efx_data_engg.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
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  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.5 64-bit"
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   "hash": "db954a3dfd3e2d6d0f27bd3bcb2986fe8f335d9177c092fd9f93a298a3366a1b"
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "$$\n",
    "\\Large \\text{Feature extraction for the developing  a  scorecard  for  credit  analysis}\n",
    "$$"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math,os,sys\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from functools import partial\n",
    "\n",
    "sns.set_style(style= 'darkgrid')\n",
    "sns.set_context(\"notebook\")\n",
    "%matplotlib inline\n",
    "%load_ext lab_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CSV_PATH = r\"\"\"C:\\Users\\M1049231\\Downloads\\UCI_Credit_Card.csv\"\"\"\n",
    "df_raw = pd.read_csv(CSV_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0   1    20000.0    2          2         1   24      2      2     -1     -1   \n",
       "1   2   120000.0    2          2         2   26     -1      2      0      0   \n",
       "2   3    90000.0    2          2         2   34      0      0      0      0   \n",
       "3   4    50000.0    2          2         1   37      0      0      0      0   \n",
       "4   5    50000.0    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "   ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0  ...        0.0        0.0        0.0       0.0     689.0       0.0   \n",
       "1  ...     3272.0     3455.0     3261.0       0.0    1000.0    1000.0   \n",
       "2  ...    14331.0    14948.0    15549.0    1518.0    1500.0    1000.0   \n",
       "3  ...    28314.0    28959.0    29547.0    2000.0    2019.0    1200.0   \n",
       "4  ...    20940.0    19146.0    19131.0    2000.0   36681.0   10000.0   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  default.payment.next.month  \n",
       "0       0.0       0.0       0.0                           1  \n",
       "1    1000.0       0.0    2000.0                           1  \n",
       "2    1000.0    1000.0    5000.0                           0  \n",
       "3    1100.0    1069.0    1000.0                           0  \n",
       "4    9000.0     689.0     679.0                           0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>LIMIT_BAL</th>\n      <th>SEX</th>\n      <th>EDUCATION</th>\n      <th>MARRIAGE</th>\n      <th>AGE</th>\n      <th>PAY_0</th>\n      <th>PAY_2</th>\n      <th>PAY_3</th>\n      <th>PAY_4</th>\n      <th>...</th>\n      <th>BILL_AMT4</th>\n      <th>BILL_AMT5</th>\n      <th>BILL_AMT6</th>\n      <th>PAY_AMT1</th>\n      <th>PAY_AMT2</th>\n      <th>PAY_AMT3</th>\n      <th>PAY_AMT4</th>\n      <th>PAY_AMT5</th>\n      <th>PAY_AMT6</th>\n      <th>default.payment.next.month</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>20000.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>24</td>\n      <td>2</td>\n      <td>2</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>...</td>\n      <td>0.0</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    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>120000.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>26</td>\n      <td>-1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3272.0</td>\n      <td>3455.0</td>\n      <td>3261.0</td>\n      <td>0.0</td>\n      <td>1000.0</td>\n      <td>1000.0</td>\n      <td>1000.0</td>\n      <td>0.0</td>\n      <td>2000.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>90000.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>34</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>14331.0</td>\n      <td>14948.0</td>\n      <td>15549.0</td>\n      <td>1518.0</td>\n      <td>1500.0</td>\n      <td>1000.0</td>\n      <td>1000.0</td>\n      <td>1000.0</td>\n      <td>5000.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>50000.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>37</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>28314.0</td>\n      <td>28959.0</td>\n      <td>29547.0</td>\n      <td>2000.0</td>\n      <td>2019.0</td>\n      <td>1200.0</td>\n      <td>1100.0</td>\n      <td>1069.0</td>\n      <td>1000.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>50000.0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>57</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>20940.0</td>\n      <td>19146.0</td>\n      <td>19131.0</td>\n      <td>2000.0</td>\n      <td>36681.0</td>\n      <td>10000.0</td>\n      <td>9000.0</td>\n      <td>689.0</td>\n      <td>679.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 25 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the target variable \n",
    "y_raw = df_raw['default.payment.next.month']\n",
    "df_raw[\"MARRIAGE\"] = np.where(df_raw.MARRIAGE == 0 , 2, df_raw.MARRIAGE )\n",
    "df_raw[\"EDUCATION\"] = np.where(df_raw.EDUCATION == 0 , 2, df_raw.EDUCATION )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx in range(1,7):\n",
    "    df_raw[f\"BAL_AMT{idx}\"]  = df_raw[f\"BILL_AMT{idx}\"] - df_raw[f\"PAY_AMT{idx}\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 ID       LIMIT_BAL           SEX     EDUCATION      MARRIAGE  \\\n",
       "count  30000.000000    30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean   15000.500000   167484.322667      1.603733      1.854067      1.555467   \n",
       "std     8660.398374   129747.661567      0.489129      0.789340      0.518137   \n",
       "min        1.000000    10000.000000      1.000000      1.000000      1.000000   \n",
       "25%     7500.750000    50000.000000      1.000000      1.000000      1.000000   \n",
       "50%    15000.500000   140000.000000      2.000000      2.000000      2.000000   \n",
       "75%    22500.250000   240000.000000      2.000000      2.000000      2.000000   \n",
       "max    30000.000000  1000000.000000      2.000000      6.000000      3.000000   \n",
       "\n",
       "                AGE         PAY_0         PAY_2         PAY_3         PAY_4  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean      35.485500     -0.016700     -0.133767     -0.166200     -0.220667   \n",
       "std        9.217904      1.123802      1.197186      1.196868      1.169139   \n",
