global_shap_resid.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License \n",
    "\n",
    "Copyright 2017 Patrick Hall and the H2O.ai team\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "\n",
    "    http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DISCLAIMER:** This notebook is not legal compliance advice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/patrickh/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "# h2o Python API with specific classes\n",
    "import h2o \n",
    "from h2o.estimators.gbm import H2OGradientBoostingEstimator\n",
    "\n",
    "import numpy as np   # array, vector, matrix calculations\n",
    "import pandas as pd  # DataFrame handling\n",
    "\n",
    "pd.options.display.max_columns = 999 # enable display of all columns in notebook\n",
    "\n",
    "# plotting functionality\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import shap # Python Shapley value package\n",
    "\n",
    "# display plots in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Start h2o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: java version \"1.8.0_201\"; Java(TM) SE Runtime Environment (build 1.8.0_201-b09); Java HotSpot(TM) 64-Bit Server VM (build 25.201-b09, mixed mode)\n",
      "  Starting server from /home/patrickh/anaconda3/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /tmp/tmpl3mfxavo\n",
      "  JVM stdout: /tmp/tmpl3mfxavo/h2o_patrickh_started_from_python.out\n",
      "  JVM stderr: /tmp/tmpl3mfxavo/h2o_patrickh_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>01 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>America/New_York</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.24.0.2</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>6 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_patrickh_mh0r6r</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>1.778 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O API Extensions:</td>\n",
       "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.6.4 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ---------------------------------------------------\n",
       "H2O cluster uptime:         01 secs\n",
       "H2O cluster timezone:       America/New_York\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.24.0.2\n",
       "H2O cluster version age:    6 days\n",
       "H2O cluster name:           H2O_from_python_patrickh_mh0r6r\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    1.778 Gb\n",
       "H2O cluster total cores:    8\n",
       "H2O cluster allowed cores:  8\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4\n",
       "Python version:             3.6.4 final\n",
       "--------------------------  ---------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init(max_mem_size='2G')       # start h2o\n",
    "h2o.remove_all()                  # remove any existing data structures from h2o memory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Download, explore, and prepare UCI credit card default data\n",
    "\n",
    "UCI credit card default data: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients\n",
    "\n",
    "The UCI credit card default data contains demographic and payment information about credit card customers in Taiwan in the year 2005. The data set contains 23 input variables: \n",
    "\n",
    "* **`LIMIT_BAL`**: Amount of given credit (NT dollar)\n",
    "* **`SEX`**: 1 = male; 2 = female\n",
    "* **`EDUCATION`**: 1 = graduate school; 2 = university; 3 = high school; 4 = others \n",
    "* **`MARRIAGE`**: 1 = married; 2 = single; 3 = others\n",
    "* **`AGE`**: Age in years \n",
    "* **`PAY_0`, `PAY_2` - `PAY_6`**: History of past payment; `PAY_0` = the repayment status in September, 2005; `PAY_2` = the repayment status in August, 2005; ...; `PAY_6` = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; ...; 8 = payment delay for eight months; 9 = payment delay for nine months and above. \n",
    "* **`BILL_AMT1` - `BILL_AMT6`**: Amount of bill statement (NT dollar). `BILL_AMNT1` = amount of bill statement in September, 2005; `BILL_AMT2` = amount of bill statement in August, 2005; ...; `BILL_AMT6` = amount of bill statement in April, 2005. \n",
    "* **`PAY_AMT1` - `PAY_AMT6`**: Amount of previous payment (NT dollar). `PAY_AMT1` = amount paid in September, 2005; `PAY_AMT2` = amount paid in August, 2005; ...; `PAY_AMT6` = amount paid in April, 2005. \n",
    "\n",
    "These 23 input variables are used to predict the target variable, whether or not a customer defaulted on their credit card bill in late 2005.\n",
    "\n",
    "Because h2o accepts both numeric and character inputs, some variables will be recoded into more transparent character values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import data and clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import XLS file\n",
    "path = 'default_of_credit_card_clients.xls'\n",
    "data = pd.read_excel(path,\n",
    "                     skiprows=1)\n",
    "\n",
    "# remove spaces from target column name \n",
    "data = data.rename(columns={'default payment next month': 'DEFAULT_NEXT_MONTH'}) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assign modeling roles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = DEFAULT_NEXT_MONTH\n",
      "X = ['PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n"
     ]
    }
   ],
   "source": [
    "# assign target and inputs for GBM\n",
    "y = 'DEFAULT_NEXT_MONTH'\n",
    "X = [name for name in data.columns if name not in [y, 'ID', 'AGE', 'EDUCATION', 'SEX', 'MARRIAGE', 'LIMIT_BAL']]\n",
    "print('y =', y)\n",
    "print('X =', X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Helper function for recoding values in the UCI credict card default data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "def recode_cc_data(frame):\n",
    "    \n",
    "    \"\"\" Recodes numeric categorical variables into categorical character variables\n",
    "    with more transparent values. \n",
    "    \n",
    "    Args:\n",
    "        frame: Pandas DataFrame version of UCI credit card default data.\n",
    "        \n",
    "    Returns: \n",
    "        H2OFrame with recoded values.\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    # define recoded values\n",
    "    sex_dict = {1:'male', 2:'female'}\n",
    "    education_dict = {0:'other', 1:'graduate school', 2:'university', 3:'high school', \n",
    "                      4:'other', 5:'other', 6:'other'}\n",
    "    marriage_dict = {0:'other', 1:'married', 2:'single', 3:'divorced'}\n",
    "    pay_dict = {-2:'no consumption (-2)', -1:'pay duly (-1)', 0:'use of revolving credit (0)', 1:'1 month delay', \n",
    "                2:'2 month delay', 3:'3 month delay', 4:'4 month delay', 5:'5 month delay', 6:'6 month delay', \n",
    "                7:'7 month delay', 8:'8 month delay', 9:'9+ month delay'}\n",
    "    \n",
    "    # recode values using Pandas apply() and anonymous function\n",
    "    frame['SEX'] = frame['SEX'].apply(lambda i: sex_dict[i])\n",
    "    frame['EDUCATION'] = frame['EDUCATION'].apply(lambda i: education_dict[i])    \n",
    "    frame['MARRIAGE'] = frame['MARRIAGE'].apply(lambda i: marriage_dict[i]) \n",
    "    for name in frame.columns:\n",
    "        if name in ['PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']:\n",
    "            frame[name] = frame[name].apply(lambda i: pay_dict[i])            \n",
    "                \n",
    "    return h2o.H2OFrame(frame)\n",
    "\n",
    "data = recode_cc_data(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ensure target is handled as a categorical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[y] = data[y].asfactor() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:30000\n",
      "Cols:25\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>       </th><th>ID               </th><th>LIMIT_BAL         </th><th>SEX   </th><th>EDUCATION      </th><th>MARRIAGE  </th><th>AGE              </th><th>PAY_0                      </th><th>PAY_2                      </th><th>PAY_3                      </th><th>PAY_4                      </th><th>PAY_5                      </th><th>PAY_6                      </th><th>BILL_AMT1         </th><th>BILL_AMT2        </th><th>BILL_AMT3        </th><th>BILL_AMT4         </th><th>BILL_AMT5       </th><th>BILL_AMT6        </th><th>PAY_AMT1          </th><th>PAY_AMT2         </th><th>PAY_AMT3          </th><th>PAY_AMT4          </th><th>PAY_AMT5          </th><th>PAY_AMT6          </th><th>DEFAULT_NEXT_MONTH  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>int              </td><td>int               </td><td>enum  </td><td>enum           </td><td>enum      </td><td>int              </td><td>enum                       </td><td>enum                       </td><td>enum                       </td><td>enum                       </td><td>enum                       </td><td>enum                       </td><td>int               </td><td>int              </td><td>int              </td><td>int               </td><td>int             </td><td>int              </td><td>int               </td><td>int              </td><td>int               </td><td>int               </td><td>int               </td><td>int               </td><td>enum                </td></tr>\n",
       "<tr><td>mins   </td><td>1.0              </td><td>10000.0           </td><td>      </td><td>               </td><td>          </td><td>21.0             </td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>-165580.0         </td><td>-69777.0         </td><td>-157264.0        </td><td>-170000.0         </td><td>-81334.0        </td><td>-339603.0        </td><td>0.0               </td><td>0.0              </td><td>0.0               </td><td>0.0               </td><td>0.0               </td><td>0.0               </td><td>                    </td></tr>\n",
       "<tr><td>mean   </td><td>15000.5          </td><td>167484.32266666667</td><td>      </td><td>               </td><td>          </td><td>35.48549999999987</td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>51223.330899999935</td><td>49179.0751666667 </td><td>47013.15479999978</td><td>43262.94896666669 </td><td>40311.4009666666</td><td>38871.76040000009</td><td>5663.580500000006 </td><td>5921.1635        </td><td>5225.681500000008 </td><td>4826.076866666684 </td><td>4799.3876333333155</td><td>5215.502566666679 </td><td>                    </td></tr>\n",
       "<tr><td>maxs   </td><td>30000.0          </td><td>1000000.0         </td><td>      </td><td>               </td><td>          </td><td>79.0             </td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>964511.0          </td><td>983931.0         </td><td>1664089.0        </td><td>891586.0          </td><td>927171.0        </td><td>961664.0         </td><td>873552.0          </td><td>1684259.0        </td><td>896040.0          </td><td>621000.0          </td><td>426529.0          </td><td>528666.0          </td><td>                    </td></tr>\n",
       "<tr><td>sigma  </td><td>8660.398374208891</td><td>129747.66156720246</td><td>      </td><td>               </td><td>          </td><td>9.217904068090172</td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>73635.86057552964 </td><td>71173.76878252838</td><td>69349.38742703681</td><td>64332.856133916415</td><td>60797.1557702648</td><td>59554.10753674574</td><td>16563.280354025763</td><td>23040.87040205722</td><td>17606.961469803107</td><td>15666.159744031986</td><td>15278.305679144814</td><td>17777.465775435325</td><td>                    </td></tr>\n",
       "<tr><td>zeros  </td><td>0                </td><td>0                 </td><td>      </td><td>               </td><td>          </td><td>0                </td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>                           </td><td>2008              </td><td>2506             </td><td>2870             </td><td>3195              </td><td>3506            </td><td>4020             </td><td>5249              </td><td>5396             </td><td>5968              </td><td>6408              </td><td>6703              </td><td>7173              </td><td>                    </td></tr>\n",
