EDUCATION','MARRIAGE','AGE','PAY_0',\n",
    "                   'PAY_2','PAY_3','PAY_4','PAY_5','PAY_6','BILL_AMT1',\n",
    "                   'BILL_AMT2','BILL_AMT3','BILL_AMT4','BILL_AMT5','BILL_AMT6','PAY_AMT1',\n",
    "                   'PAY_AMT2','PAY_AMT3','PAY_AMT4','PAY_AMT5','PAY_AMT6']]\n",
    "train_c_a=train_a.dpnm\n",
    "test_b_a=test_a[['LIMIT_BAL','SEX','EDUCATION','MARRIAGE','AGE','PAY_0',\n",
    "                 'PAY_2','PAY_3','PAY_4','PAY_5','PAY_6','BILL_AMT1',\n",
    "                 'BILL_AMT2','BILL_AMT3','BILL_AMT4','BILL_AMT5','BILL_AMT6','PAY_AMT1',\n",
    "                 'PAY_AMT2','PAY_AMT3','PAY_AMT4','PAY_AMT5','PAY_AMT6']]\n",
    "test_c_a=test_a.dpnm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After standardizing our features, the first 5 rows of our data now look like this:\n",
      "\n",
      "   LIMIT_BAL       SEX  EDUCATION  MARRIAGE       AGE     PAY_0     PAY_2  \\\n",
      "0  -1.063195  0.817793   0.187191  0.858058 -0.485236 -1.756704 -1.554090   \n",
      "1   0.253364 -1.222803   0.187191 -1.059696 -0.485236  0.890707  1.756174   \n",
      "2   2.576705  0.817793   2.719242  0.858058  0.276931 -1.756704 -1.554090   \n",
      "3  -1.140640  0.817793   0.187191  0.858058 -1.465165  0.008236  0.101042   \n",
      "4  -0.908306  0.817793   0.187191 -1.059696  1.365741  0.890707  1.756174   \n",
      "\n",
      "      PAY_3     PAY_4     PAY_5    ...     BILL_AMT4  BILL_AMT5  BILL_AMT6  \\\n",
      "0 -1.526818 -1.523691 -1.532501    ...     -0.675090  -0.668727  -0.656920   \n",
      "1  1.785922  0.180389  0.227164    ...     -0.233607  -0.190694  -0.175440   \n",
      "2 -1.526818 -1.523691 -1.532501    ...     -0.038395  -0.514911  -0.510393   \n",
      "3  0.129552  0.180389  0.227164    ...     -0.360786  -0.342881  -0.319751   \n",
      "4  0.129552  0.180389  0.227164    ...     -0.371396  -0.355046  -0.329295   \n",
      "\n",
      "   PAY_AMT1  PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6      dpnm  \n",
      "0 -0.334535 -0.245768 -0.301666 -0.311311 -0.316269 -0.297365 -0.536682  \n",
      "1 -0.156089 -0.245520 -0.243881 -0.245227 -0.229656 -0.183567 -0.536682  \n",
      "2 -0.200656  0.783499  2.078955  1.663779  0.258613 -0.096398 -0.536682  \n",
      "3 -0.257813 -0.166874 -0.204522  0.008863 -0.269789 -0.253268 -0.536682  \n",
      "4 -0.334535 -0.163113 -0.214201 -0.247276 -0.184036 -0.240466  1.863301  \n",
      "\n",
      "[5 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "sc.fit(train_a)\n",
    "X_train_std = sc.transform(train_a)\n",
    "X_test_std = sc.transform(test_a)\n",
    "\n",
    "print('After standardizing our features, the first 5 rows of our data now look like this:\\n')\n",
    "print(pd.DataFrame(X_train_std, columns=cc.columns).head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the SVM using all variables is: 0.7848888888888889\n"
     ]
    }
   ],
   "source": [
    "model=svm.SVC()\n",
    "model.fit(train_b_a,train_c_a) \n",
    "prediction=model.predict(test_b_a) \n",
    "print('The accuracy of the SVM using all variables is:',metrics.accuracy_score(prediction,test_c_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Logistic Regression using all variables is: 0.7844444444444445\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(train_b_a,train_c_a) \n",
    "prediction=model.predict(test_b_a) \n",
    "print('The accuracy of the Logistic Regression using all variables is:',metrics.accuracy_score(prediction,test_c_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Decision Tree using all variables is: 0.7342222222222222\n"
     ]
    }
   ],
   "source": [
    "model=DecisionTreeClassifier()\n",
    "model.fit(train_b_a,train_c_a) \n",
    "prediction=model.predict(test_b_a) \n",
    "print('The accuracy of the Decision Tree using all variables is:',metrics.accuracy_score(prediction,test_c_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the KNN using all variables is: 0.739\n"
     ]
    }
   ],
   "source": [
    "model=KNeighborsClassifier(n_neighbors=3) \n",
    "model.fit(train_b_a,train_c_a) \n",
    "prediction=model.predict(test_b_a) \n",