       "min       21.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       28.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%       34.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%       41.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max       79.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "       ...       PAY_AMT4       PAY_AMT5       PAY_AMT6  \\\n",
       "count  ...   30000.000000   30000.000000   30000.000000   \n",
       "mean   ...    4826.076867    4799.387633    5215.502567   \n",
       "std    ...   15666.159744   15278.305679   17777.465775   \n",
       "min    ...       0.000000       0.000000       0.000000   \n",
       "25%    ...     296.000000     252.500000     117.750000   \n",
       "50%    ...    1500.000000    1500.000000    1500.000000   \n",
       "75%    ...    4013.250000    4031.500000    4000.000000   \n",
       "max    ...  621000.000000  426529.000000  528666.000000   \n",
       "\n",
       "       default.payment.next.month       BAL_AMT1      BAL_AMT2      BAL_AMT3  \\\n",
       "count                30000.000000   30000.000000  3.000000e+04  3.000000e+04   \n",
       "mean                     0.221200   45559.750400  4.325791e+04  4.178747e+04   \n",
       "std                      0.415062   73173.789447  7.256594e+04  6.929536e+04   \n",
       "min                      0.000000 -733744.000000 -1.702347e+06 -8.546410e+05   \n",
       "25%                      0.000000     745.000000  3.295000e+02  2.627500e+02   \n",
       "50%                      0.000000   18550.500000  1.810250e+04  1.776900e+04   \n",
       "75%                      0.000000   62241.500000  5.907775e+04  5.629425e+04   \n",
       "max                      1.000000  913727.000000  9.332080e+05  1.542258e+06   \n",
       "\n",
       "           BAL_AMT4       BAL_AMT5       BAL_AMT6  \n",
       "count   30000.00000   30000.000000   30000.000000  \n",
       "mean    38436.87210   35512.013333   33656.257833  \n",
       "std     64200.61083   60553.370054   60151.290836  \n",
       "min   -667000.00000 -414380.000000 -684896.000000  \n",
       "25%       230.00000       0.000000       0.000000  \n",
       "50%     16970.00000   15538.000000   13926.500000  \n",
       "75%     50259.50000   46961.500000   46067.250000  \n",
       "max    841586.00000  877171.000000  911408.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>LIMIT_BAL</th>\n      <th>SEX</th>\n      <th>EDUCATION</th>\n      <th>MARRIAGE</th>\n      <th>AGE</th>\n      <th>PAY_0</th>\n      <th>PAY_2</th>\n      <th>PAY_3</th>\n      <th>PAY_4</th>\n      <th>...</th>\n      <th>PAY_AMT4</th>\n      <th>PAY_AMT5</th>\n      <th>PAY_AMT6</th>\n      <th>default.payment.next.month</th>\n      <th>BAL_AMT1</th>\n      <th>BAL_AMT2</th>\n      <th>BAL_AMT3</th>\n      <th>BAL_AMT4</th>\n      <th>BAL_AMT5</th>\n      <th>BAL_AMT6</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>...</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>3.000000e+04</td>\n      <td>3.000000e+04</td>\n      <td>30000.00000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>15000.500000</td>\n      <td>167484.322667</td>\n      <td>1.603733</td>\n      <td>1.854067</td>\n      <td>1.555467</td>\n      <td>35.485500</td>\n      <td>-0.016700</td>\n      <td>-0.133767</td>\n      <td>-0.166200</td>\n      <td>-0.220667</td>\n      <td>...</td>\n      <td>4826.076867</td>\n      <td>4799.387633</td>\n      <td>5215.502567</td>\n      <td>0.221200</td>\n      <td>45559.750400</td>\n      <td>4.325791e+04</td>\n      <td>4.178747e+04</td>\n      <td>38436.87210</td>\n      <td>35512.013333</td>\n      <td>33656.257833</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>8660.398374</td>\n      <td>129747.661567</td>\n      <td>0.489129</td>\n      <td>0.789340</td>\n      <td>0.518137</td>\n      <td>9.217904</td>\n      <td>1.123802</td>\n      <td>1.197186</td>\n      <td>1.196868</td>\n      <td>1.169139</td>\n      <td>...</td>\n      <td>15666.159744</td>\n      <td>15278.305679</td>\n      <td>17777.465775</td>\n      <td>0.415062</td>\n      <td>73173.789447</td>\n      <td>7.256594e+04</td>\n      <td>6.929536e+04</td>\n      <td>64200.61083</td>\n      <td>60553.370054</td>\n      <td>60151.290836</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>10000.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>21.000000</td>\n      <td>-2.000000</td>\n      <td>-2.000000</td>\n      <td>-2.000000</td>\n      <td>-2.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-733744.000000</td>\n      <td>-1.702347e+06</td>\n      <td>-8.546410e+05</td>\n      <td>-667000.00000</td>\n      <td>-414380.000000</td>\n      <td>-684896.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>7500.750000</td>\n      <td>50000.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>28.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>...</td>\n      <td>296.000000</td>\n      <td>252.500000</td>\n      <td>117.750000</td>\n      <td>0.000000</td>\n      <td>745.000000</td>\n      <td>3.295000e+02</td>\n      <td>2.627500e+02</td>\n      <td>230.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>15000.500000</td>\n      <td>140000.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>34.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1500.000000</td>\n      <td>1500.000000</td>\n      <td>1500.000000</td>\n      <td>0.000000</td>\n      <td>18550.500000</td>\n      <td>1.810250e+04</td>\n      <td>1.776900e+04</td>\n      <td>16970.00000</td>\n      <td>15538.000000</td>\n      <td>13926.500000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>22500.250000</td>\n      <td>240000.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>41.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>4013.250000</td>\n      <td>4031.500000</td>\n      <td>4000.000000</td>\n      <td>0.000000</td>\n      <td>62241.500000</td>\n      <td>5.907775e+04</td>\n      <td>5.629425e+04</td>\n      <td>50259.50000</td>\n      <td>46961.500000</td>\n      <td>46067.250000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>30000.000000</td>\n      <td>1000000.000000</td>\n      <td>2.000000</td>\n      <td>6.000000</td>\n      <td>3.000000</td>\n      <td>79.000000</td>\n      <td>8.000000</td>\n      <td>8.000000</td>\n      <td>8.000000</td>\n      <td>8.000000</td>\n      <td>...</td>\n      <td>621000.000000</td>\n      <td>426529.000000</td>\n      <td>528666.000000</td>\n      <td>1.000000</td>\n      <td>913727.000000</td>\n      <td>9.332080e+05</td>\n      <td>1.542258e+06</td>\n      <td>841586.00000</td>\n      <td>877171.000000</td>\n      <td>911408.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 31 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "df_raw.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_raw[\"BAL_AMT_MEAN\"]  = df_raw[['BAL_AMT1', 'BAL_AMT2', 'BAL_AMT3', 'BAL_AMT4', 'BAL_AMT5', 'BAL_AMT6']].mean(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0',\n",