       "<tr><td>missing</td><td>0                </td><td>0                 </td><td>0     </td><td>0              </td><td>0         </td><td>0                </td><td>0                          </td><td>0                          </td><td>0                          </td><td>0                          </td><td>0                          </td><td>0                          </td><td>0                 </td><td>0                </td><td>0                </td><td>0                 </td><td>0               </td><td>0                </td><td>0                 </td><td>0                </td><td>0                 </td><td>0                 </td><td>0                 </td><td>0                 </td><td>0                   </td></tr>\n",
       "<tr><td>0      </td><td>1.0              </td><td>20000.0           </td><td>female</td><td>university     </td><td>married   </td><td>24.0             </td><td>2 month delay              </td><td>2 month delay              </td><td>pay duly (-1)              </td><td>pay duly (-1)              </td><td>no consumption (-2)        </td><td>no consumption (-2)        </td><td>3913.0            </td><td>3102.0           </td><td>689.0            </td><td>0.0               </td><td>0.0             </td><td>0.0              </td><td>0.0               </td><td>689.0            </td><td>0.0               </td><td>0.0               </td><td>0.0               </td><td>0.0               </td><td>1                   </td></tr>\n",
       "<tr><td>1      </td><td>2.0              </td><td>120000.0          </td><td>female</td><td>university     </td><td>single    </td><td>26.0             </td><td>pay duly (-1)              </td><td>2 month delay              </td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>2 month delay              </td><td>2682.0            </td><td>1725.0           </td><td>2682.0           </td><td>3272.0            </td><td>3455.0          </td><td>3261.0           </td><td>0.0               </td><td>1000.0           </td><td>1000.0            </td><td>1000.0            </td><td>0.0               </td><td>2000.0            </td><td>1                   </td></tr>\n",
       "<tr><td>2      </td><td>3.0              </td><td>90000.0           </td><td>female</td><td>university     </td><td>single    </td><td>34.0             </td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>29239.0           </td><td>14027.0          </td><td>13559.0          </td><td>14331.0           </td><td>14948.0         </td><td>15549.0          </td><td>1518.0            </td><td>1500.0           </td><td>1000.0            </td><td>1000.0            </td><td>1000.0            </td><td>5000.0            </td><td>0                   </td></tr>\n",
       "<tr><td>3      </td><td>4.0              </td><td>50000.0           </td><td>female</td><td>university     </td><td>married   </td><td>37.0             </td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>46990.0           </td><td>48233.0          </td><td>49291.0          </td><td>28314.0           </td><td>28959.0         </td><td>29547.0          </td><td>2000.0            </td><td>2019.0           </td><td>1200.0            </td><td>1100.0            </td><td>1069.0            </td><td>1000.0            </td><td>0                   </td></tr>\n",
       "<tr><td>4      </td><td>5.0              </td><td>50000.0           </td><td>male  </td><td>university     </td><td>married   </td><td>57.0             </td><td>pay duly (-1)              </td><td>use of revolving credit (0)</td><td>pay duly (-1)              </td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>8617.0            </td><td>5670.0           </td><td>35835.0          </td><td>20940.0           </td><td>19146.0         </td><td>19131.0          </td><td>2000.0            </td><td>36681.0          </td><td>10000.0           </td><td>9000.0            </td><td>689.0             </td><td>679.0             </td><td>0                   </td></tr>\n",
       "<tr><td>5      </td><td>6.0              </td><td>50000.0           </td><td>male  </td><td>graduate school</td><td>single    </td><td>37.0             </td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>64400.0           </td><td>57069.0          </td><td>57608.0          </td><td>19394.0           </td><td>19619.0         </td><td>20024.0          </td><td>2500.0            </td><td>1815.0           </td><td>657.0             </td><td>1000.0            </td><td>1000.0            </td><td>800.0             </td><td>0                   </td></tr>\n",
       "<tr><td>6      </td><td>7.0              </td><td>500000.0          </td><td>male  </td><td>graduate school</td><td>single    </td><td>29.0             </td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>367965.0          </td><td>412023.0         </td><td>445007.0         </td><td>542653.0          </td><td>483003.0        </td><td>473944.0         </td><td>55000.0           </td><td>40000.0          </td><td>38000.0           </td><td>20239.0           </td><td>13750.0           </td><td>13770.0           </td><td>0                   </td></tr>\n",
       "<tr><td>7      </td><td>8.0              </td><td>100000.0          </td><td>female</td><td>university     </td><td>single    </td><td>23.0             </td><td>use of revolving credit (0)</td><td>pay duly (-1)              </td><td>pay duly (-1)              </td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>pay duly (-1)              </td><td>11876.0           </td><td>380.0            </td><td>601.0            </td><td>221.0             </td><td>-159.0          </td><td>567.0            </td><td>380.0             </td><td>601.0            </td><td>0.0               </td><td>581.0             </td><td>1687.0            </td><td>1542.0            </td><td>0                   </td></tr>\n",
       "<tr><td>8      </td><td>9.0              </td><td>140000.0          </td><td>female</td><td>high school    </td><td>married   </td><td>28.0             </td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>2 month delay              </td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>use of revolving credit (0)</td><td>11285.0           </td><td>14096.0          </td><td>12108.0          </td><td>12211.0           </td><td>11793.0         </td><td>3719.0           </td><td>3329.0            </td><td>0.0              </td><td>432.0             </td><td>1000.0            </td><td>1000.0            </td><td>1000.0            </td><td>0                   </td></tr>\n",
       "<tr><td>9      </td><td>10.0             </td><td>20000.0           </td><td>male  </td><td>high school    </td><td>single    </td><td>35.0             </td><td>no consumption (-2)        </td><td>no consumption (-2)        </td><td>no consumption (-2)        </td><td>no consumption (-2)        </td><td>pay duly (-1)              </td><td>pay duly (-1)              </td><td>0.0               </td><td>0.0              </td><td>0.0              </td><td>0.0               </td><td>13007.0         </td><td>13912.0          </td><td>0.0               </td><td>0.0              </td><td>0.0               </td><td>13007.0           </td><td>1122.0            </td><td>0.0               </td><td>0                   </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Train an H2O GBM classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split data into training and test sets for early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data rows = 21060, columns = 25\n",
      "Test data rows = 8940, columns = 25\n"
     ]
    }
   ],
   "source": [
    "# split into training and validation\n",
    "train, test = data.split_frame([0.7], seed=12345)\n",
    "\n",
    "# summarize split\n",
    "print('Train data rows = %d, columns = %d' % (train.shape[0], train.shape[1]))\n",
    "print('Test data rows = %d, columns = %d' % (test.shape[0], test.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train h2o GBM classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "GBM Test AUC = 0.7714\n"
     ]
    }
   ],
   "source": [
    "# initialize GBM model\n",
    "model = H2OGradientBoostingEstimator(ntrees=150,            # maximum 150 trees in GBM\n",
    "                                     max_depth=4,           # trees can have maximum depth of 4\n",
    "                                     sample_rate=0.9,       # use 90% of rows in each iteration (tree)\n",
    "                                     col_sample_rate=0.9,   # use 90% of variables in each iteration (tree)\n",
    "                                     stopping_rounds=5,     # stop if validation error does not decrease for 5 iterations (trees)\n",
    "                                     seed=12345)            # for reproducibility\n",
    "\n",
    "# train a GBM model\n",
    "model.train(y=y, x=X, training_frame=train, validation_frame=test)\n",
    "\n",
    "# print AUC\n",
    "print('GBM Test AUC = %.4f' % model.auc(valid=True))\n",
    "\n",
    "# uncomment to see model details\n",
    "# print(model) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display Shapley variable importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/patrickh/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x626.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contributions = model.predict_contributions(test)\n",
    "contributions_matrix = contributions.as_data_frame().as_matrix()\n",
    "shap_values = contributions_matrix[:,:-1]\n",
    "shap.summary_plot(shap_values, X, plot_type='bar', color='royalblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Conduct residual analysis to debug model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bind model predictions onto test data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "yhat = 'p_DEFAULT_NEXT_MONTH'\n",
    "preds1 = model.predict(test).drop(['predict', 'p0'])\n",
    "preds1.columns = [yhat]\n",
    "test_yhat = test.cbind(preds1[yhat])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate deviance residuals for binomial classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use Pandas for adding columns and plotting\n",
    "test_yhat = test_yhat.as_data_frame()\n",
    "test_yhat['s'] = 1\n",
    "test_yhat.loc[test_yhat['DEFAULT_NEXT_MONTH'] == 0, 's'] = -1\n",
    "test_yhat['r_DEFAULT_NEXT_MONTH'] = test_yhat['s'] * np.sqrt(-2*(test_yhat[y]*np.log(test_yhat[yhat]) +\n",
    "                                                                 ((1 - test_yhat[y])*np.log(1 - test_yhat[yhat]))))\n",
    "test_yhat = test_yhat.drop('s', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sort data by residuals and display data and residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_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_NEXT_MONTH</th>\n",
       "      <th>p_DEFAULT_NEXT_MONTH</th>\n",
       "      <th>r_DEFAULT_NEXT_MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6933</td>\n",
       "      <td>500000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>37</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>4331</td>\n",
       "      <td>60446</td>\n",
       "      <td>30592</td>\n",
       "      <td>154167</td>\n",
       "      <td>13410</td>\n",
       "      <td>25426</td>\n",
       "      <td>60446</td>\n",
       "      <td>30594</td>\n",
       "      <td>150843</td>\n",
       "      <td>163881</td>\n",
       "      <td>25426</td>\n",
       "      <td>39526</td>\n",
       "      <td>1</td>\n",
       "      <td>0.048142</td>\n",
       "      <td>2.463165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22505</td>\n",
       "      <td>260000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>33</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>5188</td>\n",
       "      <td>12357</td>\n",
       "      <td>28656</td>\n",
