03-UCI with functions_clean.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " --- \n",
    "# UCI - Default from Credit Card Clients\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Dataset presentation\n",
    "\n",
    "This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005.\n",
    "\n",
    "It can be found here:\n",
    "https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables\n",
    "There are 25 variables:\n",
    "\n",
    "* ID: ID of each client\n",
    "* LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit\n",
    "* SEX: Gender (1=male, 2=female)\n",
    "* EDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)\n",
    "* MARRIAGE: Marital status (1=married, 2=single, 3=others)\n",
    "* AGE: Age in years\n",
    "* PAY_0: Repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months, ... 8=payment delay for eight months, 9=payment delay for nine months and above)\n",
    "* PAY_2: Repayment status in August, 2005 (scale same as above)\n",
    "* PAY_3: Repayment status in July, 2005 (scale same as above)\n",
    "* PAY_4: Repayment status in June, 2005 (scale same as above)\n",
    "* PAY_5: Repayment status in May, 2005 (scale same as above)\n",
    "* PAY_6: Repayment status in April, 2005 (scale same as above)\n",
    "* BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar)\n",
    "* BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar)\n",
    "* BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar)\n",
    "* BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar)\n",
    "* BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar)\n",
    "* BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar)\n",
    "* PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar)\n",
    "* PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar)\n",
    "* PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar)\n",
    "* PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar)\n",
    "* PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar)\n",
    "* PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)\n",
    "* default.payment.next.month: Default payment (1=yes, 0=no)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nimport os\\nfor dirname, _, filenames in os.walk('/kaggle/input'):\\nfor filename in filenames:\\n    print(os.path.join(dirname, filename))\\n\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    # This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "\"\"\"\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\"\"\"\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Useful imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import catboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing our Data easily!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'UCI_Credit_Card.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataprep.load import load_raw_data\n",
    "from dataprep.load import load_data\n",
    "from dataprep.load import load_data_xy\n",
    "\n",
    "\n",
    "df = load_data(path)\n",
    "X, y = load_data_xy(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "# Splitting Train/test/val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stratified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataprep.feature_eng import bin_age\n",
    "\n",
    "df = bin_age(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>limit_bal</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>marriage</th>\n",
       "      <th>pay_1</th>\n",
       "      <th>pay_2</th>\n",
       "      <th>pay_3</th>\n",
       "      <th>pay_4</th>\n",
       "      <th>pay_5</th>\n",
       "      <th>pay_6</th>\n",
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       "      <th>bill_amt3</th>\n",
       "      <th>bill_amt4</th>\n",
       "      <th>bill_amt5</th>\n",
       "      <th>bill_amt6</th>\n",
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       "      <th>pay_amt3</th>\n",
       "      <th>pay_amt4</th>\n",
       "      <th>pay_amt5</th>\n",
       "      <th>pay_amt6</th>\n",
       "      <th>default</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
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       "      <td>20s</td>\n",
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       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>29239.0</td>\n",
       "      <td>14027.0</td>\n",
       "      <td>13559.0</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
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       "      <td>1000.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>30s</td>\n",
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       "      <td>2</td>\n",
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       "      <td>28959.0</td>\n",
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       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
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       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
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       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "      <td>50s</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   limit_bal  gender  education  marriage  pay_1  pay_2  pay_3  pay_4  pay_5  \\\n",
       "0    20000.0       2          2         1      1      1      0      0      0   \n",
       "1   120000.0       2          2         2      0      1      0      0      0   \n",
       "2    90000.0       2          2         2      0      0      0      0      0   \n",
       "3    50000.0       2          2         1      0      0      0      0      0   \n",
       "4    50000.0       1          2         1      0      0      0      0      0   \n",
       "\n",
       "   pay_6  bill_amt1  bill_amt2  bill_amt3  bill_amt4  bill_amt5  bill_amt6  \\\n",
       "0      0     3913.0     3102.0      689.0        0.0        0.0        0.0   \n",
       "1      1     2682.0     1725.0     2682.0     3272.0     3455.0     3261.0   \n",
       "2      0    29239.0    14027.0    13559.0    14331.0    14948.0    15549.0   \n",
       "3      0    46990.0    48233.0    49291.0    28314.0    28959.0    29547.0   \n",
       "4      0     8617.0     5670.0    35835.0    20940.0    19146.0    19131.0   \n",
       "\n",
       "   pay_amt1  pay_amt2  pay_amt3  pay_amt4  pay_amt5  pay_amt6  default age_bin  \n",
       "0       0.0     689.0       0.0       0.0       0.0       0.0        1     20s  \n",
       "1       0.0    1000.0    1000.0    1000.0       0.0    2000.0        1     20s  \n",
       "2    1518.0    1500.0    1000.0    1000.0    1000.0    5000.0        0     30s  \n",
       "3    2000.0    2019.0    1200.0    1100.0    1069.0    1000.0        0     30s  \n",
       "4    2000.0   36681.0   10000.0    9000.0     689.0     679.0        0     50s  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New category Gender x Marriage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataprep.feature_eng import gender_x_marriage\n",
    "\n",
    "df = gender_x_marriage(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <th>pay_amt3</th>\n",
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       "      <th>pay_amt5</th>\n",
       "      <th>pay_amt6</th>\n",
       "      <th>default</th>\n",
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       "      <td>1</td>\n",
       "      <td>20s</td>\n",
       "      <td>(2, 1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2682.0</td>\n",
       "      <td>1725.0</td>\n",
       "      <td>2682.0</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20s</td>\n",
       "      <td>(2, 2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>29239.0</td>\n",
       "      <td>14027.0</td>\n",
       "      <td>13559.0</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
       "      <td>(2, 2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>46990.0</td>\n",
       "      <td>48233.0</td>\n",
       "      <td>49291.0</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
       "      <td>(2, 1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>8617.0</td>\n",
       "      <td>5670.0</td>\n",
       "      <td>35835.0</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "      <td>50s</td>\n",
       "      <td>(1, 1)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   limit_bal  gender  education  marriage  pay_1  pay_2  pay_3  pay_4  pay_5  \\\n",
       "0    20000.0       2          2         1      1      1      0      0      0   \n",
       "1   120000.0       2          2         2      0      1      0      0      0   \n",
       "2    90000.0       2          2         2      0      0      0      0      0   \n",
       "3    50000.0       2          2         1      0      0      0      0      0   \n",
       "4    50000.0       1          2         1      0      0      0      0      0   \n",
       "\n",
       "   pay_6  bill_amt1  bill_amt2  bill_amt3  bill_amt4  bill_amt5  bill_amt6  \\\n",
       "0      0     3913.0     3102.0      689.0        0.0        0.0        0.0   \n",
       "1      1     2682.0     1725.0     2682.0     3272.0     3455.0     3261.0   \n",
       "2      0    29239.0    14027.0    13559.0    14331.0    14948.0    15549.0   \n",
       "3      0    46990.0    48233.0    49291.0    28314.0    28959.0    29547.0   \n",
       "4      0     8617.0     5670.0    35835.0    20940.0    19146.0    19131.0   \n",
       "\n",
       "   pay_amt1  pay_amt2  pay_amt3  pay_amt4  pay_amt5  pay_amt6  default  \\\n",
       "0       0.0     689.0       0.0       0.0       0.0       0.0        1   \n",
       "1       0.0    1000.0    1000.0    1000.0       0.0    2000.0        1   \n",
       "2    1518.0    1500.0    1000.0    1000.0    1000.0    5000.0        0   \n",
       "3    2000.0    2019.0    1200.0    1100.0    1069.0    1000.0        0   \n",
       "4    2000.0   36681.0   10000.0    9000.0     689.0     679.0        0   \n",
       "\n",
       "  age_bin gen_mar  \n",
       "0     20s  (2, 1)  \n",
       "1     20s  (2, 2)  \n",
       "2     30s  (2, 2)  \n",
       "3     30s  (2, 1)  \n",
       "4     50s  (1, 1)  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New category Gender x Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataprep.feature_eng import gender_x_agebin\n",
    "\n",
    "df = gender_x_agebin(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>limit_bal</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>marriage</th>\n",
       "      <th>pay_1</th>\n",
       "      <th>pay_2</th>\n",
       "      <th>pay_3</th>\n",
       "      <th>pay_4</th>\n",
       "      <th>pay_5</th>\n",
       "      <th>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</th>\n",
       "      <th>age_bin</th>\n",
       "      <th>gen_mar</th>\n",
       "      <th>gen_ageBin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3913.0</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20s</td>\n",
       "      <td>(2, 1)</td>\n",
       "      <td>(2, 20s)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2682.0</td>\n",
       "      <td>1725.0</td>\n",
       "      <td>2682.0</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20s</td>\n",
       "      <td>(2, 2)</td>\n",
       "      <td>(2, 20s)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>29239.0</td>\n",
       "      <td>14027.0</td>\n",
       "      <td>13559.0</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
       "      <td>(2, 2)</td>\n",
       "      <td>(2, 30s)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>46990.0</td>\n",
       "      <td>48233.0</td>\n",
       "      <td>49291.0</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
       "      <td>(2, 1)</td>\n",
       "      <td>(2, 30s)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>8617.0</td>\n",
       "      <td>5670.0</td>\n",
       "      <td>35835.0</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "      <td>50s</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(1, 50s)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   limit_bal  gender  education  marriage  pay_1  pay_2  pay_3  pay_4  pay_5  \\\n",
       "0    20000.0       2          2         1      1      1      0      0      0   \n",
       "1   120000.0       2          2         2      0      1      0      0      0   \n",
       "2    90000.0       2          2         2      0      0      0      0      0   \n",
       "3    50000.0       2          2         1      0      0      0      0      0   \n",
       "4    50000.0       1          2         1      0      0      0      0      0   \n",
       "\n",
       "   pay_6  bill_amt1  bill_amt2  bill_amt3  bill_amt4  bill_amt5  bill_amt6  \\\n",
       "0      0     3913.0     3102.0      689.0        0.0        0.0        0.0   \n",
       "1      1     2682.0     1725.0     2682.0     3272.0     3455.0     3261.0   \n",
       "2      0    29239.0    14027.0    13559.0    14331.0    14948.0    15549.0   \n",
       "3      0    46990.0    48233.0    49291.0    28314.0    28959.0    29547.0   \n",
       "4      0     8617.0     5670.0    35835.0    20940.0    19146.0    19131.0   \n",
       "\n",
       "   pay_amt1  pay_amt2  pay_amt3  pay_amt4  pay_amt5  pay_amt6  default  \\\n",
       "0       0.0     689.0       0.0       0.0       0.0       0.0        1   \n",
       "1       0.0    1000.0    1000.0    1000.0       0.0    2000.0        1   \n",
       "2    1518.0    1500.0    1000.0    1000.0    1000.0    5000.0        0   \n",
       "3    2000.0    2019.0    1200.0    1100.0    1069.0    1000.0        0   \n",
       "4    2000.0   36681.0   10000.0    9000.0     689.0     679.0        0   \n",
       "\n",
       "  age_bin gen_mar gen_ageBin  \n",
       "0     20s  (2, 1)   (2, 20s)  \n",
       "1     20s  (2, 2)   (2, 20s)  \n",
       "2     30s  (2, 2)   (2, 30s)  \n",
       "3     30s  (2, 1)   (2, 30s)  \n",
       "4     50s  (1, 1)   (1, 50s)  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's add a linear Bill_amt0 prediction to our features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataprep.feature_eng import next_bill_amt\n",
    "    \n",
    "df = next_bill_amt(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>limit_bal</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>marriage</th>\n",
       "      <th>pay_1</th>\n",
       "      <th>pay_2</th>\n",
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       "      <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",
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       "      <th>bill_amt6</th>\n",
       "      <th>pay_amt1</th>\n",
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       "      <th>pay_amt3</th>\n",
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       "      <th>pay_amt6</th>\n",
       "      <th>default</th>\n",
       "      <th>age_bin</th>\n",
       "      <th>gen_mar</th>\n",
       "      <th>gen_ageBin</th>\n",
       "      <th>pred_bill_amt0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3913.0</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20s</td>\n",
       "      <td>(2, 1)</td>\n",
       "      <td>(2, 20s)</td>\n",
       "      <td>3909.683795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>2682.0</td>\n",
       "      <td>1725.0</td>\n",
       "      <td>2682.0</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20s</td>\n",
       "      <td>(2, 2)</td>\n",
       "      <td>(2, 20s)</td>\n",
       "      <td>2678.148995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>29239.0</td>\n",
       "      <td>14027.0</td>\n",
       "      <td>13559.0</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
       "      <td>(2, 2)</td>\n",
       "      <td>(2, 30s)</td>\n",
       "      <td>29177.529785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>46990.0</td>\n",
       "      <td>48233.0</td>\n",
       "      <td>49291.0</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
       "      <td>(2, 1)</td>\n",
       "      <td>(2, 30s)</td>\n",
       "      <td>46995.038110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>8617.0</td>\n",
       "      <td>5670.0</td>\n",
       "      <td>35835.0</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "      <td>50s</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(1, 50s)</td>\n",
       "      <td>8605.584386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   limit_bal  gender  education  marriage  pay_1  pay_2  pay_3  pay_4  pay_5  \\\n",
       "0    20000.0       2          2         1      1      1      0      0      0   \n",
       "1   120000.0       2          2         2      0      1      0      0      0   \n",
       "2    90000.0       2          2         2      0      0      0      0      0   \n",
       "3    50000.0       2          2         1      0      0      0      0      0   \n",
       "4    50000.0       1          2         1      0      0      0      0      0   \n",
       "\n",
       "   pay_6  bill_amt1  bill_amt2  bill_amt3  bill_amt4  bill_amt5  bill_amt6  \\\n",
       "0      0     3913.0     3102.0      689.0        0.0        0.0        0.0   \n",
       "1      1     2682.0     1725.0     2682.0     3272.0     3455.0     3261.0   \n",
       "2      0    29239.0    14027.0    13559.0    14331.0    14948.0    15549.0   \n",
       "3      0    46990.0    48233.0    49291.0    28314.0    28959.0    29547.0   \n",
       "4      0     8617.0     5670.0    35835.0    20940.0    19146.0    19131.0   \n",
       "\n",
       "   pay_amt1  pay_amt2  pay_amt3  pay_amt4  pay_amt5  pay_amt6  default  \\\n",
       "0       0.0     689.0       0.0       0.0       0.0       0.0        1   \n",
       "1       0.0    1000.0    1000.0    1000.0       0.0    2000.0        1   \n",
       "2    1518.0    1500.0    1000.0    1000.0    1000.0    5000.0        0   \n",
       "3    2000.0    2019.0    1200.0    1100.0    1069.0    1000.0        0   \n",
       "4    2000.0   36681.0   10000.0    9000.0     689.0     679.0        0   \n",
       "\n",
       "  age_bin gen_mar gen_ageBin  pred_bill_amt0  \n",
       "0     20s  (2, 1)   (2, 20s)     3909.683795  \n",
       "1     20s  (2, 2)   (2, 20s)     2678.148995  \n",
       "2     30s  (2, 2)   (2, 30s)    29177.529785  \n",
       "3     30s  (2, 1)   (2, 30s)    46995.038110  \n",
       "4     50s  (1, 1)   (1, 50s)     8605.584386  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing our data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One-hot encoding our categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cat = df.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns=['gender', 'education', 'marriage', 'gen_mar', 'gen_ageBin', 'age_bin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>limit_bal</th>\n",
       "      <th>pay_1</th>\n",
       "      <th>pay_2</th>\n",
       "      <th>pay_3</th>\n",
       "      <th>pay_4</th>\n",
       "      <th>pay_5</th>\n",
       "      <th>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</th>\n",
       "      <th>pred_bill_amt0</th>\n",
       "      <th>gender_1</th>\n",
       "      <th>gender_2</th>\n",
       "      <th>education_1</th>\n",
       "      <th>education_2</th>\n",
       "      <th>education_3</th>\n",
       "      <th>education_4</th>\n",
       "      <th>marriage_1</th>\n",
       "      <th>marriage_2</th>\n",
       "      <th>marriage_3</th>\n",
       "      <th>gen_mar_(1, 1)</th>\n",
       "      <th>gen_mar_(1, 2)</th>\n",