       "       'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
       "       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n",
       "       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6',\n",
       "       'default.payment.next.month', 'BAL_AMT1', 'BAL_AMT2', 'BAL_AMT3',\n",
       "       'BAL_AMT4', 'BAL_AMT5', 'BAL_AMT6', 'BAL_AMT_MEAN'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "df_raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ENGG_FEAT_COL = ['AGE','LIMIT_BAL','BAL_AMT1', 'BAL_AMT2', 'BAL_AMT3',\n",
    "       'BAL_AMT4', 'BAL_AMT5', 'BAL_AMT6', 'BAL_AMT_MEAN' ]\n",
    "REV_FEAT_COL = [ 'PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']\n",
    "OTHER_FEAT_COL = ['SEX', 'EDUCATION', 'MARRIAGE']\n",
    "FINAL_FEAT = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binning(df, colname, bins:list):\n",
    "\n",
    "    assert len(bins) == 3\n",
    "    res_df = pd.DataFrame()\n",
    "    res_df[f'{colname}_<={bins[0]}'] = np.where(df[colname] <= bins[0] ,1 , 0)\n",
    "    res_df[f'{colname}_{bins[0]}<{bins[1]}'] = np.where(((df[colname] >bins[0]) & (df[colname] <bins[1])) ,1 , 0)\n",
    "    res_df[f'{colname}_{bins[1]}<{bins[2]}'] = np.where(((df[colname] >bins[1]) & (df[colname] <bins[2])) ,1 , 0)\n",
    "    res_df[f'{colname}_>{bins[2]}'] = np.where(df[colname] > bins[2] ,1 , 0)\n",
    "\n",
    "    return res_df\n",
    "\n",
    "def get_bins(df):\n",
    "    _ = df.describe()\n",
    "    bin_range = {}\n",
    "    for col in _.columns:\n",
    "        bin_range[col] = [_[col]['25%'].round(2), _[col]['50%'].round(2), _[col]['75%'].round(2)]\n",
    "    \n",
    "    return bin_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "source": [
    "<h2>BINNING OF ENGG FEATURES"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "['BAL_AMT_MEAN_16987.17<50952.96', 'AGE_34.0<41.0', 'AGE_>41.0', 'BAL_AMT5_>46961.5', 'LIMIT_BAL_<=50000.0', 'BAL_AMT3_262.75<17769.0', 'BAL_AMT2_>59077.75', 'BAL_AMT_MEAN_753.46<16987.17', 'BAL_AMT3_>56294.25', 'BAL_AMT2_329.5<18102.5', 'LIMIT_BAL_140000.0<240000.0', 'BAL_AMT1_18550.5<62241.5', 'BAL_AMT1_<=745.0', 'BAL_AMT5_0.0<15538.0', 'AGE_28.0<34.0', 'BAL_AMT4_230.0<16970.0', 'BAL_AMT3_<=262.75', 'BAL_AMT4_<=230.0', 'BAL_AMT4_16970.0<50259.5', 'BAL_AMT6_<=0.0', 'BAL_AMT2_18102.5<59077.75', 'BAL_AMT6_>46067.25', 'LIMIT_BAL_>240000.0', 'BAL_AMT_MEAN_>50952.96', 'BAL_AMT2_<=329.5', 'BAL_AMT5_<=0.0', 'LIMIT_BAL_50000.0<140000.0', 'BAL_AMT_MEAN_<=753.46', 'AGE_<=28.0', 'BAL_AMT1_745.0<18550.5', 'BAL_AMT6_0.0<13926.5', 'BAL_AMT5_15538.0<46961.5', 'BAL_AMT6_13926.5<46067.25', 'BAL_AMT4_>50259.5', 'BAL_AMT1_>62241.5', 'BAL_AMT3_17769.0<56294.25']\n"
     ]
    }
   ],
   "source": [
    "bin_val = get_bins(df_raw)\n",
    "inital_columns = df_raw.columns.values\n",
    "\n",
    "for i in ENGG_FEAT_COL:\n",
    "    df_raw = pd.concat([df_raw, binning(df_raw, colname= i, bins= bin_val[i])], axis = 1)\n",
    "\n",
    "# add all the new columns to the feature list\n",
    "new_cols = set(df_raw.columns.values) ^ set(inital_columns)\n",
    "assert len(new_cols) == 4*len(ENGG_FEAT_COL) , \"do not continue\"\n",
    "FINAL_FEAT.extend(new_cols)\n",
    "\n",
    "print(FINAL_FEAT)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "4*len(ENGG_FEAT_COL)"
   ]
  },
  {
   "source": [
    "<h3>OPERATING ON 'PAY_i'"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_payment_info(row, cols):\n",
    "    num_defaults = 0\n",
    "    sum_defaults = 0\n",
    "    for i in cols:\n",
    "        if row[i] > 0:\n",
    "            num_defaults = 1 # change this to get total defaluts in last 6 months\n",
    "            sum_defaults += row[i] # gives total months defaults\n",
    "    return pd.Series([num_defaults, sum_defaults])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get number of ontime payments  the category >5, 5, 5>\n",
    "# get sum of payment dues ie, >2 , 2-1, 1>\n",
    "# number of default months \n",
    "# df_raw[REV_FEAT_COL].sum(axis = 1)\n",
    "_get_payment_info = partial(get_payment_info, cols = REV_FEAT_COL)\n",
    "df_raw[['HAS_DEFAULT', 'TOT_DEFAULT']] = df_raw.apply(_get_payment_info, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 ID       LIMIT_BAL           SEX     EDUCATION      MARRIAGE  \\\n",
       "count  30000.000000    30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean   15000.500000   167484.322667      1.603733      1.854067      1.555467   \n",
       "std     8660.398374   129747.661567      0.489129      0.789340      0.518137   \n",
       "min        1.000000    10000.000000      1.000000      1.000000      1.000000   \n",
       "25%     7500.750000    50000.000000      1.000000      1.000000      1.000000   \n",
       "50%    15000.500000   140000.000000      2.000000      2.000000      2.000000   \n",
       "75%    22500.250000   240000.000000      2.000000      2.000000      2.000000   \n",
       "max    30000.000000  1000000.000000      2.000000      6.000000      3.000000   \n",
       "\n",
       "                AGE         PAY_0         PAY_2         PAY_3         PAY_4  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean      35.485500     -0.016700     -0.133767     -0.166200     -0.220667   \n",
       "std        9.217904      1.123802      1.197186      1.196868      1.169139   \n",
       "min       21.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       28.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%       34.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%       41.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max       79.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "       ...  BAL_AMT6_<=0.0  BAL_AMT6_0.0<13926.5  BAL_AMT6_13926.5<46067.25  \\\n",