       "      <td>7497</td>\n",
       "      <td>7685</td>\n",
       "      <td>15434</td>\n",
       "      <td>13000</td>\n",
       "      <td>29022</td>\n",
       "      <td>7500</td>\n",
       "      <td>27769</td>\n",
       "      <td>12000</td>\n",
       "      <td>6200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.048142</td>\n",
       "      <td>2.463165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2561</td>\n",
       "      <td>310000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>32</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>20138</td>\n",
       "      <td>8267</td>\n",
       "      <td>65993</td>\n",
       "      <td>8543</td>\n",
       "      <td>1695</td>\n",
       "      <td>750</td>\n",
       "      <td>8267</td>\n",
       "      <td>66008</td>\n",
       "      <td>8543</td>\n",
       "      <td>1695</td>\n",
       "      <td>750</td>\n",
       "      <td>7350</td>\n",
       "      <td>1</td>\n",
       "      <td>0.056123</td>\n",
       "      <td>2.400086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11745</td>\n",
       "      <td>220000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>51</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>20730</td>\n",
       "      <td>-270</td>\n",
       "      <td>53895</td>\n",
       "      <td>-105</td>\n",
       "      <td>20895</td>\n",
       "      <td>20835</td>\n",
       "      <td>0</td>\n",
       "      <td>54165</td>\n",
       "      <td>0</td>\n",
       "      <td>21000</td>\n",
       "      <td>20940</td>\n",
       "      <td>33460</td>\n",
       "      <td>1</td>\n",
       "      <td>0.056634</td>\n",
       "      <td>2.396310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25772</td>\n",
       "      <td>350000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>33</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>82964</td>\n",
       "      <td>68532</td>\n",
       "      <td>17926</td>\n",
       "      <td>17966</td>\n",
       "      <td>30741</td>\n",
       "      <td>31088</td>\n",
       "      <td>68940</td>\n",
       "      <td>18018</td>\n",
       "      <td>18058</td>\n",
       "      <td>30897</td>\n",
       "      <td>31244</td>\n",
       "      <td>88461</td>\n",
       "      <td>1</td>\n",
       "      <td>0.061981</td>\n",
       "      <td>2.358360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29294</td>\n",
       "      <td>50000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>25</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>9716</td>\n",
       "      <td>25045</td>\n",
       "      <td>15180</td>\n",
       "      <td>6964</td>\n",
       "      <td>7604</td>\n",
       "      <td>2173</td>\n",
       "      <td>25045</td>\n",
       "      <td>15522</td>\n",
       "      <td>6964</td>\n",
       "      <td>7604</td>\n",
       "      <td>2173</td>\n",
       "      <td>6586</td>\n",
       "      <td>1</td>\n",
       "      <td>0.061981</td>\n",
       "      <td>2.358360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>25692</td>\n",
       "      <td>330000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>42</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>565</td>\n",
       "      <td>20650</td>\n",
       "      <td>15360</td>\n",
       "      <td>0</td>\n",
       "      <td>12923</td>\n",
       "      <td>1816</td>\n",
       "      <td>20650</td>\n",
       "      <td>15360</td>\n",
       "      <td>0</td>\n",
       "      <td>12923</td>\n",
       "      <td>1816</td>\n",
       "      <td>17050</td>\n",
       "      <td>1</td>\n",
       "      <td>0.062990</td>\n",
       "      <td>2.351502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8339</td>\n",
       "      <td>480000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>58</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>24610</td>\n",
       "      <td>-310</td>\n",
       "      <td>148544</td>\n",
       "      <td>18791</td>\n",
       "      <td>5909</td>\n",
       "      <td>68988</td>\n",
       "      <td>4</td>\n",
       "      <td>149654</td>\n",
       "      <td>18885</td>\n",
       "      <td>5940</td>\n",
       "      <td>69337</td>\n",
       "      <td>200655</td>\n",
       "      <td>1</td>\n",
       "      <td>0.066306</td>\n",
       "      <td>2.329584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15482</td>\n",
       "      <td>150000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>37</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>22109</td>\n",
       "      <td>10876</td>\n",
       "      <td>10268</td>\n",
       "      <td>5872</td>\n",
       "      <td>3068</td>\n",
       "      <td>2181</td>\n",
       "      <td>10943</td>\n",
       "      <td>10273</td>\n",
       "      <td>5978</td>\n",
       "      <td>3068</td>\n",
       "      <td>2181</td>\n",
       "      <td>3242</td>\n",
       "      <td>1</td>\n",
       "      <td>0.067178</td>\n",
       "      <td>2.323969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3016</td>\n",
       "      <td>350000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>38</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>16459</td>\n",
       "      <td>4120</td>\n",
       "      <td>44164</td>\n",
       "      <td>35233</td>\n",
       "      <td>884</td>\n",
       "      <td>9924</td>\n",
       "      <td>941</td>\n",
       "      <td>44743</td>\n",
       "      <td>0</td>\n",
       "      <td>884</td>\n",
       "      <td>9924</td>\n",
       "      <td>10824</td>\n",
       "      <td>1</td>\n",
       "      <td>0.068563</td>\n",
       "      <td>2.315165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22751</td>\n",
       "      <td>350000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>32</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>30625</td>\n",
       "      <td>60003</td>\n",
       "      <td>7147</td>\n",
       "      <td>9950</td>\n",
       "      <td>22117</td>\n",
       "      <td>4874</td>\n",
       "      <td>60396</td>\n",
       "      <td>7147</td>\n",
       "      <td>9950</td>\n",
       "      <td>22117</td>\n",
       "      <td>4874</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.069769</td>\n",
       "      <td>2.307623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9078</td>\n",
       "      <td>320000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>34</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>800</td>\n",
       "      <td>18873</td>\n",
       "      <td>-150</td>\n",
       "      <td>150</td>\n",
       "      <td>21565</td>\n",
       "      <td>4818</td>\n",
       "      <td>18873</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>21565</td>\n",
       "      <td>5000</td>\n",
       "      <td>168492</td>\n",
       "      <td>1</td>\n",
       "      <td>0.070637</td>\n",
       "      <td>2.302260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11462</td>\n",
       "      <td>210000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>46</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>15655</td>\n",
       "      <td>3918</td>\n",
       "      <td>29881</td>\n",
       "      <td>24247</td>\n",
       "      <td>21664</td>\n",
       "      <td>1556</td>\n",
       "      <td>4854</td>\n",
       "      <td>30366</td>\n",
       "      <td>0</td>\n",
       "      <td>433</td>\n",
       "      <td>1556</td>\n",
       "      <td>14047</td>\n",
       "      <td>1</td>\n",
       "      <td>0.070963</td>\n",
       "      <td>2.300256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>971</td>\n",
       "      <td>300000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>42</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>11973</td>\n",
       "      <td>61834</td>\n",
       "      <td>25145</td>\n",
       "      <td>37666</td>\n",
       "      <td>19453</td>\n",
       "      <td>10492</td>\n",
       "      <td>20979</td>\n",
       "      <td>5000</td>\n",
       "      <td>37676</td>\n",
       "      <td>8808</td>\n",
       "      <td>2000</td>\n",
       "      <td>2709</td>\n",
       "      <td>1</td>\n",
       "      <td>0.072148</td>\n",
       "      <td>2.293051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18350</td>\n",
       "      <td>50000</td>\n",
       "      <td>female</td>\n",
       "      <td>high school</td>\n",
       "      <td>single</td>\n",
       "      <td>23</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>3237</td>\n",
       "      <td>5365</td>\n",
       "      <td>8263</td>\n",
       "      <td>14054</td>\n",
       "      <td>22071</td>\n",
       "      <td>20506</td>\n",
       "      <td>5389</td>\n",
       "      <td>8276</td>\n",
       "      <td>14075</td>\n",
       "      <td>15022</td>\n",
       "      <td>3012</td>\n",
       "      <td>3018</td>\n",
       "      <td>1</td>\n",
       "      <td>0.073202</td>\n",
       "      <td>2.286714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6074</td>\n",
       "      <td>250000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>48</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>205323</td>\n",
       "      <td>5317</td>\n",
       "      <td>4898</td>\n",
       "      <td>19351</td>\n",
       "      <td>4858</td>\n",
       "      <td>6973</td>\n",
       "      <td>10021</td>\n",
       "      <td>5033</td>\n",
       "      <td>19619</td>\n",
       "      <td>4874</td>\n",
       "      <td>7050</td>\n",
       "      <td>12073</td>\n",
       "      <td>1</td>\n",
       "      <td>0.073489</td>\n",
       "      <td>2.285002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5706</td>\n",
       "      <td>80000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>28</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>8598</td>\n",
       "      <td>9467</td>\n",
       "      <td>-270</td>\n",
       "      <td>3697</td>\n",
       "      <td>9455</td>\n",
       "      <td>4144</td>\n",
       "      <td>9467</td>\n",
       "      <td>0</td>\n",
       "      <td>3967</td>\n",
       "      <td>9455</td>\n",
       "      <td>5000</td>\n",
       "      <td>2900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.073560</td>\n",
       "      <td>2.284580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15549</td>\n",
       "      <td>450000</td>\n",
       "      <td>male</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>36</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>8012</td>\n",
       "      <td>4009</td>\n",
       "      <td>5226</td>\n",
       "      <td>4715</td>\n",
       "      <td>3275</td>\n",
       "      <td>6422</td>\n",
       "      <td>4021</td>\n",
       "      <td>5241</td>\n",
       "      <td>4729</td>\n",
       "      <td>3284</td>\n",
       "      <td>6441</td>\n",
       "      <td>4285</td>\n",
       "      <td>1</td>\n",
       "      <td>0.074203</td>\n",
       "      <td>2.280771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2302</td>\n",
       "      <td>230000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>30</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>2212</td>\n",
       "      <td>17402</td>\n",
       "      <td>32450</td>\n",
       "      <td>17285</td>\n",
       "      <td>9766</td>\n",
       "      <td>9981</td>\n",
       "      <td>17402</td>\n",
       "      <td>20013</td>\n",
       "      <td>346</td>\n",
       "      <td>5000</td>\n",
       "      <td>8000</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.074854</td>\n",
       "      <td>2.276935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7966</td>\n",
       "      <td>130000</td>\n",
       "      <td>male</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>27</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>53119</td>\n",
       "      <td>48994</td>\n",
       "      <td>17285</td>\n",
       "      <td>12448</td>\n",