       "      <th>gen_mar_(1, 3)</th>\n",
       "      <th>gen_mar_(2, 1)</th>\n",
       "      <th>gen_mar_(2, 2)</th>\n",
       "      <th>gen_mar_(2, 3)</th>\n",
       "      <th>gen_ageBin_(1, '20s')</th>\n",
       "      <th>gen_ageBin_(1, '30s')</th>\n",
       "      <th>gen_ageBin_(1, '40s')</th>\n",
       "      <th>gen_ageBin_(1, '50s')</th>\n",
       "      <th>gen_ageBin_(1, '60+')</th>\n",
       "      <th>gen_ageBin_(2, '20s')</th>\n",
       "      <th>gen_ageBin_(2, '30s')</th>\n",
       "      <th>gen_ageBin_(2, '40s')</th>\n",
       "      <th>gen_ageBin_(2, '50s')</th>\n",
       "      <th>gen_ageBin_(2, '60+')</th>\n",
       "      <th>age_bin_20s</th>\n",
       "      <th>age_bin_30s</th>\n",
       "      <th>age_bin_40s</th>\n",
       "      <th>age_bin_50s</th>\n",
       "      <th>age_bin_60+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3913.0</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3909.683795</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2682.0</td>\n",
       "      <td>1725.0</td>\n",
       "      <td>2682.0</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2678.148995</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90000.0</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>29239.0</td>\n",
       "      <td>14027.0</td>\n",
       "      <td>13559.0</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29177.529785</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000.0</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>46990.0</td>\n",
       "      <td>48233.0</td>\n",
       "      <td>49291.0</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>46995.038110</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000.0</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>8617.0</td>\n",
       "      <td>5670.0</td>\n",
       "      <td>35835.0</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8605.584386</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   limit_bal  pay_1  pay_2  pay_3  pay_4  pay_5  pay_6  bill_amt1  bill_amt2  \\\n",
       "0    20000.0      1      1      0      0      0      0     3913.0     3102.0   \n",
       "1   120000.0      0      1      0      0      0      1     2682.0     1725.0   \n",
       "2    90000.0      0      0      0      0      0      0    29239.0    14027.0   \n",
       "3    50000.0      0      0      0      0      0      0    46990.0    48233.0   \n",
       "4    50000.0      0      0      0      0      0      0     8617.0     5670.0   \n",
       "\n",
       "   bill_amt3  bill_amt4  bill_amt5  bill_amt6  pay_amt1  pay_amt2  pay_amt3  \\\n",
       "0      689.0        0.0        0.0        0.0       0.0     689.0       0.0   \n",
       "1     2682.0     3272.0     3455.0     3261.0       0.0    1000.0    1000.0   \n",
       "2    13559.0    14331.0    14948.0    15549.0    1518.0    1500.0    1000.0   \n",
       "3    49291.0    28314.0    28959.0    29547.0    2000.0    2019.0    1200.0   \n",
       "4    35835.0    20940.0    19146.0    19131.0    2000.0   36681.0   10000.0   \n",
       "\n",
       "   pay_amt4  pay_amt5  pay_amt6  default  pred_bill_amt0  gender_1  gender_2  \\\n",
       "0       0.0       0.0       0.0        1     3909.683795         0         1   \n",
       "1    1000.0       0.0    2000.0        1     2678.148995         0         1   \n",
       "2    1000.0    1000.0    5000.0        0    29177.529785         0         1   \n",
       "3    1100.0    1069.0    1000.0        0    46995.038110         0         1   \n",
       "4    9000.0     689.0     679.0        0     8605.584386         1         0   \n",
       "\n",
       "   education_1  education_2  education_3  education_4  marriage_1  marriage_2  \\\n",
       "0            0            1            0            0           1           0   \n",
       "1            0            1            0            0           0           1   \n",
       "2            0            1            0            0           0           1   \n",
       "3            0            1            0            0           1           0   \n",
       "4            0            1            0            0           1           0   \n",
       "\n",
       "   marriage_3  gen_mar_(1, 1)  gen_mar_(1, 2)  gen_mar_(1, 3)  gen_mar_(2, 1)  \\\n",
       "0           0               0               0               0               1   \n",
       "1           0               0               0               0               0   \n",
       "2           0               0               0               0               0   \n",
       "3           0               0               0               0               1   \n",
       "4           0               1               0               0               0   \n",
       "\n",
       "   gen_mar_(2, 2)  gen_mar_(2, 3)  gen_ageBin_(1, '20s')  \\\n",
       "0               0               0                      0   \n",
       "1               1               0                      0   \n",
       "2               1               0                      0   \n",
       "3               0               0                      0   \n",
       "4               0               0                      0   \n",
       "\n",
       "   gen_ageBin_(1, '30s')  gen_ageBin_(1, '40s')  gen_ageBin_(1, '50s')  \\\n",
       "0                      0                      0                      0   \n",
       "1                      0                      0                      0   \n",
       "2                      0                      0                      0   \n",
       "3                      0                      0                      0   \n",
       "4                      0                      0                      1   \n",
       "\n",
       "   gen_ageBin_(1, '60+')  gen_ageBin_(2, '20s')  gen_ageBin_(2, '30s')  \\\n",
       "0                      0                      1                      0   \n",
       "1                      0                      1                      0   \n",
       "2                      0                      0                      1   \n",
       "3                      0                      0                      1   \n",
       "4                      0                      0                      0   \n",
       "\n",
       "   gen_ageBin_(2, '40s')  gen_ageBin_(2, '50s')  gen_ageBin_(2, '60+')  \\\n",
       "0                      0                      0                      0   \n",
       "1                      0                      0                      0   \n",
       "2                      0                      0                      0   \n",
       "3                      0                      0                      0   \n",
       "4                      0                      0                      0   \n",
       "\n",
       "   age_bin_20s  age_bin_30s  age_bin_40s  age_bin_50s  age_bin_60+  \n",
       "0            1            0            0            0            0  \n",
       "1            1            0            0            0            0  \n",
       "2            0            1            0            0            0  \n",
       "3            0            1            0            0            0  \n",
       "4            0            0            0            1            0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting test/train/val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_train, df_val = train_test_split(df, test_size=6000, stratify=df.default, random_state=42)\n",
    "df_train, df_test = train_test_split(df_train, test_size=6000, stratify=df_train.default, random_state=42)\n",
    "\n",
    "df_train_cat, df_val_cat = train_test_split(df_cat, test_size=6000, stratify=df_cat.default, random_state=42)\n",
    "df_train_cat, df_test_cat = train_test_split(df_train_cat, test_size=6000, stratify=df_train_cat.default, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting into (X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataprep.load import df2xy\n",
    "\n",
    "X_train, y_train = df2xy(df_train, 'default')\n",
    "X_test, y_test = df2xy(df_test, 'default')\n",
    "X_val, y_val = df2xy(df_val, 'default')\n",
    "\n",
    "X_train_cat, y_train_cat = df2xy(df_train_cat, 'default')\n",
    "X_test_cat, y_test_cat= df2xy(df_test_cat, 'default')\n",
    "X_val_cat, y_va_cat = df2xy(df_val_cat, 'default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>default</th>\n",
       "      <th>default</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18000.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.221222</td>\n",
       "      <td>0.221167</td>\n",
       "      <td>0.221167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.415081</td>\n",
       "      <td>0.415067</td>\n",
       "      <td>0.415067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            default      default      default\n",
       "count  18000.000000  6000.000000  6000.000000\n",
       "mean       0.221222     0.221167     0.221167\n",
       "std        0.415081     0.415067     0.415067\n",
       "min        0.000000     0.000000     0.000000\n",
       "25%        0.000000     0.000000     0.000000\n",
       "50%        0.000000     0.000000     0.000000\n",
       "75%        0.000000     0.000000     0.000000\n",
       "max        1.000000     1.000000     1.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([y_train,y_test, y_val], axis=1).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like our data is well stratified: the proportions of defaults in our 3 data sets are all ~22%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "colonnes_dummies = list(X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalizing our training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_norm = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_norm = scaler.transform(X_test)\n",
    "X_val_norm = scaler.transform(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training our models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\twang\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:1544: UserWarning: 'n_jobs' > 1 does not have any effect when 'solver' is set to 'liblinear'. Got 'n_jobs' = 8.\n",
      "  \" = {}.\".format(effective_n_jobs(self.n_jobs)))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1, class_weight='balanced', dual=False,\n",
       "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=100, multi_class='warn', n_jobs=-1, penalty='l2',\n",
       "                   random_state=42, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_reg = LogisticRegression(C=0.1, \n",
    "                             solver='liblinear',\n",
    "                             penalty='l2',\n",
    "                             class_weight='balanced', \n",
    "                             random_state=42, \n",
    "                             n_jobs=-1)\n",
    "log_reg.fit(X_train_norm, np.ravel(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--TRAIN--\n",
      "accuracy:  0.7720555555555556\n",
      "recall:  0.5873932697137118\n",
      "F1:  0.5327411456553922\n",
      "--TEST--\n",
      "accuracy:  0.772\n",
      "recall:  0.5719668425018839\n",
      "F1:  0.525987525987526\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = log_reg.predict(X_train_norm)\n",
    "\n",
    "print('--TRAIN--')\n",
    "print('accuracy: ', accuracy_score(y_train, y_pred_train))\n",
    "print('recall: ', recall_score(y_train, y_pred_train))\n",
    "print('F1: ', f1_score(y_train, y_pred_train))\n",
    "\n",
    "\n",
    "y_pred = log_reg.predict(X_test_norm)\n",
    "\n",
    "print('--TEST--')\n",
    "print('accuracy: ', accuracy_score(y_test, y_pred))\n",
    "print('recall: ', recall_score(y_test, y_pred))\n",
    "print('F1: ', f1_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " non-default       0.88      0.82      0.85     14018\n",
      "     default       0.49      0.59      0.53      3982\n",
      "\n",
      "    accuracy                           0.77     18000\n",
      "   macro avg       0.68      0.71      0.69     18000\n",
      "weighted avg       0.79      0.77      0.78     18000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, \n",
    "                            y_pred_train, \n",
    "                            target_names=['non-default', 'default']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " non-default       0.87      0.83      0.85      4673\n",
      "     default       0.49      0.57      0.53      1327\n",
      "\n",
      "    accuracy                           0.77      6000\n",
      "   macro avg       0.68      0.70      0.69      6000\n",
      "weighted avg       0.79      0.77      0.78      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, \n",
    "                            y_pred, \n",
    "                            target_names=['non-default', 'default']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7059523061366391\n",
      "0.7003853044095125\n"
     ]
    }
   ],
   "source": [
    "print(roc_auc_score(y_train, y_pred_train))\n",
    "print(roc_auc_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.32278946e-01  5.64895451e-01  6.56883946e-02  1.42400872e-01\n",
      "   1.06009074e-01  7.94765090e-02  1.14277127e-01 -2.56964706e-02\n",
      "   3.83900659e-02  1.11602020e-01  7.29962386e-02  1.61440957e-02\n",
      "  -1.11443708e-01 -1.32503030e-01 -1.69791682e-01  4.52516705e-03\n",
      "  -2.49229460e-02 -2.68836286e-02 -3.53613522e-02 -2.54500030e-02\n",
      "   1.11175062e-02 -1.11175062e-02  3.71651278e-03  2.97274120e-02\n",
      "  -7.98987983e-03 -1.09443329e-01  1.99611205e-02 -2.01149401e-02\n",
      "   8.87150931e-04  2.38543271e-02 -1.45042376e-02  3.55251854e-02\n",
      "   2.03755972e-03 -8.69602438e-03 -2.65060972e-02 -5.89520955e-03\n",
      "   1.92046120e-02  1.23667626e-02 -9.88239149e-03 -1.48819539e-02\n",
      "  -1.58834304e-04 -2.04306445e-02 -6.96881389e-03  2.08197922e-02\n",
      "   2.01003740e-02 -4.20849903e-03 -3.30776945e-03  3.21757124e-03\n",
      "   8.59094966e-03  2.23613731e-03]]\n"
     ]
    }
   ],
   "source": [
    "print(log_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "coefs=np.array(log_reg.coef_[0])\n",
    "abs_coefs = np.abs(coefs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances_log = pd.DataFrame(list(zip(colonnes_dummies, coefs, abs_coefs)),\n",
    "                                  columns=['Feature', 'Coef', 'Abs Coef'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Feature</th>\n",
       "      <th>Coef</th>\n",
       "      <th>Abs Coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pay_1</td>\n",
       "      <td>0.564895</td>\n",
       "      <td>0.564895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>limit_bal</td>\n",
       "      <td>-0.232279</td>\n",
       "      <td>0.232279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>pay_amt2</td>\n",
       "      <td>-0.169792</td>\n",
       "      <td>0.169792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pay_3</td>\n",
       "      <td>0.142401</td>\n",
       "      <td>0.142401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pay_amt1</td>\n",
       "      <td>-0.132503</td>\n",
       "      <td>0.132503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pay_6</td>\n",
       "      <td>0.114277</td>\n",
       "      <td>0.114277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>bill_amt3</td>\n",
       "      <td>0.111602</td>\n",
       "      <td>0.111602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>bill_amt6</td>\n",
       "      <td>-0.111444</td>\n",
       "      <td>0.111444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>education_4</td>\n",
       "      <td>-0.109443</td>\n",
       "      <td>0.109443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pay_4</td>\n",
       "      <td>0.106009</td>\n",
       "      <td>0.106009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pay_5</td>\n",
       "      <td>0.079477</td>\n",
       "      <td>0.079477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>bill_amt4</td>\n",
       "      <td>0.072996</td>\n",
       "      <td>0.072996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pay_2</td>\n",
       "      <td>0.065688</td>\n",
       "      <td>0.065688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bill_amt2</td>\n",
       "      <td>0.038390</td>\n",
       "      <td>0.038390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>gen_mar_(1, 3)</td>\n",
       "      <td>0.035525</td>\n",
       "      <td>0.035525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>pay_amt6</td>\n",
       "      <td>-0.035361</td>\n",
       "      <td>0.035361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>education_2</td>\n",
       "      <td>0.029727</td>\n",
       "      <td>0.029727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>pay_amt5</td>\n",
       "      <td>-0.026884</td>\n",
       "      <td>0.026884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>gen_mar_(2, 3)</td>\n",
       "      <td>-0.026506</td>\n",
       "      <td>0.026506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bill_amt1</td>\n",
       "      <td>-0.025696</td>\n",
       "      <td>0.025696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>pred_bill_amt0</td>\n",
       "      <td>-0.025450</td>\n",
       "      <td>0.025450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>pay_amt4</td>\n",
       "      <td>-0.024923</td>\n",
       "      <td>0.024923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>gen_mar_(1, 1)</td>\n",
       "      <td>0.023854</td>\n",
       "      <td>0.023854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>gen_ageBin_(2, '50s')</td>\n",
       "      <td>0.020820</td>\n",
       "      <td>0.020820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>gen_ageBin_(2, '30s')</td>\n",
       "      <td>-0.020431</td>\n",
       "      <td>0.020431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>marriage_2</td>\n",
       "      <td>-0.020115</td>\n",
       "      <td>0.020115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>gen_ageBin_(2, '60+')</td>\n",
       "      <td>0.020100</td>\n",
       "      <td>0.020100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>marriage_1</td>\n",
       "      <td>0.019961</td>\n",
       "      <td>0.019961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>gen_ageBin_(1, '30s')</td>\n",
       "      <td>0.019205</td>\n",
       "      <td>0.019205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bill_amt5</td>\n",
       "      <td>0.016144</td>\n",
       "      <td>0.016144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>gen_ageBin_(1, '60+')</td>\n",
       "      <td>-0.014882</td>\n",
       "      <td>0.014882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>gen_mar_(1, 2)</td>\n",
       "      <td>-0.014504</td>\n",
       "      <td>0.014504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>gen_ageBin_(1, '40s')</td>\n",
       "      <td>0.012367</td>\n",
       "      <td>0.012367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>gender_1</td>\n",
       "      <td>0.011118</td>\n",
       "      <td>0.011118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>gender_2</td>\n",
       "      <td>-0.011118</td>\n",
       "      <td>0.011118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>gen_ageBin_(1, '50s')</td>\n",
       "      <td>-0.009882</td>\n",
       "      <td>0.009882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>gen_mar_(2, 2)</td>\n",
