credit-card-default-prediction (1).ipynb

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   "source": [
    "#importing the necessary library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "from fastai.tabular.all import *"
   ]
  },
  {
   "cell_type": "code",
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   "id": "37076850",
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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",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>24</td>\n",
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       "      <td>...</td>\n",
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       "      <td>689.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <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",
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       "    <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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
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       "   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",
       "\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",
       "\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",
       "\n",
       "[5 rows x 25 columns]"
      ]
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     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "#importing and reading the dataset\n",
    "df = pd.read_csv(\"../input/credit-card-default-predictions/UCI_Credit_Card.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "52514fce",
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    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "#Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02d04ea6",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 25)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Knowing the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f19d8bda",
   "metadata": {
    "execution": {
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   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "71cd28c2",
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   "outputs": [
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       "<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",
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       "      <th>PAY_3</th>\n",
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       "      <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>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_0         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  default.payment.next.month  \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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#undertanding how the data is distributed\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "258ce9b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:22.915537Z",
     "iopub.status.busy": "2022-06-16T10:19:22.915206Z",
     "iopub.status.idle": "2022-06-16T10:19:22.932166Z",
     "shell.execute_reply": "2022-06-16T10:19:22.930613Z"
    },
    "papermill": {
     "duration": 0.031544,
     "end_time": "2022-06-16T10:19:22.934172",
     "exception": false,
     "start_time": "2022-06-16T10:19:22.902628",
     "status": "completed"
    },
    "tags": []
   },
   "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": [
    "#Getting the information of the dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f666f49",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:22.958899Z",
     "iopub.status.busy": "2022-06-16T10:19:22.958595Z",
     "iopub.status.idle": "2022-06-16T10:19:23.275365Z",
     "shell.execute_reply": "2022-06-16T10:19:23.274557Z"
    },
    "papermill": {
     "duration": 0.332557,
     "end_time": "2022-06-16T10:19:23.278646",
     "exception": false,
     "start_time": "2022-06-16T10:19:22.946089",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row1_col12, #T_cf8af_row1_col15, #T_cf8af_row3_col8, #T_cf8af_row7_col4, #T_cf8af_row10_col24, #T_cf8af_row18_col5, #T_cf8af_row21_col16, #T_cf8af_row22_col5 {\n",
       "  background-color: #bbe4bd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row1_col13, #T_cf8af_row1_col14, #T_cf8af_row11_col17, #T_cf8af_row14_col18 {\n",
       "  background-color: #bee6bf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row1_col16, #T_cf8af_row8_col4, #T_cf8af_row9_col4, #T_cf8af_row20_col5, #T_cf8af_row20_col15 {\n",
       "  background-color: #bae4bd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row1_col18, #T_cf8af_row3_col11, #T_cf8af_row7_col5, #T_cf8af_row8_col5, #T_cf8af_row9_col5, #T_cf8af_row9_col17, #T_cf8af_row10_col5 {\n",
       "  background-color: #cbeac4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row1_col19, #T_cf8af_row8_col12, #T_cf8af_row10_col12, #T_cf8af_row11_col12, #T_cf8af_row18_col23, #T_cf8af_row21_col19 {\n",
       "  background-color: #cfecc8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row1_col20, #T_cf8af_row1_col23, #T_cf8af_row6_col5, #T_cf8af_row9_col14, #T_cf8af_row9_col15, #T_cf8af_row10_col14, #T_cf8af_row18_col14, #T_cf8af_row20_col21 {\n",
       "  background-color: #c8eac3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row1_col21, #T_cf8af_row10_col13, #T_cf8af_row11_col13, #T_cf8af_row18_col15 {\n",
       "  background-color: #ccebc5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row1_col22, #T_cf8af_row7_col12, #T_cf8af_row7_col13, #T_cf8af_row8_col13, #T_cf8af_row9_col16, #T_cf8af_row10_col3, #T_cf8af_row11_col5, #T_cf8af_row11_col14, #T_cf8af_row15_col19, #T_cf8af_row17_col18, #T_cf8af_row21_col18 {\n",
       "  background-color: #c9eac4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row2_col3, #T_cf8af_row3_col4, #T_cf8af_row6_col17, #T_cf8af_row13_col3, #T_cf8af_row14_col21, #T_cf8af_row18_col10, #T_cf8af_row18_col22, #T_cf8af_row20_col11, #T_cf8af_row21_col7, #T_cf8af_row22_col8, #T_cf8af_row22_col12, #T_cf8af_row23_col8 {\n",
       "  background-color: #d5efcf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row2_col5, #T_cf8af_row4_col6, #T_cf8af_row4_col10, #T_cf8af_row9_col12, #T_cf8af_row14_col23, #T_cf8af_row17_col19, #T_cf8af_row19_col21 {\n",
       "  background-color: #d0ecc9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row2_col6, #T_cf8af_row2_col8, #T_cf8af_row5_col9, #T_cf8af_row18_col12, #T_cf8af_row19_col8, #T_cf8af_row23_col11 {\n",
       "  background-color: #d9f0d3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row2_col9, #T_cf8af_row21_col6, #T_cf8af_row22_col16 {\n",
       "  background-color: #daf1d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row2_col10, #T_cf8af_row2_col11, #T_cf8af_row19_col3, #T_cf8af_row21_col10, #T_cf8af_row22_col11 {\n",
       "  background-color: #dcf1d6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row2_col16, #T_cf8af_row4_col12, #T_cf8af_row4_col13, #T_cf8af_row7_col19, #T_cf8af_row8_col1, #T_cf8af_row10_col0, #T_cf8af_row11_col0, #T_cf8af_row11_col22, #T_cf8af_row24_col14, #T_cf8af_row24_col19 {\n",
       "  background-color: #f6fbef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row2_col17, #T_cf8af_row10_col21, #T_cf8af_row24_col18, #T_cf8af_row24_col21, #T_cf8af_row24_col23 {\n",
       "  background-color: #f6fcef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row2_col18, #T_cf8af_row5_col14, #T_cf8af_row5_col22, #T_cf8af_row8_col18, #T_cf8af_row10_col20, #T_cf8af_row11_col21, #T_cf8af_row20_col2 {\n",
       "  background-color: #e9f7e3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row3_col2 {\n",
       "  background-color: #e6f5e0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row3_col5, #T_cf8af_row6_col24, #T_cf8af_row15_col10, #T_cf8af_row16_col10 {\n",
       "  background-color: #99d7ba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row3_col6, #T_cf8af_row3_col9, #T_cf8af_row11_col24, #T_cf8af_row17_col21, #T_cf8af_row19_col20, #T_cf8af_row20_col19 {\n",
       "  background-color: #bfe6bf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row3_col7, #T_cf8af_row5_col3, #T_cf8af_row9_col24, #T_cf8af_row22_col17 {\n",
       "  background-color: #b7e3bc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row3_col10, #T_cf8af_row7_col3, #T_cf8af_row10_col17, #T_cf8af_row13_col4, #T_cf8af_row17_col4 {\n",
       "  background-color: #c4e8c1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row3_col15, #T_cf8af_row3_col23, #T_cf8af_row7_col22, #T_cf8af_row7_col23, #T_cf8af_row8_col2, #T_cf8af_row8_col22, #T_cf8af_row8_col23, #T_cf8af_row9_col1, #T_cf8af_row9_col22 {\n",
       "  background-color: #f3fbed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row3_col16, #T_cf8af_row3_col22, #T_cf8af_row6_col1, #T_cf8af_row7_col0, #T_cf8af_row7_col2, #T_cf8af_row7_col21, #T_cf8af_row8_col20, #T_cf8af_row8_col21, #T_cf8af_row9_col21, #T_cf8af_row24_col0, #T_cf8af_row24_col13, #T_cf8af_row24_col16 {\n",
       "  background-color: #f4fbed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row3_col17, #T_cf8af_row7_col20, #T_cf8af_row8_col0, #T_cf8af_row24_col12, #T_cf8af_row24_col15, #T_cf8af_row24_col17, #T_cf8af_row24_col20 {\n",
       "  background-color: #f5fbee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row3_col20, #T_cf8af_row6_col2, #T_cf8af_row9_col2, #T_cf8af_row9_col23, #T_cf8af_row22_col0 {\n",
       "  background-color: #f2faeb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row3_col21, #T_cf8af_row9_col0, #T_cf8af_row10_col22 {\n",
       "  background-color: #f3faec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row3_col24, #T_cf8af_row5_col10, #T_cf8af_row12_col19, #T_cf8af_row13_col19, #T_cf8af_row19_col6, #T_cf8af_row20_col6, #T_cf8af_row20_col9 {\n",
       "  background-color: #dbf1d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row4_col1, #T_cf8af_row5_col24, #T_cf8af_row20_col3, #T_cf8af_row21_col3, #T_cf8af_row22_col3 {\n",
       "  background-color: #ddf2d7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row4_col9, #T_cf8af_row7_col17, #T_cf8af_row16_col19, #T_cf8af_row18_col16, #T_cf8af_row22_col19 {\n",
       "  background-color: #ceecc8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row4_col24, #T_cf8af_row24_col1 {\n",
       "  background-color: #e3f4de;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row5_col1, #T_cf8af_row24_col11 {\n",
       "  background-color: #b2e1b9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row5_col11, #T_cf8af_row18_col3, #T_cf8af_row18_col6, #T_cf8af_row20_col14, #T_cf8af_row21_col15, #T_cf8af_row23_col3 {\n",
       "  background-color: #dcf1d7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row5_col12, #T_cf8af_row23_col24 {\n",
       "  background-color: #e7f6e2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row5_col20 {\n",
       "  background-color: #e6f6e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row5_col21, #T_cf8af_row19_col2, #T_cf8af_row21_col2, #T_cf8af_row22_col2, #T_cf8af_row23_col2 {\n",
       "  background-color: #e9f6e3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row6_col3, #T_cf8af_row9_col3, #T_cf8af_row21_col17, #T_cf8af_row21_col20 {\n",
       "  background-color: #c7e9c3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row6_col7, #T_cf8af_row8_col10 {\n",
       "  background-color: #2b8cbe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row6_col8 {\n",
       "  background-color: #41a5cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row6_col9 {\n",
       "  background-color: #4bb0d1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row6_col10 {\n",
       "  background-color: #54b6d1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row6_col11 {\n",
       "  background-color: #5fbdcd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row6_col14, #T_cf8af_row6_col16, #T_cf8af_row19_col16, #T_cf8af_row22_col7, #T_cf8af_row22_col14, #T_cf8af_row22_col21, #T_cf8af_row22_col23, #T_cf8af_row23_col7, #T_cf8af_row23_col13, #T_cf8af_row23_col14, #T_cf8af_row23_col22, #T_cf8af_row24_col3 {\n",
       "  background-color: #d4eece;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row6_col15, #T_cf8af_row12_col3, #T_cf8af_row13_col21, #T_cf8af_row19_col10, #T_cf8af_row21_col22, #T_cf8af_row23_col15 {\n",
       "  background-color: #d5eece;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row7_col6, #T_cf8af_row9_col7 {\n",
       "  background-color: #2d8fbf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row7_col8, #T_cf8af_row8_col7 {\n",
       "  background-color: #1878b4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row7_col9 {\n",
       "  background-color: #3091c1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row7_col10 {\n",
       "  background-color: #3a9cc7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row7_col11 {\n",
       "  background-color: #45a8cd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row7_col14, #T_cf8af_row8_col15, #T_cf8af_row8_col16 {\n",
       "  background-color: #cdebc6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row7_col15, #T_cf8af_row7_col16, #T_cf8af_row8_col17 {\n",
       "  background-color: #ceecc7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row7_col24, #T_cf8af_row12_col6, #T_cf8af_row13_col6, #T_cf8af_row14_col19, #T_cf8af_row24_col10 {\n",
       "  background-color: #acdfb7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row8_col6 {\n",
       "  background-color: #42a6cc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row8_col9, #T_cf8af_row9_col8 {\n",
       "  background-color: #1576b3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row8_col11 {\n",
       "  background-color: #389bc6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row8_col24, #T_cf8af_row19_col18 {\n",
       "  background-color: #b3e1ba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row9_col6 {\n",
       "  background-color: #4aafd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row9_col10, #T_cf8af_row10_col9, #T_cf8af_row14_col17, #T_cf8af_row17_col14 {\n",
       "  background-color: #0d6dae;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row9_col11 {\n",
       "  background-color: #2586bb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row10_col2, #T_cf8af_row10_col23, #T_cf8af_row11_col23, #T_cf8af_row23_col0 {\n",
       "  background-color: #f1faeb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row10_col4, #T_cf8af_row11_col4 {\n",
       "  background-color: #b9e3bc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row10_col6 {\n",
       "  background-color: #51b5d2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row10_col7 {\n",
       "  background-color: #3699c5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row10_col8 {\n",
       "  background-color: #298abd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row10_col11, #T_cf8af_row11_col10 {\n",
       "  background-color: #0e6eaf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row10_col15, #T_cf8af_row10_col16, #T_cf8af_row18_col4, #T_cf8af_row19_col4, #T_cf8af_row23_col4 {\n",
       "  background-color: #c2e7c0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row11_col6 {\n",
       "  background-color: #5bbacf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row11_col7 {\n",