       "      <td>12855</td>\n",
       "      <td>11795</td>\n",
       "      <td>3106</td>\n",
       "      <td>22511</td>\n",
       "      <td>600</td>\n",
       "      <td>1005</td>\n",
       "      <td>1004</td>\n",
       "      <td>1006</td>\n",
       "      <td>1</td>\n",
       "      <td>0.074996</td>\n",
       "      <td>2.276101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12918</td>\n",
       "      <td>210000</td>\n",
       "      <td>male</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>33</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>106669</td>\n",
       "      <td>13333</td>\n",
       "      <td>12780</td>\n",
       "      <td>18472</td>\n",
       "      <td>0</td>\n",
       "      <td>106669</td>\n",
       "      <td>13333</td>\n",
       "      <td>12780</td>\n",
       "      <td>19136</td>\n",
       "      <td>18660</td>\n",
       "      <td>1</td>\n",
       "      <td>0.075092</td>\n",
       "      <td>2.275540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>28158</td>\n",
       "      <td>130000</td>\n",
       "      <td>female</td>\n",
       "      <td>high school</td>\n",
       "      <td>married</td>\n",
       "      <td>31</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>62518</td>\n",
       "      <td>4049</td>\n",
       "      <td>6690</td>\n",
       "      <td>-1018</td>\n",
       "      <td>89445</td>\n",
       "      <td>90179</td>\n",
       "      <td>4065</td>\n",
       "      <td>6704</td>\n",
       "      <td>21018</td>\n",
       "      <td>92610</td>\n",
       "      <td>3272</td>\n",
       "      <td>3332</td>\n",
       "      <td>1</td>\n",
       "      <td>0.075593</td>\n",
       "      <td>2.272618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22014</td>\n",
       "      <td>40000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>27</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>27025</td>\n",
       "      <td>32228</td>\n",
       "      <td>38904</td>\n",
       "      <td>4235</td>\n",
       "      <td>87230</td>\n",
       "      <td>72647</td>\n",
       "      <td>6000</td>\n",
       "      <td>8040</td>\n",
       "      <td>4235</td>\n",
       "      <td>87230</td>\n",
       "      <td>3000</td>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.075666</td>\n",
       "      <td>2.272189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7315</td>\n",
       "      <td>90000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>28</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>12759</td>\n",
       "      <td>10941</td>\n",
       "      <td>10420</td>\n",
       "      <td>14420</td>\n",
       "      <td>9855</td>\n",
       "      <td>9097</td>\n",
       "      <td>5000</td>\n",
       "      <td>6000</td>\n",
       "      <td>4000</td>\n",
       "      <td>5000</td>\n",
       "      <td>3500</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.075955</td>\n",
       "      <td>2.270512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12668</td>\n",
       "      <td>210000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>37</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>24547</td>\n",
       "      <td>48302</td>\n",
       "      <td>4549</td>\n",
       "      <td>3085</td>\n",
       "      <td>7300</td>\n",
       "      <td>6583</td>\n",
       "      <td>4519</td>\n",
       "      <td>9098</td>\n",
       "      <td>3085</td>\n",
       "      <td>7300</td>\n",
       "      <td>6583</td>\n",
       "      <td>5060</td>\n",
       "      <td>1</td>\n",
       "      <td>0.076111</td>\n",
       "      <td>2.269607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>22712</td>\n",
       "      <td>320000</td>\n",
       "      <td>female</td>\n",
       "      <td>high school</td>\n",
       "      <td>married</td>\n",
       "      <td>35</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>157249</td>\n",
       "      <td>148123</td>\n",
       "      <td>142852</td>\n",
       "      <td>133583</td>\n",
       "      <td>129049</td>\n",
       "      <td>128325</td>\n",
       "      <td>6000</td>\n",
       "      <td>6048</td>\n",
       "      <td>6000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.076998</td>\n",
       "      <td>2.264501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1409</td>\n",
       "      <td>220000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>25</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>165040</td>\n",
       "      <td>167485</td>\n",
       "      <td>148133</td>\n",
       "      <td>123266</td>\n",
       "      <td>125736</td>\n",
       "      <td>128342</td>\n",
       "      <td>6809</td>\n",
       "      <td>7038</td>\n",
       "      <td>5000</td>\n",
       "      <td>4500</td>\n",
       "      <td>5000</td>\n",
       "      <td>4473</td>\n",
       "      <td>1</td>\n",
       "      <td>0.076998</td>\n",
       "      <td>2.264501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>14021</td>\n",
       "      <td>500000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>39</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>no consumption (-2)</td>\n",
       "      <td>3220</td>\n",
       "      <td>0</td>\n",
       "      <td>15040</td>\n",
       "      <td>35498</td>\n",
       "      <td>19508</td>\n",
       "      <td>21199</td>\n",
       "      <td>0</td>\n",
       "      <td>15040</td>\n",
       "      <td>35498</td>\n",
       "      <td>19508</td>\n",
       "      <td>21199</td>\n",
       "      <td>10894</td>\n",
       "      <td>1</td>\n",
       "      <td>0.077184</td>\n",
       "      <td>2.263434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>12404</td>\n",
       "      <td>60000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>38</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>13138</td>\n",
       "      <td>22389</td>\n",
       "      <td>16586</td>\n",
       "      <td>5516</td>\n",
       "      <td>16378</td>\n",
       "      <td>2670</td>\n",
       "      <td>20000</td>\n",
       "      <td>5000</td>\n",
       "      <td>3000</td>\n",
       "      <td>16000</td>\n",
       "      <td>3000</td>\n",
       "      <td>980</td>\n",
       "      <td>1</td>\n",
       "      <td>0.078592</td>\n",
       "      <td>2.255431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5387</td>\n",
       "      <td>50000</td>\n",
       "      <td>female</td>\n",
       "      <td>high school</td>\n",
       "      <td>married</td>\n",
       "      <td>44</td>\n",
       "      <td>1 month delay</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>-42</td>\n",
       "      <td>26267</td>\n",
       "      <td>26372</td>\n",
       "      <td>7490</td>\n",
       "      <td>5155</td>\n",
       "      <td>0</td>\n",
       "      <td>26309</td>\n",
       "      <td>26479</td>\n",
       "      <td>7490</td>\n",
       "      <td>5155</td>\n",
       "      <td>0</td>\n",
       "      <td>12754</td>\n",
       "      <td>1</td>\n",
       "      <td>0.079116</td>\n",
       "      <td>2.252486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8910</th>\n",
       "      <td>13739</td>\n",
       "      <td>30000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>22</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>26163</td>\n",
       "      <td>26449</td>\n",
       "      <td>27915</td>\n",
       "      <td>27170</td>\n",
       "      <td>29071</td>\n",
       "      <td>29446</td>\n",
       "      <td>1000</td>\n",
       "      <td>2201</td>\n",
       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "      <td>1001</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.771650</td>\n",
       "      <td>-1.718648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8911</th>\n",
       "      <td>400</td>\n",
       "      <td>120000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>25</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>120298</td>\n",
       "      <td>120822</td>\n",
       "      <td>109887</td>\n",
       "      <td>116313</td>\n",
       "      <td>119311</td>\n",
       "      <td>117167</td>\n",
       "      <td>4007</td>\n",
       "      <td>0</td>\n",
       "      <td>8272</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>4400</td>\n",
       "      <td>0</td>\n",
       "      <td>0.774923</td>\n",
       "      <td>-1.727028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8912</th>\n",
       "      <td>13422</td>\n",
       "      <td>100000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>29</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>74032</td>\n",
       "      <td>75557</td>\n",
       "      <td>76434</td>\n",
       "      <td>74611</td>\n",
       "      <td>79292</td>\n",
       "      <td>80945</td>\n",
       "      <td>3300</td>\n",
       "      <td>2700</td>\n",
       "      <td>0</td>\n",
       "      <td>5900</td>\n",
       "      <td>3100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.776469</td>\n",
       "      <td>-1.731013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8913</th>\n",
       "      <td>3641</td>\n",
       "      <td>50000</td>\n",
       "      <td>male</td>\n",
       "      <td>high school</td>\n",
       "      <td>single</td>\n",
       "      <td>41</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>29368</td>\n",
       "      <td>28612</td>\n",
       "      <td>31623</td>\n",
       "      <td>33393</td>\n",
       "      <td>32587</td>\n",
       "      <td>31914</td>\n",
       "      <td>0</td>\n",
       "      <td>3500</td>\n",
       "      <td>2589</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1332</td>\n",
       "      <td>0</td>\n",
       "      <td>0.776631</td>\n",
       "      <td>-1.731433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8914</th>\n",
       "      <td>18926</td>\n",
       "      <td>30000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>29</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>26799</td>\n",
       "      <td>26002</td>\n",
       "      <td>27293</td>\n",
       "      <td>26551</td>\n",
       "      <td>30223</td>\n",
       "      <td>30592</td>\n",
       "      <td>5000</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>4400</td>\n",
       "      <td>1600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.776654</td>\n",
       "      <td>-1.731492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8915</th>\n",
       "      <td>18688</td>\n",
       "      <td>60000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>28</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>48322</td>\n",
       "      <td>51587</td>\n",
       "      <td>52664</td>\n",
       "      <td>53059</td>\n",
       "      <td>51468</td>\n",
       "      <td>54511</td>\n",
       "      <td>4692</td>\n",
       "      <td>2500</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>4019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.776736</td>\n",
       "      <td>-1.731705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8916</th>\n",
       "      <td>1629</td>\n",
       "      <td>140000</td>\n",
       "      <td>female</td>\n",
       "      <td>high school</td>\n",
       "      <td>married</td>\n",
       "      <td>31</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>89910</td>\n",
       "      <td>92588</td>\n",
       "      <td>91936</td>\n",
       "      <td>94623</td>\n",
       "      <td>94952</td>\n",
       "      <td>95234</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>5000</td>\n",
       "      <td>1900</td>\n",
       "      <td>4700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.776736</td>\n",
       "      <td>-1.731705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8917</th>\n",
       "      <td>14806</td>\n",
       "      <td>20000</td>\n",
       "      <td>male</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>24</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>16276</td>\n",