       "      <td>-0.008696</td>\n",
       "      <td>0.008696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>age_bin_50s</td>\n",
       "      <td>0.008591</td>\n",
       "      <td>0.008591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>education_3</td>\n",
       "      <td>-0.007990</td>\n",
       "      <td>0.007990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>gen_ageBin_(2, '40s')</td>\n",
       "      <td>-0.006969</td>\n",
       "      <td>0.006969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>gen_ageBin_(1, '20s')</td>\n",
       "      <td>-0.005895</td>\n",
       "      <td>0.005895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>pay_amt3</td>\n",
       "      <td>0.004525</td>\n",
       "      <td>0.004525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>age_bin_20s</td>\n",
       "      <td>-0.004208</td>\n",
       "      <td>0.004208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>education_1</td>\n",
       "      <td>0.003717</td>\n",
       "      <td>0.003717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>age_bin_30s</td>\n",
       "      <td>-0.003308</td>\n",
       "      <td>0.003308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>age_bin_40s</td>\n",
       "      <td>0.003218</td>\n",
       "      <td>0.003218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>age_bin_60+</td>\n",
       "      <td>0.002236</td>\n",
       "      <td>0.002236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>gen_mar_(2, 1)</td>\n",
       "      <td>0.002038</td>\n",
       "      <td>0.002038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>marriage_3</td>\n",
       "      <td>0.000887</td>\n",
       "      <td>0.000887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>gen_ageBin_(2, '20s')</td>\n",
       "      <td>-0.000159</td>\n",
       "      <td>0.000159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Feature      Coef  Abs Coef\n",
       "1                   pay_1  0.564895  0.564895\n",
       "0               limit_bal -0.232279  0.232279\n",
       "14               pay_amt2 -0.169792  0.169792\n",
       "3                   pay_3  0.142401  0.142401\n",
       "13               pay_amt1 -0.132503  0.132503\n",
       "6                   pay_6  0.114277  0.114277\n",
       "9               bill_amt3  0.111602  0.111602\n",
       "12              bill_amt6 -0.111444  0.111444\n",
       "25            education_4 -0.109443  0.109443\n",
       "4                   pay_4  0.106009  0.106009\n",
       "5                   pay_5  0.079477  0.079477\n",
       "10              bill_amt4  0.072996  0.072996\n",
       "2                   pay_2  0.065688  0.065688\n",
       "8               bill_amt2  0.038390  0.038390\n",
       "31         gen_mar_(1, 3)  0.035525  0.035525\n",
       "18               pay_amt6 -0.035361  0.035361\n",
       "23            education_2  0.029727  0.029727\n",
       "17               pay_amt5 -0.026884  0.026884\n",
       "34         gen_mar_(2, 3) -0.026506  0.026506\n",
       "7               bill_amt1 -0.025696  0.025696\n",
       "19         pred_bill_amt0 -0.025450  0.025450\n",
       "16               pay_amt4 -0.024923  0.024923\n",
       "29         gen_mar_(1, 1)  0.023854  0.023854\n",
       "43  gen_ageBin_(2, '50s')  0.020820  0.020820\n",
       "41  gen_ageBin_(2, '30s') -0.020431  0.020431\n",
       "27             marriage_2 -0.020115  0.020115\n",
       "44  gen_ageBin_(2, '60+')  0.020100  0.020100\n",
       "26             marriage_1  0.019961  0.019961\n",
       "36  gen_ageBin_(1, '30s')  0.019205  0.019205\n",
       "11              bill_amt5  0.016144  0.016144\n",
       "39  gen_ageBin_(1, '60+') -0.014882  0.014882\n",
       "30         gen_mar_(1, 2) -0.014504  0.014504\n",
       "37  gen_ageBin_(1, '40s')  0.012367  0.012367\n",
       "20               gender_1  0.011118  0.011118\n",
       "21               gender_2 -0.011118  0.011118\n",
       "38  gen_ageBin_(1, '50s') -0.009882  0.009882\n",
       "33         gen_mar_(2, 2) -0.008696  0.008696\n",
       "48            age_bin_50s  0.008591  0.008591\n",
       "24            education_3 -0.007990  0.007990\n",
       "42  gen_ageBin_(2, '40s') -0.006969  0.006969\n",
       "35  gen_ageBin_(1, '20s') -0.005895  0.005895\n",
       "15               pay_amt3  0.004525  0.004525\n",
       "45            age_bin_20s -0.004208  0.004208\n",
       "22            education_1  0.003717  0.003717\n",
       "46            age_bin_30s -0.003308  0.003308\n",
       "47            age_bin_40s  0.003218  0.003218\n",
       "49            age_bin_60+  0.002236  0.002236\n",
       "32         gen_mar_(2, 1)  0.002038  0.002038\n",
       "28             marriage_3  0.000887  0.000887\n",
       "40  gen_ageBin_(2, '20s') -0.000159  0.000159"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances_log.sort_values(by=['Abs Coef'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, it appears that our most important variables seem to be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pay_1',\n",
       " 'limit_bal',\n",
       " 'pay_amt2',\n",
       " 'pay_3',\n",
       " 'pay_amt1',\n",
       " 'pay_6',\n",
       " 'bill_amt3',\n",
       " 'bill_amt6',\n",
       " 'education_4']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(feature_importances_log.sort_values(by=['Abs Coef'], ascending=False)['Feature'])[:9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can note that they are the same as before we did our little feature engineering...\n",
    "😅"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight='balanced', coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svc_clf = SVC(gamma='auto', C=1, class_weight='balanced')\n",
    "svc_clf.fit(X_train_norm, np.ravel(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--TRAIN--\n",
      "accuracy:  0.773\n",
      "recall:  0.6479156202913109\n",
      "F1:  0.5580791693705386\n",
      "--TEST--\n",
      "accuracy:  0.7596666666666667\n",
      "recall:  0.5855312735493594\n",
      "F1:  0.5186915887850467\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = svc_clf.predict(X_train_norm)\n",
    "\n",
    "print('--TRAIN--')\n",
    "print('accuracy: ', accuracy_score(y_train, y_pred_train))\n",
    "print('recall: ', recall_score(y_train, y_pred_train))\n",
    "print('F1: ', f1_score(y_train, y_pred_train))\n",
    "\n",
    "\n",
    "y_pred = svc_clf.predict(X_test_norm)\n",
    "\n",
    "print('--TEST--')\n",
    "print('accuracy: ', accuracy_score(y_test, y_pred))\n",
    "print('recall: ', recall_score(y_test, y_pred))\n",
    "print('F1: ', f1_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " non-default       0.89      0.81      0.85     14018\n",
      "     default       0.49      0.65      0.56      3982\n",
      "\n",
      "    accuracy                           0.77     18000\n",
      "   macro avg       0.69      0.73      0.70     18000\n",
      "weighted avg       0.80      0.77      0.78     18000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, \n",
    "                            y_pred_train, \n",
    "                            target_names=['non-default', 'default']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " non-default       0.87      0.81      0.84      4673\n",
      "     default       0.47      0.59      0.52      1327\n",
      "\n",
      "    accuracy                           0.76      6000\n",
      "   macro avg       0.67      0.70      0.68      6000\n",
      "weighted avg       0.78      0.76      0.77      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, \n",
    "                            y_pred, \n",
    "                            target_names=['non-default', 'default']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7282237539322156\n",
      "0.6973237364964858\n"
     ]
    }
   ],
   "source": [
    "print(roc_auc_score(y_train, y_pred_train))\n",
    "print(roc_auc_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight='balanced', criterion='entropy',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=5,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=42, splitter='best')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(criterion='entropy', \n",
    "                                  random_state=42, \n",
    "                                 max_leaf_nodes=5,\n",
    "                                 class_weight='balanced')\n",
    "tree_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--TRAIN--\n",
      "accuracy:  0.7675555555555555\n",
      "recall:  0.5595178302360623\n",
      "F1:  0.5157407407407408\n",
      "--TEST--\n",
      "accuracy:  0.7683333333333333\n",
      "recall:  0.5395629238884703\n",
      "F1:  0.5074415308291991\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = tree_clf.predict(X_train)\n",
    "\n",
    "print('--TRAIN--')\n",
    "print('accuracy: ', accuracy_score(y_train, y_pred_train))\n",
    "print('recall: ', recall_score(y_train, y_pred_train))\n",
    "print('F1: ', f1_score(y_train, y_pred_train))\n",
    "\n",
    "\n",
    "y_pred = tree_clf.predict(X_test)\n",
    "\n",
    "print('--TEST--')\n",
    "print('accuracy: ', accuracy_score(y_test, y_pred))\n",
    "print('recall: ', recall_score(y_test, y_pred))\n",
    "print('F1: ', f1_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " non-default       0.87      0.83      0.85     14018\n",
      "     default       0.48      0.56      0.52      3982\n",
      "\n",
      "    accuracy                           0.77     18000\n",
      "   macro avg       0.67      0.69      0.68     18000\n",
      "weighted avg       0.78      0.77      0.77     18000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, \n",
    "                            y_pred_train, \n",
    "                            target_names=['non-default', 'default']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " non-default       0.86      0.83      0.85      4673\n",
      "     default       0.48      0.54      0.51      1327\n",
      "\n",
      "    accuracy                           0.77      6000\n",
      "   macro avg       0.67      0.69      0.68      6000\n",
      "weighted avg       0.78      0.77      0.77      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, \n",
    "                            y_pred, \n",
    "                            target_names=['non-default', 'default']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize our Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "export_graphviz(tree_clf, \n",
    "                out_file='tree_credit_default_featureEngineered.dot',\n",
    "                feature_names=list(X_train.columns),\n",
    "                class_names=['no-default', 'default'],\n",
    "                rounded=True,\n",
    "                filled=True\n",
    "               )"
   ]
  },
  {
   "attachments": {
    "DecisionTreeViz-CreditDefault-FeatureEngineered.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![DecisionTreeViz-CreditDefault-FeatureEngineered.png](attachment:DecisionTreeViz-CreditDefault-FeatureEngineered.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6930846391870852\n",
      "0.6864302956698932\n"
     ]
    }
   ],
   "source": [
    "print(roc_auc_score(y_train, y_pred_train))\n",
    "print(roc_auc_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance_tree = pd.DataFrame(list(zip(X_train.columns, tree_clf.feature_importances_)), \n",
    "                   columns=['Feature', 'Importance']).sort_values(by=['Importance'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, it appears that our most important variables seem to be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pay_1',\n",
       " 'pred_bill_amt0',\n",
       " 'pay_amt2',\n",
       " 'pay_4',\n",
       " 'marriage_1',\n",
       " 'education_3',\n",
       " 'pay_2',\n",
       " 'pay_3',\n",
       " 'pay_5']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(feature_importance_tree.sort_values(by=['Importance'], ascending=False)['Feature'])[:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=10, min_impurity_decrease=0.0,\n",
       "                       min_impurity_split=None, min_samples_leaf=1,\n",
       "                       min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                       n_estimators=500, n_jobs=-1, oob_score=False,\n",
       "                       random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, \n",
    "                                 max_leaf_nodes=10, \n",
    "                                 n_jobs=-1, \n",
    "                                 class_weight='balanced',\n",
    "                                random_state=42)\n",
    "rnd_clf.fit(X_train, np.ravel(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--TRAIN--\n",
      "accuracy:  0.7608888888888888\n",
      "recall:  0.6353591160220995\n",
      "F1:  0.5403673643741991\n",
      "--TEST--\n",
      "accuracy:  0.7581666666666667\n",
      "recall:  0.6141672946495855\n",
      "F1:  0.5290490100616683\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = rnd_clf.predict(X_train)\n",
    "\n",
    "print('--TRAIN--')\n",
    "print('accuracy: ', accuracy_score(y_train, y_pred_train))\n",
    "print('recall: ', recall_score(y_train, y_pred_train))\n",
    "print('F1: ', f1_score(y_train, y_pred_train))\n",
    "\n",
    "\n",
    "y_pred = rnd_clf.predict(X_test)\n",
    "\n",
    "print('--TEST--')\n",
    "print('accuracy: ', accuracy_score(y_test, y_pred))\n",
    "print('recall: ', recall_score(y_test, y_pred))\n",
    "print('F1: ', f1_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " non-default       0.88      0.80      0.84     14018\n",
      "     default       0.47      0.64      0.54      3982\n",
      "\n",
      "    accuracy                           0.76     18000\n",
      "   macro avg       0.68      0.72      0.69     18000\n",
      "weighted avg       0.79      0.76      0.77     18000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, \n",
    "                            y_pred_train, \n",
    "                            target_names=['non-default', 'default']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " non-default       0.88      0.80      0.84      4673\n",
      "     default       0.46      0.61      0.53      1327\n",
      "\n",
      "    accuracy                           0.76      6000\n",
      "   macro avg       0.67      0.71      0.68      6000\n",
      "weighted avg       0.79      0.76      0.77      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, \n",
    "                            y_pred, \n",
    "                            target_names=['non-default', 'default']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7159532061776926\n",
      "0.7066128576821649\n"
     ]
    }
   ],
   "source": [
    "print(roc_auc_score(y_train, y_pred_train))\n",
    "print(roc_auc_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.41927838e-02, 2.95190886e-01, 1.64873830e-01, 1.05946884e-01,\n",
       "       7.51477589e-02, 7.13559657e-02, 4.73644141e-02, 1.28282874e-02,\n",
       "       9.72154308e-03, 6.94881237e-03, 6.52472670e-03, 5.33753836e-03,\n",
       "       3.90694076e-03, 3.73269728e-02, 3.02318074e-02, 2.62259574e-02,\n",
       "       1.35800295e-02, 1.13064879e-02, 1.42696702e-02, 1.36451465e-02,\n",
       "       4.80049820e-05, 1.98988034e-04, 1.58766408e-03, 1.74824754e-04,\n",
       "       2.87393129e-05, 7.33658255e-04, 1.24036474e-05, 3.74663882e-05,\n",
       "       0.00000000e+00, 1.98253656e-05, 2.93014447e-05, 5.07252679e-05,\n",
       "       0.00000000e+00, 2.49899622e-04, 0.00000000e+00, 2.02447334e-05,\n",
       "       1.08180129e-05, 2.40019326e-05, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 7.28657601e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 1.18333943e-04, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "coefs=np.array(rnd_clf.feature_importances_)\n",
    "abs_coefs = np.abs(coefs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances_rf = pd.DataFrame(list(zip(colonnes_dummies, coefs, abs_coefs)),\n",
    "                                  columns=['Feature', 'Coef', 'Abs Coef'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Feature</th>\n",
       "      <th>Coef</th>\n",
       "      <th>Abs Coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pay_1</td>\n",
       "      <td>0.295191</td>\n",
       "      <td>0.295191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pay_2</td>\n",
       "      <td>0.164874</td>\n",
       "      <td>0.164874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pay_3</td>\n",
       "      <td>0.105947</td>\n",
       "      <td>0.105947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pay_4</td>\n",
       "      <td>0.075148</td>\n",
       "      <td>0.075148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pay_5</td>\n",
       "      <td>0.071356</td>\n",
       "      <td>0.071356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pay_6</td>\n",
       "      <td>0.047364</td>\n",
       "      <td>0.047364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>limit_bal</td>\n",
       "      <td>0.044193</td>\n",
       "      <td>0.044193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pay_amt1</td>\n",
       "      <td>0.037327</td>\n",
       "      <td>0.037327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>pay_amt2</td>\n",
       "      <td>0.030232</td>\n",
       "      <td>0.030232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>pay_amt3</td>\n",
       "      <td>0.026226</td>\n",
       "      <td>0.026226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>pay_amt6</td>\n",
       "      <td>0.014270</td>\n",
       "      <td>0.014270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>pred_bill_amt0</td>\n",
       "      <td>0.013645</td>\n",
       "      <td>0.013645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>pay_amt4</td>\n",
       "      <td>0.013580</td>\n",
       "      <td>0.013580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bill_amt1</td>\n",
       "      <td>0.012828</td>\n",
       "      <td>0.012828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>pay_amt5</td>\n",
       "      <td>0.011306</td>\n",
       "      <td>0.011306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bill_amt2</td>\n",
       "      <td>0.009722</td>\n",
       "      <td>0.009722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>bill_amt3</td>\n",
       "      <td>0.006949</td>\n",
       "      <td>0.006949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>bill_amt4</td>\n",
       "      <td>0.006525</td>\n",
       "      <td>0.006525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bill_amt5</td>\n",
       "      <td>0.005338</td>\n",
       "      <td>0.005338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>bill_amt6</td>\n",
       "      <td>0.003907</td>\n",
       "      <td>0.003907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>education_1</td>\n",
       "      <td>0.001588</td>\n",
       "      <td>0.001588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>education_4</td>\n",
       "      <td>0.000734</td>\n",
       "      <td>0.000734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>gen_ageBin_(2, '30s')</td>\n",
       "      <td>0.000729</td>\n",