       "  background-color: #40a4cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row11_col8 {\n",
       "  background-color: #3597c4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row11_col9 {\n",
       "  background-color: #2484ba;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row11_col15, #T_cf8af_row17_col20, #T_cf8af_row21_col4 {\n",
       "  background-color: #c3e7c1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row12_col1, #T_cf8af_row14_col1 {\n",
       "  background-color: #8ed3be;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row12_col4, #T_cf8af_row14_col4, #T_cf8af_row15_col4, #T_cf8af_row16_col4, #T_cf8af_row16_col18, #T_cf8af_row24_col4 {\n",
       "  background-color: #c5e8c2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row12_col5, #T_cf8af_row13_col18, #T_cf8af_row16_col21, #T_cf8af_row18_col19, #T_cf8af_row19_col14 {\n",
       "  background-color: #b4e2ba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row12_col7, #T_cf8af_row13_col7, #T_cf8af_row13_col8, #T_cf8af_row17_col10 {\n",
       "  background-color: #9cd9b9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row12_col8, #T_cf8af_row14_col11, #T_cf8af_row21_col1 {\n",
       "  background-color: #a5dcb6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row12_col9 {\n",
       "  background-color: #aadeb6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row12_col10, #T_cf8af_row19_col1 {\n",
       "  background-color: #abdeb6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row12_col11 {\n",
       "  background-color: #addfb7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row12_col13, #T_cf8af_row13_col12 {\n",
       "  background-color: #084f91;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row12_col14, #T_cf8af_row13_col15, #T_cf8af_row14_col12, #T_cf8af_row15_col13 {\n",
       "  background-color: #0861a4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row12_col15, #T_cf8af_row15_col12, #T_cf8af_row16_col13 {\n",
       "  background-color: #0a6aad;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row12_col16, #T_cf8af_row13_col17, #T_cf8af_row16_col12 {\n",
       "  background-color: #1373b2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row12_col17 {\n",
       "  background-color: #1b7bb6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row12_col18, #T_cf8af_row13_col20, #T_cf8af_row13_col22, #T_cf8af_row15_col22, #T_cf8af_row18_col9, #T_cf8af_row19_col23, #T_cf8af_row20_col10, #T_cf8af_row20_col22, #T_cf8af_row21_col11, #T_cf8af_row21_col23, #T_cf8af_row23_col12 {\n",
       "  background-color: #d3eecd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row12_col23, #T_cf8af_row14_col22, #T_cf8af_row15_col23, #T_cf8af_row18_col8, #T_cf8af_row19_col15, #T_cf8af_row19_col22 {\n",
       "  background-color: #d0edca;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row12_col24 {\n",
       "  background-color: #e2f4dd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row13_col1 {\n",
       "  background-color: #8fd4bd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row13_col5, #T_cf8af_row14_col5, #T_cf8af_row15_col5 {\n",
       "  background-color: #b5e2bb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row13_col9 {\n",
       "  background-color: #a3dbb7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row13_col10, #T_cf8af_row18_col1, #T_cf8af_row24_col9 {\n",
       "  background-color: #a6dcb6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row13_col11 {\n",
       "  background-color: #a9ddb5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row13_col14, #T_cf8af_row14_col13 {\n",
       "  background-color: #085598;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row13_col16 {\n",
       "  background-color: #0b6cae;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row14_col6, #T_cf8af_row15_col6, #T_cf8af_row16_col6, #T_cf8af_row17_col6 {\n",
       "  background-color: #aedfb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row14_col7, #T_cf8af_row14_col8, #T_cf8af_row15_col7, #T_cf8af_row15_col8, #T_cf8af_row16_col7, #T_cf8af_row17_col9 {\n",
       "  background-color: #9fdab8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row14_col9, #T_cf8af_row15_col9, #T_cf8af_row15_col11, #T_cf8af_row16_col9, #T_cf8af_row24_col8 {\n",
       "  background-color: #9ed9b8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row14_col10, #T_cf8af_row20_col1 {\n",
       "  background-color: #a2dbb7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row14_col15 {\n",
       "  background-color: #08589b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row14_col16, #T_cf8af_row16_col14 {\n",
       "  background-color: #0863a7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row15_col1, #T_cf8af_row16_col1 {\n",
       "  background-color: #8bd2bf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row15_col3, #T_cf8af_row20_col13, #T_cf8af_row21_col9, #T_cf8af_row22_col15 {\n",
       "  background-color: #d8f0d2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row15_col14 {\n",
       "  background-color: #085799;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row15_col16, #T_cf8af_row16_col15 {\n",
       "  background-color: #085294;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row15_col17, #T_cf8af_row17_col15 {\n",
       "  background-color: #085ea1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row15_col18, #T_cf8af_row20_col4, #T_cf8af_row22_col4 {\n",
       "  background-color: #c0e6c0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row15_col20, #T_cf8af_row17_col22 {\n",
       "  background-color: #b1e0b9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row15_col24 {\n",
       "  background-color: #e1f3dc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row16_col0, #T_cf8af_row17_col0, #T_cf8af_row24_col2 {\n",
       "  background-color: #eff9e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row16_col5, #T_cf8af_row17_col5 {\n",
       "  background-color: #b6e3bb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row16_col11 {\n",
       "  background-color: #95d6bb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row16_col17, #T_cf8af_row17_col16 {\n",
       "  background-color: #085093;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row16_col20, #T_cf8af_row18_col13, #T_cf8af_row18_col20, #T_cf8af_row24_col5 {\n",
       "  background-color: #bde5be;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row16_col24, #T_cf8af_row17_col24, #T_cf8af_row19_col12, #T_cf8af_row19_col13 {\n",
       "  background-color: #e0f3db;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row17_col1 {\n",
       "  background-color: #8dd3be;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row17_col11 {\n",
       "  background-color: #98d7ba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row17_col12 {\n",
       "  background-color: #1a7ab5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row17_col13 {\n",
       "  background-color: #1272b1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf8af_row17_col23, #T_cf8af_row18_col7, #T_cf8af_row21_col14, #T_cf8af_row22_col6, #T_cf8af_row23_col6 {\n",
       "  background-color: #daf0d4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row23_col17 {\n",
       "  background-color: #dff2da;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row24_col6 {\n",
       "  background-color: #86d0c0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf8af_row24_col7 {\n",
       "  background-color: #94d5bc;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_cf8af_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >ID</th>\n",
       "      <th class=\"col_heading level0 col1\" >LIMIT_BAL</th>\n",
       "      <th class=\"col_heading level0 col2\" >SEX</th>\n",
       "      <th class=\"col_heading level0 col3\" >EDUCATION</th>\n",
       "      <th class=\"col_heading level0 col4\" >MARRIAGE</th>\n",
       "      <th class=\"col_heading level0 col5\" >AGE</th>\n",
       "      <th class=\"col_heading level0 col6\" >PAY_0</th>\n",
       "      <th class=\"col_heading level0 col7\" >PAY_2</th>\n",
       "      <th class=\"col_heading level0 col8\" >PAY_3</th>\n",
       "      <th class=\"col_heading level0 col9\" >PAY_4</th>\n",
       "      <th class=\"col_heading level0 col10\" >PAY_5</th>\n",
       "      <th class=\"col_heading level0 col11\" >PAY_6</th>\n",
       "      <th class=\"col_heading level0 col12\" >BILL_AMT1</th>\n",
       "      <th class=\"col_heading level0 col13\" >BILL_AMT2</th>\n",
       "      <th class=\"col_heading level0 col14\" >BILL_AMT3</th>\n",
       "      <th class=\"col_heading level0 col15\" >BILL_AMT4</th>\n",
       "      <th class=\"col_heading level0 col16\" >BILL_AMT5</th>\n",
       "      <th class=\"col_heading level0 col17\" >BILL_AMT6</th>\n",
       "      <th class=\"col_heading level0 col18\" >PAY_AMT1</th>\n",
       "      <th class=\"col_heading level0 col19\" >PAY_AMT2</th>\n",
       "      <th class=\"col_heading level0 col20\" >PAY_AMT3</th>\n",
       "      <th class=\"col_heading level0 col21\" >PAY_AMT4</th>\n",
       "      <th class=\"col_heading level0 col22\" >PAY_AMT5</th>\n",
       "      <th class=\"col_heading level0 col23\" >PAY_AMT6</th>\n",
       "      <th class=\"col_heading level0 col24\" >default.payment.next.month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row0\" class=\"row_heading level0 row0\" >ID</th>\n",
       "      <td id=\"T_cf8af_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row0_col1\" class=\"data row0 col1\" >0.026179</td>\n",
       "      <td id=\"T_cf8af_row0_col2\" class=\"data row0 col2\" >0.018497</td>\n",
       "      <td id=\"T_cf8af_row0_col3\" class=\"data row0 col3\" >0.039177</td>\n",
       "      <td id=\"T_cf8af_row0_col4\" class=\"data row0 col4\" >-0.029079</td>\n",
       "      <td id=\"T_cf8af_row0_col5\" class=\"data row0 col5\" >0.018678</td>\n",
       "      <td id=\"T_cf8af_row0_col6\" class=\"data row0 col6\" >-0.030575</td>\n",
       "      <td id=\"T_cf8af_row0_col7\" class=\"data row0 col7\" >-0.011215</td>\n",
       "      <td id=\"T_cf8af_row0_col8\" class=\"data row0 col8\" >-0.018494</td>\n",
       "      <td id=\"T_cf8af_row0_col9\" class=\"data row0 col9\" >-0.002735</td>\n",
       "      <td id=\"T_cf8af_row0_col10\" class=\"data row0 col10\" >-0.022199</td>\n",
       "      <td id=\"T_cf8af_row0_col11\" class=\"data row0 col11\" >-0.020270</td>\n",
       "      <td id=\"T_cf8af_row0_col12\" class=\"data row0 col12\" >0.019389</td>\n",
       "      <td id=\"T_cf8af_row0_col13\" class=\"data row0 col13\" >0.017982</td>\n",
       "      <td id=\"T_cf8af_row0_col14\" class=\"data row0 col14\" >0.024354</td>\n",
       "      <td id=\"T_cf8af_row0_col15\" class=\"data row0 col15\" >0.040351</td>\n",
       "      <td id=\"T_cf8af_row0_col16\" class=\"data row0 col16\" >0.016705</td>\n",
       "      <td id=\"T_cf8af_row0_col17\" class=\"data row0 col17\" >0.016730</td>\n",
       "      <td id=\"T_cf8af_row0_col18\" class=\"data row0 col18\" >0.009742</td>\n",
       "      <td id=\"T_cf8af_row0_col19\" class=\"data row0 col19\" >0.008406</td>\n",
       "      <td id=\"T_cf8af_row0_col20\" class=\"data row0 col20\" >0.039151</td>\n",
       "      <td id=\"T_cf8af_row0_col21\" class=\"data row0 col21\" >0.007793</td>\n",
       "      <td id=\"T_cf8af_row0_col22\" class=\"data row0 col22\" >0.000652</td>\n",
       "      <td id=\"T_cf8af_row0_col23\" class=\"data row0 col23\" >0.003000</td>\n",
       "      <td id=\"T_cf8af_row0_col24\" class=\"data row0 col24\" >-0.013952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row1\" class=\"row_heading level0 row1\" >LIMIT_BAL</th>\n",
       "      <td id=\"T_cf8af_row1_col0\" class=\"data row1 col0\" >0.026179</td>\n",
       "      <td id=\"T_cf8af_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row1_col2\" class=\"data row1 col2\" >0.024755</td>\n",
       "      <td id=\"T_cf8af_row1_col3\" class=\"data row1 col3\" >-0.219161</td>\n",
       "      <td id=\"T_cf8af_row1_col4\" class=\"data row1 col4\" >-0.108139</td>\n",
       "      <td id=\"T_cf8af_row1_col5\" class=\"data row1 col5\" >0.144713</td>\n",
       "      <td id=\"T_cf8af_row1_col6\" class=\"data row1 col6\" >-0.271214</td>\n",
       "      <td id=\"T_cf8af_row1_col7\" class=\"data row1 col7\" >-0.296382</td>\n",
       "      <td id=\"T_cf8af_row1_col8\" class=\"data row1 col8\" >-0.286123</td>\n",
       "      <td id=\"T_cf8af_row1_col9\" class=\"data row1 col9\" >-0.267460</td>\n",
       "      <td id=\"T_cf8af_row1_col10\" class=\"data row1 col10\" >-0.249411</td>\n",
       "      <td id=\"T_cf8af_row1_col11\" class=\"data row1 col11\" >-0.235195</td>\n",
       "      <td id=\"T_cf8af_row1_col12\" class=\"data row1 col12\" >0.285430</td>\n",
       "      <td id=\"T_cf8af_row1_col13\" class=\"data row1 col13\" >0.278314</td>\n",
       "      <td id=\"T_cf8af_row1_col14\" class=\"data row1 col14\" >0.283236</td>\n",
       "      <td id=\"T_cf8af_row1_col15\" class=\"data row1 col15\" >0.293988</td>\n",
       "      <td id=\"T_cf8af_row1_col16\" class=\"data row1 col16\" >0.295562</td>\n",
       "      <td id=\"T_cf8af_row1_col17\" class=\"data row1 col17\" >0.290389</td>\n",
       "      <td id=\"T_cf8af_row1_col18\" class=\"data row1 col18\" >0.195236</td>\n",
       "      <td id=\"T_cf8af_row1_col19\" class=\"data row1 col19\" >0.178408</td>\n",
       "      <td id=\"T_cf8af_row1_col20\" class=\"data row1 col20\" >0.210167</td>\n",
       "      <td id=\"T_cf8af_row1_col21\" class=\"data row1 col21\" >0.203242</td>\n",
       "      <td id=\"T_cf8af_row1_col22\" class=\"data row1 col22\" >0.217202</td>\n",
       "      <td id=\"T_cf8af_row1_col23\" class=\"data row1 col23\" >0.219595</td>\n",
       "      <td id=\"T_cf8af_row1_col24\" class=\"data row1 col24\" >-0.153520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row2\" class=\"row_heading level0 row2\" >SEX</th>\n",
       "      <td id=\"T_cf8af_row2_col0\" class=\"data row2 col0\" >0.018497</td>\n",
       "      <td id=\"T_cf8af_row2_col1\" class=\"data row2 col1\" >0.024755</td>\n",
       "      <td id=\"T_cf8af_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row2_col3\" class=\"data row2 col3\" >0.014232</td>\n",
       "      <td id=\"T_cf8af_row2_col4\" class=\"data row2 col4\" >-0.031389</td>\n",
       "      <td id=\"T_cf8af_row2_col5\" class=\"data row2 col5\" >-0.090874</td>\n",
       "      <td id=\"T_cf8af_row2_col6\" class=\"data row2 col6\" >-0.057643</td>\n",
       "      <td id=\"T_cf8af_row2_col7\" class=\"data row2 col7\" >-0.070771</td>\n",
       "      <td id=\"T_cf8af_row2_col8\" class=\"data row2 col8\" >-0.066096</td>\n",
       "      <td id=\"T_cf8af_row2_col9\" class=\"data row2 col9\" >-0.060173</td>\n",
       "      <td id=\"T_cf8af_row2_col10\" class=\"data row2 col10\" >-0.055064</td>\n",
       "      <td id=\"T_cf8af_row2_col11\" class=\"data row2 col11\" >-0.044008</td>\n",
       "      <td id=\"T_cf8af_row2_col12\" class=\"data row2 col12\" >-0.033642</td>\n",
       "      <td id=\"T_cf8af_row2_col13\" class=\"data row2 col13\" >-0.031183</td>\n",