       "      <td>16715</td>\n",
       "      <td>18327</td>\n",
       "      <td>17735</td>\n",
       "      <td>19446</td>\n",
       "      <td>18988</td>\n",
       "      <td>1000</td>\n",
       "      <td>2200</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0.777931</td>\n",
       "      <td>-1.734800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8918</th>\n",
       "      <td>17748</td>\n",
       "      <td>30000</td>\n",
       "      <td>female</td>\n",
       "      <td>high school</td>\n",
       "      <td>married</td>\n",
       "      <td>54</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>4 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>22147</td>\n",
       "      <td>24770</td>\n",
       "      <td>26068</td>\n",
       "      <td>28842</td>\n",
       "      <td>28094</td>\n",
       "      <td>27361</td>\n",
       "      <td>3000</td>\n",
       "      <td>2000</td>\n",
       "      <td>3500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.779158</td>\n",
       "      <td>-1.737991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8919</th>\n",
       "      <td>26580</td>\n",
       "      <td>50000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>51</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>15902</td>\n",
       "      <td>17328</td>\n",
       "      <td>18746</td>\n",
       "      <td>20623</td>\n",
       "      <td>20019</td>\n",
       "      <td>19227</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.779862</td>\n",
       "      <td>-1.739829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8920</th>\n",
       "      <td>2865</td>\n",
       "      <td>50000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>46</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>28390</td>\n",
       "      <td>29639</td>\n",
       "      <td>30854</td>\n",
       "      <td>30062</td>\n",
       "      <td>32705</td>\n",
       "      <td>33519</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>3300</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.780487</td>\n",
       "      <td>-1.741461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8921</th>\n",
       "      <td>18659</td>\n",
       "      <td>40000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>28</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>31131</td>\n",
       "      <td>33815</td>\n",
       "      <td>33002</td>\n",
       "      <td>32173</td>\n",
       "      <td>34629</td>\n",
       "      <td>33940</td>\n",
       "      <td>3500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.781771</td>\n",
       "      <td>-1.744828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8922</th>\n",
       "      <td>17703</td>\n",
       "      <td>60000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>35</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3167</td>\n",
       "      <td>5601</td>\n",
       "      <td>5366</td>\n",
       "      <td>6772</td>\n",
       "      <td>6515</td>\n",
       "      <td>7906</td>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.784593</td>\n",
       "      <td>-1.752270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8923</th>\n",
       "      <td>26713</td>\n",
       "      <td>30000</td>\n",
       "      <td>male</td>\n",
       "      <td>high school</td>\n",
       "      <td>single</td>\n",
       "      <td>26</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>14598</td>\n",
       "      <td>13998</td>\n",
       "      <td>15467</td>\n",
       "      <td>14913</td>\n",
       "      <td>17692</td>\n",
       "      <td>16302</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.785128</td>\n",
       "      <td>-1.753689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8924</th>\n",
       "      <td>15186</td>\n",
       "      <td>30000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>25</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>7593</td>\n",
       "      <td>9634</td>\n",
       "      <td>4476</td>\n",
       "      <td>8830</td>\n",
       "      <td>8153</td>\n",
       "      <td>6422</td>\n",
       "      <td>2379</td>\n",
       "      <td>7</td>\n",
       "      <td>7002</td>\n",
       "      <td>13</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.787911</td>\n",
       "      <td>-1.761108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8925</th>\n",
       "      <td>9821</td>\n",
       "      <td>20000</td>\n",
       "      <td>male</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>26</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>36060</td>\n",
       "      <td>31741</td>\n",
       "      <td>30013</td>\n",
       "      <td>25938</td>\n",
       "      <td>28447</td>\n",
       "      <td>24288</td>\n",
       "      <td>0</td>\n",
       "      <td>2600</td>\n",
       "      <td>0</td>\n",
       "      <td>6500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.788631</td>\n",
       "      <td>-1.763036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8926</th>\n",
       "      <td>7068</td>\n",
       "      <td>90000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>30</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>2450</td>\n",
       "      <td>2150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.789552</td>\n",
       "      <td>-1.765512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8927</th>\n",
       "      <td>2974</td>\n",
       "      <td>30000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>24</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.789718</td>\n",
       "      <td>-1.765960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8928</th>\n",
       "      <td>19215</td>\n",
       "      <td>150000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>34</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>4 month delay</td>\n",
       "      <td>4 month delay</td>\n",
       "      <td>5 month delay</td>\n",
       "      <td>5 month delay</td>\n",
       "      <td>103389</td>\n",
       "      <td>108189</td>\n",
       "      <td>112968</td>\n",
       "      <td>117613</td>\n",
       "      <td>122161</td>\n",
       "      <td>129616</td>\n",
       "      <td>8000</td>\n",
       "      <td>8000</td>\n",
       "      <td>8000</td>\n",
       "      <td>8000</td>\n",
       "      <td>10000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.792339</td>\n",
       "      <td>-1.773046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8929</th>\n",
       "      <td>13811</td>\n",
       "      <td>40000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>47</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>11084</td>\n",
       "      <td>12605</td>\n",
       "      <td>13102</td>\n",
       "      <td>12595</td>\n",
       "      <td>14386</td>\n",
       "      <td>14005</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.792621</td>\n",
       "      <td>-1.773813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8930</th>\n",
       "      <td>11559</td>\n",
       "      <td>30000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>35</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>14489</td>\n",
       "      <td>15460</td>\n",
       "      <td>15514</td>\n",
       "      <td>14968</td>\n",
       "      <td>16026</td>\n",
       "      <td>15619</td>\n",
       "      <td>1500</td>\n",
       "      <td>600</td>\n",
       "      <td>0</td>\n",
       "      <td>1300</td>\n",
       "      <td>0</td>\n",
       "      <td>1300</td>\n",
       "      <td>0</td>\n",
       "      <td>0.796064</td>\n",
       "      <td>-1.783227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8931</th>\n",
       "      <td>16594</td>\n",
       "      <td>20000</td>\n",
       "      <td>male</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>28</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>1150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.797397</td>\n",
       "      <td>-1.786902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8932</th>\n",
       "      <td>8115</td>\n",
       "      <td>120000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>26</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>12034</td>\n",
       "      <td>12548</td>\n",
       "      <td>12056</td>\n",
       "      <td>13958</td>\n",
       "      <td>13468</td>\n",
       "      <td>6144</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2400</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>57258</td>\n",
       "      <td>0</td>\n",
       "      <td>0.809014</td>\n",
       "      <td>-1.819646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8933</th>\n",
       "      <td>14589</td>\n",
       "      <td>280000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>50</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>5 month delay</td>\n",
       "      <td>4 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>use of revolving credit (0)</td>\n",
       "      <td>327918</td>\n",
       "      <td>321476</td>\n",
       "      <td>314931</td>\n",
       "      <td>176439</td>\n",
       "      <td>154010</td>\n",
       "      <td>134334</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>6267</td>\n",
       "      <td>2257</td>\n",
       "      <td>0</td>\n",
       "      <td>0.809038</td>\n",
       "      <td>-1.819714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8934</th>\n",
       "      <td>3087</td>\n",
       "      <td>30000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>single</td>\n",
       "      <td>24</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.810125</td>\n",
       "      <td>-1.822848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8935</th>\n",
       "      <td>5916</td>\n",
       "      <td>110000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>41</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.810125</td>\n",
       "      <td>-1.822848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8936</th>\n",
       "      <td>565</td>\n",
       "      <td>390000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>45</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>pay duly (-1)</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>834</td>\n",
       "      <td>155</td>\n",
       "      <td>-1690</td>\n",
       "      <td>4240</td>\n",
       "      <td>3346</td>\n",
       "      <td>2698</td>\n",
       "      <td>1</td>\n",
       "      <td>1693</td>\n",
       "      <td>6940</td>\n",
       "      <td>7</td>\n",
       "      <td>1230</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.825543</td>\n",
       "      <td>-1.868731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8937</th>\n",
       "      <td>9672</td>\n",
       "      <td>170000</td>\n",
       "      <td>male</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>single</td>\n",
       "      <td>48</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.850514</td>\n",
       "      <td>-1.949642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8938</th>\n",
       "      <td>19316</td>\n",
       "      <td>110000</td>\n",
       "      <td>female</td>\n",
       "      <td>graduate school</td>\n",
       "      <td>married</td>\n",
       "      <td>41</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>7 month delay</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.854803</td>\n",
       "      <td>-1.964518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8939</th>\n",
       "      <td>22725</td>\n",
       "      <td>100000</td>\n",
       "      <td>female</td>\n",
       "      <td>university</td>\n",
       "      <td>married</td>\n",
       "      <td>38</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>2 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>3 month delay</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.875889</td>\n",