       "      <td>0.000729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>gen_mar_(2, 2)</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>gender_2</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>0.000199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>education_2</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>age_bin_30s</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>gen_mar_(1, 3)</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>gender_1</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>marriage_2</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>gen_mar_(1, 2)</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>education_3</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>gen_ageBin_(1, '40s')</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>gen_ageBin_(1, '20s')</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>gen_mar_(1, 1)</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>marriage_1</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>gen_ageBin_(1, '30s')</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>gen_mar_(2, 3)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>gen_ageBin_(1, '50s')</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>gen_ageBin_(1, '60+')</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>gen_ageBin_(2, '20s')</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>gen_mar_(2, 1)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>gen_ageBin_(2, '40s')</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>gen_ageBin_(2, '50s')</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>gen_ageBin_(2, '60+')</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>age_bin_20s</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>marriage_3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>age_bin_40s</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>age_bin_50s</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>age_bin_60+</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Feature      Coef  Abs Coef\n",
       "1                   pay_1  0.295191  0.295191\n",
       "2                   pay_2  0.164874  0.164874\n",
       "3                   pay_3  0.105947  0.105947\n",
       "4                   pay_4  0.075148  0.075148\n",
       "5                   pay_5  0.071356  0.071356\n",
       "6                   pay_6  0.047364  0.047364\n",
       "0               limit_bal  0.044193  0.044193\n",
       "13               pay_amt1  0.037327  0.037327\n",
       "14               pay_amt2  0.030232  0.030232\n",
       "15               pay_amt3  0.026226  0.026226\n",
       "18               pay_amt6  0.014270  0.014270\n",
       "19         pred_bill_amt0  0.013645  0.013645\n",
       "16               pay_amt4  0.013580  0.013580\n",
       "7               bill_amt1  0.012828  0.012828\n",
       "17               pay_amt5  0.011306  0.011306\n",
       "8               bill_amt2  0.009722  0.009722\n",
       "9               bill_amt3  0.006949  0.006949\n",
       "10              bill_amt4  0.006525  0.006525\n",
       "11              bill_amt5  0.005338  0.005338\n",
       "12              bill_amt6  0.003907  0.003907\n",
       "22            education_1  0.001588  0.001588\n",
       "25            education_4  0.000734  0.000734\n",
       "41  gen_ageBin_(2, '30s')  0.000729  0.000729\n",
       "33         gen_mar_(2, 2)  0.000250  0.000250\n",
       "21               gender_2  0.000199  0.000199\n",
       "23            education_2  0.000175  0.000175\n",
       "46            age_bin_30s  0.000118  0.000118\n",
       "31         gen_mar_(1, 3)  0.000051  0.000051\n",
       "20               gender_1  0.000048  0.000048\n",
       "27             marriage_2  0.000037  0.000037\n",
       "30         gen_mar_(1, 2)  0.000029  0.000029\n",
       "24            education_3  0.000029  0.000029\n",
       "37  gen_ageBin_(1, '40s')  0.000024  0.000024\n",
       "35  gen_ageBin_(1, '20s')  0.000020  0.000020\n",
       "29         gen_mar_(1, 1)  0.000020  0.000020\n",
       "26             marriage_1  0.000012  0.000012\n",
       "36  gen_ageBin_(1, '30s')  0.000011  0.000011\n",
       "34         gen_mar_(2, 3)  0.000000  0.000000\n",
       "38  gen_ageBin_(1, '50s')  0.000000  0.000000\n",
       "39  gen_ageBin_(1, '60+')  0.000000  0.000000\n",
       "40  gen_ageBin_(2, '20s')  0.000000  0.000000\n",
       "32         gen_mar_(2, 1)  0.000000  0.000000\n",
       "42  gen_ageBin_(2, '40s')  0.000000  0.000000\n",
       "43  gen_ageBin_(2, '50s')  0.000000  0.000000\n",
       "44  gen_ageBin_(2, '60+')  0.000000  0.000000\n",
       "45            age_bin_20s  0.000000  0.000000\n",
       "28             marriage_3  0.000000  0.000000\n",
       "47            age_bin_40s  0.000000  0.000000\n",
       "48            age_bin_50s  0.000000  0.000000\n",
       "49            age_bin_60+  0.000000  0.000000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances_rf.sort_values(by=['Abs Coef'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, it appears that our most important variables seem to be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pay_1',\n",
       " 'pay_2',\n",
       " 'pay_3',\n",
       " 'pay_4',\n",
       " 'pay_5',\n",
       " 'pay_6',\n",
       " 'limit_bal',\n",
       " 'pay_amt1',\n",
       " 'pay_amt2']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(feature_importances_rf.sort_values(by=['Abs Coef'], ascending=False)['Feature'])[:9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our new predicted bill_amt0 seems to be more important than bill_amt1, and the previous ones, which is a good sign!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boostings models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,\n",
       "                   n_estimators=50, random_state=None)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "ada_clf = AdaBoostClassifier()\n",
    "ada_clf.fit(X_train_norm, np.ravel(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--TEST--\n",
      "accuracy:  0.8088333333333333\n",
      "recall:  0.32169763937719736\n",
      "F1:  0.42678660669665164\n",
      "--TEST--\n",
      "accuracy:  0.8055\n",
      "recall:  0.306706857573474\n",
      "F1:  0.41090358404846034\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = ada_clf.predict(X_train_norm)\n",
    "\n",
    "print('--TEST--')\n",
    "print('accuracy: ', accuracy_score(y_train, y_pred_train))\n",
    "print('recall: ', recall_score(y_train, y_pred_train))\n",
    "print('F1: ', f1_score(y_train, y_pred_train))\n",
    "\n",
    "\n",
    "y_pred = ada_clf.predict(X_test_norm)\n",
    "\n",
    "print('--TEST--')\n",
    "print('accuracy: ', accuracy_score(y_test, y_pred))\n",
    "print('recall: ', recall_score(y_test, y_pred))\n",
    "print('F1: ', f1_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " non-default       0.83      0.95      0.89     14018\n",
      "     default       0.63      0.32      0.43      3982\n",
      "\n",
      "    accuracy                           0.81     18000\n",
      "   macro avg       0.73      0.63      0.66     18000\n",
      "weighted avg       0.79      0.81      0.78     18000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, \n",
    "                            y_pred_train, \n",
    "                            target_names=['non-default', 'default']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " non-default       0.83      0.95      0.88      4673\n",
      "     default       0.62      0.31      0.41      1327\n",
      "\n",
      "    accuracy                           0.81      6000\n",
      "   macro avg       0.73      0.63      0.65      6000\n",
      "weighted avg       0.78      0.81      0.78      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, \n",
    "                            y_pred, \n",
    "                            target_names=['non-default', 'default']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6344541842199155\n",
      "0.6269250102119457\n"
     ]
    }
   ],
   "source": [
    "print(roc_auc_score(y_train, y_pred_train))\n",
    "print(roc_auc_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_gbm = {\n",
    "          'boosting_type': 'gbdt',\n",
    "          'objective': 'binary',\n",
    "          'metric':'auc',\n",
    "          'learning_rate': 0.05,\n",
    "          'num_leaves': 7,  # we should let it be smaller than 2^(max_depth)\n",
    "          'max_depth': 4,  # -1 means no limit\n",
    "          'min_child_samples': 100,  # Minimum number of data need in a child(min_data_in_leaf)\n",
    "          'max_bin': 100,  # Number of bucketed bin for feature values\n",
    "          'subsample': 0.9,  # Subsample ratio of the training instance.\n",
    "          'subsample_freq': 1,  # frequence of subsample, <=0 means no enable\n",
    "          'colsample_bytree': 0.7,  # Subsample ratio of columns when constructing each tree.\n",
    "          'min_child_weight': 0,  # Minimum sum of instance weight(hessian) needed in a child(leaf)\n",
    "          'min_split_gain': 0,  # lambda_l1, lambda_l2 and min_gain_to_split to regularization\n",
    "          'nthread': 8,\n",
    "          'verbose': 0,\n",
    "          'scale_pos_weight':50, # because training data is sightly unbalanced \n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>limit_bal</th>\n",
       "      <th>pay_1</th>\n",
       "      <th>pay_2</th>\n",
       "      <th>pay_3</th>\n",
       "      <th>pay_4</th>\n",
       "      <th>pay_5</th>\n",
       "      <th>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>pred_bill_amt0</th>\n",
       "      <th>gender_1</th>\n",
       "      <th>gender_2</th>\n",
       "      <th>education_1</th>\n",
       "      <th>education_2</th>\n",
       "      <th>education_3</th>\n",
       "      <th>education_4</th>\n",
       "      <th>marriage_1</th>\n",
       "      <th>marriage_2</th>\n",
       "      <th>marriage_3</th>\n",
       "      <th>gen_mar_(1, 1)</th>\n",
       "      <th>gen_mar_(1, 2)</th>\n",
       "      <th>gen_mar_(1, 3)</th>\n",
       "      <th>gen_mar_(2, 1)</th>\n",
       "      <th>gen_mar_(2, 2)</th>\n",
       "      <th>gen_mar_(2, 3)</th>\n",
       "      <th>gen_ageBin_(1, '20s')</th>\n",
       "      <th>gen_ageBin_(1, '30s')</th>\n",
       "      <th>gen_ageBin_(1, '40s')</th>\n",
       "      <th>gen_ageBin_(1, '50s')</th>\n",
       "      <th>gen_ageBin_(1, '60+')</th>\n",
       "      <th>gen_ageBin_(2, '20s')</th>\n",
       "      <th>gen_ageBin_(2, '30s')</th>\n",
       "      <th>gen_ageBin_(2, '40s')</th>\n",
       "      <th>gen_ageBin_(2, '50s')</th>\n",
       "      <th>gen_ageBin_(2, '60+')</th>\n",
       "      <th>age_bin_20s</th>\n",
       "      <th>age_bin_30s</th>\n",
       "      <th>age_bin_40s</th>\n",
       "      <th>age_bin_50s</th>\n",
       "      <th>age_bin_60+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10574</th>\n",
       "      <td>20000.0</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>19255.0</td>\n",
       "      <td>20650.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19260.299255</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11330</th>\n",
       "      <td>300000.0</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>285038.0</td>\n",
       "      <td>291480.0</td>\n",
       "      <td>298378.0</td>\n",
       "      <td>131103.0</td>\n",
       "      <td>128742.0</td>\n",
       "      <td>124882.0</td>\n",
       "      <td>12507.0</td>\n",
       "      <td>15056.0</td>\n",
       "      <td>5027.0</td>\n",
       "      <td>5007.0</td>\n",
       "      <td>5063.0</td>\n",
       "      <td>3039.0</td>\n",
       "      <td>285064.129858</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7127</th>\n",
       "      <td>500000.0</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>33179.0</td>\n",
       "      <td>97882.0</td>\n",
       "      <td>48017.0</td>\n",
       "      <td>49403.0</td>\n",
       "      <td>40018.0</td>\n",
       "      <td>54109.0</td>\n",
       "      <td>99159.0</td>\n",
       "      <td>48276.0</td>\n",
       "      <td>49450.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>54379.0</td>\n",
       "      <td>9905.0</td>\n",
       "      <td>33439.612312</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25150</th>\n",
       "      <td>70000.0</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>70349.0</td>\n",
       "      <td>70068.0</td>\n",
       "      <td>71318.0</td>\n",
       "      <td>59665.0</td>\n",
       "      <td>29510.0</td>\n",
       "      <td>26738.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>70347.884276</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6000</th>\n",
       "      <td>50000.0</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>13538.0</td>\n",
       "      <td>9199.0</td>\n",
       "      <td>6879.0</td>\n",
       "      <td>6753.0</td>\n",
       "      <td>5440.0</td>\n",
       "      <td>5440.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1284.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13520.430805</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20951</th>\n",
       "      <td>340000.0</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>11379.0</td>\n",
       "      <td>11086.0</td>\n",
       "      <td>3112.0</td>\n",
       "      <td>3351.0</td>\n",
       "      <td>1259.0</td>\n",
       "      <td>19271.0</td>\n",
       "      <td>11258.0</td>\n",
       "      <td>3112.0</td>\n",
       "      <td>3351.0</td>\n",
       "      <td>1259.0</td>\n",
       "      <td>19271.0</td>\n",
       "      <td>16992.0</td>\n",
       "      <td>11377.686002</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13894</th>\n",
       "      <td>300000.0</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>145138.0</td>\n",
       "      <td>148586.0</td>\n",
       "      <td>151878.0</td>\n",
       "      <td>123417.0</td>\n",
       "      <td>104922.0</td>\n",
       "      <td>107005.0</td>\n",
       "      <td>5785.0</td>\n",
       "      <td>6163.0</td>\n",
       "      <td>4728.0</td>\n",
       "      <td>3811.0</td>\n",
       "      <td>3830.0</td>\n",
       "      <td>3970.0</td>\n",
       "      <td>145151.983172</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15285</th>\n",
       "      <td>230000.0</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>970.0</td>\n",
       "      <td>1475.0</td>\n",
       "      <td>1636.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>1483.0</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>972.043000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6881</th>\n",
       "      <td>200000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26344.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>2865.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2865.0</td>\n",
       "      <td>801.0</td>\n",
       "      <td>26237.559617</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22145</th>\n",
       "      <td>60000.0</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>23304.0</td>\n",
       "      <td>20517.0</td>\n",
       "      <td>20261.0</td>\n",
       "      <td>22832.0</td>\n",
       "      <td>24234.0</td>\n",
       "      <td>24928.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>23292.735385</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10270</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49423.0</td>\n",
       "      <td>48332.0</td>\n",
       "      <td>46043.0</td>\n",
       "      <td>18759.0</td>\n",
       "      <td>19161.0</td>\n",
       "      <td>19516.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>688.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>49418.552752</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28740</th>\n",
       "      <td>160000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4145.0</td>\n",
       "      <td>3930.0</td>\n",
       "      <td>5859.0</td>\n",
       "      <td>10299.0</td>\n",
       "      <td>11825.0</td>\n",
       "      <td>13121.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>5008.0</td>\n",
       "      <td>3007.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>4144.163097</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19310</th>\n",
       "      <td>360000.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4087</th>\n",
       "      <td>150000.0</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>135587.0</td>\n",
       "      <td>138473.0</td>\n",
       "      <td>141297.0</td>\n",
       "      <td>144605.0</td>\n",
       "      <td>148420.0</td>\n",
       "      <td>150500.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135598.706927</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13760</th>\n",
       "      <td>480000.0</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>7174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7145.014141</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27714</th>\n",
       "      <td>130000.0</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>44930.0</td>\n",
       "      <td>40688.0</td>\n",
       "      <td>40880.0</td>\n",
       "      <td>37851.0</td>\n",
       "      <td>37816.0</td>\n",
       "      <td>39849.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8002.0</td>\n",
       "      <td>44912.863541</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7584</th>\n",
       "      <td>90000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83480.0</td>\n",
       "      <td>89264.0</td>\n",
       "      <td>89896.0</td>\n",
       "      <td>86706.0</td>\n",
       "      <td>87385.0</td>\n",
       "      <td>88351.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>83503.379804</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29650</th>\n",
       "      <td>260000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>334214.0</td>\n",
       "      <td>310546.0</td>\n",
       "      <td>288566.0</td>\n",
       "      <td>291597.0</td>\n",
       "      <td>214353.0</td>\n",
       "      <td>216238.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>8300.0</td>\n",
       "      <td>334118.013076</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9472</th>\n",
       "      <td>440000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>225416.0</td>\n",
       "      <td>220405.0</td>\n",
       "      <td>226731.0</td>\n",
       "      <td>231456.0</td>\n",
       "      <td>237707.0</td>\n",
       "      <td>243555.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>225395.857120</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7022</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59586.0</td>\n",
       "      <td>60842.0</td>\n",
       "      <td>61749.0</td>\n",
       "      <td>65130.0</td>\n",
       "      <td>63816.0</td>\n",
       "      <td>65011.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>59591.089777</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21323</th>\n",
       "      <td>250000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.003247</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23409</th>\n",
       "      <td>200000.0</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>198595.0</td>\n",
       "      <td>194734.0</td>\n",
       "      <td>190447.0</td>\n",
       "      <td>187224.0</td>\n",
       "      <td>183641.0</td>\n",
       "      <td>178239.0</td>\n",
       "      <td>7003.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>6510.0</td>\n",
       "      <td>6550.0</td>\n",