       "      <td id=\"T_cf8af_row2_col14\" class=\"data row2 col14\" >-0.024563</td>\n",
       "      <td id=\"T_cf8af_row2_col15\" class=\"data row2 col15\" >-0.021880</td>\n",
       "      <td id=\"T_cf8af_row2_col16\" class=\"data row2 col16\" >-0.017005</td>\n",
       "      <td id=\"T_cf8af_row2_col17\" class=\"data row2 col17\" >-0.016733</td>\n",
       "      <td id=\"T_cf8af_row2_col18\" class=\"data row2 col18\" >-0.000242</td>\n",
       "      <td id=\"T_cf8af_row2_col19\" class=\"data row2 col19\" >-0.001391</td>\n",
       "      <td id=\"T_cf8af_row2_col20\" class=\"data row2 col20\" >-0.008597</td>\n",
       "      <td id=\"T_cf8af_row2_col21\" class=\"data row2 col21\" >-0.002229</td>\n",
       "      <td id=\"T_cf8af_row2_col22\" class=\"data row2 col22\" >-0.001667</td>\n",
       "      <td id=\"T_cf8af_row2_col23\" class=\"data row2 col23\" >-0.002766</td>\n",
       "      <td id=\"T_cf8af_row2_col24\" class=\"data row2 col24\" >-0.039961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row3\" class=\"row_heading level0 row3\" >EDUCATION</th>\n",
       "      <td id=\"T_cf8af_row3_col0\" class=\"data row3 col0\" >0.039177</td>\n",
       "      <td id=\"T_cf8af_row3_col1\" class=\"data row3 col1\" >-0.219161</td>\n",
       "      <td id=\"T_cf8af_row3_col2\" class=\"data row3 col2\" >0.014232</td>\n",
       "      <td id=\"T_cf8af_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row3_col4\" class=\"data row3 col4\" >-0.143464</td>\n",
       "      <td id=\"T_cf8af_row3_col5\" class=\"data row3 col5\" >0.175061</td>\n",
       "      <td id=\"T_cf8af_row3_col6\" class=\"data row3 col6\" >0.105364</td>\n",
       "      <td id=\"T_cf8af_row3_col7\" class=\"data row3 col7\" >0.121566</td>\n",
       "      <td id=\"T_cf8af_row3_col8\" class=\"data row3 col8\" >0.114025</td>\n",
       "      <td id=\"T_cf8af_row3_col9\" class=\"data row3 col9\" >0.108793</td>\n",
       "      <td id=\"T_cf8af_row3_col10\" class=\"data row3 col10\" >0.097520</td>\n",
       "      <td id=\"T_cf8af_row3_col11\" class=\"data row3 col11\" >0.082316</td>\n",
       "      <td id=\"T_cf8af_row3_col12\" class=\"data row3 col12\" >0.023581</td>\n",
       "      <td id=\"T_cf8af_row3_col13\" class=\"data row3 col13\" >0.018749</td>\n",
       "      <td id=\"T_cf8af_row3_col14\" class=\"data row3 col14\" >0.013002</td>\n",
       "      <td id=\"T_cf8af_row3_col15\" class=\"data row3 col15\" >-0.000451</td>\n",
       "      <td id=\"T_cf8af_row3_col16\" class=\"data row3 col16\" >-0.007567</td>\n",
       "      <td id=\"T_cf8af_row3_col17\" class=\"data row3 col17\" >-0.009099</td>\n",
       "      <td id=\"T_cf8af_row3_col18\" class=\"data row3 col18\" >-0.037456</td>\n",
       "      <td id=\"T_cf8af_row3_col19\" class=\"data row3 col19\" >-0.030038</td>\n",
       "      <td id=\"T_cf8af_row3_col20\" class=\"data row3 col20\" >-0.039943</td>\n",
       "      <td id=\"T_cf8af_row3_col21\" class=\"data row3 col21\" >-0.038218</td>\n",
       "      <td id=\"T_cf8af_row3_col22\" class=\"data row3 col22\" >-0.040358</td>\n",
       "      <td id=\"T_cf8af_row3_col23\" class=\"data row3 col23\" >-0.037200</td>\n",
       "      <td id=\"T_cf8af_row3_col24\" class=\"data row3 col24\" >0.028006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row4\" class=\"row_heading level0 row4\" >MARRIAGE</th>\n",
       "      <td id=\"T_cf8af_row4_col0\" class=\"data row4 col0\" >-0.029079</td>\n",
       "      <td id=\"T_cf8af_row4_col1\" class=\"data row4 col1\" >-0.108139</td>\n",
       "      <td id=\"T_cf8af_row4_col2\" class=\"data row4 col2\" >-0.031389</td>\n",
       "      <td id=\"T_cf8af_row4_col3\" class=\"data row4 col3\" >-0.143464</td>\n",
       "      <td id=\"T_cf8af_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row4_col5\" class=\"data row4 col5\" >-0.414170</td>\n",
       "      <td id=\"T_cf8af_row4_col6\" class=\"data row4 col6\" >0.019917</td>\n",
       "      <td id=\"T_cf8af_row4_col7\" class=\"data row4 col7\" >0.024199</td>\n",
       "      <td id=\"T_cf8af_row4_col8\" class=\"data row4 col8\" >0.032688</td>\n",
       "      <td id=\"T_cf8af_row4_col9\" class=\"data row4 col9\" >0.033122</td>\n",
       "      <td id=\"T_cf8af_row4_col10\" class=\"data row4 col10\" >0.035629</td>\n",
       "      <td id=\"T_cf8af_row4_col11\" class=\"data row4 col11\" >0.034345</td>\n",
       "      <td id=\"T_cf8af_row4_col12\" class=\"data row4 col12\" >-0.023472</td>\n",
       "      <td id=\"T_cf8af_row4_col13\" class=\"data row4 col13\" >-0.021602</td>\n",
       "      <td id=\"T_cf8af_row4_col14\" class=\"data row4 col14\" >-0.024909</td>\n",
       "      <td id=\"T_cf8af_row4_col15\" class=\"data row4 col15\" >-0.023344</td>\n",
       "      <td id=\"T_cf8af_row4_col16\" class=\"data row4 col16\" >-0.025393</td>\n",
       "      <td id=\"T_cf8af_row4_col17\" class=\"data row4 col17\" >-0.021207</td>\n",
       "      <td id=\"T_cf8af_row4_col18\" class=\"data row4 col18\" >-0.005979</td>\n",
       "      <td id=\"T_cf8af_row4_col19\" class=\"data row4 col19\" >-0.008093</td>\n",
       "      <td id=\"T_cf8af_row4_col20\" class=\"data row4 col20\" >-0.003541</td>\n",
       "      <td id=\"T_cf8af_row4_col21\" class=\"data row4 col21\" >-0.012659</td>\n",
       "      <td id=\"T_cf8af_row4_col22\" class=\"data row4 col22\" >-0.001205</td>\n",
       "      <td id=\"T_cf8af_row4_col23\" class=\"data row4 col23\" >-0.006641</td>\n",
       "      <td id=\"T_cf8af_row4_col24\" class=\"data row4 col24\" >-0.024339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row5\" class=\"row_heading level0 row5\" >AGE</th>\n",
       "      <td id=\"T_cf8af_row5_col0\" class=\"data row5 col0\" >0.018678</td>\n",
       "      <td id=\"T_cf8af_row5_col1\" class=\"data row5 col1\" >0.144713</td>\n",
       "      <td id=\"T_cf8af_row5_col2\" class=\"data row5 col2\" >-0.090874</td>\n",
       "      <td id=\"T_cf8af_row5_col3\" class=\"data row5 col3\" >0.175061</td>\n",
       "      <td id=\"T_cf8af_row5_col4\" class=\"data row5 col4\" >-0.414170</td>\n",
       "      <td id=\"T_cf8af_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row5_col6\" class=\"data row5 col6\" >-0.039447</td>\n",
       "      <td id=\"T_cf8af_row5_col7\" class=\"data row5 col7\" >-0.050148</td>\n",
       "      <td id=\"T_cf8af_row5_col8\" class=\"data row5 col8\" >-0.053048</td>\n",
       "      <td id=\"T_cf8af_row5_col9\" class=\"data row5 col9\" >-0.049722</td>\n",
       "      <td id=\"T_cf8af_row5_col10\" class=\"data row5 col10\" >-0.053826</td>\n",
       "      <td id=\"T_cf8af_row5_col11\" class=\"data row5 col11\" >-0.048773</td>\n",
       "      <td id=\"T_cf8af_row5_col12\" class=\"data row5 col12\" >0.056239</td>\n",
       "      <td id=\"T_cf8af_row5_col13\" class=\"data row5 col13\" >0.054283</td>\n",
       "      <td id=\"T_cf8af_row5_col14\" class=\"data row5 col14\" >0.053710</td>\n",
       "      <td id=\"T_cf8af_row5_col15\" class=\"data row5 col15\" >0.051353</td>\n",
       "      <td id=\"T_cf8af_row5_col16\" class=\"data row5 col16\" >0.049345</td>\n",
       "      <td id=\"T_cf8af_row5_col17\" class=\"data row5 col17\" >0.047613</td>\n",
       "      <td id=\"T_cf8af_row5_col18\" class=\"data row5 col18\" >0.026147</td>\n",
       "      <td id=\"T_cf8af_row5_col19\" class=\"data row5 col19\" >0.021785</td>\n",
       "      <td id=\"T_cf8af_row5_col20\" class=\"data row5 col20\" >0.029247</td>\n",
       "      <td id=\"T_cf8af_row5_col21\" class=\"data row5 col21\" >0.021379</td>\n",
       "      <td id=\"T_cf8af_row5_col22\" class=\"data row5 col22\" >0.022850</td>\n",
       "      <td id=\"T_cf8af_row5_col23\" class=\"data row5 col23\" >0.019478</td>\n",
       "      <td id=\"T_cf8af_row5_col24\" class=\"data row5 col24\" >0.013890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row6\" class=\"row_heading level0 row6\" >PAY_0</th>\n",
       "      <td id=\"T_cf8af_row6_col0\" class=\"data row6 col0\" >-0.030575</td>\n",
       "      <td id=\"T_cf8af_row6_col1\" class=\"data row6 col1\" >-0.271214</td>\n",
       "      <td id=\"T_cf8af_row6_col2\" class=\"data row6 col2\" >-0.057643</td>\n",
       "      <td id=\"T_cf8af_row6_col3\" class=\"data row6 col3\" >0.105364</td>\n",
       "      <td id=\"T_cf8af_row6_col4\" class=\"data row6 col4\" >0.019917</td>\n",
       "      <td id=\"T_cf8af_row6_col5\" class=\"data row6 col5\" >-0.039447</td>\n",
       "      <td id=\"T_cf8af_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row6_col7\" class=\"data row6 col7\" >0.672164</td>\n",
       "      <td id=\"T_cf8af_row6_col8\" class=\"data row6 col8\" >0.574245</td>\n",
       "      <td id=\"T_cf8af_row6_col9\" class=\"data row6 col9\" >0.538841</td>\n",
       "      <td id=\"T_cf8af_row6_col10\" class=\"data row6 col10\" >0.509426</td>\n",
       "      <td id=\"T_cf8af_row6_col11\" class=\"data row6 col11\" >0.474553</td>\n",
       "      <td id=\"T_cf8af_row6_col12\" class=\"data row6 col12\" >0.187068</td>\n",
       "      <td id=\"T_cf8af_row6_col13\" class=\"data row6 col13\" >0.189859</td>\n",
       "      <td id=\"T_cf8af_row6_col14\" class=\"data row6 col14\" >0.179785</td>\n",
       "      <td id=\"T_cf8af_row6_col15\" class=\"data row6 col15\" >0.179125</td>\n",
       "      <td id=\"T_cf8af_row6_col16\" class=\"data row6 col16\" >0.180635</td>\n",
       "      <td id=\"T_cf8af_row6_col17\" class=\"data row6 col17\" >0.176980</td>\n",
       "      <td id=\"T_cf8af_row6_col18\" class=\"data row6 col18\" >-0.079269</td>\n",
       "      <td id=\"T_cf8af_row6_col19\" class=\"data row6 col19\" >-0.070101</td>\n",
       "      <td id=\"T_cf8af_row6_col20\" class=\"data row6 col20\" >-0.070561</td>\n",
       "      <td id=\"T_cf8af_row6_col21\" class=\"data row6 col21\" >-0.064005</td>\n",
       "      <td id=\"T_cf8af_row6_col22\" class=\"data row6 col22\" >-0.058190</td>\n",
       "      <td id=\"T_cf8af_row6_col23\" class=\"data row6 col23\" >-0.058673</td>\n",
       "      <td id=\"T_cf8af_row6_col24\" class=\"data row6 col24\" >0.324794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row7\" class=\"row_heading level0 row7\" >PAY_2</th>\n",
       "      <td id=\"T_cf8af_row7_col0\" class=\"data row7 col0\" >-0.011215</td>\n",
       "      <td id=\"T_cf8af_row7_col1\" class=\"data row7 col1\" >-0.296382</td>\n",
       "      <td id=\"T_cf8af_row7_col2\" class=\"data row7 col2\" >-0.070771</td>\n",
       "      <td id=\"T_cf8af_row7_col3\" class=\"data row7 col3\" >0.121566</td>\n",
       "      <td id=\"T_cf8af_row7_col4\" class=\"data row7 col4\" >0.024199</td>\n",
       "      <td id=\"T_cf8af_row7_col5\" class=\"data row7 col5\" >-0.050148</td>\n",
       "      <td id=\"T_cf8af_row7_col6\" class=\"data row7 col6\" >0.672164</td>\n",
       "      <td id=\"T_cf8af_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row7_col8\" class=\"data row7 col8\" >0.766552</td>\n",
       "      <td id=\"T_cf8af_row7_col9\" class=\"data row7 col9\" >0.662067</td>\n",
       "      <td id=\"T_cf8af_row7_col10\" class=\"data row7 col10\" >0.622780</td>\n",
       "      <td id=\"T_cf8af_row7_col11\" class=\"data row7 col11\" >0.575501</td>\n",
       "      <td id=\"T_cf8af_row7_col12\" class=\"data row7 col12\" >0.234887</td>\n",
       "      <td id=\"T_cf8af_row7_col13\" class=\"data row7 col13\" >0.235257</td>\n",
       "      <td id=\"T_cf8af_row7_col14\" class=\"data row7 col14\" >0.224146</td>\n",
       "      <td id=\"T_cf8af_row7_col15\" class=\"data row7 col15\" >0.222237</td>\n",
       "      <td id=\"T_cf8af_row7_col16\" class=\"data row7 col16\" >0.221348</td>\n",
       "      <td id=\"T_cf8af_row7_col17\" class=\"data row7 col17\" >0.219403</td>\n",
       "      <td id=\"T_cf8af_row7_col18\" class=\"data row7 col18\" >-0.080701</td>\n",
       "      <td id=\"T_cf8af_row7_col19\" class=\"data row7 col19\" >-0.058990</td>\n",
       "      <td id=\"T_cf8af_row7_col20\" class=\"data row7 col20\" >-0.055901</td>\n",
       "      <td id=\"T_cf8af_row7_col21\" class=\"data row7 col21\" >-0.046858</td>\n",
       "      <td id=\"T_cf8af_row7_col22\" class=\"data row7 col22\" >-0.037093</td>\n",
       "      <td id=\"T_cf8af_row7_col23\" class=\"data row7 col23\" >-0.036500</td>\n",
       "      <td id=\"T_cf8af_row7_col24\" class=\"data row7 col24\" >0.263551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row8\" class=\"row_heading level0 row8\" >PAY_3</th>\n",
       "      <td id=\"T_cf8af_row8_col0\" class=\"data row8 col0\" >-0.018494</td>\n",
       "      <td id=\"T_cf8af_row8_col1\" class=\"data row8 col1\" >-0.286123</td>\n",
       "      <td id=\"T_cf8af_row8_col2\" class=\"data row8 col2\" >-0.066096</td>\n",
       "      <td id=\"T_cf8af_row8_col3\" class=\"data row8 col3\" >0.114025</td>\n",
       "      <td id=\"T_cf8af_row8_col4\" class=\"data row8 col4\" >0.032688</td>\n",
       "      <td id=\"T_cf8af_row8_col5\" class=\"data row8 col5\" >-0.053048</td>\n",
       "      <td id=\"T_cf8af_row8_col6\" class=\"data row8 col6\" >0.574245</td>\n",
       "      <td id=\"T_cf8af_row8_col7\" class=\"data row8 col7\" >0.766552</td>\n",
       "      <td id=\"T_cf8af_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row8_col9\" class=\"data row8 col9\" >0.777359</td>\n",
       "      <td id=\"T_cf8af_row8_col10\" class=\"data row8 col10\" >0.686775</td>\n",
       "      <td id=\"T_cf8af_row8_col11\" class=\"data row8 col11\" >0.632684</td>\n",
       "      <td id=\"T_cf8af_row8_col12\" class=\"data row8 col12\" >0.208473</td>\n",
       "      <td id=\"T_cf8af_row8_col13\" class=\"data row8 col13\" >0.237295</td>\n",
       "      <td id=\"T_cf8af_row8_col14\" class=\"data row8 col14\" >0.227494</td>\n",
       "      <td id=\"T_cf8af_row8_col15\" class=\"data row8 col15\" >0.227202</td>\n",
       "      <td id=\"T_cf8af_row8_col16\" class=\"data row8 col16\" >0.225145</td>\n",
       "      <td id=\"T_cf8af_row8_col17\" class=\"data row8 col17\" >0.222327</td>\n",
       "      <td id=\"T_cf8af_row8_col18\" class=\"data row8 col18\" >0.001295</td>\n",
       "      <td id=\"T_cf8af_row8_col19\" class=\"data row8 col19\" >-0.066793</td>\n",
       "      <td id=\"T_cf8af_row8_col20\" class=\"data row8 col20\" >-0.053311</td>\n",
       "      <td id=\"T_cf8af_row8_col21\" class=\"data row8 col21\" >-0.046067</td>\n",
       "      <td id=\"T_cf8af_row8_col22\" class=\"data row8 col22\" >-0.035863</td>\n",
       "      <td id=\"T_cf8af_row8_col23\" class=\"data row8 col23\" >-0.035861</td>\n",
       "      <td id=\"T_cf8af_row8_col24\" class=\"data row8 col24\" >0.235253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row9\" class=\"row_heading level0 row9\" >PAY_4</th>\n",
       "      <td id=\"T_cf8af_row9_col0\" class=\"data row9 col0\" >-0.002735</td>\n",
       "      <td id=\"T_cf8af_row9_col1\" class=\"data row9 col1\" >-0.267460</td>\n",
       "      <td id=\"T_cf8af_row9_col2\" class=\"data row9 col2\" >-0.060173</td>\n",
       "      <td id=\"T_cf8af_row9_col3\" class=\"data row9 col3\" >0.108793</td>\n",
       "      <td id=\"T_cf8af_row9_col4\" class=\"data row9 col4\" >0.033122</td>\n",