       "      <td>-2.042829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8940 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  LIMIT_BAL     SEX        EDUCATION MARRIAGE  AGE  \\\n",
       "0      6933     500000    male  graduate school   single   37   \n",
       "1     22505     260000  female       university   single   33   \n",
       "2      2561     310000  female  graduate school   single   32   \n",
       "3     11745     220000    male  graduate school   single   51   \n",
       "4     25772     350000  female  graduate school  married   33   \n",
       "5     29294      50000    male  graduate school   single   25   \n",
       "6     25692     330000  female  graduate school   single   42   \n",
       "7      8339     480000    male  graduate school  married   58   \n",
       "8     15482     150000    male  graduate school  married   37   \n",
       "9      3016     350000    male  graduate school  married   38   \n",
       "10    22751     350000  female  graduate school  married   32   \n",
       "11     9078     320000  female  graduate school  married   34   \n",
       "12    11462     210000  female  graduate school   single   46   \n",
       "13      971     300000    male  graduate school  married   42   \n",
       "14    18350      50000  female      high school   single   23   \n",
       "15     6074     250000    male  graduate school  married   48   \n",
       "16     5706      80000    male  graduate school   single   28   \n",
       "17    15549     450000    male       university   single   36   \n",
       "18     2302     230000  female  graduate school  married   30   \n",
       "19     7966     130000    male       university  married   27   \n",
       "20    12918     210000    male       university  married   33   \n",
       "21    28158     130000  female      high school  married   31   \n",
       "22    22014      40000  female  graduate school   single   27   \n",
       "23     7315      90000  female  graduate school  married   28   \n",
       "24    12668     210000  female  graduate school  married   37   \n",
       "25    22712     320000  female      high school  married   35   \n",
       "26     1409     220000  female       university   single   25   \n",
       "27    14021     500000  female  graduate school  married   39   \n",
       "28    12404      60000  female       university   single   38   \n",
       "29     5387      50000  female      high school  married   44   \n",
       "...     ...        ...     ...              ...      ...  ...   \n",
       "8910  13739      30000  female       university   single   22   \n",
       "8911    400     120000  female       university   single   25   \n",
       "8912  13422     100000  female  graduate school   single   29   \n",
       "8913   3641      50000    male      high school   single   41   \n",
       "8914  18926      30000  female       university  married   29   \n",
       "8915  18688      60000  female       university   single   28   \n",
       "8916   1629     140000  female      high school  married   31   \n",
       "8917  14806      20000    male       university   single   24   \n",
       "8918  17748      30000  female      high school  married   54   \n",
       "8919  26580      50000  female       university  married   51   \n",
       "8920   2865      50000  female       university  married   46   \n",
       "8921  18659      40000  female       university  married   28   \n",
       "8922  17703      60000  female       university  married   35   \n",
       "8923  26713      30000    male      high school   single   26   \n",
       "8924  15186      30000  female  graduate school   single   25   \n",
       "8925   9821      20000    male       university   single   26   \n",
       "8926   7068      90000  female  graduate school   single   30   \n",
       "8927   2974      30000  female       university  married   24   \n",
       "8928  19215     150000  female       university   single   34   \n",
       "8929  13811      40000    male  graduate school  married   47   \n",
       "8930  11559      30000    male  graduate school   single   35   \n",
       "8931  16594      20000    male       university   single   28   \n",
       "8932   8115     120000  female       university   single   26   \n",
       "8933  14589     280000    male  graduate school  married   50   \n",
       "8934   3087      30000  female       university   single   24   \n",
       "8935   5916     110000  female  graduate school  married   41   \n",
       "8936    565     390000  female  graduate school   single   45   \n",
       "8937   9672     170000    male  graduate school   single   48   \n",
       "8938  19316     110000  female  graduate school  married   41   \n",
       "8939  22725     100000  female       university  married   38   \n",
       "\n",
       "                            PAY_0                        PAY_2  \\\n",
       "0                   pay duly (-1)                pay duly (-1)   \n",
       "1                   pay duly (-1)                pay duly (-1)   \n",
       "2             no consumption (-2)          no consumption (-2)   \n",
       "3                   pay duly (-1)                pay duly (-1)   \n",
       "4     use of revolving credit (0)                pay duly (-1)   \n",
       "5                   pay duly (-1)                pay duly (-1)   \n",
       "6             no consumption (-2)          no consumption (-2)   \n",
       "7             no consumption (-2)          no consumption (-2)   \n",
       "8             no consumption (-2)          no consumption (-2)   \n",
       "9             no consumption (-2)          no consumption (-2)   \n",
       "10                  pay duly (-1)                pay duly (-1)   \n",
       "11                  pay duly (-1)                pay duly (-1)   \n",
       "12                  pay duly (-1)                pay duly (-1)   \n",
       "13                  pay duly (-1)  use of revolving credit (0)   \n",
       "14                  pay duly (-1)                pay duly (-1)   \n",
       "15    use of revolving credit (0)                pay duly (-1)   \n",
       "16            no consumption (-2)          no consumption (-2)   \n",
       "17            no consumption (-2)          no consumption (-2)   \n",
       "18                  pay duly (-1)                pay duly (-1)   \n",
       "19    use of revolving credit (0)  use of revolving credit (0)   \n",
       "20            no consumption (-2)          no consumption (-2)   \n",
       "21    use of revolving credit (0)                pay duly (-1)   \n",
       "22                  pay duly (-1)  use of revolving credit (0)   \n",
       "23                  pay duly (-1)  use of revolving credit (0)   \n",
       "24    use of revolving credit (0)  use of revolving credit (0)   \n",
       "25    use of revolving credit (0)  use of revolving credit (0)   \n",
       "26    use of revolving credit (0)  use of revolving credit (0)   \n",
       "27            no consumption (-2)          no consumption (-2)   \n",
       "28    use of revolving credit (0)  use of revolving credit (0)   \n",
       "29                  1 month delay                pay duly (-1)   \n",
       "...                           ...                          ...   \n",
       "8910                2 month delay                2 month delay   \n",
       "8911                2 month delay                2 month delay   \n",
       "8912                2 month delay                2 month delay   \n",
       "8913                2 month delay                2 month delay   \n",
       "8914                2 month delay                2 month delay   \n",
       "8915                2 month delay                2 month delay   \n",
       "8916                2 month delay                2 month delay   \n",
       "8917                2 month delay                2 month delay   \n",
       "8918                2 month delay                2 month delay   \n",
       "8919                2 month delay                2 month delay   \n",
       "8920                2 month delay                2 month delay   \n",
       "8921                2 month delay                2 month delay   \n",
       "8922                2 month delay                2 month delay   \n",
       "8923                2 month delay                2 month delay   \n",
       "8924                2 month delay                2 month delay   \n",
       "8925                2 month delay                2 month delay   \n",
       "8926                2 month delay                2 month delay   \n",
       "8927                2 month delay                2 month delay   \n",
       "8928                3 month delay                3 month delay   \n",
       "8929                2 month delay                2 month delay   \n",
       "8930                2 month delay                2 month delay   \n",
       "8931                3 month delay                2 month delay   \n",
       "8932                3 month delay                3 month delay   \n",
       "8933                3 month delay                5 month delay   \n",
       "8934                2 month delay                2 month delay   \n",
       "8935                2 month delay                2 month delay   \n",
       "8936                2 month delay                2 month delay   \n",
       "8937                2 month delay                2 month delay   \n",
       "8938                3 month delay                2 month delay   \n",
       "8939                3 month delay                2 month delay   \n",
       "\n",
       "                            PAY_3                        PAY_4  \\\n",
       "0                   pay duly (-1)                pay duly (-1)   \n",
       "1                   pay duly (-1)                pay duly (-1)   \n",
       "2             no consumption (-2)          no consumption (-2)   \n",
       "3                   pay duly (-1)                pay duly (-1)   \n",
       "4                   pay duly (-1)                pay duly (-1)   \n",
       "5                   pay duly (-1)                pay duly (-1)   \n",
       "6             no consumption (-2)          no consumption (-2)   \n",
       "7             no consumption (-2)          no consumption (-2)   \n",
       "8             no consumption (-2)          no consumption (-2)   \n",
       "9                   pay duly (-1)  use of revolving credit (0)   \n",
       "10            no consumption (-2)          no consumption (-2)   \n",
       "11                  pay duly (-1)                pay duly (-1)   \n",
       "12                  pay duly (-1)  use of revolving credit (0)   \n",
       "13    use of revolving credit (0)                pay duly (-1)   \n",
       "14                  pay duly (-1)                pay duly (-1)   \n",
       "15                  pay duly (-1)                pay duly (-1)   \n",
       "16            no consumption (-2)          no consumption (-2)   \n",
       "17            no consumption (-2)          no consumption (-2)   \n",
       "18    use of revolving credit (0)  use of revolving credit (0)   \n",
       "19                  pay duly (-1)  use of revolving credit (0)   \n",
       "20                  pay duly (-1)                pay duly (-1)   \n",
       "21                  pay duly (-1)                pay duly (-1)   \n",
       "22    use of revolving credit (0)                pay duly (-1)   \n",
       "23    use of revolving credit (0)  use of revolving credit (0)   \n",
       "24                  pay duly (-1)                pay duly (-1)   \n",
       "25    use of revolving credit (0)  use of revolving credit (0)   \n",
       "26    use of revolving credit (0)  use of revolving credit (0)   \n",
       "27            no consumption (-2)          no consumption (-2)   \n",