    "print('The accuracy of the KNN using all variables is:',metrics.accuracy_score(prediction,test_c_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Linear SVC using all variables is: 0.32655555555555554\n"
     ]
    }
   ],
   "source": [
    "model=svm.LinearSVC()\n",
    "model.fit(train_b_a,train_c_a) \n",
    "prediction=model.predict(test_b_a) \n",
    "print('The accuracy of the Linear SVC using all variables is:',metrics.accuracy_score(prediction,test_c_a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM.SVC is giving the best accuracy with 78.48% score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ccz=cc[['EDUCATION','AGE','PAY_0','PAY_2','PAY_3','PAY_4','PAY_5','PAY_6','dpnm']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_d,test_d=train_test_split(ccz,test_size=0.3,random_state=0) \n",
    "train_e_d=train_d[['EDUCATION','AGE','PAY_0','PAY_2','PAY_3','PAY_4','PAY_5','PAY_6']]\n",
    "train_f_d=train_d.dpnm\n",
    "test_e_d=test_d[['EDUCATION','AGE','PAY_0','PAY_2','PAY_3','PAY_4','PAY_5','PAY_6']]\n",
    "test_f_d=test_d.dpnm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the SVM using Education, Age, and Payment History is: 0.8203333333333334\n"
     ]
    }
   ],
   "source": [
    "model=svm.SVC()\n",
    "model.fit(train_e_d,train_f_d) \n",
    "prediction=model.predict(test_e_d) \n",
    "print('The accuracy of the SVM using Education, Age, and Payment History is:',metrics.accuracy_score(prediction,test_f_d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "\n",
    "   1. Using all data for training the data gives a much better accuracy when classified with SVM.SVC.\n",
    "   2. Using Education, Age, and Payment History as chosen features for training the data gives a much better accuracy when classified with SVM.SVC.\n",
    "   2. This was expected as we saw in the heatmap above that the correlation between Education, Age, and Payment History to Default Payment was higher than the others.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "from sklearn.decomposition import PCA\n",
    "from pylab import plot,show\n",
    "from numpy import genfromtxt, zeros\n",
    "target = genfromtxt('default of credit card clients.csv',delimiter=',',usecols=(24),skip_header=2,dtype=int)\n",
    "pca = PCA(n_components=2)\n",
    "pcad = pca.fit_transform(cc[cc.columns[:23]])\n",
    "plot(pcad[target==0,0],pcad[target==0,1],'bo')\n",
    "plot(pcad[target==1,0],pcad[target==1,1],'ro')\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.61043701 0.29535381]\n"
     ]
    }
   ],
   "source": [
    "print(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first principal component accounts for 61% of the information of the original dataset and the secon one accounts for the remaining 29%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 24)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09420917415705488\n"
     ]
    }
   ],
   "source": [
    "print (1-sum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The percentage of information we lost is 9%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61.04370130752248 %\n",
      "90.57908258396043 %\n",
      "93.63150183258672 %\n",
      "95.32436109196878 %\n",
      "96.26640301792403 %\n",
      "97.17057756242585 %\n",
      "97.92502335277258 %\n",
      "98.5635047319761 %\n",
      "99.1472135027531 %\n",
      "99.44388491430063 %\n",
      "99.68239091675927 %\n",
      "99.85216527023782 %\n",
      "99.99999979997565 %\n",
      "99.99999998313865 %\n",
      "99.99999999253919 %\n",
      "99.9999999944921 %\n",
      "99.99999999574094 %\n",
      "99.99999999692207 %\n",
      "99.99999999770635 %\n",
      "99.99999999828394 %\n",
      "99.99999999882122 %\n",
      "99.99999999928684 %\n",
      "99.9999999996727 %\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,24):\n",
    "    pca = PCA(n_components=i)\n",
    "    pca.fit(cc)\n",
    "    print (sum(pca.explained_variance_ratio_) * 100,'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the output of the snippet above we can see that on the Default of Credit Card Clients dataset we can save almost 100% of the information just by using nine PCs."
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}