       "      <td id=\"T_cf8af_row9_col5\" class=\"data row9 col5\" >-0.049722</td>\n",
       "      <td id=\"T_cf8af_row9_col6\" class=\"data row9 col6\" >0.538841</td>\n",
       "      <td id=\"T_cf8af_row9_col7\" class=\"data row9 col7\" >0.662067</td>\n",
       "      <td id=\"T_cf8af_row9_col8\" class=\"data row9 col8\" >0.777359</td>\n",
       "      <td id=\"T_cf8af_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row9_col10\" class=\"data row9 col10\" >0.819835</td>\n",
       "      <td id=\"T_cf8af_row9_col11\" class=\"data row9 col11\" >0.716449</td>\n",
       "      <td id=\"T_cf8af_row9_col12\" class=\"data row9 col12\" >0.202812</td>\n",
       "      <td id=\"T_cf8af_row9_col13\" class=\"data row9 col13\" >0.225816</td>\n",
       "      <td id=\"T_cf8af_row9_col14\" class=\"data row9 col14\" >0.244983</td>\n",
       "      <td id=\"T_cf8af_row9_col15\" class=\"data row9 col15\" >0.245917</td>\n",
       "      <td id=\"T_cf8af_row9_col16\" class=\"data row9 col16\" >0.242902</td>\n",
       "      <td id=\"T_cf8af_row9_col17\" class=\"data row9 col17\" >0.239154</td>\n",
       "      <td id=\"T_cf8af_row9_col18\" class=\"data row9 col18\" >-0.009362</td>\n",
       "      <td id=\"T_cf8af_row9_col19\" class=\"data row9 col19\" >-0.001944</td>\n",
       "      <td id=\"T_cf8af_row9_col20\" class=\"data row9 col20\" >-0.069235</td>\n",
       "      <td id=\"T_cf8af_row9_col21\" class=\"data row9 col21\" >-0.043461</td>\n",
       "      <td id=\"T_cf8af_row9_col22\" class=\"data row9 col22\" >-0.033590</td>\n",
       "      <td id=\"T_cf8af_row9_col23\" class=\"data row9 col23\" >-0.026565</td>\n",
       "      <td id=\"T_cf8af_row9_col24\" class=\"data row9 col24\" >0.216614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row10\" class=\"row_heading level0 row10\" >PAY_5</th>\n",
       "      <td id=\"T_cf8af_row10_col0\" class=\"data row10 col0\" >-0.022199</td>\n",
       "      <td id=\"T_cf8af_row10_col1\" class=\"data row10 col1\" >-0.249411</td>\n",
       "      <td id=\"T_cf8af_row10_col2\" class=\"data row10 col2\" >-0.055064</td>\n",
       "      <td id=\"T_cf8af_row10_col3\" class=\"data row10 col3\" >0.097520</td>\n",
       "      <td id=\"T_cf8af_row10_col4\" class=\"data row10 col4\" >0.035629</td>\n",
       "      <td id=\"T_cf8af_row10_col5\" class=\"data row10 col5\" >-0.053826</td>\n",
       "      <td id=\"T_cf8af_row10_col6\" class=\"data row10 col6\" >0.509426</td>\n",
       "      <td id=\"T_cf8af_row10_col7\" class=\"data row10 col7\" >0.622780</td>\n",
       "      <td id=\"T_cf8af_row10_col8\" class=\"data row10 col8\" >0.686775</td>\n",
       "      <td id=\"T_cf8af_row10_col9\" class=\"data row10 col9\" >0.819835</td>\n",
       "      <td id=\"T_cf8af_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row10_col11\" class=\"data row10 col11\" >0.816900</td>\n",
       "      <td id=\"T_cf8af_row10_col12\" class=\"data row10 col12\" >0.206684</td>\n",
       "      <td id=\"T_cf8af_row10_col13\" class=\"data row10 col13\" >0.226913</td>\n",
       "      <td id=\"T_cf8af_row10_col14\" class=\"data row10 col14\" >0.243335</td>\n",
       "      <td id=\"T_cf8af_row10_col15\" class=\"data row10 col15\" >0.271915</td>\n",
       "      <td id=\"T_cf8af_row10_col16\" class=\"data row10 col16\" >0.269783</td>\n",
       "      <td id=\"T_cf8af_row10_col17\" class=\"data row10 col17\" >0.262509</td>\n",
       "      <td id=\"T_cf8af_row10_col18\" class=\"data row10 col18\" >-0.006089</td>\n",
       "      <td id=\"T_cf8af_row10_col19\" class=\"data row10 col19\" >-0.003191</td>\n",
       "      <td id=\"T_cf8af_row10_col20\" class=\"data row10 col20\" >0.009062</td>\n",
       "      <td id=\"T_cf8af_row10_col21\" class=\"data row10 col21\" >-0.058299</td>\n",
       "      <td id=\"T_cf8af_row10_col22\" class=\"data row10 col22\" >-0.033337</td>\n",
       "      <td id=\"T_cf8af_row10_col23\" class=\"data row10 col23\" >-0.023027</td>\n",
       "      <td id=\"T_cf8af_row10_col24\" class=\"data row10 col24\" >0.204149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row11\" class=\"row_heading level0 row11\" >PAY_6</th>\n",
       "      <td id=\"T_cf8af_row11_col0\" class=\"data row11 col0\" >-0.020270</td>\n",
       "      <td id=\"T_cf8af_row11_col1\" class=\"data row11 col1\" >-0.235195</td>\n",
       "      <td id=\"T_cf8af_row11_col2\" class=\"data row11 col2\" >-0.044008</td>\n",
       "      <td id=\"T_cf8af_row11_col3\" class=\"data row11 col3\" >0.082316</td>\n",
       "      <td id=\"T_cf8af_row11_col4\" class=\"data row11 col4\" >0.034345</td>\n",
       "      <td id=\"T_cf8af_row11_col5\" class=\"data row11 col5\" >-0.048773</td>\n",
       "      <td id=\"T_cf8af_row11_col6\" class=\"data row11 col6\" >0.474553</td>\n",
       "      <td id=\"T_cf8af_row11_col7\" class=\"data row11 col7\" >0.575501</td>\n",
       "      <td id=\"T_cf8af_row11_col8\" class=\"data row11 col8\" >0.632684</td>\n",
       "      <td id=\"T_cf8af_row11_col9\" class=\"data row11 col9\" >0.716449</td>\n",
       "      <td id=\"T_cf8af_row11_col10\" class=\"data row11 col10\" >0.816900</td>\n",
       "      <td id=\"T_cf8af_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row11_col12\" class=\"data row11 col12\" >0.207373</td>\n",
       "      <td id=\"T_cf8af_row11_col13\" class=\"data row11 col13\" >0.226924</td>\n",
       "      <td id=\"T_cf8af_row11_col14\" class=\"data row11 col14\" >0.241181</td>\n",
       "      <td id=\"T_cf8af_row11_col15\" class=\"data row11 col15\" >0.266356</td>\n",
       "      <td id=\"T_cf8af_row11_col16\" class=\"data row11 col16\" >0.290894</td>\n",
       "      <td id=\"T_cf8af_row11_col17\" class=\"data row11 col17\" >0.285091</td>\n",
       "      <td id=\"T_cf8af_row11_col18\" class=\"data row11 col18\" >-0.001496</td>\n",
       "      <td id=\"T_cf8af_row11_col19\" class=\"data row11 col19\" >-0.005223</td>\n",
       "      <td id=\"T_cf8af_row11_col20\" class=\"data row11 col20\" >0.005834</td>\n",
       "      <td id=\"T_cf8af_row11_col21\" class=\"data row11 col21\" >0.019018</td>\n",
       "      <td id=\"T_cf8af_row11_col22\" class=\"data row11 col22\" >-0.046434</td>\n",
       "      <td id=\"T_cf8af_row11_col23\" class=\"data row11 col23\" >-0.025299</td>\n",
       "      <td id=\"T_cf8af_row11_col24\" class=\"data row11 col24\" >0.186866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row12\" class=\"row_heading level0 row12\" >BILL_AMT1</th>\n",
       "      <td id=\"T_cf8af_row12_col0\" class=\"data row12 col0\" >0.019389</td>\n",
       "      <td id=\"T_cf8af_row12_col1\" class=\"data row12 col1\" >0.285430</td>\n",
       "      <td id=\"T_cf8af_row12_col2\" class=\"data row12 col2\" >-0.033642</td>\n",
       "      <td id=\"T_cf8af_row12_col3\" class=\"data row12 col3\" >0.023581</td>\n",
       "      <td id=\"T_cf8af_row12_col4\" class=\"data row12 col4\" >-0.023472</td>\n",
       "      <td id=\"T_cf8af_row12_col5\" class=\"data row12 col5\" >0.056239</td>\n",
       "      <td id=\"T_cf8af_row12_col6\" class=\"data row12 col6\" >0.187068</td>\n",
       "      <td id=\"T_cf8af_row12_col7\" class=\"data row12 col7\" >0.234887</td>\n",
       "      <td id=\"T_cf8af_row12_col8\" class=\"data row12 col8\" >0.208473</td>\n",
       "      <td id=\"T_cf8af_row12_col9\" class=\"data row12 col9\" >0.202812</td>\n",
       "      <td id=\"T_cf8af_row12_col10\" class=\"data row12 col10\" >0.206684</td>\n",
       "      <td id=\"T_cf8af_row12_col11\" class=\"data row12 col11\" >0.207373</td>\n",
       "      <td id=\"T_cf8af_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row12_col13\" class=\"data row12 col13\" >0.951484</td>\n",
       "      <td id=\"T_cf8af_row12_col14\" class=\"data row12 col14\" >0.892279</td>\n",
       "      <td id=\"T_cf8af_row12_col15\" class=\"data row12 col15\" >0.860272</td>\n",
       "      <td id=\"T_cf8af_row12_col16\" class=\"data row12 col16\" >0.829779</td>\n",
       "      <td id=\"T_cf8af_row12_col17\" class=\"data row12 col17\" >0.802650</td>\n",
       "      <td id=\"T_cf8af_row12_col18\" class=\"data row12 col18\" >0.140277</td>\n",
       "      <td id=\"T_cf8af_row12_col19\" class=\"data row12 col19\" >0.099355</td>\n",
       "      <td id=\"T_cf8af_row12_col20\" class=\"data row12 col20\" >0.156887</td>\n",
       "      <td id=\"T_cf8af_row12_col21\" class=\"data row12 col21\" >0.158303</td>\n",
       "      <td id=\"T_cf8af_row12_col22\" class=\"data row12 col22\" >0.167026</td>\n",
       "      <td id=\"T_cf8af_row12_col23\" class=\"data row12 col23\" >0.179341</td>\n",
       "      <td id=\"T_cf8af_row12_col24\" class=\"data row12 col24\" >-0.019644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row13\" class=\"row_heading level0 row13\" >BILL_AMT2</th>\n",
       "      <td id=\"T_cf8af_row13_col0\" class=\"data row13 col0\" >0.017982</td>\n",
       "      <td id=\"T_cf8af_row13_col1\" class=\"data row13 col1\" >0.278314</td>\n",
       "      <td id=\"T_cf8af_row13_col2\" class=\"data row13 col2\" >-0.031183</td>\n",
       "      <td id=\"T_cf8af_row13_col3\" class=\"data row13 col3\" >0.018749</td>\n",
       "      <td id=\"T_cf8af_row13_col4\" class=\"data row13 col4\" >-0.021602</td>\n",
       "      <td id=\"T_cf8af_row13_col5\" class=\"data row13 col5\" >0.054283</td>\n",
       "      <td id=\"T_cf8af_row13_col6\" class=\"data row13 col6\" >0.189859</td>\n",
       "      <td id=\"T_cf8af_row13_col7\" class=\"data row13 col7\" >0.235257</td>\n",
       "      <td id=\"T_cf8af_row13_col8\" class=\"data row13 col8\" >0.237295</td>\n",
       "      <td id=\"T_cf8af_row13_col9\" class=\"data row13 col9\" >0.225816</td>\n",
       "      <td id=\"T_cf8af_row13_col10\" class=\"data row13 col10\" >0.226913</td>\n",
       "      <td id=\"T_cf8af_row13_col11\" class=\"data row13 col11\" >0.226924</td>\n",
       "      <td id=\"T_cf8af_row13_col12\" class=\"data row13 col12\" >0.951484</td>\n",
       "      <td id=\"T_cf8af_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row13_col14\" class=\"data row13 col14\" >0.928326</td>\n",
       "      <td id=\"T_cf8af_row13_col15\" class=\"data row13 col15\" >0.892482</td>\n",
       "      <td id=\"T_cf8af_row13_col16\" class=\"data row13 col16\" >0.859778</td>\n",
       "      <td id=\"T_cf8af_row13_col17\" class=\"data row13 col17\" >0.831594</td>\n",
       "      <td id=\"T_cf8af_row13_col18\" class=\"data row13 col18\" >0.280365</td>\n",
       "      <td id=\"T_cf8af_row13_col19\" class=\"data row13 col19\" >0.100851</td>\n",
       "      <td id=\"T_cf8af_row13_col20\" class=\"data row13 col20\" >0.150718</td>\n",
       "      <td id=\"T_cf8af_row13_col21\" class=\"data row13 col21\" >0.147398</td>\n",
       "      <td id=\"T_cf8af_row13_col22\" class=\"data row13 col22\" >0.157957</td>\n",
       "      <td id=\"T_cf8af_row13_col23\" class=\"data row13 col23\" >0.174256</td>\n",
       "      <td id=\"T_cf8af_row13_col24\" class=\"data row13 col24\" >-0.014193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row14\" class=\"row_heading level0 row14\" >BILL_AMT3</th>\n",
       "      <td id=\"T_cf8af_row14_col0\" class=\"data row14 col0\" >0.024354</td>\n",
       "      <td id=\"T_cf8af_row14_col1\" class=\"data row14 col1\" >0.283236</td>\n",
       "      <td id=\"T_cf8af_row14_col2\" class=\"data row14 col2\" >-0.024563</td>\n",
       "      <td id=\"T_cf8af_row14_col3\" class=\"data row14 col3\" >0.013002</td>\n",
       "      <td id=\"T_cf8af_row14_col4\" class=\"data row14 col4\" >-0.024909</td>\n",
       "      <td id=\"T_cf8af_row14_col5\" class=\"data row14 col5\" >0.053710</td>\n",
       "      <td id=\"T_cf8af_row14_col6\" class=\"data row14 col6\" >0.179785</td>\n",
       "      <td id=\"T_cf8af_row14_col7\" class=\"data row14 col7\" >0.224146</td>\n",
       "      <td id=\"T_cf8af_row14_col8\" class=\"data row14 col8\" >0.227494</td>\n",
       "      <td id=\"T_cf8af_row14_col9\" class=\"data row14 col9\" >0.244983</td>\n",
       "      <td id=\"T_cf8af_row14_col10\" class=\"data row14 col10\" >0.243335</td>\n",
       "      <td id=\"T_cf8af_row14_col11\" class=\"data row14 col11\" >0.241181</td>\n",
       "      <td id=\"T_cf8af_row14_col12\" class=\"data row14 col12\" >0.892279</td>\n",
       "      <td id=\"T_cf8af_row14_col13\" class=\"data row14 col13\" >0.928326</td>\n",
       "      <td id=\"T_cf8af_row14_col14\" class=\"data row14 col14\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row14_col15\" class=\"data row14 col15\" >0.923969</td>\n",
       "      <td id=\"T_cf8af_row14_col16\" class=\"data row14 col16\" >0.883910</td>\n",
       "      <td id=\"T_cf8af_row14_col17\" class=\"data row14 col17\" >0.853320</td>\n",
       "      <td id=\"T_cf8af_row14_col18\" class=\"data row14 col18\" >0.244335</td>\n",
       "      <td id=\"T_cf8af_row14_col19\" class=\"data row14 col19\" >0.316936</td>\n",
       "      <td id=\"T_cf8af_row14_col20\" class=\"data row14 col20\" >0.130011</td>\n",
       "      <td id=\"T_cf8af_row14_col21\" class=\"data row14 col21\" >0.143405</td>\n",
       "      <td id=\"T_cf8af_row14_col22\" class=\"data row14 col22\" >0.179712</td>\n",
       "      <td id=\"T_cf8af_row14_col23\" class=\"data row14 col23\" >0.182326</td>\n",
       "      <td id=\"T_cf8af_row14_col24\" class=\"data row14 col24\" >-0.014076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row15\" class=\"row_heading level0 row15\" >BILL_AMT4</th>\n",
       "      <td id=\"T_cf8af_row15_col0\" class=\"data row15 col0\" >0.040351</td>\n",
       "      <td id=\"T_cf8af_row15_col1\" class=\"data row15 col1\" >0.293988</td>\n",
       "      <td id=\"T_cf8af_row15_col2\" class=\"data row15 col2\" >-0.021880</td>\n",
       "      <td id=\"T_cf8af_row15_col3\" class=\"data row15 col3\" >-0.000451</td>\n",
       "      <td id=\"T_cf8af_row15_col4\" class=\"data row15 col4\" >-0.023344</td>\n",
       "      <td id=\"T_cf8af_row15_col5\" class=\"data row15 col5\" >0.051353</td>\n",
       "      <td id=\"T_cf8af_row15_col6\" class=\"data row15 col6\" >0.179125</td>\n",
       "      <td id=\"T_cf8af_row15_col7\" class=\"data row15 col7\" >0.222237</td>\n",
       "      <td id=\"T_cf8af_row15_col8\" class=\"data row15 col8\" >0.227202</td>\n",
       "      <td id=\"T_cf8af_row15_col9\" class=\"data row15 col9\" >0.245917</td>\n",
       "      <td id=\"T_cf8af_row15_col10\" class=\"data row15 col10\" >0.271915</td>\n",
       "      <td id=\"T_cf8af_row15_col11\" class=\"data row15 col11\" >0.266356</td>\n",
       "      <td id=\"T_cf8af_row15_col12\" class=\"data row15 col12\" >0.860272</td>\n",
       "      <td id=\"T_cf8af_row15_col13\" class=\"data row15 col13\" >0.892482</td>\n",
       "      <td id=\"T_cf8af_row15_col14\" class=\"data row15 col14\" >0.923969</td>\n",
       "      <td id=\"T_cf8af_row15_col15\" class=\"data row15 col15\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row15_col16\" class=\"data row15 col16\" >0.940134</td>\n",
       "      <td id=\"T_cf8af_row15_col17\" class=\"data row15 col17\" >0.900941</td>\n",
       "      <td id=\"T_cf8af_row15_col18\" class=\"data row15 col18\" >0.233012</td>\n",