       "      <td>6001.0</td>\n",
       "      <td>198579.329802</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3884</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>391.569417</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9456</th>\n",
       "      <td>310000.0</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>2948.0</td>\n",
       "      <td>62593.0</td>\n",
       "      <td>152141.0</td>\n",
       "      <td>125325.0</td>\n",
       "      <td>109142.0</td>\n",
       "      <td>103020.0</td>\n",
       "      <td>62635.0</td>\n",
       "      <td>100042.0</td>\n",
       "      <td>4017.0</td>\n",
       "      <td>9184.0</td>\n",
       "      <td>4081.0</td>\n",
       "      <td>6042.0</td>\n",
       "      <td>3190.449985</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15820</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23575.0</td>\n",
       "      <td>24584.0</td>\n",
       "      <td>25885.0</td>\n",
       "      <td>26851.0</td>\n",
       "      <td>26808.0</td>\n",
       "      <td>27599.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>557.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>23579.098070</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13152</th>\n",
       "      <td>180000.0</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>43507.0</td>\n",
       "      <td>44282.0</td>\n",
       "      <td>23946.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43509.797751</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10707</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17294.0</td>\n",
       "      <td>22256.0</td>\n",
       "      <td>21660.0</td>\n",
       "      <td>22055.0</td>\n",
       "      <td>22603.0</td>\n",
       "      <td>23133.0</td>\n",
       "      <td>5210.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>17314.038781</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23062</th>\n",
       "      <td>150000.0</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>23364.0</td>\n",
       "      <td>22846.0</td>\n",
       "      <td>25779.0</td>\n",
       "      <td>22303.0</td>\n",
       "      <td>16981.0</td>\n",
       "      <td>16837.0</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>5779.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>23361.954721</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11118</th>\n",
       "      <td>200000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>1879.0</td>\n",
       "      <td>1707.0</td>\n",
       "      <td>617.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1879.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>617.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.934494</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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",
       "      <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>3063</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>43685.0</td>\n",
       "      <td>44694.0</td>\n",
       "      <td>43673.0</td>\n",
       "      <td>44659.0</td>\n",
       "      <td>49512.0</td>\n",
       "      <td>48454.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>43689.060166</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21234</th>\n",
       "      <td>300000.0</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>50281.0</td>\n",
       "      <td>51441.0</td>\n",
       "      <td>54107.0</td>\n",
       "      <td>55111.0</td>\n",
       "      <td>56688.0</td>\n",
       "      <td>58272.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>50285.730457</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2459</th>\n",
       "      <td>250000.0</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>37137.0</td>\n",
       "      <td>51803.0</td>\n",
       "      <td>4997.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51811.0</td>\n",
       "      <td>9544.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37195.492134</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24360</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-342.0</td>\n",
       "      <td>-342.0</td>\n",
       "      <td>-342.0</td>\n",
       "      <td>-342.0</td>\n",
       "      <td>1158.0</td>\n",
       "      <td>2485.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-342.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9048</th>\n",
       "      <td>140000.0</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>854.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>850.549495</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25080</th>\n",
       "      <td>30000.0</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>14476.0</td>\n",
       "      <td>15297.0</td>\n",
       "      <td>8531.0</td>\n",
       "      <td>8020.0</td>\n",
       "      <td>-620.0</td>\n",
       "      <td>-620.0</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>1081.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14479.206682</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6886</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>911.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>930.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>911.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.008516</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18264</th>\n",
       "      <td>180000.0</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>77408.0</td>\n",
       "      <td>74156.0</td>\n",
       "      <td>75659.0</td>\n",
       "      <td>72046.0</td>\n",
       "      <td>70355.0</td>\n",
       "      <td>71208.0</td>\n",
       "      <td>3293.0</td>\n",
       "      <td>3350.0</td>\n",
       "      <td>3111.0</td>\n",
       "      <td>2615.0</td>\n",
       "      <td>2689.0</td>\n",
       "      <td>2766.0</td>\n",
       "      <td>77394.884904</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17546</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31422.0</td>\n",
       "      <td>32420.0</td>\n",
       "      <td>33462.0</td>\n",
       "      <td>36199.0</td>\n",
       "      <td>36803.0</td>\n",
       "      <td>37566.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>31426.049514</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12326</th>\n",
       "      <td>240000.0</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>133230.0</td>\n",
       "      <td>136579.0</td>\n",
       "      <td>139281.0</td>\n",
       "      <td>132487.0</td>\n",
       "      <td>107437.0</td>\n",
       "      <td>109237.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>5036.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>133243.574968</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2440</th>\n",
       "      <td>160000.0</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>67925.0</td>\n",
       "      <td>69771.0</td>\n",
       "      <td>73233.0</td>\n",
       "      <td>72328.0</td>\n",
       "      <td>74234.0</td>\n",
       "      <td>75500.0</td>\n",
       "      <td>3509.0</td>\n",
       "      <td>5824.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2204.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67932.515043</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8905</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>42883.0</td>\n",
       "      <td>47887.0</td>\n",
       "      <td>46836.0</td>\n",
       "      <td>45785.0</td>\n",
       "      <td>47003.0</td>\n",
       "      <td>48085.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42903.200955</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5739</th>\n",
       "      <td>150000.0</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>112391.0</td>\n",
       "      <td>98583.0</td>\n",
       "      <td>95810.0</td>\n",
       "      <td>87216.0</td>\n",
       "      <td>88201.0</td>\n",
       "      <td>87483.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>3290.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>3215.0</td>\n",
       "      <td>112335.164266</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>500000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>507726.0</td>\n",
       "      <td>509229.0</td>\n",
       "      <td>499936.0</td>\n",
       "      <td>505507.0</td>\n",
       "      <td>484612.0</td>\n",
       "      <td>419643.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>507731.921382</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4816</th>\n",
       "      <td>50000.0</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>40019.0</td>\n",
       "      <td>20394.0</td>\n",
       "      <td>19597.0</td>\n",
       "      <td>17830.0</td>\n",
       "      <td>18397.0</td>\n",
       "      <td>19064.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1519.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>39939.693943</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15178</th>\n",
       "      <td>90000.0</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>28276.0</td>\n",
       "      <td>29305.0</td>\n",
       "      <td>30336.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>9852.0</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>9852.0</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>28280.174300</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18321</th>\n",
       "      <td>480000.0</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>3655.0</td>\n",
       "      <td>38022.0</td>\n",
       "      <td>5785.0</td>\n",
       "      <td>9233.0</td>\n",
       "      <td>4893.0</td>\n",
       "      <td>2168.0</td>\n",
       "      <td>38024.0</td>\n",
       "      <td>5785.0</td>\n",
       "      <td>9233.0</td>\n",
       "      <td>4893.0</td>\n",
       "      <td>2168.0</td>\n",
       "      <td>3625.0</td>\n",
       "      <td>3793.330527</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23609</th>\n",
       "      <td>50000.0</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>43886.0</td>\n",
       "      <td>50411.0</td>\n",
       "      <td>9688.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>3810.0</td>\n",
       "      <td>4741.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>7026.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>43911.698257</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16230</th>\n",
       "      <td>50000.0</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>49450.0</td>\n",
       "      <td>50067.0</td>\n",
       "      <td>69594.0</td>\n",
       "      <td>46181.0</td>\n",
       "      <td>16770.0</td>\n",
       "      <td>19890.0</td>\n",
       "      <td>1743.0</td>\n",
       "      <td>2236.0</td>\n",
       "      <td>2807.0</td>\n",
       "      <td>629.0</td>\n",
       "      <td>3810.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>49452.810153</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21927</th>\n",
       "      <td>160000.0</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>3849.0</td>\n",
       "      <td>6393.0</td>\n",
       "      <td>6969.0</td>\n",
       "      <td>9044.0</td>\n",
       "      <td>2325.0</td>\n",
       "      <td>3998.0</td>\n",
       "      <td>6458.0</td>\n",
       "      <td>6980.0</td>\n",
       "      <td>9044.0</td>\n",
       "      <td>2325.0</td>\n",
       "      <td>4997.0</td>\n",
       "      <td>9947.0</td>\n",
       "      <td>3859.288326</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27267</th>\n",
       "      <td>160000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14137.0</td>\n",
       "      <td>13613.0</td>\n",
       "      <td>14634.0</td>\n",
       "      <td>16532.0</td>\n",
       "      <td>15969.0</td>\n",
       "      <td>17701.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1247.0</td>\n",
       "      <td>2145.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14134.899621</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11593</th>\n",
       "      <td>170000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1020.0</td>\n",
       "      <td>25607.0</td>\n",
       "      <td>23922.0</td>\n",
       "      <td>24399.0</td>\n",
       "      <td>24919.0</td>\n",
       "      <td>26884.0</td>\n",
       "      <td>27107.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>863.0</td>\n",
       "      <td>903.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>6186.0</td>\n",
       "      <td>-912.443637</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15732</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27369.0</td>\n",
       "      <td>6425.0</td>\n",
       "      <td>4580.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27284.347356</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25950</th>\n",
       "      <td>410000.0</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>388.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>110345.0</td>\n",
       "      <td>116128.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>110345.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>388.000029</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5863</th>\n",
       "      <td>320000.0</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>36831.0</td>\n",
       "      <td>28785.0</td>\n",
       "      <td>13104.0</td>\n",
       "      <td>2621.0</td>\n",
       "      <td>9075.0</td>\n",
       "      <td>6564.0</td>\n",
       "      <td>28791.0</td>\n",
       "      <td>13227.0</td>\n",
       "      <td>2621.0</td>\n",
       "      <td>9075.0</td>\n",
       "      <td>6564.0</td>\n",
       "      <td>12767.0</td>\n",
       "      <td>36798.234229</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22847</th>\n",
       "      <td>310000.0</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>67940.0</td>\n",
       "      <td>45746.0</td>\n",
       "      <td>46979.0</td>\n",
       "      <td>42045.0</td>\n",
       "      <td>40334.0</td>\n",
       "      <td>49542.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>67850.347075</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17496</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52062.0</td>\n",
       "      <td>49884.0</td>\n",
       "      <td>22348.0</td>\n",
       "      <td>22063.0</td>\n",
       "      <td>8723.0</td>\n",
       "      <td>7254.0</td>\n",
       "      <td>2158.0</td>\n",
       "      <td>1708.0</td>\n",
       "      <td>1411.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>52052.750456</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2145</th>\n",
       "      <td>170000.0</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>171098.0</td>\n",
       "      <td>166176.0</td>\n",
       "      <td>169759.0</td>\n",
       "      <td>11760.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4902.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>5695.0</td>\n",
       "      <td>11760.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4902.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>171078.161199</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15305</th>\n",
       "      <td>100000.0</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>83013.0</td>\n",
       "      <td>89589.0</td>\n",
       "      <td>92887.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83039.617410</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25509</th>\n",
       "      <td>60000.0</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>56136.0</td>\n",
       "      <td>57280.0</td>\n",
       "      <td>56969.0</td>\n",
       "      <td>58695.0</td>\n",
       "      <td>57453.0</td>\n",
       "      <td>61153.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>2685.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>56140.617259</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18000 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       limit_bal  pay_1  pay_2  pay_3  pay_4  pay_5  pay_6  bill_amt1  \\\n",
       "10574    20000.0      0      0      0      0      0      0    19255.0   \n",
       "11330   300000.0      0      0      0      0      0      0   285038.0   \n",
       "7127    500000.0      0      0      0      0      0      0    33179.0   \n",
       "25150    70000.0      0      0      0      0      0      0    70349.0   \n",
       "6000     50000.0      0      0      0      0      0      0    13538.0   \n",
       "20951   340000.0      0      0      0      0      0      0    11379.0   \n",
       "13894   300000.0      0      0      0      0      0      0   145138.0   \n",
       "15285   230000.0      0      0      0      0      0      0      970.0   \n",
       "6881    200000.0      0      0      0      0      1      0    26344.0   \n",
       "22145    60000.0      0      0      0      0      0      0    23304.0   \n",
       "10270    50000.0      1      1      0      0      0      0    49423.0   \n",
       "28740   160000.0      0      1      0      0      0      0     4145.0   \n",
       "19310   360000.0      0      0      0      0      0      0        0.0   \n",
       "4087    150000.0      0      0      0      0      0      0   135587.0   \n",
       "13760   480000.0      0      0      0      0      0      0     7174.0   \n",
       "27714   130000.0      0      0      0      0      0      0    44930.0   \n",
       "7584     90000.0      1      1      1      1      0      0    83480.0   \n",
       "29650   260000.0      1      0      0      0      0      0   334214.0   \n",
       "9472    440000.0      1      1      0      0      0      0   225416.0   \n",
       "7022    100000.0      1      1      1      1      1      1    59586.0   \n",
       "21323   250000.0      1      0      0      0      0      0        0.0   \n",
       "23409   200000.0      0      0      0      0      0      0   198595.0   \n",
       "3884     20000.0      0      0      1      0      0      0      390.0   \n",
       "9456    310000.0      0      0      0      0      0      0     2948.0   \n",
       "15820    30000.0      1      0      0      0      0      0    23575.0   \n",
       "13152   180000.0      0      0      0      0      0      0    43507.0   \n",
       "10707    80000.0      1      1      1      0      0      0    17294.0   \n",
       "1541     50000.0      1      0      0      0      0      0        0.0   \n",
       "23062   150000.0      0      0      0      0      0      0    23364.0   \n",
       "11118   200000.0      0      0      1      0      0      0      162.0   \n",
       "...          ...    ...    ...    ...    ...    ...    ...        ...   \n",
       "3063     50000.0      1      1      1      0      0      1    43685.0   \n",
       "21234   300000.0      0      0      0      0      0      0    50281.0   \n",
       "2459    250000.0      0      0      0      0      0      0    37137.0   \n",
       "24360   100000.0      1      0      0      0      0      0     -342.0   \n",
       "9048    140000.0      0      0      0      0      0      0      854.0   \n",
       "25080    30000.0      0      0      0      0      0      0    14476.0   \n",
       "6886     50000.0      0      0      0      0      0      1      390.0   \n",
       "18264   180000.0      0      0      0      0      0      0    77408.0   \n",
       "17546    50000.0      1      0      0      0      1      1    31422.0   \n",
       "12326   240000.0      0      0      0      0      0      0   133230.0   \n",
       "2440    160000.0      0      0      0      0      0      0    67925.0   \n",