       "count  ...    30000.000000          30000.000000                30000.00000   \n",
       "mean   ...        0.279367              0.220633                    0.25000   \n",
       "std    ...        0.448695              0.414681                    0.43302   \n",
       "min    ...        0.000000              0.000000                    0.00000   \n",
       "25%    ...        0.000000              0.000000                    0.00000   \n",
       "50%    ...        0.000000              0.000000                    0.00000   \n",
       "75%    ...        1.000000              0.000000                    0.25000   \n",
       "max    ...        1.000000              1.000000                    1.00000   \n",
       "\n",
       "       BAL_AMT6_>46067.25  BAL_AMT_MEAN_<=753.46  \\\n",
       "count         30000.00000            30000.00000   \n",
       "mean              0.25000                0.25000   \n",
       "std               0.43302                0.43302   \n",
       "min               0.00000                0.00000   \n",
       "25%               0.00000                0.00000   \n",
       "50%               0.00000                0.00000   \n",
       "75%               0.25000                0.25000   \n",
       "max               1.00000                1.00000   \n",
       "\n",
       "       BAL_AMT_MEAN_753.46<16987.17  BAL_AMT_MEAN_16987.17<50952.96  \\\n",
       "count                   30000.00000                     30000.00000   \n",
       "mean                        0.25000                         0.25000   \n",
       "std                         0.43302                         0.43302   \n",
       "min                         0.00000                         0.00000   \n",
       "25%                         0.00000                         0.00000   \n",
       "50%                         0.00000                         0.00000   \n",
       "75%                         0.25000                         0.25000   \n",
       "max                         1.00000                         1.00000   \n",
       "\n",
       "       BAL_AMT_MEAN_>50952.96   HAS_DEFAULT   TOT_DEFAULT  \n",
       "count             30000.00000  30000.000000  30000.000000  \n",
       "mean                  0.25000      0.335633      1.687700  \n",
       "std                   0.43302      0.472219      3.609322  \n",
       "min                   0.00000      0.000000      0.000000  \n",
       "25%                   0.00000      0.000000      0.000000  \n",
       "50%                   0.00000      0.000000      0.000000  \n",
       "75%                   0.25000      1.000000      2.000000  \n",
       "max                   1.00000      1.000000     36.000000  \n",
       "\n",
       "[8 rows x 70 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>LIMIT_BAL</th>\n      <th>SEX</th>\n      <th>EDUCATION</th>\n      <th>MARRIAGE</th>\n      <th>AGE</th>\n      <th>PAY_0</th>\n      <th>PAY_2</th>\n      <th>PAY_3</th>\n      <th>PAY_4</th>\n      <th>...</th>\n      <th>BAL_AMT6_&lt;=0.0</th>\n      <th>BAL_AMT6_0.0&lt;13926.5</th>\n      <th>BAL_AMT6_13926.5&lt;46067.25</th>\n      <th>BAL_AMT6_&gt;46067.25</th>\n      <th>BAL_AMT_MEAN_&lt;=753.46</th>\n      <th>BAL_AMT_MEAN_753.46&lt;16987.17</th>\n      <th>BAL_AMT_MEAN_16987.17&lt;50952.96</th>\n      <th>BAL_AMT_MEAN_&gt;50952.96</th>\n      <th>HAS_DEFAULT</th>\n      <th>TOT_DEFAULT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>...</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.00000</td>\n      <td>30000.00000</td>\n      <td>30000.00000</td>\n      <td>30000.00000</td>\n      <td>30000.00000</td>\n      <td>30000.00000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>15000.500000</td>\n      <td>167484.322667</td>\n      <td>1.603733</td>\n      <td>1.854067</td>\n      <td>1.555467</td>\n      <td>35.485500</td>\n      <td>-0.016700</td>\n      <td>-0.133767</td>\n      <td>-0.166200</td>\n      <td>-0.220667</td>\n      <td>...</td>\n      <td>0.279367</td>\n      <td>0.220633</td>\n      <td>0.25000</td>\n      <td>0.25000</td>\n      <td>0.25000</td>\n      <td>0.25000</td>\n      <td>0.25000</td>\n      <td>0.25000</td>\n      <td>0.335633</td>\n      <td>1.687700</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>8660.398374</td>\n      <td>129747.661567</td>\n      <td>0.489129</td>\n      <td>0.789340</td>\n      <td>0.518137</td>\n      <td>9.217904</td>\n      <td>1.123802</td>\n      <td>1.197186</td>\n      <td>1.196868</td>\n      <td>1.169139</td>\n      <td>...</td>\n      <td>0.448695</td>\n      <td>0.414681</td>\n      <td>0.43302</td>\n      <td>0.43302</td>\n      <td>0.43302</td>\n      <td>0.43302</td>\n      <td>0.43302</td>\n      <td>0.43302</td>\n      <td>0.472219</td>\n      <td>3.609322</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>10000.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>21.000000</td>\n      <td>-2.000000</td>\n      <td>-2.000000</td>\n      <td>-2.000000</td>\n      <td>-2.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>7500.750000</td>\n      <td>50000.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>28.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>15000.500000</td>\n      <td>140000.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>34.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>22500.250000</td>\n      <td>240000.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>41.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.25000</td>\n      <td>0.25000</td>\n      <td>0.25000</td>\n      <td>0.25000</td>\n      <td>0.25000</td>\n      <td>0.25000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>30000.000000</td>\n      <td>1000000.000000</td>\n      <td>2.000000</td>\n      <td>6.000000</td>\n      <td>3.000000</td>\n      <td>79.000000</td>\n      <td>8.000000</td>\n      <td>8.000000</td>\n      <td>8.000000</td>\n      <td>8.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.00000</td>\n      <td>1.00000</td>\n      <td>1.00000</td>\n      <td>1.00000</td>\n      <td>1.00000</td>\n      <td>1.00000</td>\n      <td>1.000000</td>\n      <td>36.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 70 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "df_raw.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw['TOT_DEFAULT_0'] = np.where(df_raw.TOT_DEFAULT == 0 ,1 , 0)\n",