       "      <td id=\"T_cf8af_row15_col19\" class=\"data row15 col19\" >0.207564</td>\n",
       "      <td id=\"T_cf8af_row15_col20\" class=\"data row15 col20\" >0.300023</td>\n",
       "      <td id=\"T_cf8af_row15_col21\" class=\"data row15 col21\" >0.130191</td>\n",
       "      <td id=\"T_cf8af_row15_col22\" class=\"data row15 col22\" >0.160433</td>\n",
       "      <td id=\"T_cf8af_row15_col23\" class=\"data row15 col23\" >0.177637</td>\n",
       "      <td id=\"T_cf8af_row15_col24\" class=\"data row15 col24\" >-0.010156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row16\" class=\"row_heading level0 row16\" >BILL_AMT5</th>\n",
       "      <td id=\"T_cf8af_row16_col0\" class=\"data row16 col0\" >0.016705</td>\n",
       "      <td id=\"T_cf8af_row16_col1\" class=\"data row16 col1\" >0.295562</td>\n",
       "      <td id=\"T_cf8af_row16_col2\" class=\"data row16 col2\" >-0.017005</td>\n",
       "      <td id=\"T_cf8af_row16_col3\" class=\"data row16 col3\" >-0.007567</td>\n",
       "      <td id=\"T_cf8af_row16_col4\" class=\"data row16 col4\" >-0.025393</td>\n",
       "      <td id=\"T_cf8af_row16_col5\" class=\"data row16 col5\" >0.049345</td>\n",
       "      <td id=\"T_cf8af_row16_col6\" class=\"data row16 col6\" >0.180635</td>\n",
       "      <td id=\"T_cf8af_row16_col7\" class=\"data row16 col7\" >0.221348</td>\n",
       "      <td id=\"T_cf8af_row16_col8\" class=\"data row16 col8\" >0.225145</td>\n",
       "      <td id=\"T_cf8af_row16_col9\" class=\"data row16 col9\" >0.242902</td>\n",
       "      <td id=\"T_cf8af_row16_col10\" class=\"data row16 col10\" >0.269783</td>\n",
       "      <td id=\"T_cf8af_row16_col11\" class=\"data row16 col11\" >0.290894</td>\n",
       "      <td id=\"T_cf8af_row16_col12\" class=\"data row16 col12\" >0.829779</td>\n",
       "      <td id=\"T_cf8af_row16_col13\" class=\"data row16 col13\" >0.859778</td>\n",
       "      <td id=\"T_cf8af_row16_col14\" class=\"data row16 col14\" >0.883910</td>\n",
       "      <td id=\"T_cf8af_row16_col15\" class=\"data row16 col15\" >0.940134</td>\n",
       "      <td id=\"T_cf8af_row16_col16\" class=\"data row16 col16\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row16_col17\" class=\"data row16 col17\" >0.946197</td>\n",
       "      <td id=\"T_cf8af_row16_col18\" class=\"data row16 col18\" >0.217031</td>\n",
       "      <td id=\"T_cf8af_row16_col19\" class=\"data row16 col19\" >0.181246</td>\n",
       "      <td id=\"T_cf8af_row16_col20\" class=\"data row16 col20\" >0.252305</td>\n",
       "      <td id=\"T_cf8af_row16_col21\" class=\"data row16 col21\" >0.293118</td>\n",
       "      <td id=\"T_cf8af_row16_col22\" class=\"data row16 col22\" >0.141574</td>\n",
       "      <td id=\"T_cf8af_row16_col23\" class=\"data row16 col23\" >0.164184</td>\n",
       "      <td id=\"T_cf8af_row16_col24\" class=\"data row16 col24\" >-0.006760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row17\" class=\"row_heading level0 row17\" >BILL_AMT6</th>\n",
       "      <td id=\"T_cf8af_row17_col0\" class=\"data row17 col0\" >0.016730</td>\n",
       "      <td id=\"T_cf8af_row17_col1\" class=\"data row17 col1\" >0.290389</td>\n",
       "      <td id=\"T_cf8af_row17_col2\" class=\"data row17 col2\" >-0.016733</td>\n",
       "      <td id=\"T_cf8af_row17_col3\" class=\"data row17 col3\" >-0.009099</td>\n",
       "      <td id=\"T_cf8af_row17_col4\" class=\"data row17 col4\" >-0.021207</td>\n",
       "      <td id=\"T_cf8af_row17_col5\" class=\"data row17 col5\" >0.047613</td>\n",
       "      <td id=\"T_cf8af_row17_col6\" class=\"data row17 col6\" >0.176980</td>\n",
       "      <td id=\"T_cf8af_row17_col7\" class=\"data row17 col7\" >0.219403</td>\n",
       "      <td id=\"T_cf8af_row17_col8\" class=\"data row17 col8\" >0.222327</td>\n",
       "      <td id=\"T_cf8af_row17_col9\" class=\"data row17 col9\" >0.239154</td>\n",
       "      <td id=\"T_cf8af_row17_col10\" class=\"data row17 col10\" >0.262509</td>\n",
       "      <td id=\"T_cf8af_row17_col11\" class=\"data row17 col11\" >0.285091</td>\n",
       "      <td id=\"T_cf8af_row17_col12\" class=\"data row17 col12\" >0.802650</td>\n",
       "      <td id=\"T_cf8af_row17_col13\" class=\"data row17 col13\" >0.831594</td>\n",
       "      <td id=\"T_cf8af_row17_col14\" class=\"data row17 col14\" >0.853320</td>\n",
       "      <td id=\"T_cf8af_row17_col15\" class=\"data row17 col15\" >0.900941</td>\n",
       "      <td id=\"T_cf8af_row17_col16\" class=\"data row17 col16\" >0.946197</td>\n",
       "      <td id=\"T_cf8af_row17_col17\" class=\"data row17 col17\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row17_col18\" class=\"data row17 col18\" >0.199965</td>\n",
       "      <td id=\"T_cf8af_row17_col19\" class=\"data row17 col19\" >0.172663</td>\n",
       "      <td id=\"T_cf8af_row17_col20\" class=\"data row17 col20\" >0.233770</td>\n",
       "      <td id=\"T_cf8af_row17_col21\" class=\"data row17 col21\" >0.250237</td>\n",
       "      <td id=\"T_cf8af_row17_col22\" class=\"data row17 col22\" >0.307729</td>\n",
       "      <td id=\"T_cf8af_row17_col23\" class=\"data row17 col23\" >0.115494</td>\n",
       "      <td id=\"T_cf8af_row17_col24\" class=\"data row17 col24\" >-0.005372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row18\" class=\"row_heading level0 row18\" >PAY_AMT1</th>\n",
       "      <td id=\"T_cf8af_row18_col0\" class=\"data row18 col0\" >0.009742</td>\n",
       "      <td id=\"T_cf8af_row18_col1\" class=\"data row18 col1\" >0.195236</td>\n",
       "      <td id=\"T_cf8af_row18_col2\" class=\"data row18 col2\" >-0.000242</td>\n",
       "      <td id=\"T_cf8af_row18_col3\" class=\"data row18 col3\" >-0.037456</td>\n",
       "      <td id=\"T_cf8af_row18_col4\" class=\"data row18 col4\" >-0.005979</td>\n",
       "      <td id=\"T_cf8af_row18_col5\" class=\"data row18 col5\" >0.026147</td>\n",
       "      <td id=\"T_cf8af_row18_col6\" class=\"data row18 col6\" >-0.079269</td>\n",
       "      <td id=\"T_cf8af_row18_col7\" class=\"data row18 col7\" >-0.080701</td>\n",
       "      <td id=\"T_cf8af_row18_col8\" class=\"data row18 col8\" >0.001295</td>\n",
       "      <td id=\"T_cf8af_row18_col9\" class=\"data row18 col9\" >-0.009362</td>\n",
       "      <td id=\"T_cf8af_row18_col10\" class=\"data row18 col10\" >-0.006089</td>\n",
       "      <td id=\"T_cf8af_row18_col11\" class=\"data row18 col11\" >-0.001496</td>\n",
       "      <td id=\"T_cf8af_row18_col12\" class=\"data row18 col12\" >0.140277</td>\n",
       "      <td id=\"T_cf8af_row18_col13\" class=\"data row18 col13\" >0.280365</td>\n",
       "      <td id=\"T_cf8af_row18_col14\" class=\"data row18 col14\" >0.244335</td>\n",
       "      <td id=\"T_cf8af_row18_col15\" class=\"data row18 col15\" >0.233012</td>\n",
       "      <td id=\"T_cf8af_row18_col16\" class=\"data row18 col16\" >0.217031</td>\n",
       "      <td id=\"T_cf8af_row18_col17\" class=\"data row18 col17\" >0.199965</td>\n",
       "      <td id=\"T_cf8af_row18_col18\" class=\"data row18 col18\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row18_col19\" class=\"data row18 col19\" >0.285576</td>\n",
       "      <td id=\"T_cf8af_row18_col20\" class=\"data row18 col20\" >0.252191</td>\n",
       "      <td id=\"T_cf8af_row18_col21\" class=\"data row18 col21\" >0.199558</td>\n",
       "      <td id=\"T_cf8af_row18_col22\" class=\"data row18 col22\" >0.148459</td>\n",
       "      <td id=\"T_cf8af_row18_col23\" class=\"data row18 col23\" >0.185735</td>\n",
       "      <td id=\"T_cf8af_row18_col24\" class=\"data row18 col24\" >-0.072929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row19\" class=\"row_heading level0 row19\" >PAY_AMT2</th>\n",
       "      <td id=\"T_cf8af_row19_col0\" class=\"data row19 col0\" >0.008406</td>\n",
       "      <td id=\"T_cf8af_row19_col1\" class=\"data row19 col1\" >0.178408</td>\n",
       "      <td id=\"T_cf8af_row19_col2\" class=\"data row19 col2\" >-0.001391</td>\n",
       "      <td id=\"T_cf8af_row19_col3\" class=\"data row19 col3\" >-0.030038</td>\n",
       "      <td id=\"T_cf8af_row19_col4\" class=\"data row19 col4\" >-0.008093</td>\n",
       "      <td id=\"T_cf8af_row19_col5\" class=\"data row19 col5\" >0.021785</td>\n",
       "      <td id=\"T_cf8af_row19_col6\" class=\"data row19 col6\" >-0.070101</td>\n",
       "      <td id=\"T_cf8af_row19_col7\" class=\"data row19 col7\" >-0.058990</td>\n",
       "      <td id=\"T_cf8af_row19_col8\" class=\"data row19 col8\" >-0.066793</td>\n",
       "      <td id=\"T_cf8af_row19_col9\" class=\"data row19 col9\" >-0.001944</td>\n",
       "      <td id=\"T_cf8af_row19_col10\" class=\"data row19 col10\" >-0.003191</td>\n",
       "      <td id=\"T_cf8af_row19_col11\" class=\"data row19 col11\" >-0.005223</td>\n",
       "      <td id=\"T_cf8af_row19_col12\" class=\"data row19 col12\" >0.099355</td>\n",
       "      <td id=\"T_cf8af_row19_col13\" class=\"data row19 col13\" >0.100851</td>\n",
       "      <td id=\"T_cf8af_row19_col14\" class=\"data row19 col14\" >0.316936</td>\n",
       "      <td id=\"T_cf8af_row19_col15\" class=\"data row19 col15\" >0.207564</td>\n",
       "      <td id=\"T_cf8af_row19_col16\" class=\"data row19 col16\" >0.181246</td>\n",
       "      <td id=\"T_cf8af_row19_col17\" class=\"data row19 col17\" >0.172663</td>\n",
       "      <td id=\"T_cf8af_row19_col18\" class=\"data row19 col18\" >0.285576</td>\n",
       "      <td id=\"T_cf8af_row19_col19\" class=\"data row19 col19\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row19_col20\" class=\"data row19 col20\" >0.244770</td>\n",
       "      <td id=\"T_cf8af_row19_col21\" class=\"data row19 col21\" >0.180107</td>\n",
       "      <td id=\"T_cf8af_row19_col22\" class=\"data row19 col22\" >0.180908</td>\n",
       "      <td id=\"T_cf8af_row19_col23\" class=\"data row19 col23\" >0.157634</td>\n",
       "      <td id=\"T_cf8af_row19_col24\" class=\"data row19 col24\" >-0.058579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row20\" class=\"row_heading level0 row20\" >PAY_AMT3</th>\n",
       "      <td id=\"T_cf8af_row20_col0\" class=\"data row20 col0\" >0.039151</td>\n",
       "      <td id=\"T_cf8af_row20_col1\" class=\"data row20 col1\" >0.210167</td>\n",
       "      <td id=\"T_cf8af_row20_col2\" class=\"data row20 col2\" >-0.008597</td>\n",
       "      <td id=\"T_cf8af_row20_col3\" class=\"data row20 col3\" >-0.039943</td>\n",
       "      <td id=\"T_cf8af_row20_col4\" class=\"data row20 col4\" >-0.003541</td>\n",
       "      <td id=\"T_cf8af_row20_col5\" class=\"data row20 col5\" >0.029247</td>\n",
       "      <td id=\"T_cf8af_row20_col6\" class=\"data row20 col6\" >-0.070561</td>\n",
       "      <td id=\"T_cf8af_row20_col7\" class=\"data row20 col7\" >-0.055901</td>\n",
       "      <td id=\"T_cf8af_row20_col8\" class=\"data row20 col8\" >-0.053311</td>\n",
       "      <td id=\"T_cf8af_row20_col9\" class=\"data row20 col9\" >-0.069235</td>\n",
       "      <td id=\"T_cf8af_row20_col10\" class=\"data row20 col10\" >0.009062</td>\n",
       "      <td id=\"T_cf8af_row20_col11\" class=\"data row20 col11\" >0.005834</td>\n",
       "      <td id=\"T_cf8af_row20_col12\" class=\"data row20 col12\" >0.156887</td>\n",
       "      <td id=\"T_cf8af_row20_col13\" class=\"data row20 col13\" >0.150718</td>\n",
       "      <td id=\"T_cf8af_row20_col14\" class=\"data row20 col14\" >0.130011</td>\n",
       "      <td id=\"T_cf8af_row20_col15\" class=\"data row20 col15\" >0.300023</td>\n",
       "      <td id=\"T_cf8af_row20_col16\" class=\"data row20 col16\" >0.252305</td>\n",
       "      <td id=\"T_cf8af_row20_col17\" class=\"data row20 col17\" >0.233770</td>\n",
       "      <td id=\"T_cf8af_row20_col18\" class=\"data row20 col18\" >0.252191</td>\n",
       "      <td id=\"T_cf8af_row20_col19\" class=\"data row20 col19\" >0.244770</td>\n",
       "      <td id=\"T_cf8af_row20_col20\" class=\"data row20 col20\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row20_col21\" class=\"data row20 col21\" >0.216325</td>\n",
       "      <td id=\"T_cf8af_row20_col22\" class=\"data row20 col22\" >0.159214</td>\n",
       "      <td id=\"T_cf8af_row20_col23\" class=\"data row20 col23\" >0.162740</td>\n",
       "      <td id=\"T_cf8af_row20_col24\" class=\"data row20 col24\" >-0.056250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row21\" class=\"row_heading level0 row21\" >PAY_AMT4</th>\n",
       "      <td id=\"T_cf8af_row21_col0\" class=\"data row21 col0\" >0.007793</td>\n",
       "      <td id=\"T_cf8af_row21_col1\" class=\"data row21 col1\" >0.203242</td>\n",
       "      <td id=\"T_cf8af_row21_col2\" class=\"data row21 col2\" >-0.002229</td>\n",
       "      <td id=\"T_cf8af_row21_col3\" class=\"data row21 col3\" >-0.038218</td>\n",
       "      <td id=\"T_cf8af_row21_col4\" class=\"data row21 col4\" >-0.012659</td>\n",
       "      <td id=\"T_cf8af_row21_col5\" class=\"data row21 col5\" >0.021379</td>\n",
       "      <td id=\"T_cf8af_row21_col6\" class=\"data row21 col6\" >-0.064005</td>\n",
       "      <td id=\"T_cf8af_row21_col7\" class=\"data row21 col7\" >-0.046858</td>\n",
       "      <td id=\"T_cf8af_row21_col8\" class=\"data row21 col8\" >-0.046067</td>\n",
       "      <td id=\"T_cf8af_row21_col9\" class=\"data row21 col9\" >-0.043461</td>\n",
       "      <td id=\"T_cf8af_row21_col10\" class=\"data row21 col10\" >-0.058299</td>\n",
       "      <td id=\"T_cf8af_row21_col11\" class=\"data row21 col11\" >0.019018</td>\n",
       "      <td id=\"T_cf8af_row21_col12\" class=\"data row21 col12\" >0.158303</td>\n",
       "      <td id=\"T_cf8af_row21_col13\" class=\"data row21 col13\" >0.147398</td>\n",
       "      <td id=\"T_cf8af_row21_col14\" class=\"data row21 col14\" >0.143405</td>\n",
       "      <td id=\"T_cf8af_row21_col15\" class=\"data row21 col15\" >0.130191</td>\n",
       "      <td id=\"T_cf8af_row21_col16\" class=\"data row21 col16\" >0.293118</td>\n",
       "      <td id=\"T_cf8af_row21_col17\" class=\"data row21 col17\" >0.250237</td>\n",
       "      <td id=\"T_cf8af_row21_col18\" class=\"data row21 col18\" >0.199558</td>\n",
       "      <td id=\"T_cf8af_row21_col19\" class=\"data row21 col19\" >0.180107</td>\n",
       "      <td id=\"T_cf8af_row21_col20\" class=\"data row21 col20\" >0.216325</td>\n",
       "      <td id=\"T_cf8af_row21_col21\" class=\"data row21 col21\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row21_col22\" class=\"data row21 col22\" >0.151830</td>\n",
       "      <td id=\"T_cf8af_row21_col23\" class=\"data row21 col23\" >0.157834</td>\n",
       "      <td id=\"T_cf8af_row21_col24\" class=\"data row21 col24\" >-0.056827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row22\" class=\"row_heading level0 row22\" >PAY_AMT5</th>\n",
       "      <td id=\"T_cf8af_row22_col0\" class=\"data row22 col0\" >0.000652</td>\n",
       "      <td id=\"T_cf8af_row22_col1\" class=\"data row22 col1\" >0.217202</td>\n",
       "      <td id=\"T_cf8af_row22_col2\" class=\"data row22 col2\" >-0.001667</td>\n",
       "      <td id=\"T_cf8af_row22_col3\" class=\"data row22 col3\" >-0.040358</td>\n",