       "8905     50000.0      1      1      1      1      1      1    42883.0   \n",
       "5739    150000.0      0      0      0      0      0      0   112391.0   \n",
       "360     500000.0      1      1      1      0      0      0   507726.0   \n",
       "4816     50000.0      0      0      0      0      0      0    40019.0   \n",
       "15178    90000.0      0      0      0      0      0      0    28276.0   \n",
       "18321   480000.0      0      0      0      0      0      0     3655.0   \n",
       "23609    50000.0      0      0      0      0      0      0    43886.0   \n",
       "16230    50000.0      0      0      0      0      0      0    49450.0   \n",
       "21927   160000.0      0      0      0      0      0      0     3849.0   \n",
       "27267   160000.0      1      1      0      0      1      1    14137.0   \n",
       "11593   170000.0      1      0      0      0      0      0    -1020.0   \n",
       "15732    50000.0      1      1      0      0      0      0    27369.0   \n",
       "25950   410000.0      0      0      0      0      0      0      388.0   \n",
       "5863    320000.0      0      0      0      0      0      0    36831.0   \n",
       "22847   310000.0      0      0      0      0      0      0    67940.0   \n",
       "17496    80000.0      1      0      0      0      0      0    52062.0   \n",
       "2145    170000.0      0      0      0      0      0      0   171098.0   \n",
       "15305   100000.0      0      0      0      0      0      0    83013.0   \n",
       "25509    60000.0      0      0      0      0      0      0    56136.0   \n",
       "\n",
       "       bill_amt2  bill_amt3  bill_amt4  bill_amt5  bill_amt6  pay_amt1  \\\n",
       "10574    20650.0        0.0        0.0        0.0        0.0    2000.0   \n",
       "11330   291480.0   298378.0   131103.0   128742.0   124882.0   12507.0   \n",
       "7127     97882.0    48017.0    49403.0    40018.0    54109.0   99159.0   \n",
       "25150    70068.0    71318.0    59665.0    29510.0    26738.0    2600.0   \n",
       "6000      9199.0     6879.0     6753.0     5440.0     5440.0    2000.0   \n",
       "20951    11086.0     3112.0     3351.0     1259.0    19271.0   11258.0   \n",
       "13894   148586.0   151878.0   123417.0   104922.0   107005.0    5785.0   \n",
       "15285     1475.0     1636.0     1149.0      334.0      459.0    1483.0   \n",
       "6881         0.0        0.0      320.0      170.0     2865.0       0.0   \n",
       "22145    20517.0    20261.0    22832.0    24234.0    24928.0    3000.0   \n",
       "10270    48332.0    46043.0    18759.0    19161.0    19516.0      10.0   \n",
       "28740     3930.0     5859.0    10299.0    11825.0    13121.0       0.0   \n",
       "19310        0.0        0.0        0.0        0.0        0.0       0.0   \n",
       "4087    138473.0   141297.0   144605.0   148420.0   150500.0    4000.0   \n",
       "13760        0.0        0.0        0.0        0.0        0.0       0.0   \n",
       "27714    40688.0    40880.0    37851.0    37816.0    39849.0    6000.0   \n",
       "7584     89264.0    89896.0    86706.0    87385.0    88351.0    8000.0   \n",
       "29650   310546.0   288566.0   291597.0   214353.0   216238.0   12000.0   \n",
       "9472    220405.0   226731.0   231456.0   237707.0   243555.0       0.0   \n",
       "7022     60842.0    61749.0    65130.0    63816.0    65011.0    2800.0   \n",
       "21323        0.0      191.0     2160.0        0.0      900.0       0.0   \n",
       "23409   194734.0   190447.0   187224.0   183641.0   178239.0    7003.0   \n",
       "3884       780.0      390.0      780.0      780.0        0.0     780.0   \n",
       "9456     62593.0   152141.0   125325.0   109142.0   103020.0   62635.0   \n",
       "15820    24584.0    25885.0    26851.0    26808.0    27599.0    1400.0   \n",
       "13152    44282.0    23946.0        0.0        0.0        0.0    2000.0   \n",
       "10707    22256.0    21660.0    22055.0    22603.0    23133.0    5210.0   \n",
       "1541         0.0        0.0        0.0        0.0        0.0       0.0   \n",
       "23062    22846.0    25779.0    22303.0    16981.0    16837.0    1450.0   \n",
       "11118     1879.0     1707.0      617.0      620.0        0.0    1879.0   \n",
       "...          ...        ...        ...        ...        ...       ...   \n",
       "3063     44694.0    43673.0    44659.0    49512.0    48454.0    2000.0   \n",
       "21234    51441.0    54107.0    55111.0    56688.0    58272.0    2000.0   \n",
       "2459     51803.0     4997.0        0.0        0.0        0.0   51811.0   \n",
       "24360     -342.0     -342.0     -342.0     1158.0     2485.0       0.0   \n",
       "9048         0.0        0.0        0.0        0.0        0.0       0.0   \n",
       "25080    15297.0     8531.0     8020.0     -620.0     -620.0    1129.0   \n",
       "6886       390.0      911.0     1080.0      930.0      390.0     390.0   \n",
       "18264    74156.0    75659.0    72046.0    70355.0    71208.0    3293.0   \n",
       "17546    32420.0    33462.0    36199.0    36803.0    37566.0    1521.0   \n",
       "12326   136579.0   139281.0   132487.0   107437.0   109237.0    5500.0   \n",
       "2440     69771.0    73233.0    72328.0    74234.0    75500.0    3509.0   \n",
       "8905     47887.0    46836.0    45785.0    47003.0    48085.0    6000.0   \n",
       "5739     98583.0    95810.0    87216.0    88201.0    87483.0    5000.0   \n",
       "360     509229.0   499936.0   505507.0   484612.0   419643.0   10000.0   \n",
       "4816     20394.0    19597.0    17830.0    18397.0    19064.0    2006.0   \n",
       "15178    29305.0    30336.0    28800.0     9852.0     2160.0    1505.0   \n",
       "18321    38022.0     5785.0     9233.0     4893.0     2168.0   38024.0   \n",
       "23609    50411.0     9688.0      857.0     3810.0     4741.0    2500.0   \n",
       "16230    50067.0    69594.0    46181.0    16770.0    19890.0    1743.0   \n",
       "21927     6393.0     6969.0     9044.0     2325.0     3998.0    6458.0   \n",
       "27267    13613.0    14634.0    16532.0    15969.0    17701.0       0.0   \n",
       "11593    25607.0    23922.0    24399.0    24919.0    26884.0   27107.0   \n",
       "15732     6425.0     4580.0        0.0        0.0        0.0       0.0   \n",
       "25950      388.0      388.0      388.0   110345.0   116128.0     388.0   \n",
       "5863     28785.0    13104.0     2621.0     9075.0     6564.0   28791.0   \n",
       "22847    45746.0    46979.0    42045.0    40334.0    49542.0    2000.0   \n",
       "17496    49884.0    22348.0    22063.0     8723.0     7254.0    2158.0   \n",
       "2145    166176.0   169759.0    11760.0        0.0     4902.0   14000.0   \n",
       "15305    89589.0    92887.0        0.0        0.0        0.0    8000.0   \n",
       "25509    57280.0    56969.0    58695.0    57453.0    61153.0    2100.0   \n",
       "\n",
       "       pay_amt2  pay_amt3  pay_amt4  pay_amt5  pay_amt6  pred_bill_amt0  \\\n",
       "10574       0.0       0.0       0.0       0.0       0.0    19260.299255   \n",
       "11330   15056.0    5027.0    5007.0    5063.0    3039.0   285064.129858   \n",
       "7127    48276.0   49450.0     201.0   54379.0    9905.0    33439.612312   \n",
       "25150    3700.0    2600.0    1200.0    1200.0    1000.0    70347.884276   \n",
       "6000     1284.0     264.0     109.0     390.0       0.0    13520.430805   \n",
       "20951    3112.0    3351.0    1259.0   19271.0   16992.0    11377.686002   \n",
       "13894    6163.0    4728.0    3811.0    3830.0    3970.0   145151.983172   \n",
       "15285    1640.0    1152.0     335.0     460.0     553.0      972.043000   \n",
       "6881        0.0     329.0       0.0    2865.0     801.0    26237.559617   \n",
       "22145    3000.0    3000.0    2000.0    2000.0    2000.0    23292.735385   \n",
       "10270    2000.0     655.0     688.0     666.0     649.0    49418.552752   \n",
       "28740    2000.0    5008.0    3007.0    2000.0    2000.0     4144.163097   \n",
       "19310       0.0       0.0       0.0       0.0       0.0        0.000000   \n",
       "4087     4000.0    4500.0    5000.0    3500.0       0.0   135598.706927   \n",
       "13760       0.0       0.0       0.0       0.0       0.0     7145.014141   \n",
       "27714    6000.0    4000.0   10000.0    5000.0    8002.0    44912.863541   \n",
       "7584     3000.0       0.0    3000.0    5000.0    5000.0    83503.379804   \n",
       "29650   11000.0   11000.0    8000.0    8500.0    8300.0   334118.013076   \n",
       "9472    10000.0   10000.0   10000.0   10000.0   10000.0   225395.857120   \n",
       "7022     2500.0    5000.0       0.0    2400.0    2400.0    59591.089777   \n",
       "21323     191.0    2160.0       0.0     900.0    2000.0        0.003247   \n",
       "23409    7000.0    7000.0    6510.0    6550.0    6001.0   198579.329802   \n",
       "3884        0.0     780.0       0.0       0.0       0.0      391.569417   \n",
       "9456   100042.0    4017.0    9184.0    4081.0    6042.0     3190.449985   \n",
       "15820    1700.0    1900.0     557.0    1000.0    2000.0    23579.098070   \n",
       "13152    2000.0       0.0       0.0       0.0       0.0    43509.797751   \n",
       "10707       0.0    1000.0    1000.0    1000.0    1000.0    17314.038781   \n",
       "1541        0.0       0.0       0.0       0.0       0.0        0.000000   \n",
       "23062    5779.0    1300.0     700.0    1000.0    1000.0    23361.954721   \n",
       "11118       8.0     617.0       3.0       0.0       0.0      168.934494   \n",
       "...         ...       ...       ...       ...       ...             ...   \n",
       "3063        0.0    2000.0    6000.0     300.0    3000.0    43689.060166   \n",
       "21234    3500.0    3000.0    2500.0    2500.0    2500.0    50285.730457   \n",
       "2459     9544.0       0.0       0.0       0.0       0.0    37195.492134   \n",
       "24360       0.0       0.0    1500.0    1500.0       0.0     -342.000000   \n",
       "9048        0.0       0.0       0.0       0.0       0.0      850.549495   \n",
       "25080    1152.0    1081.0       0.0       0.0       0.0    14479.206682   \n",
       "6886      911.0     559.0     390.0       0.0     390.0      390.008516   \n",
       "18264    3350.0    3111.0    2615.0    2689.0    2766.0    77394.884904   \n",
       "17546    1563.0    3300.0    1500.0    1500.0    1600.0    31426.049514   \n",
       "12326    5036.0    4000.0    6200.0    5500.0    5000.0   133243.574968   \n",
       "2440     5824.0       0.0    2204.0    2000.0       0.0    67932.515043   \n",
       "8905        0.0       0.0    2100.0    2000.0       0.0    42903.200955   \n",
       "5739     4000.0    4000.0    3290.0    3200.0    3215.0   112335.164266   \n",
       "360         0.0   14000.0   16500.0   16000.0   19000.0   507731.921382   \n",
       "4816     1519.0    1000.0    1000.0    1000.0    1000.0    39939.693943   \n",
       "15178    1536.0    1000.0    9852.0    2160.0     400.0    28280.174300   \n",
       "18321    5785.0    9233.0    4893.0    2168.0    3625.0     3793.330527   \n",
       "23609    7026.0    1000.0    3000.0    1000.0    1000.0    43911.698257   \n",
       "16230    2236.0    2807.0     629.0    3810.0     719.0    49452.810153   \n",
       "21927    6980.0    9044.0    2325.0    4997.0    9947.0     3859.288326   \n",
       "27267    1247.0    2145.0       0.0    2000.0       0.0    14134.899621   \n",
       "11593    1500.0     863.0     903.0    3000.0    6186.0     -912.443637   \n",
       "15732    1050.0       0.0       0.0       0.0       0.0    27284.347356   \n",
       "25950     388.0     388.0  110345.0    8000.0    4200.0      388.000029   \n",
       "5863    13227.0    2621.0    9075.0    6564.0   12767.0    36798.234229   \n",
       "22847    2000.0    2000.0    2000.0   13000.0    2000.0    67850.347075   \n",
       "17496    1708.0    1411.0       4.0      21.0     220.0    52052.750456   \n",
       "2145     5695.0   11760.0       0.0    4902.0    6000.0   171078.161199   \n",
       "15305    5000.0       0.0       0.0       0.0       0.0    83039.617410   \n",
       "25509    2100.0    2685.0    2300.0    4800.0    3400.0    56140.617259   \n",
       "\n",
       "       gender_1  gender_2  education_1  education_2  education_3  education_4  \\\n",
       "10574         0         1            0            0            1            0   \n",
       "11330         1         0            0            1            0            0   \n",
       "7127          1         0            0            1            0            0   \n",
       "25150         0         1            0            1            0            0   \n",
       "6000          1         0            0            1            0            0   \n",
       "20951         1         0            1            0            0            0   \n",
       "13894         0         1            0            1            0            0   \n",
       "15285         0         1            1            0            0            0   \n",
       "6881          0         1            1            0            0            0   \n",
       "22145         0         1            1            0            0            0   \n",
       "10270         1         0            0            1            0            0   \n",
       "28740         0         1            1            0            0            0   \n",
       "19310         0         1            1            0            0            0   \n",
       "4087          1         0            0            1            0            0   \n",
       "13760         1         0            0            0            0            1   \n",
       "27714         0         1            0            1            0            0   \n",
       "7584          0         1            0            1            0            0   \n",
       "29650         1         0            0            1            0            0   \n",
       "9472          0         1            0            1            0            0   \n",
       "7022          1         0            0            1            0            0   \n",
       "21323         1         0            1            0            0            0   \n",
       "23409         0         1            0            0            0            1   \n",
       "3884          1         0            0            1            0            0   \n",
       "9456          0         1            1            0            0            0   \n",
       "15820         0         1            1            0            0            0   \n",
       "13152         0         1            1            0            0            0   \n",
       "10707         0         1            0            1            0            0   \n",
       "1541          1         0            0            0            1            0   \n",
       "23062         0         1            1            0            0            0   \n",
       "11118         1         0            0            1            0            0   \n",
       "...         ...       ...          ...          ...          ...          ...   \n",
       "3063          1         0            0            0            1            0   \n",
       "21234         1         0            0            1            0            0   \n",
       "2459          0         1            0            1            0            0   \n",
       "24360         1         0            0            1            0            0   \n",
       "9048          0         1            0            1            0            0   \n",
       "25080         1         0            1            0            0            0   \n",
       "6886          0         1            0            0            1            0   \n",
       "18264         1         0            0            0            1            0   \n",
       "17546         0         1            0            0            1            0   \n",
       "12326         0         1            1            0            0            0   \n",
       "2440          0         1            1            0            0            0   \n",
       "8905          1         0            0            1            0            0   \n",
       "5739          0         1            0            0            1            0   \n",
       "360           0         1            0            1            0            0   \n",
       "4816          0         1            0            1            0            0   \n",
       "15178         0         1            0            1            0            0   \n",
       "18321         0         1            1            0            0            0   \n",
       "23609         0         1            1            0            0            0   \n",
       "16230         0         1            0            1            0            0   \n",
       "21927         0         1            1            0            0            0   \n",
       "27267         1         0            1            0            0            0   \n",
       "11593         0         1            0            1            0            0   \n",
       "15732         0         1            0            1            0            0   \n",
       "25950         0         1            0            0            1            0   \n",
       "5863          1         0            0            1            0            0   \n",
       "22847         0         1            1            0            0            0   \n",
       "17496         0         1            0            1            0            0   \n",
       "2145          1         0            0            0            0            1   \n",
       "15305         0         1            0            1            0            0   \n",
       "25509         0         1            1            0            0            0   \n",
       "\n",
       "       marriage_1  marriage_2  marriage_3  gen_mar_(1, 1)  gen_mar_(1, 2)  \\\n",
       "10574           1           0           0               0               0   \n",
       "11330           0           1           0               0               1   \n",
       "7127            0           1           0               0               1   \n",
       "25150           1           0           0               0               0   \n",
       "6000            1           0           0               1               0   \n",
       "20951           1           0           0               1               0   \n",
       "13894           1           0           0               0               0   \n",
       "15285           1           0           0               0               0   \n",
       "6881            1           0           0               0               0   \n",
       "22145           0           1           0               0               0   \n",
       "10270           0           1           0               0               1   \n",
       "28740           0           1           0               0               0   \n",
       "19310           0           1           0               0               0   \n",
       "4087            0           1           0               0               1   \n",
       "13760           0           1           0               0               1   \n",
       "27714           0           1           0               0               0   \n",