    "df_raw['TOT_DEFAULT_0<2'] = np.where(((df_raw.TOT_DEFAULT >0) & (df_raw.TOT_DEFAULT <2)) ,1 , 0)\n",
    "df_raw['TOT_DEFAULT_>2'] = np.where(df_raw.TOT_DEFAULT > 2 ,1 , 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "FINAL_FEAT.extend(['HAS_DEFAULT','TOT_DEFAULT_0','TOT_DEFAULT_0<2','TOT_DEFAULT_>2'])\n",
    "\n",
    "#DONE with pay_i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "['BAL_AMT_MEAN_16987.17<50952.96', 'AGE_34.0<41.0', 'AGE_>41.0', 'BAL_AMT5_>46961.5', 'LIMIT_BAL_<=50000.0', 'BAL_AMT3_262.75<17769.0', 'BAL_AMT2_>59077.75', 'BAL_AMT_MEAN_753.46<16987.17', 'BAL_AMT3_>56294.25', 'BAL_AMT2_329.5<18102.5', 'LIMIT_BAL_140000.0<240000.0', 'BAL_AMT1_18550.5<62241.5', 'BAL_AMT1_<=745.0', 'BAL_AMT5_0.0<15538.0', 'AGE_28.0<34.0', 'BAL_AMT4_230.0<16970.0', 'BAL_AMT3_<=262.75', 'BAL_AMT4_<=230.0', 'BAL_AMT4_16970.0<50259.5', 'BAL_AMT6_<=0.0', 'BAL_AMT2_18102.5<59077.75', 'BAL_AMT6_>46067.25', 'LIMIT_BAL_>240000.0', 'BAL_AMT_MEAN_>50952.96', 'BAL_AMT2_<=329.5', 'BAL_AMT5_<=0.0', 'LIMIT_BAL_50000.0<140000.0', 'BAL_AMT_MEAN_<=753.46', 'AGE_<=28.0', 'BAL_AMT1_745.0<18550.5', 'BAL_AMT6_0.0<13926.5', 'BAL_AMT5_15538.0<46961.5', 'BAL_AMT6_13926.5<46067.25', 'BAL_AMT4_>50259.5', 'BAL_AMT1_>62241.5', 'BAL_AMT3_17769.0<56294.25', 'HAS_DEFAULT', 'TOT_DEFAULT_0', 'TOT_DEFAULT_0<2', 'TOT_DEFAULT_>2'] \n\n 40\n"
     ]
    }
   ],
   "source": [
    "print(FINAL_FEAT, \"\\n\\n\",len(FINAL_FEAT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig,ax = plt.subplots(3,2,figsize=(13, 7))\n",
    "# # sns.catplot(x = 'default.payment.next.month' , y = 'BAL_AMT_MEAN', data = df_raw, kind = \"box\")\n",
    "# l,r = 0,0\n",
    "# for idx in range(1,7):\n",
    "    \n",
    "#     sns.kdeplot(df_raw[df_raw['default.payment.next.month'] == 0][f'BAL_AMT{idx}'], ax = ax[l,r])\n",
    "#     sns.kdeplot(df_raw[df_raw['default.payment.next.month'] == 1][f'BAL_AMT{idx}'], ax = ax[l,r])\n",
    "#     if r == 0:\n",
    "#         r += 1\n",
    "#     elif r == 1:\n",
    "#         l +=1\n",
    "#         r = 0\n",
    "\n",
    "#     else:\n",
    "#         assert False, 'this can not happen'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.kdeplot(df_raw[df_raw['default.payment.next.month'] == 0][f'LIMIT_BAL'],)\n",
    "# sns.kdeplot(df_raw[df_raw['default.payment.next.month'] == 1][f'LIMIT_BAL'],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "source": [
    "<h3> Let us handle the OTHER_FEAT"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to create dummy variables\n",
    "def dummy_creation(df, columns_list):\n",
    "    df_dummies = []\n",
    "    for col in columns_list:\n",
    "        df_dummies.append(pd.get_dummies(df[col], prefix = col, prefix_sep = ':'))\n",
    "    df_dummies = pd.concat(df_dummies, axis = 1)\n",
    "    df = pd.concat([df, df_dummies], axis = 1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "the other features are :  ['SEX', 'EDUCATION', 'MARRIAGE']\nnew columns were : {'EDUCATION:5', 'MARRIAGE:1', 'EDUCATION:2', 'SEX:2', 'MARRIAGE:2', 'EDUCATION:1', 'EDUCATION:4', 'EDUCATION:3', 'SEX:1', 'MARRIAGE:3', 'EDUCATION:6'}\n"
     ]
    }
   ],
   "source": [
    "inital_columns = df_raw.columns.values\n",
    "\n",
    "# apply to our final four categorical variables\n",
    "print(\"the other features are : \", OTHER_FEAT_COL)\n",
    "df_raw = dummy_creation(df_raw, OTHER_FEAT_COL)\n",
    "\n",
    "# drop_cols = [f'{i}:1' for i in OTHER_FEAT_COL]\n",
    "# df_raw.drop(drop_cols, axis= 1, inplace= True)\n",
    "\n",
    "new_cols = set(df_raw.columns.values) ^ set(inital_columns)\n",
    "print(\"new columns were :\", new_cols)\n",
    "FINAL_FEAT.extend(new_cols)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0',\n",
       "       'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
       "       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n",
       "       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6',\n",
       "       'default.payment.next.month', 'BAL_AMT1', 'BAL_AMT2', 'BAL_AMT3',\n",
       "       'BAL_AMT4', 'BAL_AMT5', 'BAL_AMT6', 'BAL_AMT_MEAN', 'AGE_<=28.0',\n",
       "       'AGE_28.0<34.0', 'AGE_34.0<41.0', 'AGE_>41.0', 'LIMIT_BAL_<=50000.0',\n",
       "       'LIMIT_BAL_50000.0<140000.0', 'LIMIT_BAL_140000.0<240000.0',\n",
       "       'LIMIT_BAL_>240000.0', 'BAL_AMT1_<=745.0', 'BAL_AMT1_745.0<18550.5',\n",
       "       'BAL_AMT1_18550.5<62241.5', 'BAL_AMT1_>62241.5', 'BAL_AMT2_<=329.5',\n",
       "       'BAL_AMT2_329.5<18102.5', 'BAL_AMT2_18102.5<59077.75',\n",
       "       'BAL_AMT2_>59077.75', 'BAL_AMT3_<=262.75', 'BAL_AMT3_262.75<17769.0',\n",
       "       'BAL_AMT3_17769.0<56294.25', 'BAL_AMT3_>56294.25', 'BAL_AMT4_<=230.0',\n",
       "       'BAL_AMT4_230.0<16970.0', 'BAL_AMT4_16970.0<50259.5',\n",
       "       'BAL_AMT4_>50259.5', 'BAL_AMT5_<=0.0', 'BAL_AMT5_0.0<15538.0',\n",
       "       'BAL_AMT5_15538.0<46961.5', 'BAL_AMT5_>46961.5', 'BAL_AMT6_<=0.0',\n",
       "       'BAL_AMT6_0.0<13926.5', 'BAL_AMT6_13926.5<46067.25',\n",
       "       'BAL_AMT6_>46067.25', 'BAL_AMT_MEAN_<=753.46',\n",
       "       'BAL_AMT_MEAN_753.46<16987.17', 'BAL_AMT_MEAN_16987.17<50952.96',\n",
       "       'BAL_AMT_MEAN_>50952.96', 'HAS_DEFAULT', 'TOT_DEFAULT', 'TOT_DEFAULT_0',\n",
       "       'TOT_DEFAULT_0<2', 'TOT_DEFAULT_>2', 'SEX:1', 'SEX:2', 'EDUCATION:1',\n",
       "       'EDUCATION:2', 'EDUCATION:3', 'EDUCATION:4', 'EDUCATION:5',\n",
       "       'EDUCATION:6', 'MARRIAGE:1', 'MARRIAGE:2', 'MARRIAGE:3'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "df_raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['BAL_AMT_MEAN_16987.17<50952.96',\n",
       " 'AGE_34.0<41.0',\n",
       " 'AGE_>41.0',\n",
       " 'LIMIT_BAL_<=50000.0',\n",
       " 'BAL_AMT_MEAN_753.46<16987.17',\n",
       " 'LIMIT_BAL_140000.0<240000.0',\n",
       " 'AGE_28.0<34.0',\n",
       " 'LIMIT_BAL_>240000.0',\n",
       " 'BAL_AMT_MEAN_>50952.96',\n",
       " 'LIMIT_BAL_50000.0<140000.0',\n",
       " 'BAL_AMT_MEAN_<=753.46',\n",