       "      <td id=\"T_cf8af_row22_col4\" class=\"data row22 col4\" >-0.001205</td>\n",
       "      <td id=\"T_cf8af_row22_col5\" class=\"data row22 col5\" >0.022850</td>\n",
       "      <td id=\"T_cf8af_row22_col6\" class=\"data row22 col6\" >-0.058190</td>\n",
       "      <td id=\"T_cf8af_row22_col7\" class=\"data row22 col7\" >-0.037093</td>\n",
       "      <td id=\"T_cf8af_row22_col8\" class=\"data row22 col8\" >-0.035863</td>\n",
       "      <td id=\"T_cf8af_row22_col9\" class=\"data row22 col9\" >-0.033590</td>\n",
       "      <td id=\"T_cf8af_row22_col10\" class=\"data row22 col10\" >-0.033337</td>\n",
       "      <td id=\"T_cf8af_row22_col11\" class=\"data row22 col11\" >-0.046434</td>\n",
       "      <td id=\"T_cf8af_row22_col12\" class=\"data row22 col12\" >0.167026</td>\n",
       "      <td id=\"T_cf8af_row22_col13\" class=\"data row22 col13\" >0.157957</td>\n",
       "      <td id=\"T_cf8af_row22_col14\" class=\"data row22 col14\" >0.179712</td>\n",
       "      <td id=\"T_cf8af_row22_col15\" class=\"data row22 col15\" >0.160433</td>\n",
       "      <td id=\"T_cf8af_row22_col16\" class=\"data row22 col16\" >0.141574</td>\n",
       "      <td id=\"T_cf8af_row22_col17\" class=\"data row22 col17\" >0.307729</td>\n",
       "      <td id=\"T_cf8af_row22_col18\" class=\"data row22 col18\" >0.148459</td>\n",
       "      <td id=\"T_cf8af_row22_col19\" class=\"data row22 col19\" >0.180908</td>\n",
       "      <td id=\"T_cf8af_row22_col20\" class=\"data row22 col20\" >0.159214</td>\n",
       "      <td id=\"T_cf8af_row22_col21\" class=\"data row22 col21\" >0.151830</td>\n",
       "      <td id=\"T_cf8af_row22_col22\" class=\"data row22 col22\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row22_col23\" class=\"data row22 col23\" >0.154896</td>\n",
       "      <td id=\"T_cf8af_row22_col24\" class=\"data row22 col24\" >-0.055124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row23\" class=\"row_heading level0 row23\" >PAY_AMT6</th>\n",
       "      <td id=\"T_cf8af_row23_col0\" class=\"data row23 col0\" >0.003000</td>\n",
       "      <td id=\"T_cf8af_row23_col1\" class=\"data row23 col1\" >0.219595</td>\n",
       "      <td id=\"T_cf8af_row23_col2\" class=\"data row23 col2\" >-0.002766</td>\n",
       "      <td id=\"T_cf8af_row23_col3\" class=\"data row23 col3\" >-0.037200</td>\n",
       "      <td id=\"T_cf8af_row23_col4\" class=\"data row23 col4\" >-0.006641</td>\n",
       "      <td id=\"T_cf8af_row23_col5\" class=\"data row23 col5\" >0.019478</td>\n",
       "      <td id=\"T_cf8af_row23_col6\" class=\"data row23 col6\" >-0.058673</td>\n",
       "      <td id=\"T_cf8af_row23_col7\" class=\"data row23 col7\" >-0.036500</td>\n",
       "      <td id=\"T_cf8af_row23_col8\" class=\"data row23 col8\" >-0.035861</td>\n",
       "      <td id=\"T_cf8af_row23_col9\" class=\"data row23 col9\" >-0.026565</td>\n",
       "      <td id=\"T_cf8af_row23_col10\" class=\"data row23 col10\" >-0.023027</td>\n",
       "      <td id=\"T_cf8af_row23_col11\" class=\"data row23 col11\" >-0.025299</td>\n",
       "      <td id=\"T_cf8af_row23_col12\" class=\"data row23 col12\" >0.179341</td>\n",
       "      <td id=\"T_cf8af_row23_col13\" class=\"data row23 col13\" >0.174256</td>\n",
       "      <td id=\"T_cf8af_row23_col14\" class=\"data row23 col14\" >0.182326</td>\n",
       "      <td id=\"T_cf8af_row23_col15\" class=\"data row23 col15\" >0.177637</td>\n",
       "      <td id=\"T_cf8af_row23_col16\" class=\"data row23 col16\" >0.164184</td>\n",
       "      <td id=\"T_cf8af_row23_col17\" class=\"data row23 col17\" >0.115494</td>\n",
       "      <td id=\"T_cf8af_row23_col18\" class=\"data row23 col18\" >0.185735</td>\n",
       "      <td id=\"T_cf8af_row23_col19\" class=\"data row23 col19\" >0.157634</td>\n",
       "      <td id=\"T_cf8af_row23_col20\" class=\"data row23 col20\" >0.162740</td>\n",
       "      <td id=\"T_cf8af_row23_col21\" class=\"data row23 col21\" >0.157834</td>\n",
       "      <td id=\"T_cf8af_row23_col22\" class=\"data row23 col22\" >0.154896</td>\n",
       "      <td id=\"T_cf8af_row23_col23\" class=\"data row23 col23\" >1.000000</td>\n",
       "      <td id=\"T_cf8af_row23_col24\" class=\"data row23 col24\" >-0.053183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf8af_level0_row24\" class=\"row_heading level0 row24\" >default.payment.next.month</th>\n",
       "      <td id=\"T_cf8af_row24_col0\" class=\"data row24 col0\" >-0.013952</td>\n",
       "      <td id=\"T_cf8af_row24_col1\" class=\"data row24 col1\" >-0.153520</td>\n",
       "      <td id=\"T_cf8af_row24_col2\" class=\"data row24 col2\" >-0.039961</td>\n",
       "      <td id=\"T_cf8af_row24_col3\" class=\"data row24 col3\" >0.028006</td>\n",
       "      <td id=\"T_cf8af_row24_col4\" class=\"data row24 col4\" >-0.024339</td>\n",
       "      <td id=\"T_cf8af_row24_col5\" class=\"data row24 col5\" >0.013890</td>\n",
       "      <td id=\"T_cf8af_row24_col6\" class=\"data row24 col6\" >0.324794</td>\n",
       "      <td id=\"T_cf8af_row24_col7\" class=\"data row24 col7\" >0.263551</td>\n",
       "      <td id=\"T_cf8af_row24_col8\" class=\"data row24 col8\" >0.235253</td>\n",
       "      <td id=\"T_cf8af_row24_col9\" class=\"data row24 col9\" >0.216614</td>\n",
       "      <td id=\"T_cf8af_row24_col10\" class=\"data row24 col10\" >0.204149</td>\n",
       "      <td id=\"T_cf8af_row24_col11\" class=\"data row24 col11\" >0.186866</td>\n",
       "      <td id=\"T_cf8af_row24_col12\" class=\"data row24 col12\" >-0.019644</td>\n",
       "      <td id=\"T_cf8af_row24_col13\" class=\"data row24 col13\" >-0.014193</td>\n",
       "      <td id=\"T_cf8af_row24_col14\" class=\"data row24 col14\" >-0.014076</td>\n",
       "      <td id=\"T_cf8af_row24_col15\" class=\"data row24 col15\" >-0.010156</td>\n",
       "      <td id=\"T_cf8af_row24_col16\" class=\"data row24 col16\" >-0.006760</td>\n",
       "      <td id=\"T_cf8af_row24_col17\" class=\"data row24 col17\" >-0.005372</td>\n",
       "      <td id=\"T_cf8af_row24_col18\" class=\"data row24 col18\" >-0.072929</td>\n",
       "      <td id=\"T_cf8af_row24_col19\" class=\"data row24 col19\" >-0.058579</td>\n",
       "      <td id=\"T_cf8af_row24_col20\" class=\"data row24 col20\" >-0.056250</td>\n",
       "      <td id=\"T_cf8af_row24_col21\" class=\"data row24 col21\" >-0.056827</td>\n",
       "      <td id=\"T_cf8af_row24_col22\" class=\"data row24 col22\" >-0.055124</td>\n",
       "      <td id=\"T_cf8af_row24_col23\" class=\"data row24 col23\" >-0.053183</td>\n",
       "      <td id=\"T_cf8af_row24_col24\" class=\"data row24 col24\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8032cf0ed0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding the co-relation of given datasets\n",
    "df.corr().style.background_gradient(cmap = \"GnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b106003c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:23.307377Z",
     "iopub.status.busy": "2022-06-16T10:19:23.307105Z",
     "iopub.status.idle": "2022-06-16T10:19:23.311674Z",
     "shell.execute_reply": "2022-06-16T10:19:23.310839Z"
    },
    "papermill": {
     "duration": 0.02091,
     "end_time": "2022-06-16T10:19:23.313482",
     "exception": false,
     "start_time": "2022-06-16T10:19:23.292572",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sex = df.SEX.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "45a4cd92",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:23.341252Z",
     "iopub.status.busy": "2022-06-16T10:19:23.340993Z",
     "iopub.status.idle": "2022-06-16T10:19:23.853046Z",
     "shell.execute_reply": "2022-06-16T10:19:23.852239Z"
    },
    "papermill": {
     "duration": 0.528186,
     "end_time": "2022-06-16T10:19:23.855010",
     "exception": false,
     "start_time": "2022-06-16T10:19:23.326824",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,6))\n",
    "sns.barplot(x=\"SEX\", y=\"AGE\", data=df)\n",
    "plt.title(\"Sex vs AGE\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c69f7f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:23.883389Z",
     "iopub.status.busy": "2022-06-16T10:19:23.883106Z",
     "iopub.status.idle": "2022-06-16T10:19:23.889731Z",
     "shell.execute_reply": "2022-06-16T10:19:23.889021Z"
    },
    "papermill": {
     "duration": 0.022489,
     "end_time": "2022-06-16T10:19:23.891285",
     "exception": false,
     "start_time": "2022-06-16T10:19:23.868796",
     "status": "completed"
    },
    "tags": []
   },
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.EDUCATION.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b9b40432",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:23.920177Z",
     "iopub.status.busy": "2022-06-16T10:19:23.919724Z",
     "iopub.status.idle": "2022-06-16T10:19:23.925520Z",
     "shell.execute_reply": "2022-06-16T10:19:23.924726Z"
    },
    "papermill": {
     "duration": 0.02244,
     "end_time": "2022-06-16T10:19:23.927201",
     "exception": false,
     "start_time": "2022-06-16T10:19:23.904761",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167484.32266666667"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df.LIMIT_BAL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82eff560",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:23.956159Z",
     "iopub.status.busy": "2022-06-16T10:19:23.955755Z",
     "iopub.status.idle": "2022-06-16T10:19:23.963213Z",
     "shell.execute_reply": "2022-06-16T10:19:23.962454Z"
    },
    "papermill": {
     "duration": 0.023729,
     "end_time": "2022-06-16T10:19:23.964869",
     "exception": false,
     "start_time": "2022-06-16T10:19:23.941140",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         20000.0\n",
       "1        120000.0\n",
       "2         90000.0\n",
       "3         50000.0\n",
       "4         50000.0\n",
       "           ...   \n",
       "29995    220000.0\n",
       "29996    150000.0\n",
       "29997     30000.0\n",
       "29998     80000.0\n",
       "29999     50000.0\n",
       "Name: LIMIT_BAL, Length: 30000, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.LIMIT_BAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e42a9275",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:23.994536Z",
     "iopub.status.busy": "2022-06-16T10:19:23.993969Z",
     "iopub.status.idle": "2022-06-16T10:19:23.999827Z",
     "shell.execute_reply": "2022-06-16T10:19:23.999149Z"
    },
    "papermill": {
     "duration": 0.022431,
     "end_time": "2022-06-16T10:19:24.001445",
     "exception": false,
     "start_time": "2022-06-16T10:19:23.979014",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "535.7142857142857"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.AGE.value_counts().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5ea507b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:24.030728Z",
     "iopub.status.busy": "2022-06-16T10:19:24.029999Z",
     "iopub.status.idle": "2022-06-16T10:19:24.506750Z",
     "shell.execute_reply": "2022-06-16T10:19:24.505949Z"
    },
    "papermill": {
     "duration": 0.493212,
     "end_time": "2022-06-16T10:19:24.508457",
     "exception": false,
     "start_time": "2022-06-16T10:19:24.015245",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "df[\"AGE\"].plot.hist(bins=200);\n",
    "plt.title(\"Distribution of age\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e26243b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:24.539311Z",
     "iopub.status.busy": "2022-06-16T10:19:24.538717Z",
     "iopub.status.idle": "2022-06-16T10:19:24.545209Z",
     "shell.execute_reply": "2022-06-16T10:19:24.544475Z"
    },
    "papermill": {
     "duration": 0.023642,
     "end_time": "2022-06-16T10:19:24.546931",
     "exception": false,
     "start_time": "2022-06-16T10:19:24.523289",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    15964\n",
       "1    13659\n",
       "3      323\n",
       "0       54\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.MARRIAGE.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2da582f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:24.577446Z",
     "iopub.status.busy": "2022-06-16T10:19:24.576102Z",
     "iopub.status.idle": "2022-06-16T10:19:24.583344Z",
     "shell.execute_reply": "2022-06-16T10:19:24.582558Z"
    },
    "papermill": {
     "duration": 0.023933,
     "end_time": "2022-06-16T10:19:24.585033",
     "exception": false,
     "start_time": "2022-06-16T10:19:24.561100",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29    1605\n",
       "27    1477\n",
       "28    1409\n",
       "30    1395\n",
       "26    1256\n",
       "31    1217\n",
       "25    1186\n",
       "34    1162\n",
       "32    1158\n",
       "33    1146\n",
       "24    1127\n",
       "35    1113\n",
       "36    1108\n",
       "37    1041\n",
       "39     954\n",
       "38     944\n",
       "23     931\n",
       "40     870\n",
       "41     824\n",
       "42     794\n",
       "44     700\n",
       "43     670\n",
       "45     617\n",
       "46     570\n",
       "22     560\n",
       "47     501\n",
       "48     466\n",
       "49     452\n",
       "50     411\n",
       "51     340\n",
       "53     325\n",
       "52     304\n",
       "54     247\n",
       "55     209\n",
       "56     178\n",
       "58     122\n",
       "57     122\n",
       "59      83\n",
       "60      67\n",
       "21      67\n",
       "61      56\n",
       "62      44\n",
       "63      31\n",
       "64      31\n",
       "66      25\n",
       "65      24\n",
       "67      16\n",
       "69      15\n",
       "70      10\n",
       "68       5\n",
       "73       4\n",
       "72       3\n",
       "75       3\n",
       "71       3\n",
       "79       1\n",
       "74       1\n",
       "Name: AGE, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.AGE.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bbb82adb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:24.615005Z",
     "iopub.status.busy": "2022-06-16T10:19:24.614556Z",
     "iopub.status.idle": "2022-06-16T10:19:24.621201Z",
     "shell.execute_reply": "2022-06-16T10:19:24.620459Z"
    },
    "papermill": {
     "duration": 0.023524,
     "end_time": "2022-06-16T10:19:24.622879",
     "exception": false,
     "start_time": "2022-06-16T10:19:24.599355",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    14737\n",
       "-1     5686\n",
       " 1     3688\n",
       "-2     2759\n",
       " 2     2667\n",
       " 3      322\n",
       " 4       76\n",
       " 5       26\n",
       " 8       19\n",
       " 6       11\n",
       " 7        9\n",
       "Name: PAY_0, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PAY_0.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "36158089",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:24.653242Z",
     "iopub.status.busy": "2022-06-16T10:19:24.652710Z",
     "iopub.status.idle": "2022-06-16T10:19:24.658918Z",
     "shell.execute_reply": "2022-06-16T10:19:24.658073Z"
    },
    "papermill": {
     "duration": 0.023545,
     "end_time": "2022-06-16T10:19:24.660875",
     "exception": false,