       "7584            0           1           0               0               0   \n",
       "29650           0           1           0               0               1   \n",
       "9472            0           1           0               0               0   \n",
       "7022            1           0           0               1               0   \n",
       "21323           1           0           0               1               0   \n",
       "23409           1           0           0               0               0   \n",
       "3884            0           1           0               0               1   \n",
       "9456            1           0           0               0               0   \n",
       "15820           0           1           0               0               0   \n",
       "13152           1           0           0               0               0   \n",
       "10707           1           0           0               0               0   \n",
       "1541            0           1           0               0               1   \n",
       "23062           1           0           0               0               0   \n",
       "11118           1           0           0               1               0   \n",
       "...           ...         ...         ...             ...             ...   \n",
       "3063            0           1           0               0               1   \n",
       "21234           0           1           0               0               1   \n",
       "2459            1           0           0               0               0   \n",
       "24360           0           1           0               0               1   \n",
       "9048            0           1           0               0               0   \n",
       "25080           1           0           0               1               0   \n",
       "6886            0           0           1               0               0   \n",
       "18264           0           1           0               0               1   \n",
       "17546           1           0           0               0               0   \n",
       "12326           0           1           0               0               0   \n",
       "2440            0           1           0               0               0   \n",
       "8905            0           1           0               0               1   \n",
       "5739            1           0           0               0               0   \n",
       "360             0           1           0               0               0   \n",
       "4816            0           1           0               0               0   \n",
       "15178           0           1           0               0               0   \n",
       "18321           0           1           0               0               0   \n",
       "23609           0           1           0               0               0   \n",
       "16230           1           0           0               0               0   \n",
       "21927           0           1           0               0               0   \n",
       "27267           1           0           0               1               0   \n",
       "11593           0           1           0               0               0   \n",
       "15732           1           0           0               0               0   \n",
       "25950           1           0           0               0               0   \n",
       "5863            0           1           0               0               1   \n",
       "22847           0           1           0               0               0   \n",
       "17496           1           0           0               0               0   \n",
       "2145            1           0           0               1               0   \n",
       "15305           1           0           0               0               0   \n",
       "25509           0           1           0               0               0   \n",
       "\n",
       "       gen_mar_(1, 3)  gen_mar_(2, 1)  gen_mar_(2, 2)  gen_mar_(2, 3)  \\\n",
       "10574               0               1               0               0   \n",
       "11330               0               0               0               0   \n",
       "7127                0               0               0               0   \n",
       "25150               0               1               0               0   \n",
       "6000                0               0               0               0   \n",
       "20951               0               0               0               0   \n",
       "13894               0               1               0               0   \n",
       "15285               0               1               0               0   \n",
       "6881                0               1               0               0   \n",
       "22145               0               0               1               0   \n",
       "10270               0               0               0               0   \n",
       "28740               0               0               1               0   \n",
       "19310               0               0               1               0   \n",
       "4087                0               0               0               0   \n",
       "13760               0               0               0               0   \n",
       "27714               0               0               1               0   \n",
       "7584                0               0               1               0   \n",
       "29650               0               0               0               0   \n",
       "9472                0               0               1               0   \n",
       "7022                0               0               0               0   \n",
       "21323               0               0               0               0   \n",
       "23409               0               1               0               0   \n",
       "3884                0               0               0               0   \n",
       "9456                0               1               0               0   \n",
       "15820               0               0               1               0   \n",
       "13152               0               1               0               0   \n",
       "10707               0               1               0               0   \n",
       "1541                0               0               0               0   \n",
       "23062               0               1               0               0   \n",
       "11118               0               0               0               0   \n",
       "...               ...             ...             ...             ...   \n",
       "3063                0               0               0               0   \n",
       "21234               0               0               0               0   \n",
       "2459                0               1               0               0   \n",
       "24360               0               0               0               0   \n",
       "9048                0               0               1               0   \n",
       "25080               0               0               0               0   \n",
       "6886                0               0               0               1   \n",
       "18264               0               0               0               0   \n",
       "17546               0               1               0               0   \n",
       "12326               0               0               1               0   \n",
       "2440                0               0               1               0   \n",
       "8905                0               0               0               0   \n",
       "5739                0               1               0               0   \n",
       "360                 0               0               1               0   \n",
       "4816                0               0               1               0   \n",
       "15178               0               0               1               0   \n",
       "18321               0               0               1               0   \n",
       "23609               0               0               1               0   \n",
       "16230               0               1               0               0   \n",
       "21927               0               0               1               0   \n",
       "27267               0               0               0               0   \n",
       "11593               0               0               1               0   \n",
       "15732               0               1               0               0   \n",
       "25950               0               1               0               0   \n",
       "5863                0               0               0               0   \n",
       "22847               0               0               1               0   \n",
       "17496               0               1               0               0   \n",
       "2145                0               0               0               0   \n",
       "15305               0               1               0               0   \n",
       "25509               0               0               1               0   \n",
       "\n",
       "       gen_ageBin_(1, '20s')  gen_ageBin_(1, '30s')  gen_ageBin_(1, '40s')  \\\n",
       "10574                      0                      0                      0   \n",
       "11330                      0                      1                      0   \n",
       "7127                       0                      1                      0   \n",
       "25150                      0                      0                      0   \n",
       "6000                       0                      1                      0   \n",
       "20951                      0                      0                      1   \n",
       "13894                      0                      0                      0   \n",
       "15285                      0                      0                      0   \n",
       "6881                       0                      0                      0   \n",
       "22145                      0                      0                      0   \n",
       "10270                      0                      0                      1   \n",
       "28740                      0                      0                      0   \n",
       "19310                      0                      0                      0   \n",
       "4087                       0                      1                      0   \n",
       "13760                      0                      0                      1   \n",
       "27714                      0                      0                      0   \n",
       "7584                       0                      0                      0   \n",
       "29650                      0                      1                      0   \n",
       "9472                       0                      0                      0   \n",
       "7022                       0                      0                      1   \n",
       "21323                      0                      0                      1   \n",
       "23409                      0                      0                      0   \n",
       "3884                       1                      0                      0   \n",
       "9456                       0                      0                      0   \n",
       "15820                      0                      0                      0   \n",
       "13152                      0                      0                      0   \n",
       "10707                      0                      0                      0   \n",
       "1541                       1                      0                      0   \n",
       "23062                      0                      0                      0   \n",
       "11118                      0                      1                      0   \n",
       "...                      ...                    ...                    ...   \n",
       "3063                       1                      0                      0   \n",
       "21234                      0                      1                      0   \n",
       "2459                       0                      0                      0   \n",
       "24360                      0                      1                      0   \n",
       "9048                       0                      0                      0   \n",
       "25080                      0                      0                      0   \n",
       "6886                       0                      0                      0   \n",
       "18264                      0                      0                      0   \n",
       "17546                      0                      0                      0   \n",
       "12326                      0                      0                      0   \n",
       "2440                       0                      0                      0   \n",
       "8905                       1                      0                      0   \n",
       "5739                       0                      0                      0   \n",
       "360                        0                      0                      0   \n",
       "4816                       0                      0                      0   \n",
       "15178                      0                      0                      0   \n",
       "18321                      0                      0                      0   \n",
       "23609                      0                      0                      0   \n",
       "16230                      0                      0                      0   \n",
       "21927                      0                      0                      0   \n",
       "27267                      0                      0                      1   \n",
       "11593                      0                      0                      0   \n",
       "15732                      0                      0                      0   \n",
       "25950                      0                      0                      0   \n",
       "5863                       0                      0                      1   \n",
       "22847                      0                      0                      0   \n",
       "17496                      0                      0                      0   \n",
       "2145                       1                      0                      0   \n",
       "15305                      0                      0                      0   \n",
       "25509                      0                      0                      0   \n",
       "\n",
       "       gen_ageBin_(1, '50s')  gen_ageBin_(1, '60+')  gen_ageBin_(2, '20s')  \\\n",
       "10574                      0                      0                      0   \n",
       "11330                      0                      0                      0   \n",
       "7127                       0                      0                      0   \n",
       "25150                      0                      0                      0   \n",
       "6000                       0                      0                      0   \n",
       "20951                      0                      0                      0   \n",
       "13894                      0                      0                      0   \n",
       "15285                      0                      0                      0   \n",
       "6881                       0                      0                      0   \n",
       "22145                      0                      0                      1   \n",
       "10270                      0                      0                      0   \n",
       "28740                      0                      0                      1   \n",
       "19310                      0                      0                      0   \n",
       "4087                       0                      0                      0   \n",
       "13760                      0                      0                      0   \n",
       "27714                      0                      0                      1   \n",
       "7584                       0                      0                      1   \n",
       "29650                      0                      0                      0   \n",
       "9472                       0                      0                      1   \n",
       "7022                       0                      0                      0   \n",
       "21323                      0                      0                      0   \n",
       "23409                      0                      0                      0   \n",
       "3884                       0                      0                      0   \n",
       "9456                       0                      0                      0   \n",
       "15820                      0                      0                      1   \n",
       "13152                      0                      0                      0   \n",
       "10707                      0                      0                      0   \n",
       "1541                       0                      0                      0   \n",
       "23062                      0                      0                      0   \n",
       "11118                      0                      0                      0   \n",
       "...                      ...                    ...                    ...   \n",
       "3063                       0                      0                      0   \n",
       "21234                      0                      0                      0   \n",
       "2459                       0                      0                      0   \n",
       "24360                      0                      0                      0   \n",
       "9048                       0                      0                      1   \n",
       "25080                      1                      0                      0   \n",
       "6886                       0                      0                      0   \n",
       "18264                      1                      0                      0   \n",
       "17546                      0                      0                      0   \n",
       "12326                      0                      0                      1   \n",
       "2440                       0                      0                      0   \n",
       "8905                       0                      0                      0   \n",
       "5739                       0                      0                      0   \n",
       "360                        0                      0                      1   \n",
       "4816                       0                      0                      0   \n",
       "15178                      0                      0                      1   \n",
       "18321                      0                      0                      0   \n",
       "23609                      0                      0                      0   \n",
       "16230                      0                      0                      0   \n",
       "21927                      0                      0                      1   \n",
       "27267                      0                      0                      0   \n",