       " 'AGE_<=28.0',\n",
       " 'TOT_DEFAULT_0',\n",
       " 'TOT_DEFAULT_0<2',\n",
       " 'TOT_DEFAULT_>2',\n",
       " 'EDUCATION:5',\n",
       " 'MARRIAGE:1',\n",
       " 'EDUCATION:2',\n",
       " 'SEX:2',\n",
       " 'MARRIAGE:2',\n",
       " 'EDUCATION:1',\n",
       " 'EDUCATION:4',\n",
       " 'EDUCATION:3',\n",
       " 'SEX:1',\n",
       " 'MARRIAGE:3',\n",
       " 'EDUCATION:6']"
      ]
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "FINAL_FEAT_v2 = [i for i in FINAL_FEAT if 'BAL_AMT_' in i or 'BAL_AMT' not in i]\n",
    "FINAL_FEAT_v2.remove('HAS_DEFAULT')\n",
    "FINAL_FEAT_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "len(FINAL_FEAT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# added 8 features \n",
    "# pay_i added 4 features \n",
    "# ENGG_COL added 36 features\n",
    "# total should be 48 features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import efx_utils\n",
    "\n",
    "efx_utils._save(df_raw, \"X\")\n",
    "efx_utils._save(y_raw.values.reshape(30000,), \"y\")\n",
    "efx_utils._save(FINAL_FEAT, \"FINAL_FEAT\")\n",
    "efx_utils._save(FINAL_FEAT_v2, \"FINAL_FEAT_V2\")"
   ]
  },
  {
   "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": "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": []
  },
  {
   "source": [
    "## Modeling \n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, RepeatedStratifiedKFold, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_curve, roc_auc_score, confusion_matrix, precision_recall_curve, auc\n",
    "from sklearn.feature_selection import f_classif\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from scipy.stats import chi2_contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['AGE_28.0<34.0', 'LIMIT_BAL_>240000.0',\n",
       "       'BAL_AMT_MEAN_16987.17<50952.96', 'BAL_AMT_MEAN_<=753.46',\n",
       "       'AGE_34.0<41.0', 'LIMIT_BAL_<=50000.0', 'BAL_AMT_MEAN_>50952.96',\n",
       "       'BAL_AMT_MEAN_753.46<16987.17', 'AGE_<=28.0',\n",
       "       'LIMIT_BAL_140000.0<240000.0', 'AGE_>41.0',\n",
       "       'LIMIT_BAL_50000.0<140000.0', 'HAS_DEFAULT', 'TOT_DEFAULT_0',\n",
       "       'TOT_DEFAULT_0<2', 'TOT_DEFAULT_>2', 'MARRIAGE:3', 'EDUCATION:5',\n",
       "       'EDUCATION:6', 'EDUCATION:4', 'EDUCATION:3', 'MARRIAGE:2', 'SEX:2',\n",
       "       'EDUCATION:2'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 66
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mean AUROC: 0.7524\nGini: 0.5048\n"
     ]
    }
   ],
   "source": [
    "# define modeling pipeline\n",
    "model = LogisticRegression(max_iter=1000, class_weight = 'balanced')\n",
    "\n",
    "pipeline = Pipeline(steps=[('model', model)])\n",
    "\n",
    "# define cross-validation criteria. RepeatedStratifiedKFold automatially takes care of the class imbalance while splitting\n",
    "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=3, random_state=1)\n",
    "\n",
    "# fit and evaluate the logistic regression pipeline with cross-validation as defined in cv\n",
    "scores = cross_val_score(pipeline, X_trian, y_train, scoring = 'roc_auc', cv = cv)\n",
    "AUROC = np.mean(scores)\n",
    "GINI = AUROC * 2 - 1\n",
    "\n",
    "# print the mean AUROC score and Gini\n",
    "print('Mean AUROC: %.4f' % (AUROC))\n",
    "print('Gini: %.4f' % (GINI))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Pipeline(steps=[('model',\n",
       "                 LogisticRegression(class_weight='balanced', max_iter=1000))])"
      ]
     },
     "metadata": {},
     "execution_count": 68
    }
   ],
   "source": [
    "pipeline.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = X_train.columns.values\n",
    "# Create a summary table of our logistic regression model\n",
    "summary_table = pd.DataFrame(columns = ['Feature name'], data = feature_name)\n",
    "# Create a new column in the dataframe, called 'Coefficients', with row values the transposed coefficients from the 'LogisticRegression' model\n",
    "summary_table['Coefficients'] = np.transpose(pipeline['model'].coef_)\n",
    "# Increase the index of every row of the dataframe with 1 to store our model intercept in 1st row\n",
    "summary_table.index = summary_table.index + 1\n",
    "# Assign our model intercept to this new row\n",
    "summary_table.loc[0] = ['Intercept', pipeline['model'].intercept_[0]]\n",
    "# Sort the dataframe by index\n",
    "summary_table.sort_index(inplace = True)\n",
    "# summary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_table[\"coff_mag\"] = abs(summary_table.Coefficients)\n",
    "summary_table.sort_values('coff_mag', ascending= False, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Feature name', ylabel='coff_mag'>"
      ]
     },
     "metadata": {},
     "execution_count": 71
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 936x504 with 1 Axes>",
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type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n  <g id=\"patch_1\">\r\n   <path d=\"M 0 590.992736 \r\nL 779.85125 590.992736 \r\nL 779.85125 0 \r\nL 0 0 \r\nz\r\n\" style=\"fill:none;\"/>\r\n  </g>\r\n  <g id=\"axes_1\">\r\n   <g id=\"patch_2\">\r\n    <path d=\"M 47.25125 387.72 \r\nL 772.65125 387.72 \r\nL 772.65125 7.2 \r\nL 47.25125 7.2 \r\nz\r\n\" style=\"fill:#eaeaf2;\"/>\r\n   </g>\r\n   <g id=\"matplotlib.axis_1\">\r\n    <g id=\"xtick_1\">\r\n     <g id=\"line2d_1\">\r\n      <path clip-path=\"url(#p384897ad00)\" d=\"M 80.223977 387.72 \r\nL 80.223977 7.2 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n     </g>\r\n     <g id=\"text_1\">\r\n      <!-- EDUCATION:5 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(64.400888 464.500601)rotate(-60)scale(0.11 -0.11)\">\r\n       <defs>\r\n        <path d=\"M 506 0 \r\nL 506 4581 \r\nL 3819 4581 \r\nL 3819 4041 \r\nL 1113 4041 \r\nL 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(13, 7))\n",
    "plt.xticks(rotation=60)\n",
    "sns.lineplot(x = 'Feature name' , y = 'coff_mag', data= summary_table, ax = ax, )"
   ]
  },
  {
   "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": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make preditions on our test set\n",
    "y_hat_test = pipeline.predict(X_test)\n",
    "# get the predicted probabilities\n",