     "start_time": "2022-06-16T10:19:24.637330",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    15730\n",
       "-1     6050\n",
       " 2     3927\n",
       "-2     3782\n",
       " 3      326\n",
       " 4       99\n",
       " 1       28\n",
       " 5       25\n",
       " 7       20\n",
       " 6       12\n",
       " 8        1\n",
       "Name: PAY_2, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PAY_2.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b3caa55f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:24.691042Z",
     "iopub.status.busy": "2022-06-16T10:19:24.690602Z",
     "iopub.status.idle": "2022-06-16T10:19:24.696623Z",
     "shell.execute_reply": "2022-06-16T10:19:24.695921Z"
    },
    "papermill": {
     "duration": 0.022882,
     "end_time": "2022-06-16T10:19:24.698187",
     "exception": false,
     "start_time": "2022-06-16T10:19:24.675305",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    15764\n",
       "-1     5938\n",
       "-2     4085\n",
       " 2     3819\n",
       " 3      240\n",
       " 4       76\n",
       " 7       27\n",
       " 6       23\n",
       " 5       21\n",
       " 1        4\n",
       " 8        3\n",
       "Name: PAY_3, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PAY_3.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a109bfc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:24.730280Z",
     "iopub.status.busy": "2022-06-16T10:19:24.729559Z",
     "iopub.status.idle": "2022-06-16T10:19:24.736037Z",
     "shell.execute_reply": "2022-06-16T10:19:24.735225Z"
    },
    "papermill": {
     "duration": 0.024978,
     "end_time": "2022-06-16T10:19:24.737702",
     "exception": false,
     "start_time": "2022-06-16T10:19:24.712724",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    16455\n",
       "-1     5687\n",
       "-2     4348\n",
       " 2     3159\n",
       " 3      180\n",
       " 4       69\n",
       " 7       58\n",
       " 5       35\n",
       " 6        5\n",
       " 1        2\n",
       " 8        2\n",
       "Name: PAY_4, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PAY_4.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "215fa81e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:24.768476Z",
     "iopub.status.busy": "2022-06-16T10:19:24.767917Z",
     "iopub.status.idle": "2022-06-16T10:19:24.774229Z",
     "shell.execute_reply": "2022-06-16T10:19:24.773316Z"
    },
    "papermill": {
     "duration": 0.023602,
     "end_time": "2022-06-16T10:19:24.776030",
     "exception": false,
     "start_time": "2022-06-16T10:19:24.752428",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    16947\n",
       "-1     5539\n",
       "-2     4546\n",
       " 2     2626\n",
       " 3      178\n",
       " 4       84\n",
       " 7       58\n",
       " 5       17\n",
       " 6        4\n",
       " 8        1\n",
       "Name: PAY_5, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PAY_5.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b9e99440",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:24.807321Z",
     "iopub.status.busy": "2022-06-16T10:19:24.806789Z",
     "iopub.status.idle": "2022-06-16T10:19:24.812830Z",
     "shell.execute_reply": "2022-06-16T10:19:24.812139Z"
    },
    "papermill": {
     "duration": 0.023331,
     "end_time": "2022-06-16T10:19:24.814391",
     "exception": false,
     "start_time": "2022-06-16T10:19:24.791060",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    16286\n",
       "-1     5740\n",
       "-2     4895\n",
       " 2     2766\n",
       " 3      184\n",
       " 4       49\n",
       " 7       46\n",
       " 6       19\n",
       " 5       13\n",
       " 8        2\n",
       "Name: PAY_6, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PAY_6.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "df5c902c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:24.845792Z",
     "iopub.status.busy": "2022-06-16T10:19:24.845212Z",
     "iopub.status.idle": "2022-06-16T10:19:24.851730Z",
     "shell.execute_reply": "2022-06-16T10:19:24.851040Z"
    },
    "papermill": {
     "duration": 0.023921,
     "end_time": "2022-06-16T10:19:24.853297",
     "exception": false,
     "start_time": "2022-06-16T10:19:24.829376",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          3913.0\n",
       "1          2682.0\n",
       "2         29239.0\n",
       "3         46990.0\n",
       "4          8617.0\n",
       "           ...   \n",
       "29995    188948.0\n",
       "29996      1683.0\n",
       "29997      3565.0\n",
       "29998     -1645.0\n",
       "29999     47929.0\n",
       "Name: BILL_AMT1, Length: 30000, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.BILL_AMT1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cad6ef1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:24.885712Z",
     "iopub.status.busy": "2022-06-16T10:19:24.885169Z",
     "iopub.status.idle": "2022-06-16T10:19:24.893538Z",
     "shell.execute_reply": "2022-06-16T10:19:24.892797Z"
    },
    "papermill": {
     "duration": 0.025938,
     "end_time": "2022-06-16T10:19:24.895299",
     "exception": false,
     "start_time": "2022-06-16T10:19:24.869361",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          3913.0\n",
       "1          2682.0\n",
       "2         29239.0\n",
       "3         46990.0\n",
       "4          8617.0\n",
       "           ...   \n",
       "29995    188948.0\n",
       "29996      1683.0\n",
       "29997      3565.0\n",
       "29998     -1645.0\n",
       "29999     47929.0\n",
       "Name: BILL_AMT1, Length: 30000, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"BILL_AMT1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c4039403",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:24.927033Z",
     "iopub.status.busy": "2022-06-16T10:19:24.926775Z",
     "iopub.status.idle": "2022-06-16T10:19:25.182215Z",
     "shell.execute_reply": "2022-06-16T10:19:25.181477Z"
    },
    "papermill": {
     "duration": 0.27403,
     "end_time": "2022-06-16T10:19:25.184874",
     "exception": false,
     "start_time": "2022-06-16T10:19:24.910844",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "sns.scatterplot(x=df[\"BILL_AMT1\"], y =df[\"AGE\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "55883c0b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:25.220101Z",
     "iopub.status.busy": "2022-06-16T10:19:25.219818Z",
     "iopub.status.idle": "2022-06-16T10:19:25.226914Z",
     "shell.execute_reply": "2022-06-16T10:19:25.226117Z"
    },
    "papermill": {
     "duration": 0.02665,
     "end_time": "2022-06-16T10:19:25.228588",
     "exception": false,
     "start_time": "2022-06-16T10:19:25.201938",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          3102.0\n",
       "1          1725.0\n",
       "2         14027.0\n",
       "3         48233.0\n",
       "4          5670.0\n",
       "           ...   \n",
       "29995    192815.0\n",
       "29996      1828.0\n",
       "29997      3356.0\n",
       "29998     78379.0\n",
       "29999     48905.0\n",
       "Name: BILL_AMT2, Length: 30000, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.BILL_AMT2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b49fca58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:25.264130Z",
     "iopub.status.busy": "2022-06-16T10:19:25.263317Z",
     "iopub.status.idle": "2022-06-16T10:19:25.270528Z",
     "shell.execute_reply": "2022-06-16T10:19:25.269624Z"
    },
    "papermill": {
     "duration": 0.026138,
     "end_time": "2022-06-16T10:19:25.272221",
     "exception": false,
     "start_time": "2022-06-16T10:19:25.246083",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           689.0\n",
       "1          2682.0\n",
       "2         13559.0\n",
       "3         49291.0\n",
       "4         35835.0\n",
       "           ...   \n",
       "29995    208365.0\n",
       "29996      3502.0\n",
       "29997      2758.0\n",
       "29998     76304.0\n",
       "29999     49764.0\n",
       "Name: BILL_AMT3, Length: 30000, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.BILL_AMT3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a52ecbef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:25.308220Z",
     "iopub.status.busy": "2022-06-16T10:19:25.307957Z",
     "iopub.status.idle": "2022-06-16T10:19:25.314320Z",
     "shell.execute_reply": "2022-06-16T10:19:25.313640Z"
    },
    "papermill": {
     "duration": 0.026233,
     "end_time": "2022-06-16T10:19:25.316096",
     "exception": false,
     "start_time": "2022-06-16T10:19:25.289863",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            0.0\n",
       "1         3272.0\n",
       "2        14331.0\n",
       "3        28314.0\n",
       "4        20940.0\n",
       "          ...   \n",
       "29995    88004.0\n",
       "29996     8979.0\n",
       "29997    20878.0\n",
       "29998    52774.0\n",
       "29999    36535.0\n",
       "Name: BILL_AMT4, Length: 30000, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.BILL_AMT4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "86a5491d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:25.350945Z",
     "iopub.status.busy": "2022-06-16T10:19:25.350371Z",
     "iopub.status.idle": "2022-06-16T10:19:25.356716Z",
     "shell.execute_reply": "2022-06-16T10:19:25.355919Z"
    },
    "papermill": {
     "duration": 0.02557,
     "end_time": "2022-06-16T10:19:25.358433",
     "exception": false,
     "start_time": "2022-06-16T10:19:25.332863",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            0.0\n",
       "1         3455.0\n",
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   "id": "dec49bc9",
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     "metadata": {},
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   "execution_count": 38,
   "id": "5adf1d3d",
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   "source": [
    "df.rename(columns = {\"default.payment.next.month\":\"default_payment_next_month\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d9d2c3d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:25.754652Z",
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   "source": [
    "cont_names = [col for col in df if df[col].dtype!=\"O\"]\n",
    "cont_names.remove(\"default_payment_next_month\")"
   ]
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>8979.0</td>\n",
       "      <td>5190.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>3526.0</td>\n",
       "      <td>8998.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29997</th>\n",
       "      <td>29998</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>20878.0</td>\n",
       "      <td>20582.0</td>\n",
       "      <td>19357.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29998</th>\n",
       "      <td>29999</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>52774.0</td>\n",
       "      <td>11855.0</td>\n",
       "      <td>48944.0</td>\n",
       "      <td>85900.0</td>\n",
       "      <td>3409.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>52964.0</td>\n",
       "      <td>1804.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\n",
       "      <td>30000</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>36535.0</td>\n",
       "      <td>32428.0</td>\n",
       "      <td>15313.0</td>\n",
       "      <td>2078.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  \\\n",
       "0          1    20000.0    2          2         1   24      2      2     -1   \n",
       "1          2   120000.0    2          2         2   26     -1      2      0   \n",
       "2          3    90000.0    2          2         2   34      0      0      0   \n",
       "3          4    50000.0    2          2         1   37      0      0      0   \n",
       "4          5    50000.0    1          2         1   57     -1      0     -1   \n",
       "...      ...        ...  ...        ...       ...  ...    ...    ...    ...   \n",
       "29995  29996   220000.0    1          3         1   39      0      0      0   \n",
       "29996  29997   150000.0    1          3         2   43     -1     -1     -1   \n",
       "29997  29998    30000.0    1          2         2   37      4      3      2   \n",
       "29998  29999    80000.0    1          3         1   41      1     -1      0   \n",
       "29999  30000    50000.0    1          2         1   46      0      0      0   \n",
       "\n",
       "       PAY_4  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "0         -1  ...        0.0        0.0        0.0       0.0     689.0   \n",
       "1          0  ...     3272.0     3455.0     3261.0       0.0    1000.0   \n",
       "2          0  ...    14331.0    14948.0    15549.0    1518.0    1500.0   \n",
       "3          0  ...    28314.0    28959.0    29547.0    2000.0    2019.0   \n",
       "4          0  ...    20940.0    19146.0    19131.0    2000.0   36681.0   \n",
       "...      ...  ...        ...        ...        ...       ...       ...   \n",
       "29995      0  ...    88004.0    31237.0    15980.0    8500.0   20000.0   \n",
       "29996     -1  ...     8979.0     5190.0        0.0    1837.0    3526.0   \n",
       "29997     -1  ...    20878.0    20582.0    19357.0       0.0       0.0   \n",
       "29998      0  ...    52774.0    11855.0    48944.0   85900.0    3409.0   \n",
       "29999      0  ...    36535.0    32428.0    15313.0    2078.0    1800.0   \n",
       "\n",
       "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default_payment_next_month  \n",
       "0           0.0       0.0       0.0       0.0                           1  \n",
       "1        1000.0    1000.0       0.0    2000.0                           1  \n",
       "2        1000.0    1000.0    1000.0    5000.0                           0  \n",
       "3        1200.0    1100.0    1069.0    1000.0                           0  \n",
       "4       10000.0    9000.0     689.0     679.0                           0  \n",
       "...         ...       ...       ...       ...                         ...  \n",
       "29995    5003.0    3047.0    5000.0    1000.0                           0  \n",
       "29996    8998.0     129.0       0.0       0.0                           0  \n",
       "29997   22000.0    4200.0    2000.0    3100.0                           1  \n",
       "29998    1178.0    1926.0   52964.0    1804.0                           1  \n",
       "29999    1430.0    1000.0    1000.0    1000.0                           1  \n",
       "\n",
       "[30000 rows x 25 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4c475c59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:25.873043Z",
     "iopub.status.busy": "2022-06-16T10:19:25.872772Z",
     "iopub.status.idle": "2022-06-16T10:19:25.883346Z",
     "shell.execute_reply": "2022-06-16T10:19:25.882583Z"
    },
    "papermill": {
     "duration": 0.031356,
     "end_time": "2022-06-16T10:19:25.885007",
     "exception": false,
     "start_time": "2022-06-16T10:19:25.853651",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((#24000) [2354,16336,8130,26317,10354,28807,8589,989,6504,9500...],\n",