       "28    use of revolving credit (0)  use of revolving credit (0)   \n",
       "29                  pay duly (-1)                pay duly (-1)   \n",
       "...                           ...                          ...   \n",
       "8910                2 month delay                2 month delay   \n",
       "8911                2 month delay                2 month delay   \n",
       "8912                2 month delay                2 month delay   \n",
       "8913                2 month delay                2 month delay   \n",
       "8914                2 month delay                2 month delay   \n",
       "8915                2 month delay                2 month delay   \n",
       "8916                2 month delay                2 month delay   \n",
       "8917                2 month delay                2 month delay   \n",
       "8918                2 month delay                2 month delay   \n",
       "8919                2 month delay                2 month delay   \n",
       "8920                2 month delay                2 month delay   \n",
       "8921                3 month delay                2 month delay   \n",
       "8922                2 month delay                2 month delay   \n",
       "8923                2 month delay                2 month delay   \n",
       "8924                2 month delay                2 month delay   \n",
       "8925                2 month delay                2 month delay   \n",
       "8926                3 month delay                3 month delay   \n",
       "8927                2 month delay                2 month delay   \n",
       "8928                4 month delay                4 month delay   \n",
       "8929                2 month delay                2 month delay   \n",
       "8930                2 month delay                2 month delay   \n",
       "8931                2 month delay                2 month delay   \n",
       "8932                2 month delay                2 month delay   \n",
       "8933                4 month delay                3 month delay   \n",
       "8934                7 month delay                7 month delay   \n",
       "8935                7 month delay                7 month delay   \n",
       "8936                2 month delay                pay duly (-1)   \n",
       "8937                7 month delay                7 month delay   \n",
       "8938                2 month delay                7 month delay   \n",
       "8939                2 month delay                3 month delay   \n",
       "\n",
       "                            PAY_5                        PAY_6  BILL_AMT1  \\\n",
       "0                   pay duly (-1)                pay duly (-1)       4331   \n",
       "1                   pay duly (-1)  use of revolving credit (0)       5188   \n",
       "2             no consumption (-2)          no consumption (-2)      20138   \n",
       "3                   pay duly (-1)                pay duly (-1)      20730   \n",
       "4                   pay duly (-1)                pay duly (-1)      82964   \n",
       "5                   pay duly (-1)                pay duly (-1)       9716   \n",
       "6             no consumption (-2)          no consumption (-2)        565   \n",
       "7             no consumption (-2)          no consumption (-2)      24610   \n",
       "8             no consumption (-2)          no consumption (-2)      22109   \n",
       "9     use of revolving credit (0)          no consumption (-2)      16459   \n",
       "10            no consumption (-2)          no consumption (-2)      30625   \n",
       "11                  pay duly (-1)                pay duly (-1)        800   \n",
       "12    use of revolving credit (0)                pay duly (-1)      15655   \n",
       "13    use of revolving credit (0)  use of revolving credit (0)      11973   \n",
       "14    use of revolving credit (0)  use of revolving credit (0)       3237   \n",
       "15    use of revolving credit (0)                pay duly (-1)     205323   \n",
       "16            no consumption (-2)          no consumption (-2)       8598   \n",
       "17            no consumption (-2)          no consumption (-2)       8012   \n",
       "18    use of revolving credit (0)  use of revolving credit (0)       2212   \n",
       "19    use of revolving credit (0)  use of revolving credit (0)      53119   \n",
       "20                  pay duly (-1)                pay duly (-1)          0   \n",
       "21                  pay duly (-1)  use of revolving credit (0)      62518   \n",
       "22                  pay duly (-1)  use of revolving credit (0)      27025   \n",
       "23    use of revolving credit (0)  use of revolving credit (0)      12759   \n",
       "24                  pay duly (-1)                pay duly (-1)      24547   \n",
       "25    use of revolving credit (0)  use of revolving credit (0)     157249   \n",
       "26    use of revolving credit (0)  use of revolving credit (0)     165040   \n",
       "27            no consumption (-2)          no consumption (-2)       3220   \n",
       "28    use of revolving credit (0)                pay duly (-1)      13138   \n",
       "29                  pay duly (-1)                pay duly (-1)        -42   \n",
       "...                           ...                          ...        ...   \n",
       "8910                2 month delay                2 month delay      26163   \n",
       "8911                2 month delay                2 month delay     120298   \n",
       "8912                2 month delay                2 month delay      74032   \n",
       "8913                3 month delay                2 month delay      29368   \n",
       "8914                2 month delay                2 month delay      26799   \n",
       "8915                2 month delay                2 month delay      48322   \n",
       "8916                2 month delay                2 month delay      89910   \n",
       "8917                2 month delay                2 month delay      16276   \n",
       "8918                4 month delay                3 month delay      22147   \n",
       "8919                3 month delay                2 month delay      15902   \n",
       "8920                2 month delay                2 month delay      28390   \n",
       "8921                2 month delay                2 month delay      31131   \n",
       "8922                2 month delay                2 month delay       3167   \n",
       "8923                2 month delay                2 month delay      14598   \n",
       "8924                2 month delay                2 month delay       7593   \n",
       "8925                2 month delay                3 month delay      36060   \n",
       "8926                3 month delay                3 month delay        750   \n",
       "8927                2 month delay                2 month delay        150   \n",
       "8928                5 month delay                5 month delay     103389   \n",
       "8929                2 month delay                2 month delay      11084   \n",
       "8930                2 month delay                2 month delay      14489   \n",
       "8931                2 month delay                pay duly (-1)        150   \n",
       "8932                3 month delay                2 month delay      12034   \n",
       "8933                2 month delay  use of revolving credit (0)     327918   \n",
       "8934                7 month delay                7 month delay        300   \n",
       "8935                7 month delay                7 month delay        150   \n",
       "8936                2 month delay                2 month delay        834   \n",
       "8937                7 month delay                7 month delay       2400   \n",
       "8938                7 month delay                7 month delay        150   \n",
       "8939                3 month delay                3 month delay        750   \n",
       "\n",
       "      BILL_AMT2  BILL_AMT3  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  \\\n",
       "0         60446      30592     154167      13410      25426     60446   \n",
       "1         12357      28656       7497       7685      15434     13000   \n",
       "2          8267      65993       8543       1695        750      8267   \n",
       "3          -270      53895       -105      20895      20835         0   \n",
       "4         68532      17926      17966      30741      31088     68940   \n",
       "5         25045      15180       6964       7604       2173     25045   \n",
       "6         20650      15360          0      12923       1816     20650   \n",
       "7          -310     148544      18791       5909      68988         4   \n",
       "8         10876      10268       5872       3068       2181     10943   \n",
       "9          4120      44164      35233        884       9924       941   \n",
       "10        60003       7147       9950      22117       4874     60396   \n",
       "11        18873       -150        150      21565       4818     18873   \n",
       "12         3918      29881      24247      21664       1556      4854   \n",
       "13        61834      25145      37666      19453      10492     20979   \n",
       "14         5365       8263      14054      22071      20506      5389   \n",
       "15         5317       4898      19351       4858       6973     10021   \n",
       "16         9467       -270       3697       9455       4144      9467   \n",
       "17         4009       5226       4715       3275       6422      4021   \n",
       "18        17402      32450      17285       9766       9981     17402   \n",
       "19        48994      17285      12448      12855      11795      3106   \n",
       "20            0     106669      13333      12780      18472         0   \n",
       "21         4049       6690      -1018      89445      90179      4065   \n",
       "22        32228      38904       4235      87230      72647      6000   \n",
       "23        10941      10420      14420       9855       9097      5000   \n",
       "24        48302       4549       3085       7300       6583      4519   \n",
       "25       148123     142852     133583     129049     128325      6000   \n",
       "26       167485     148133     123266     125736     128342      6809   \n",
       "27            0      15040      35498      19508      21199         0   \n",
       "28        22389      16586       5516      16378       2670     20000   \n",
       "29        26267      26372       7490       5155          0     26309   \n",
       "...         ...        ...        ...        ...        ...       ...   \n",
       "8910      26449      27915      27170      29071      29446      1000   \n",
       "8911     120822     109887     116313     119311     117167      4007   \n",
       "8912      75557      76434      74611      79292      80945      3300   \n",
       "8913      28612      31623      33393      32587      31914         0   \n",
       "8914      26002      27293      26551      30223      30592      5000   \n",
       "8915      51587      52664      53059      51468      54511      4692   \n",
       "8916      92588      91936      94623      94952      95234      5000   \n",
       "8917      16715      18327      17735      19446      18988      1000   \n",
       "8918      24770      26068      28842      28094      27361      3000   \n",
       "8919      17328      18746      20623      20019      19227      2000   \n",
       "8920      29639      30854      30062      32705      33519      2000   \n",
       "8921      33815      33002      32173      34629      33940      3500   \n",
       "8922       5601       5366       6772       6515       7906      2500   \n",
       "8923      13998      15467      14913      17692      16302      2000   \n",