    "y_hat_test_proba = pipeline.predict_proba(X_test)\n",
    "# select the probabilities of only the positive class (class 1 - default) \n",
    "y_hat_test_proba = y_hat_test_proba[:][: , 1]\n",
    "\n",
    "# we will now create a new DF with actual classes and the predicted probabilities\n",
    "# create a temp y_test DF to reset its index to allow proper concaternation with y_hat_test_proba\n",
    "y_test_temp = pd.DataFrame(y_test.copy())\n",
    "y_test_temp.reset_index(drop = True, inplace = True)\n",
    "y_test_proba = pd.concat([y_test_temp, pd.DataFrame(y_hat_test_proba)], axis = 1)\n",
    "# check the shape to make sure the number of rows is same as that in y_test\n",
    "# y_test_proba.shape\n",
    "\n",
    "# Rename the columns\n",
    "y_test_proba.columns = ['y_test_class_actual', 'y_hat_test_proba']\n",
    "# Makes the index of one dataframe equal to the index of another dataframe.\n",
    "y_test_proba.index = X_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0.597     , 0.18183333],\n",
       "       [0.0865    , 0.13466667]])"
      ]
     },
     "metadata": {},
     "execution_count": 73
    }
   ],
   "source": [
    "# assign a threshold value to differentiate good with bad\n",
    "tr = 0.5\n",
    "# crate a new column for the predicted class based on predicted probabilities and threshold\n",
    "# We will determine this optimat threshold later in this project\n",
    "y_test_proba['y_test_class_predicted'] = np.where(y_test_proba['y_hat_test_proba'] > tr, 1, 0)\n",
    "# create the confusion matrix\n",
    "confusion_matrix(y_test_proba['y_test_class_actual'], y_test_proba['y_test_class_predicted'], normalize = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "# get the values required to plot a ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test_proba['y_test_class_actual'], y_test_proba['y_hat_test_proba'])\n",
    "# plot the ROC curve\n",
    "plt.plot(fpr, tpr)\n",
    "# plot a secondary diagonal line, with dashed line style and black color to represent a no-skill classifier\n",
    "plt.plot(fpr, fpr, linestyle = '--', color = 'k')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7371241193658322"
      ]
     },
     "metadata": {},
     "execution_count": 75
    }
   ],
   "source": [
    "# Calculate the Area Under the Receiver Operating Characteristic Curve (AUROC) on our test set\n",
    "AUROC = roc_auc_score(y_test_proba['y_test_class_actual'], y_test_proba['y_hat_test_proba'])\n",
    "AUROC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.47424823873166444"
      ]
     },
     "metadata": {},
     "execution_count": 76
    }
   ],
   "source": [
    "\n",
    "# calculate Gini from AUROC\n",
    "Gini = AUROC * 2 - 1\n",
    "Gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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KCCIi4lJAEBERQAFBRERcCggiIgIoIIiIiCvo5YsbY84EbgDygduttTNS0g1wD1AObADOsNbWeZknERFJL+uAYIw5ChgM+GLnrLXzM1y/O3AzUA00A68bY1601r7vpvuAJ4AfWGufNsb8CrgemLozBRERkV2TVUAwxjwA/CfwLyDqno4C7QYE4FjgBWvtZvc1HgFOBX7hph8ENFprn3aPbwHKOpN5rwU31FD44SNEgeZ9TiVUWd3TWRIR8Uy2NYTxwD7W2oZOvPYwYH3C8Xrg0ITjrwIbjDH3A2OA94ArO/H6ngpuqKFs/ikQDQNQ9P486k+Zr6AgIv1WtgHh004GA0hoWkoQSbn30cDXrbWLjTG/BH4LnJftDYYMKelkloCNhQAMHjwAKkrbv+6Nx+LBAIBomPLls+CAozt/z16iIlN5+ymVOTeozF0j24DwT2PMX4G/A9tjJzP1IQBrcWoWMUOBdQnHG4CV1trF7vFfgEeyzA8AtbUNRCLRji9MkL+liUHAlhVv4n/7MVp3Pzztt/6S7SEK2RHVokDoM0v9pq2dul9vUVFRyqY+mvedpTLnBpU5e36/L+MX6WwDwuHu78kJ5zrqQ3geuMkYUwE0AhOBixPSXwcqjDGjrbXvAN8GarLMzy4rfeEaiIQhWED9SQ+1CQrNe06gcPmDSeeihYO7K3siIt0uq4BgrT0GwBgTBHzW2tYsnrPWGDMdeBFn2Olsa+2bxpgFwI1uM9F3gVnGmAHAGuDsnS1IZ/kiIQCioWYKP3yEhpSA4G+qw4cT9WK1BP/WNQx46SfqYBaRfskXjXbc5GKM2Q24H2ekURB4Gfi+tXZdxid6ZyTw8U41Gf3rSQY9c2n8OArgD1L/3UcJVVZTsHwuBR8twNdUT3DzCnzh7UnNRg4/W4++leb9ztrVcnQbVatzg8qcG7qgyWhPYFVqerZNRr8HFgHfAwLAVcDdwEmdzlEv4wOikTB5axeS/9FTFC/9Qzwtkl+KL5x8LUCUCKUvXQ/Qp4KCiEgm2QaEUdba0xKOf2aMWe5FhnqEP0CksJzSRb9KOzQqsQ7ic3+iRCl9eRrhIfuo+UhE+oVs1zLKM8YUxg6MMcUkf072ac0jjo33GSTytzSwbczlhHY7kNYh+yal+QCiTs1CRKQ/yDYg/BV43hhzoTHmQuBZOjlEtDcr+PQFfM1b0qREiRYMpH7Sk3xxxrO0Dh2bkOLobEAIbqihqOb3BDd024AqEZGsZDvK6JfGmDXAf+EEkfuAOR7mq3uFWwluWhY/jFd9Avm07n54/Hxo8Cjy1r+R0GwE+atfpmD53A77EgqWz6XondkE61a6Z3w07/lNth90mZqcRKRXyBgQjDEDrbVbjDGDgcfdn5hyYLOXmes+UQgUuI984PPTPHJCmw/r5n1OpWj5g/GhqLGgUPT+XzIGhILlcyl9yVmzb0fHdJSCj5+m4ONnadnzuKTrw8UVXTq0tWD5XFg6g/KmRpr/43S2HTGtS15XRPqXjmoIL+EsQvc5bftWozgjjvqFQP2/AGja/2yazMS0H8ahympaho4lf/0bwI5/kODnywhuqGn3A7y4ZiaQvJZH4oil/I+fbvOcog8fov7kh3c5KBS89yClr/wEcN6s4iUzKXh3Dv5wKxF/AH8kRBQ/LaNOomHCnbt0LxHp2zIGBGvtQe7vfr2Rjg8IfPEJAA2HT4f8Ae1eu+2IaeTPP4VoNLzjQz0SYuDfzyZcWoU/0kqobC+2H3QZ+R89TeEHf8HfXB9/fiyIpAsOiaLhFkpenkb96c8Abt/D23cTrP8o/vrtBYvYKq3+xg3krX41/vrxKB5udmo4kYh7LkzhivnkrX+TunMWAVD8+i0ULbsfQi1EioZAQSkRfx6BLavxhbbTWnUkAHnr3qB12Fi2fGduu/9mqXnLW7uw3SVDRKTnZDsx7cvAWGvtE8aY24HRwNXW2ne9zmA7RtJFE9NiokC0YBC1kzseTTtwwWQKUr7VJ85oTv3Qj6Wlq2IlXpfudaL4ieSXEGhJ6fROmEwXE9xQw4DXbyHPrcGk3iPxtVPFrmutOJBo8RDyP/lH2utSyxETLq3Cv+1zCDcBECmuJPzlAwEIbLb4Gz4jEiwk0LyjlTEcLMYf2u5e/yW2HfqjtE1vxa/fQuEHD0HrdnzhJmJrJEYKyqibvKzN9TGJk3cKls9lwMJf4WveQnjgcBom3NEvA5ImaeWGnp6Ydh/wrDHmP3FmK98O3AUc1ekc9WKh8r2zui5cXNHmwzX1ceqHZmxG9LbRF1P87myi4RbAR2jgCIJbViU9J7GPAiIEWra0+SCPRkKUPvcDfE2bIRIiMmgkwdr30+TH6bwu3LQEGj5L+2HuS7g+f9PSjorfNi9AYOuapHOBbRsIpATNYHhb8nNC2xKu30TpS1MpeWkqEX8B/khz2vslBstAcz1DZlSlzWEUHxBhSJrXCG5ZRdmjJ7UzbtpP6/DxWdd4RPqTbJuChlhrbwe+Bcyz1t4HFHuWq54SCXd8DU7nsvOh44iS/ltz4vmWoWOp/+6jbDtiGvUnP0zjYddTP/ExGibcAYF89zpfPCglfrNPrUHEBLaswt+yBX9oG8Ha9+PXxoJLFCCQx/aDLoMfraB+4uM07Xc24cIhRPET9gXa5DMKtFZ8rc251Psnpvuy/EmU6bpApBk/xH/SvUZ8famUH+e6KH63FpH6/MSf1DTnOEL+6pcZMqOKITNHUP6nMU6nvEgOyLaGkG+MycMJCOe6E9N2YjOC3iXxW74PyNu4JKshpKHKaraNuYziJTPTfljGXjM8aE8Amr/yraSRPaHK6qTmivqTH05qVy97cBzBLauS8pgacNqbUZ14PlxaxdbjZsTvFaqsdhbxO/rW+DXBDTWUPn81gS8+du4RLKTh67+k8L37KVwxP2NQSFfm1GaxdM+JpaW7tqMmrcTr013b0blMNbs2x9Fwcs2ltCrexyLSH2UbEB4HNgFLrbU1xphlwDzvstVzCj5akNX6RNuOmEZ40Ajn+r2OB4jPM4g1D2099ndZtVOnBoiGCXdQ9tgkt1lpx4dhOFAAwSICzfXt9k8kfnAmBoNM9677/qttOnsbKqtpOuBc8tYuJLB5BflrFxIauAehymqCny+nea/jKfhoAXlrF+KLtLQJEumCWLhgMP7WrfgirW2ax7LpCYr4C/BFWvARzer69l43tV8l9TmJvxMFtq5JaqJqHX6UmpakX8mqUxnAGFMFrLXWRo0xX+vBDmXook7lCP540wI4Hw5bj75tlxas66pRNLHX8TVviX8AN+93VtKchlSh8r0JNGwgNHA4jUffmnR/LzveYiOg/I0baNr3ezTvdxaDHj6R4Kb3iOQPouWrJyTNq0j9Nyp+/RaK3r13R4d0oAB/eEcfQnjgyKRO4ILlcyl6/y/4Gj/D37gRSG3qc/oQAkTi727UFyBaMhRaGvAnBFRI37mf/GptxYO0L0jd5at