       " (#6000) [21543,22290,24309,5206,6942,26136,17458,29542,9150,19391...])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splits = RandomSplitter(valid_pct=0.2)(range_of(df))\n",
    "splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c4bec5ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:25.923497Z",
     "iopub.status.busy": "2022-06-16T10:19:25.922932Z",
     "iopub.status.idle": "2022-06-16T10:19:25.928059Z",
     "shell.execute_reply": "2022-06-16T10:19:25.927389Z"
    },
    "papermill": {
     "duration": 0.025768,
     "end_time": "2022-06-16T10:19:25.929722",
     "exception": false,
     "start_time": "2022-06-16T10:19:25.903954",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ID',\n",
       " 'LIMIT_BAL',\n",
       " 'SEX',\n",
       " 'EDUCATION',\n",
       " 'MARRIAGE',\n",
       " 'AGE',\n",
       " 'PAY_0',\n",
       " 'PAY_2',\n",
       " 'PAY_3',\n",
       " 'PAY_4',\n",
       " 'PAY_5',\n",
       " 'PAY_6',\n",
       " 'BILL_AMT1',\n",
       " 'BILL_AMT2',\n",
       " 'BILL_AMT3',\n",
       " 'BILL_AMT4',\n",
       " 'BILL_AMT5',\n",
       " 'BILL_AMT6',\n",
       " 'PAY_AMT1',\n",
       " 'PAY_AMT2',\n",
       " 'PAY_AMT3',\n",
       " 'PAY_AMT4',\n",
       " 'PAY_AMT5',\n",
       " 'PAY_AMT6']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a6a0aeec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:25.968333Z",
     "iopub.status.busy": "2022-06-16T10:19:25.966982Z",
     "iopub.status.idle": "2022-06-16T10:19:26.021776Z",
     "shell.execute_reply": "2022-06-16T10:19:26.020952Z"
    },
    "papermill": {
     "duration": 0.076004,
     "end_time": "2022-06-16T10:19:26.023846",
     "exception": false,
     "start_time": "2022-06-16T10:19:25.947842",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "to = TabularPandas(df,\n",
    "                  procs=[Normalize],\n",
    "                  cont_names = cont_names,\n",
    "                  y_names = \"default_payment_next_month\",\n",
    "                  splits = splits\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d5fc6ede",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:26.063189Z",
     "iopub.status.busy": "2022-06-16T10:19:26.062869Z",
     "iopub.status.idle": "2022-06-16T10:19:26.079811Z",
     "shell.execute_reply": "2022-06-16T10:19:26.079088Z"
    },
    "papermill": {
     "duration": 0.037941,
     "end_time": "2022-06-16T10:19:26.081451",
     "exception": false,
     "start_time": "2022-06-16T10:19:26.043510",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dls = to.dataloaders(bs = 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ce0733fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:26.121818Z",
     "iopub.status.busy": "2022-06-16T10:19:26.121514Z",
     "iopub.status.idle": "2022-06-16T10:19:26.181039Z",
     "shell.execute_reply": "2022-06-16T10:19:26.180306Z"
    },
    "papermill": {
     "duration": 0.082883,
     "end_time": "2022-06-16T10:19:26.182855",
     "exception": false,
     "start_time": "2022-06-16T10:19:26.099972",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>PAY_5</th>\n",
       "      <th>PAY_6</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",
       "      <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>24527.000303</td>\n",
       "      <td>500000.002852</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2.255575e-10</td>\n",
       "      <td>2.878071e-09</td>\n",
       "      <td>3.114033e-09</td>\n",
       "      <td>-4.806220e-09</td>\n",
       "      <td>7.339561e-09</td>\n",
       "      <td>-1.195189e-08</td>\n",
       "      <td>305574.999542</td>\n",
       "      <td>231984.004323</td>\n",
       "      <td>228764.993015</td>\n",
       "      <td>185693.996918</td>\n",
       "      <td>179852.997937</td>\n",
       "      <td>178762.994341</td>\n",
       "      <td>9824.000003</td>\n",
       "      <td>10909.999848</td>\n",
       "      <td>7239.000006</td>\n",
       "      <td>5576.000004</td>\n",
       "      <td>6716.999953</td>\n",
       "      <td>6127.999990</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>490.000314</td>\n",
       "      <td>260000.001617</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>16425.999975</td>\n",
       "      <td>6088.000545</td>\n",
       "      <td>4662.998630</td>\n",
       "      <td>3884.999789</td>\n",
       "      <td>4414.000724</td>\n",
       "      <td>2514.000633</td>\n",
       "      <td>6277.000029</td>\n",
       "      <td>4662.999984</td>\n",
       "      <td>4139.999969</td>\n",
       "      <td>4414.000010</td>\n",
       "      <td>2645.999998</td>\n",
       "      <td>7725.000055</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10804.999921</td>\n",
       "      <td>119999.999609</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.255575e-10</td>\n",
       "      <td>2.878071e-09</td>\n",
       "      <td>3.114033e-09</td>\n",
       "      <td>-4.806220e-09</td>\n",
       "      <td>7.339561e-09</td>\n",
       "      <td>-1.195189e-08</td>\n",
       "      <td>11513.000581</td>\n",
       "      <td>12227.000219</td>\n",
       "      <td>13320.999435</td>\n",
       "      <td>15089.999778</td>\n",
       "      <td>16836.000682</td>\n",
       "      <td>18553.999462</td>\n",
       "      <td>1205.999798</td>\n",
       "      <td>1300.000016</td>\n",
       "      <td>2000.000099</td>\n",
       "      <td>1999.999961</td>\n",
       "      <td>2000.000094</td>\n",
       "      <td>2000.000070</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16943.999937</td>\n",
       "      <td>60000.003161</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2.255575e-10</td>\n",
       "      <td>2.878071e-09</td>\n",
       "      <td>3.114033e-09</td>\n",
       "      <td>-4.806220e-09</td>\n",
       "      <td>7.339561e-09</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>58432.000081</td>\n",
       "      <td>58312.000380</td>\n",
       "      <td>59387.999745</td>\n",
       "      <td>29941.000189</td>\n",
       "      <td>1340.999781</td>\n",
       "      <td>1876.001372</td>\n",
       "      <td>20000.000102</td>\n",
       "      <td>3000.000051</td>\n",
       "      <td>3000.000051</td>\n",
       "      <td>999.999976</td>\n",
       "      <td>2000.000094</td>\n",
       "      <td>24999.999964</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22693.999938</td>\n",
       "      <td>359999.996983</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2.255575e-10</td>\n",
       "      <td>2.878071e-09</td>\n",
       "      <td>3.114033e-09</td>\n",
       "      <td>-4.806220e-09</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.195189e-08</td>\n",
       "      <td>192160.002429</td>\n",
       "      <td>197203.991986</td>\n",
       "      <td>119028.000580</td>\n",
       "      <td>103045.998333</td>\n",
       "      <td>15862.999299</td>\n",
       "      <td>10503.000614</td>\n",
       "      <td>20104.000377</td>\n",
       "      <td>4815.999961</td>\n",
       "      <td>2061.000006</td>\n",
       "      <td>15862.999924</td>\n",
       "      <td>21.000108</td>\n",
       "      <td>5016.000006</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16245.000031</td>\n",
       "      <td>50000.000535</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.255575e-10</td>\n",
       "      <td>2.878071e-09</td>\n",
       "      <td>3.114033e-09</td>\n",
       "      <td>-4.806220e-09</td>\n",
       "      <td>7.339561e-09</td>\n",
       "      <td>-1.195189e-08</td>\n",
       "      <td>3237.999290</td>\n",
       "      <td>4672.000245</td>\n",
       "      <td>2864.999399</td>\n",
       "      <td>3878.000262</td>\n",
       "      <td>4809.001258</td>\n",
       "      <td>5725.999981</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1056.000035</td>\n",
       "      <td>1069.000075</td>\n",
       "      <td>999.999976</td>\n",
       "      <td>1000.000006</td>\n",
       "      <td>254.999889</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13502.000028</td>\n",
       "      <td>20000.000381</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.255575e-10</td>\n",
       "      <td>2.878071e-09</td>\n",
       "      <td>3.114033e-09</td>\n",
       "      <td>-4.806220e-09</td>\n",
       "      <td>7.339561e-09</td>\n",
       "      <td>-1.195189e-08</td>\n",
       "      <td>11861.998316</td>\n",
       "      <td>11259.000325</td>\n",
       "      <td>12482.001224</td>\n",
       "      <td>10528.001065</td>\n",
       "      <td>9292.998789</td>\n",
       "      <td>17098.999155</td>\n",
       "      <td>1299.999893</td>\n",
       "      <td>1446.000143</td>\n",
       "      <td>500.000239</td>\n",
       "      <td>500.000098</td>\n",
       "      <td>7999.999940</td>\n",
       "      <td>1000.000028</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23294.999976</td>\n",
       "      <td>30000.003007</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.255575e-10</td>\n",
       "      <td>2.878071e-09</td>\n",
       "      <td>3.114033e-09</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>26829.999465</td>\n",
       "      <td>30900.000683</td>\n",
       "      <td>0.001942</td>\n",
       "      <td>0.000676</td>\n",
       "      <td>0.000583</td>\n",
       "      <td>0.001403</td>\n",
       "      <td>4999.999989</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>-0.000080</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>-0.000082</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15203.000008</td>\n",
       "      <td>129999.998373</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>90833.000702</td>\n",
       "      <td>2773.999150</td>\n",
       "      <td>740.001521</td>\n",
       "      <td>740.001788</td>\n",
       "      <td>73757.998458</td>\n",
       "      <td>72799.999054</td>\n",
       "      <td>2788.000026</td>\n",
       "      <td>739.999970</td>\n",
       "      <td>-0.000080</td>\n",
       "      <td>73758.002493</td>\n",
       "      <td>-0.000082</td>\n",
       "      <td>2342.000125</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5343.999868</td>\n",
       "      <td>9999.997755</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.255575e-10</td>\n",
       "      <td>2.878071e-09</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>-4.806220e-09</td>\n",
       "      <td>7.339561e-09</td>\n",
       "      <td>-1.195189e-08</td>\n",
       "      <td>10119.000434</td>\n",
       "      <td>10188.000744</td>\n",
       "      <td>9088.998778</td>\n",
       "      <td>9270.999710</td>\n",
       "      <td>9617.998311</td>\n",
       "      <td>8515.998880</td>\n",
       "      <td>3000.000049</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>295.000218</td>\n",
       "      <td>458.999884</td>\n",
       "      <td>1500.000050</td>\n",
       "      <td>2000.000070</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fbc276d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:26.222535Z",
     "iopub.status.busy": "2022-06-16T10:19:26.222242Z",
     "iopub.status.idle": "2022-06-16T10:19:26.231352Z",
     "shell.execute_reply": "2022-06-16T10:19:26.230705Z"
    },
    "papermill": {
     "duration": 0.030722,
     "end_time": "2022-06-16T10:19:26.232968",
     "exception": false,
     "start_time": "2022-06-16T10:19:26.202246",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "learn = tabular_learner(dls,layers = [512,256,256,128,64], wd = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "48d705c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:26.272762Z",
     "iopub.status.busy": "2022-06-16T10:19:26.272468Z",
     "iopub.status.idle": "2022-06-16T10:19:31.568597Z",
     "shell.execute_reply": "2022-06-16T10:19:31.567573Z"
    },
    "papermill": {
     "duration": 5.318929,
     "end_time": "2022-06-16T10:19:31.571159",
     "exception": false,
     "start_time": "2022-06-16T10:19:26.252230",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_min,lr_steep = learn.lr_find(suggest_funcs=(minimum, steep))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "05f303f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:31.647013Z",
     "iopub.status.busy": "2022-06-16T10:19:31.646597Z",
     "iopub.status.idle": "2022-06-16T10:19:31.651404Z",
     "shell.execute_reply": "2022-06-16T10:19:31.650703Z"
    },
    "papermill": {
     "duration": 0.04694,
     "end_time": "2022-06-16T10:19:31.657070",
     "exception": false,
     "start_time": "2022-06-16T10:19:31.610130",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum/10: 1.45e-02, steepest point: 6.31e-07\n"
     ]
    }
   ],
   "source": [
    "print(f\"Minimum/10: {lr_min:.2e}, steepest point: {lr_steep:.2e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6d2fa9d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:19:31.723481Z",
     "iopub.status.busy": "2022-06-16T10:19:31.723081Z",
     "iopub.status.idle": "2022-06-16T10:20:09.136103Z",
     "shell.execute_reply": "2022-06-16T10:20:09.135315Z"
    },
    "papermill": {
     "duration": 37.448196,
     "end_time": "2022-06-16T10:20:09.138003",
     "exception": false,
     "start_time": "2022-06-16T10:19:31.689807",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.465368</td>\n",
       "      <td>0.538111</td>\n",
       "      <td>00:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.424821</td>\n",
       "      <td>0.483269</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.405054</td>\n",
       "      <td>0.419467</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.379349</td>\n",
       "      <td>0.405361</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.350161</td>\n",
       "      <td>0.403460</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.340611</td>\n",
       "      <td>0.390067</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.341734</td>\n",
       "      <td>0.382231</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.341126</td>\n",
       "      <td>0.357381</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.325734</td>\n",
       "      <td>0.359916</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.336345</td>\n",
       "      <td>0.376579</td>\n",
       "      <td>00:04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(10, lr_steep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "41220aa4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-16T10:20:09.180641Z",
     "iopub.status.busy": "2022-06-16T10:20:09.179131Z",
     "iopub.status.idle": "2022-06-16T10:20:09.463334Z",
     "shell.execute_reply": "2022-06-16T10:20:09.462610Z"
    },
    "papermill": {
     "duration": 0.306694,
     "end_time": "2022-06-16T10:20:09.465040",
     "exception": false,
     "start_time": "2022-06-16T10:20:09.158346",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_loss()"
   ]
  }
 ],
 "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": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 62.485597,
   "end_time": "2022-06-16T10:20:11.749530",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-06-16T10:19:09.263933",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}