       "8924       9634       4476       8830       8153       6422      2379   \n",
       "8925      31741      30013      25938      28447      24288         0   \n",
       "8926        750        750        750       2450       2150         0   \n",
       "8927        150        150        150        150        300         0   \n",
       "8928     108189     112968     117613     122161     129616      8000   \n",
       "8929      12605      13102      12595      14386      14005      2000   \n",
       "8930      15460      15514      14968      16026      15619      1500   \n",
       "8931        150        150        150        150       1150         0   \n",
       "8932      12548      12056      13958      13468       6144      1000   \n",
       "8933     321476     314931     176439     154010     134334         0   \n",
       "8934        300        300        300        300        300         0   \n",
       "8935        150        150        150        150        150         0   \n",
       "8936        155      -1690       4240       3346       2698         1   \n",
       "8937       2400       2400       2400       2400       2400         0   \n",
       "8938        150        150        150        150        150         0   \n",
       "8939        750        750        750        750        750         0   \n",
       "\n",
       "      PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  DEFAULT_NEXT_MONTH  \\\n",
       "0        30594    150843    163881     25426     39526                   1   \n",
       "1        29022      7500     27769     12000      6200                   1   \n",
       "2        66008      8543      1695       750      7350                   1   \n",
       "3        54165         0     21000     20940     33460                   1   \n",
       "4        18018     18058     30897     31244     88461                   1   \n",
       "5        15522      6964      7604      2173      6586                   1   \n",
       "6        15360         0     12923      1816     17050                   1   \n",
       "7       149654     18885      5940     69337    200655                   1   \n",
       "8        10273      5978      3068      2181      3242                   1   \n",
       "9        44743         0       884      9924     10824                   1   \n",
       "10        7147      9950     22117      4874         0                   1   \n",
       "11           0       500     21565      5000    168492                   1   \n",
       "12       30366         0       433      1556     14047                   1   \n",
       "13        5000     37676      8808      2000      2709                   1   \n",
       "14        8276     14075     15022      3012      3018                   1   \n",
       "15        5033     19619      4874      7050     12073                   1   \n",
       "16           0      3967      9455      5000      2900                   1   \n",
       "17        5241      4729      3284      6441      4285                   1   \n",
       "18       20013       346      5000      8000      5000                   1   \n",
       "19       22511       600      1005      1004      1006                   1   \n",
       "20      106669     13333     12780     19136     18660                   1   \n",
       "21        6704     21018     92610      3272      3332                   1   \n",
       "22        8040      4235     87230      3000     10000                   1   \n",
       "23        6000      4000      5000      3500      5000                   1   \n",
       "24        9098      3085      7300      6583      5060                   1   \n",
       "25        6048      6000      5000      5000      5000                   1   \n",
       "26        7038      5000      4500      5000      4473                   1   \n",
       "27       15040     35498     19508     21199     10894                   1   \n",
       "28        5000      3000     16000      3000       980                   1   \n",
       "29       26479      7490      5155         0     12754                   1   \n",
       "...        ...       ...       ...       ...       ...                 ...   \n",
       "8910      2201         0      2500      1001         6                   0   \n",
       "8911         0      8272      5000         0      4400                   0   \n",
       "8912      2700         0      5900      3100         0                   0   \n",
       "8913      3500      2589         0         0      1332                   0   \n",
       "8914      2000         0      4400      1600         0                   0   \n",
       "8915      2500      2000         0      4019         0                   0   \n",
       "8916      1800      5000      1900      4700         0                   0   \n",
       "8917      2200         0      2000         0      1200                   0   \n",
       "8918      2000      3500         0         0      1000                   0   \n",
       "8919      2000      2500         0         0      1000                   0   \n",
       "8920      2000         0      3300      1500         0                   0   \n",
       "8921         0         0      3000         0      2000                   0   \n",
       "8922         0      1500         0      1500         0                   0   \n",
       "8923      2000         0      3009         0      2000                   0   \n",
       "8924         7      7002        13       155         1                   0   \n",
       "8925      2600         0      6500         0         0                   0   \n",
       "8926         0         0      2000         0         0                   0   \n",
       "8927         0         0         0       150         0                   0   \n",
       "8928      8000      8000      8000     10000      5000                   0   \n",
       "8929      1000         0      2000         0      2000                   0   \n",
       "8930       600         0      1300         0      1300                   0   \n",
       "8931         0         0         0      1150         0                   0   \n",
       "8932         0      2400       100         0     57258                   0   \n",
       "8933         0       500         0      6267      2257                   0   \n",
       "8934         0         0         0         0         0                   0   \n",
       "8935         0         0         0         0         0                   0   \n",
       "8936      1693      6940         7      1230         3                   0   \n",
       "8937         0         0         0         0         0                   0   \n",
       "8938         0         0         0         0         0                   0   \n",
       "8939         0         0         0         0      1500                   0   \n",
       "\n",
       "      p_DEFAULT_NEXT_MONTH  r_DEFAULT_NEXT_MONTH  \n",
       "0                 0.048142              2.463165  \n",
       "1                 0.048142              2.463165  \n",
       "2                 0.056123              2.400086  \n",
       "3                 0.056634              2.396310  \n",
       "4                 0.061981              2.358360  \n",
       "5                 0.061981              2.358360  \n",
       "6                 0.062990              2.351502  \n",
       "7                 0.066306              2.329584  \n",
       "8                 0.067178              2.323969  \n",
       "9                 0.068563              2.315165  \n",
       "10                0.069769              2.307623  \n",
       "11                0.070637              2.302260  \n",
       "12                0.070963              2.300256  \n",
       "13                0.072148              2.293051  \n",
       "14                0.073202              2.286714  \n",
       "15                0.073489              2.285002  \n",
       "16                0.073560              2.284580  \n",
       "17                0.074203              2.280771  \n",
       "18                0.074854              2.276935  \n",
       "19                0.074996              2.276101  \n",
       "20                0.075092              2.275540  \n",
       "21                0.075593              2.272618  \n",
       "22                0.075666              2.272189  \n",
       "23                0.075955              2.270512  \n",
       "24                0.076111              2.269607  \n",
       "25                0.076998              2.264501  \n",
       "26                0.076998              2.264501  \n",
       "27                0.077184              2.263434  \n",
       "28                0.078592              2.255431  \n",
       "29                0.079116              2.252486  \n",
       "...                    ...                   ...  \n",
       "8910              0.771650             -1.718648  \n",
       "8911              0.774923             -1.727028  \n",
       "8912              0.776469             -1.731013  \n",
       "8913              0.776631             -1.731433  \n",
       "8914              0.776654             -1.731492  \n",
       "8915              0.776736             -1.731705  \n",
       "8916              0.776736             -1.731705  \n",
       "8917              0.777931             -1.734800  \n",
       "8918              0.779158             -1.737991  \n",
       "8919              0.779862             -1.739829  \n",
       "8920              0.780487             -1.741461  \n",
       "8921              0.781771             -1.744828  \n",
       "8922              0.784593             -1.752270  \n",
       "8923              0.785128             -1.753689  \n",
       "8924              0.787911             -1.761108  \n",
       "8925              0.788631             -1.763036  \n",
       "8926              0.789552             -1.765512  \n",
       "8927              0.789718             -1.765960  \n",
       "8928              0.792339             -1.773046  \n",
       "8929              0.792621             -1.773813  \n",
       "8930              0.796064             -1.783227  \n",
       "8931              0.797397             -1.786902  \n",
       "8932              0.809014             -1.819646  \n",
       "8933              0.809038             -1.819714  \n",
       "8934              0.810125             -1.822848  \n",
       "8935              0.810125             -1.822848  \n",
       "8936              0.825543             -1.868731  \n",
       "8937              0.850514             -1.949642  \n",
       "8938              0.854803             -1.964518  \n",
       "8939              0.875889             -2.042829  \n",
       "\n",
       "[8940 rows x 27 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_yhat = test_yhat.sort_values(by='r_DEFAULT_NEXT_MONTH', ascending=False).reset_index(drop=True)\n",
    "test_yhat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot residuals by most important input variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 914.825x648 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sort for better organization in plot\n",
    "sorted_ = test_yhat.sort_values(by='PAY_0')\n",
    "sorted_.rename(columns={yhat: 'Predicted', \n",
    "                        'r_DEFAULT_NEXT_MONTH': 'Residual',\n",
    "                        y: 'Default'}, \n",
    "               inplace=True)\n",
    "\n",
    "# use Seaborn FacetGrid for convenience\n",
    "sns.set(font_scale=1.2) \n",
    "sns.set_style('whitegrid', {'axes.grid': False})\n",
    "sns.set_palette((sns.diverging_palette(255, 133, l=60, n=2, center='dark')))\n",
    "g = sns.FacetGrid(sorted_, col='PAY_0', hue='Default', col_wrap=4)\n",
    "_ = g.map(plt.scatter, 'Predicted', 'Residual', alpha=0.4)\n",
    "_ = g.add_legend() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}