       "11593                      0                      0                      1   \n",
       "15732                      0                      0                      1   \n",
       "25950                      0                      0                      0   \n",
       "5863                       0                      0                      0   \n",
       "22847                      0                      0                      0   \n",
       "17496                      0                      0                      1   \n",
       "2145                       0                      0                      0   \n",
       "15305                      0                      0                      0   \n",
       "25509                      0                      0                      1   \n",
       "\n",
       "       gen_ageBin_(2, '30s')  gen_ageBin_(2, '40s')  gen_ageBin_(2, '50s')  \\\n",
       "10574                      0                      0                      1   \n",
       "11330                      0                      0                      0   \n",
       "7127                       0                      0                      0   \n",
       "25150                      0                      1                      0   \n",
       "6000                       0                      0                      0   \n",
       "20951                      0                      0                      0   \n",
       "13894                      1                      0                      0   \n",
       "15285                      1                      0                      0   \n",
       "6881                       1                      0                      0   \n",
       "22145                      0                      0                      0   \n",
       "10270                      0                      0                      0   \n",
       "28740                      0                      0                      0   \n",
       "19310                      1                      0                      0   \n",
       "4087                       0                      0                      0   \n",
       "13760                      0                      0                      0   \n",
       "27714                      0                      0                      0   \n",
       "7584                       0                      0                      0   \n",
       "29650                      0                      0                      0   \n",
       "9472                       0                      0                      0   \n",
       "7022                       0                      0                      0   \n",
       "21323                      0                      0                      0   \n",
       "23409                      0                      1                      0   \n",
       "3884                       0                      0                      0   \n",
       "9456                       1                      0                      0   \n",
       "15820                      0                      0                      0   \n",
       "13152                      1                      0                      0   \n",
       "10707                      1                      0                      0   \n",
       "1541                       0                      0                      0   \n",
       "23062                      1                      0                      0   \n",
       "11118                      0                      0                      0   \n",
       "...                      ...                    ...                    ...   \n",
       "3063                       0                      0                      0   \n",
       "21234                      0                      0                      0   \n",
       "2459                       0                      1                      0   \n",
       "24360                      0                      0                      0   \n",
       "9048                       0                      0                      0   \n",
       "25080                      0                      0                      0   \n",
       "6886                       0                      0                      1   \n",
       "18264                      0                      0                      0   \n",
       "17546                      0                      1                      0   \n",
       "12326                      0                      0                      0   \n",
       "2440                       1                      0                      0   \n",
       "8905                       0                      0                      0   \n",
       "5739                       1                      0                      0   \n",
       "360                        0                      0                      0   \n",
       "4816                       1                      0                      0   \n",
       "15178                      0                      0                      0   \n",
       "18321                      1                      0                      0   \n",
       "23609                      1                      0                      0   \n",
       "16230                      1                      0                      0   \n",
       "21927                      0                      0                      0   \n",
       "27267                      0                      0                      0   \n",
       "11593                      0                      0                      0   \n",
       "15732                      0                      0                      0   \n",
       "25950                      1                      0                      0   \n",
       "5863                       0                      0                      0   \n",
       "22847                      1                      0                      0   \n",
       "17496                      0                      0                      0   \n",
       "2145                       0                      0                      0   \n",
       "15305                      1                      0                      0   \n",
       "25509                      0                      0                      0   \n",
       "\n",
       "       gen_ageBin_(2, '60+')  age_bin_20s  age_bin_30s  age_bin_40s  \\\n",
       "10574                      0            0            0            0   \n",
       "11330                      0            0            1            0   \n",
       "7127                       0            0            1            0   \n",
       "25150                      0            0            0            1   \n",
       "6000                       0            0            1            0   \n",
       "20951                      0            0            0            1   \n",
       "13894                      0            0            1            0   \n",
       "15285                      0            0            1            0   \n",
       "6881                       0            0            1            0   \n",
       "22145                      0            1            0            0   \n",
       "10270                      0            0            0            1   \n",
       "28740                      0            1            0            0   \n",
       "19310                      0            0            1            0   \n",
       "4087                       0            0            1            0   \n",
       "13760                      0            0            0            1   \n",
       "27714                      0            1            0            0   \n",
       "7584                       0            1            0            0   \n",
       "29650                      0            0            1            0   \n",
       "9472                       0            1            0            0   \n",
       "7022                       0            0            0            1   \n",
       "21323                      0            0            0            1   \n",
       "23409                      0            0            0            1   \n",
       "3884                       0            1            0            0   \n",
       "9456                       0            0            1            0   \n",
       "15820                      0            1            0            0   \n",
       "13152                      0            0            1            0   \n",
       "10707                      0            0            1            0   \n",
       "1541                       0            1            0            0   \n",
       "23062                      0            0            1            0   \n",
       "11118                      0            0            1            0   \n",
       "...                      ...          ...          ...          ...   \n",
       "3063                       0            1            0            0   \n",
       "21234                      0            0            1            0   \n",
       "2459                       0            0            0            1   \n",
       "24360                      0            0            1            0   \n",
       "9048                       0            1            0            0   \n",
       "25080                      0            0            0            0   \n",
       "6886                       0            0            0            0   \n",
       "18264                      0            0            0            0   \n",
       "17546                      0            0            0            1   \n",
       "12326                      0            1            0            0   \n",
       "2440                       0            0            1            0   \n",
       "8905                       0            1            0            0   \n",
       "5739                       0            0            1            0   \n",
       "360                        0            1            0            0   \n",
       "4816                       0            0            1            0   \n",
       "15178                      0            1            0            0   \n",
       "18321                      0            0            1            0   \n",
       "23609                      0            0            1            0   \n",
       "16230                      0            0            1            0   \n",
       "21927                      0            1            0            0   \n",
       "27267                      0            0            0            1   \n",
       "11593                      0            1            0            0   \n",
       "15732                      0            1            0            0   \n",
       "25950                      0            0            1            0   \n",
       "5863                       0            0            0            1   \n",
       "22847                      0            0            1            0   \n",
       "17496                      0            1            0            0   \n",
       "2145                       0            1            0            0   \n",
       "15305                      0            0            1            0   \n",
       "25509                      0            1            0            0   \n",
       "\n",
       "       age_bin_50s  age_bin_60+  \n",
       "10574            1            0  \n",
       "11330            0            0  \n",
       "7127             0            0  \n",
       "25150            0            0  \n",
       "6000             0            0  \n",
       "20951            0            0  \n",
       "13894            0            0  \n",
       "15285            0            0  \n",
       "6881             0            0  \n",
       "22145            0            0  \n",
       "10270            0            0  \n",
       "28740            0            0  \n",
       "19310            0            0  \n",
       "4087             0            0  \n",
       "13760            0            0  \n",
       "27714            0            0  \n",
       "7584             0            0  \n",
       "29650            0            0  \n",
       "9472             0            0  \n",
       "7022             0            0  \n",
       "21323            0            0  \n",
       "23409            0            0  \n",
       "3884             0            0  \n",
       "9456             0            0  \n",
       "15820            0            0  \n",
       "13152            0            0  \n",
       "10707            0            0  \n",
       "1541             0            0  \n",
       "23062            0            0  \n",
       "11118            0            0  \n",
       "...            ...          ...  \n",
       "3063             0            0  \n",
       "21234            0            0  \n",
       "2459             0            0  \n",
       "24360            0            0  \n",
       "9048             0            0  \n",
       "25080            1            0  \n",
       "6886             1            0  \n",
       "18264            1            0  \n",
       "17546            0            0  \n",
       "12326            0            0  \n",
       "2440             0            0  \n",
       "8905             0            0  \n",
       "5739             0            0  \n",
       "360              0            0  \n",
       "4816             0            0  \n",
       "15178            0            0  \n",
       "18321            0            0  \n",
       "23609            0            0  \n",
       "16230            0            0  \n",
       "21927            0            0  \n",
       "27267            0            0  \n",
       "11593            0            0  \n",
       "15732            0            0  \n",
       "25950            0            0  \n",
       "5863             0            0  \n",
       "22847            0            0  \n",
       "17496            0            0  \n",
       "2145             0            0  \n",
       "15305            0            0  \n",
       "25509            0            0  \n",
       "\n",
       "[18000 rows x 50 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = ['SEX', 'EDUCATION', 'MARRIAGE','PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import FunctionTransformer\n",
    "bin_age_transformer = FunctionTransformer(bin_age, validate=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = load_data_xy(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>limit_bal</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>pay_1</th>\n",
       "      <th>pay_2</th>\n",
       "      <th>pay_3</th>\n",
       "      <th>pay_4</th>\n",
       "      <th>pay_5</th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3913.0</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2682.0</td>\n",
       "      <td>1725.0</td>\n",
       "      <td>2682.0</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29239.0</td>\n",
       "      <td>14027.0</td>\n",
       "      <td>13559.0</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46990.0</td>\n",
       "      <td>48233.0</td>\n",
       "      <td>49291.0</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</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>8617.0</td>\n",
       "      <td>5670.0</td>\n",
       "      <td>35835.0</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   limit_bal  gender  education  marriage  age  pay_1  pay_2  pay_3  pay_4  \\\n",
       "0    20000.0       2          2         1   24      1      1      0      0   \n",
       "1   120000.0       2          2         2   26      0      1      0      0   \n",
       "2    90000.0       2          2         2   34      0      0      0      0   \n",
       "3    50000.0       2          2         1   37      0      0      0      0   \n",
       "4    50000.0       1          2         1   57      0      0      0      0   \n",
       "\n",
       "   pay_5  pay_6  bill_amt1  bill_amt2  bill_amt3  bill_amt4  bill_amt5  \\\n",
       "0      0      0     3913.0     3102.0      689.0        0.0        0.0   \n",
       "1      0      1     2682.0     1725.0     2682.0     3272.0     3455.0   \n",
       "2      0      0    29239.0    14027.0    13559.0    14331.0    14948.0   \n",
       "3      0      0    46990.0    48233.0    49291.0    28314.0    28959.0   \n",
       "4      0      0     8617.0     5670.0    35835.0    20940.0    19146.0   \n",
       "\n",
       "   bill_amt6  pay_amt1  pay_amt2  pay_amt3  pay_amt4  pay_amt5  pay_amt6  \n",
       "0        0.0       0.0     689.0       0.0       0.0       0.0       0.0  \n",
       "1     3261.0       0.0    1000.0    1000.0    1000.0       0.0    2000.0  \n",
       "2    15549.0    1518.0    1500.0    1000.0    1000.0    1000.0    5000.0  \n",
       "3    29547.0    2000.0    2019.0    1200.0    1100.0    1069.0    1000.0  \n",
       "4    19131.0    2000.0   36681.0   10000.0    9000.0     689.0     679.0  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'feature_eng'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-88-b626cf4277a1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mdataprep\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpipelines\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mAgeBinAdder\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mAgeBinAdder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\UCI-Credit-card-defaults\\dataprep\\pipelines.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mBaseEstimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTransformerMixin\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mFunctionTransformer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mfeature_eng\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# Age Bin Adder\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'feature_eng'"
     ]
    }
   ],
   "source": [
    "from dataprep.pipelines import AgeBinAdder\n",
    "AgeBinAdder().transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pourquoi le module n'est pas trouvable?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TO DO:\n",
    "create a pipeline for different processings of the dataset: 1 is categorical, while the other one is 1-hot encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relève la tête du guidon gros!\n",
    "## Quel est le Big Picture du problème?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notre objectif = prédire quelles sont les personnes les plus susceptibles de défault  \n",
    "ie trouver celles pour qui y=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quelles métriques sont possibles?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Classification accuracy: no beacuase of our imbalanced classes to predict\n",
    "* Logarithmic loss: \n",
    "* Area Under ROC Curve: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quelles métriques pour cet objectif?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* recall = parmi les (y=1), quelle proportion a bien été détectée?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\twang\\AppData\\Local\\Continuum\\anaconda3\\python.exe\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(sys.executable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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