6zegpdbDmBq86lTMGBGPM9621fzbGXJMu3Vr7207nqGuMZCcDQunTl1H40d+Bts0HXREQukNs1jNAqOIA/Ntr4wGjPfpPkyzdB/igh08kuHFp0nWZagyptZyYnvwb0vucG3pqlFFs2M0Bnb5zLxXcuCT+ON1/8mybjHpS835n9fo89napzXQAX0x6ss11ZfOO2dEMmCCxDyP1S0XpS1PJW/eGJvpJn5NxlJG19mfu7/OBP7m/rwUedx/3ObGOXmg7ugZwJmGJuOrPfJFtYy4nkj+QiD8vbV9NolhwKFwxn+LXb+mGHIp0nayGnRpj/hv4uXtYDFxvjLnBs1x5KPylHctYt37pa4SLd0veJnN7bY/kS3qvbUdMY/NF77P5so+pnbKGcHEFsOMLReIP7PhbKloykyEzhjNkRhVl844BtNqt9G7ZjjI6GRgDYK1dY4w5CqgB/tujfHWLrd+6h7zVr1D60tT4f+bYiCGR9tSdv4SBT5xF3upXSKwrpBvOGhsRFaxbyZAZVW2GBWsYq/Qm2QaEPGtta8JxCyQMz+lLEjrRo4GCeFt8bPio2uYlG6nDTYMbauKzn1P7FtrrawhsXUP5jCq29YGBDJIbsg0I/zTGzMXZAyEKnAu84VmuukvQWfJanbSyq0KV1YSLKwhs25TVJML4QoMQn/iGe65l1CnqkJYeke3SFVcCn+GsYfQ/7uMfeJUpbyXXEES6St35SwiXVhEFIviI+JzvW5n6GtItp1G4Yj5DZlRRfvdXurkEkuuy3TGtEbjGGFNura3zOE8eS/j+poAgXSy1T2DgE2eRt/Z1opEwPreVNd3Ks2lnRkda1KQk3SqrgGCMMcB8oMwYcwjwD+C71toPvcyc53ztzU0V6RrplrYonzECf5vZ1Y7U/obUJqVIoIDmr12oXe/EE9k2Gd0FXA1stNauc4//6FWmPJXlUh0iXqmb8okzfJVAm2ak1JVz26wEG26meMlMZzXWGVXOQogawipdpDPLXz8XO7DWzgQGepMlkdwQCwyxn21jLnf3ceg4MMR+Yns7lM8Y0c25l/4o24AQNcYU4v6NGmMq6bP7KauGIL3TtiOmUTtlNS3Dj2pTY8gUIAAChJ2VWG8aRPmMKu3hIDsl24BwN/AMsJsx5lZgETDTs1x5KaHJSFVt6Y22fGcutVPWUD/xcSL+/LRNSumCQ+w/c6zfYciMKsr/uE+35Vv6vmxHGc0xxqwETgDygIuttc96mjOPOMslO8oeP536kx7ql3vrSt8Xqqxm82X/jh+X/2kM/m2bgOS9HDJt8hNobYjv4aD9G6Qj2Y4y+oe19hvAKx7nx3OBxvU7DsKt5K1dqIAgfULd+TtW6g1uqGHgoyfjT9ksKP3yGU7wyF/9MoNnVGnim7Qr2yajMmPMAE9z0k0iAyp3HATyaN398J7LjMhOClVWs3nKauonPk7r0LFE8Wfc4ztxD+rCFfMp/9OYbsur9B3ZLl3RCHxijHkXaIidtNZ+J9OTjDFnAjcA+cDt1toZ7Vx3AvB7a+2e6dK7UmRARfyxmoukrwtVVvPFKY/Gjyv+7ySi62rS7lmduO1qYNumeFNSJK+Euov79pQi6RodBgRjzP44eyo/A6zJ9oWNMbsDNwPVQDPwujHmRWvt+ynXfRlnOYzumSWmQUbSn138ArWbtjrbkr4zGyItbfobUmdKx/oZwuV7U3/miz2Ra+klMjYZGWPOx+k3OA34IbDBWnt/7KeD1z4WeMFau9ld+uIR4NQ0181mx14LnvM3fhZ/XPb46RppJP3StiOmUXvZv90JcMnNSdB22KoPZ4nuwTOqKHnuqu7OrvQSHdUQrgL2t9auM8YcjvON/5ksX3sYkNCDy3rg0MQLjDFXAW/jDGPtNHdv0M5p3jHKyBdupby+Bg44emdu3+dUVJT2dBa6ncoM3OQuP/bL3SDc3Ob6pFFJQNGK+RStmA+lw+C0+2H4oW2e09vofe4aHTYZuUtVYK1daIyp6Oj6BOmagOJ7KLhNUROBbwBVnXjduNraBiKRzrUBDcwfQmxJu2ggj/qyakI5sEG3NiLPDRnLfOlHlM07hkDdyvipdKOS4v+jtq6DORMIDRxJw4Q7em1/m97n7Pn9voxfpDsKCKmftqFO3HstMD7heCiwLuF4kntuMU6n8zBjzKvW2sTndLlI4eD4Y3UqS65J7SMY9PCJBDcuTepXSN3IJ7Y8RhSIFFckDX+V/iXbUUYxnfk6/jxwk1uraMSpDVwcS7TW/gz4GYAxZiTwktfBAIgvQQwoGEjO+2LSkwCUzTuGYN3KjCOTfCSPTnLO+QiXf1Wd0f1ERwHha8aYLQnHxe6xD4haa9td4M5au9YYMx14EacGMNta+6YxZgFwo7V28a5mfqdE0i87LJLL6s98kYLlcymuuQv/1jVpRyalii3DF9svOnYmtNvoeKCRvqWjgLDXrry4tXYeMC/lXJtd7K21q4CRu3KvrEX75lbQIl5L3Eq2fPb++Jvr0y6RkRocdgxhda7M27iU8tn7Uzd5mddZli6WMSBYaz/prox0H01EEOlI7MPc6WN4B1/KEhmJ0gWIQHN9vGkJ/ET9AaIDvsy26iu1+1svlu3SFf2HmoxEsvbFpCepdZfICA8YShRffGmMWF07dYnuxNVXnccR/JFWAlvXxFdhHfTX4zQHqBfqbKdyn5fYqSwi2QlVVlN33lttzhcsn8uAN/8X3/ZaiIYzrrwKOwJGXu37lD16Eq27HUjTvt9TraGXyLmAoD4Eka6T2O8Q3FATH56aTrohrXkbl5K3cSklL00l4s+jefRF2i+6B+Vgk5ECgogXQpXVCU1Lic1IyduCphu95AMCkdb4ftHls/fXrm89IPdqCGoyEvFMxqalhb/C11yXcUhrfL5Dcz2lL02l+JWfUpewSZB4K/cCQkKncnBDjSaniXSDpCGt7s5vmZboxk0LRFoS5jj4aR0+Tru+eSj3mowSaLVTke5Xd/4SaqesIVS+N9GEYR7pmpRiv31E8REmf/XL2ifaQzkXEHyhph0H7haaItL96s98kdopq9k8ZQ3bxlxOJH9gm13ffCk/4OzfUD6jiuLXb+mBXPdvORcQwqW7A25Hl7bQFOkVth0xjc0XvZ9Qc3C0V2sIAMVLZlI+owp+vZeCQxfJuYAQKRkKQGvVeK12KtILOTWHNWw9+jYiBeUZN/cJAGz7nOIlMxn4hOYy7Kqc61T2RZ0/q9bdD1MwEOnFUtdWCjTXZ1xXKX/1ywnLZaAtQXdCztUQduieLZxFZNfVTV5Gy/CjMjYlxZbLiC2ZEVuFtfwPe2tOQ5ZyMCBocTuRvmjLd+bGm5KiCftEQ9v/1UkT3sLbKX1pKoPv3pPyBw5TcMggZwNC1Kcagkhf1LzfWdRO+ZTaKWsIF5TFz7e3yF7sf3riAnvlM6oUGNLIuT4EEek/6iYvo6KilPCtI/A318fPdzQbOgCUvjSVkpemxs9H8NM85tKcXksp9wJCVE1GIv1N6mY85X/YG394e4ezoRO3CA0QoXjJTIqWzHTO+Qto+eoJNEy407uM9zK5FxBi1GQk0m/VXboSgPKZI/FHQ0DbWsOOnd7a7h0N4Is0U7hiPgUr5sfTWkad0q8DRA4GBNUQRHJF3eWr4o/TraGUqfaQygdJAQIgUlpF3TmLuiy/PS33AkI0dSSziOSCuvOXAM7KqwUfLQAgb81r8Y19Mo1UiommHAe2ronPfWgdflSfX3gv9wJCnAKCSC5KnPAWU7B8LiUvT4OosxpyewEi06dGbGJclAA+f5BopLlNMOntTU45HBBERBypQaL8gcPwb10TP862BuFcF4ZI+u1EY01OvbU2kbsBQZ3KItKO1H6B4tdvoXD5n/G1bAHaBoh0I5fSiTU5JS6z0ZtqDrk3MU3DTkWkkxJXY62dsoamUackLZ2ROiku3Q8kT5bzJ/wuXDG/V0yWy8EagjqVRWTXNEy4M/6NvuS5qyhY+QS4w1vT9SGkDnkl5XG6yXKRgrI28yu8loMBwaUmIxHpAonBoT3Fr99C0ZKZneqsDjTXUz6jim1H39amE9wrORgQ1GQkIt1r2xHT2HbENMr/uA/+1ob4+dQJctB+rSF2XcSfDzdu8iSfORgQYlRDEJHuVXfxh0nH5TP2IEAkY60hmnIciLTATYNgyhq6mjqVRUR6SN2UT93lvEnbSQ3p95UGnO1Du1gO1hDcf2b1IYhIL9BmDsSMqvgIpEw1hwBODaNuyqddlhdPA4Ix5kzgBiAfuN1aOyMl/STg5zhl/Bg431pb52WeRER6szq3KajkuauS1k1K19/gJ9Kl9/asycgYsztwMzAOGA1cbIzZNyF9IHA3cIK1djTwLnCTV/mJU5ORiPQBDRPujM97qJ2yhrB7PvETLNLFH+Fe9iEcC7xgrd1srW0EHgFOTUjPAy631q51j98F9vAwPynUZCQifUfdlDWE80riASGMv0ubi8DbJqNhwPqE4/XAobEDa20t8BiAMaYIuB64y8P8AODTsFMR6aNio5QqKkqp27S1y1/fy4CQ7it4mwYvY8wgnMDwjrX2/s7cYMiQks7nqjAPgJLSIkoqSjv//D6sIsfKCypzrlCZu4aXAWEtMD7heCiwLvECY8xQ4BngBeCHnb1BbW0DkUjnvvGXNLVQBGxtaKbJgwjbW1VUlLIph8oLKnOuUJmz5/f7Mn6R9jIgPA/cZIypABqBicDFsURjTAB4Evibtfa/PcxHMrUYiYik5VlAsNauNcZMB17EGXY621r7pjFmAXAjMBwYAwSMMbHO5sXW2sle5SmJ5iGIiCTxdB6CtXYeMC/l3PHuw8X0yExpVRFERNLJ4aUrVEMQEUmUewEhRk1GIiJJcjAgqMlIRCSdHAwIMaohiIgkyr2AoLWMRETSyr2AoD2VRUTSysGA4FI8EBFJkoMBQU1GIiLp5GBAiFEVQUQkUe4FBHUqi4iklXsBQXsqi4iklYMBIUYBQUQkUe4FBLUYiYiklXsBIU41BBGRRDkXELSnsohIejkXEGJtRlF1KouIJMnBgBCjgCAikij3AoLmIYiIpJV7ASFGTUYiIklyMCCohiAikk4OBoQY1RBERBLlXkBQH4KISFq5FxDiaxn1bC5ERHqbHAwIMYoIIiKJci8gqMlIRCStYE9noKfcu2g1ryx9J358rKlg0oHDaGoN84P5y9pcf+J+X+bb+1dSv62VqX9/v036xNFDOW6f3diwpYmfPWXbpJ91cBVf32sIqzZv49bnVrZJv+CwPRg7ohy7sYHfvvhRm/TLx41k9O6DeGftF8x8bVWb9GuO2QuzWwlvfFLHvYs+bZP+m9MPZCDwyke1zF28pk36z79lqBxYyLMfbuTRd9a3Sb/t2/tSVpzH35dt4Mnln7VJv+OU/SnMC/Dw0nU8bze1Sb/n9NEAPPjWal779+aktIKgnzsnHgDA7IWf8Nan9Unpg4ry+PV39gXg969+zHvrtiSl71ZawC+P3weA/33xI1ZsbAAgLy9Aa2uYPcqLmH7cKABufnYFn9ZtT3r+qN1KuPaYvQD46YIP2bi1OSn9gGEDuWL8ngD8+In3+WJ7a1L6IXuUMfnwEQBc9eh7NIciSenjvjKYsw8ZDsAlD71Dqq7827ti/jJaW8NJ6T39t/eTCXszcnCxZ397cy8+HKBX/e3FePW3N/+KcW3K2RVyr4agYaciImn5on2zCWUk8HFtbQORSOfyX/rsFApXPs6WCXfRPOq7nmSuN6qoKGXTpq09nY1upTLnBpU5e36/jyFDSgD2BFa1Sd/lnPVZ6lQWEUmUewGhb9aIREQ8l3sBIUZrGYmIJPF0lJEx5kzgBiAfuN1aOyMl/UBgFjAIeAW41Fob8jJP6lQWEUnPsxqCMWZ34GZgHDAauNgYs2/KZX8GrrTWjsJp1L/Iq/zE+LY7w84C9R97fSsRkT7FyyajY4EXrLWbrbWNwCPAqbFEY8wIoMhau8g9dR8wycP8ENxQQ/66hQAUL76D4IYaL28nItKneNlkNAxInGWyHji0g/SqztzAHT6VvQ9r4p3KvmiE8voaOODozr1GH1ZRUdrTWeh2KnNuUJm7hpcBIV2vbaQT6R3q7DyEYFk1ZcECfOFWooE86suqCeXI+GWN1c4NKnNu6IJ5CGl5GRDWAuMTjocC61LSKzOkd7lQZTX1Jz1EeX2NEwwqq728nYhIn+JlH8LzwDeMMRXGmGJgIvB0LNFa+wnQZIw50j11DvCUh/kBnKDA+GsVDEREUngWEKy1a4HpwIvAUmCetfZNY8wCY8zB7mVnAbcbYz4ABgB3epUfERHJzNN5CNbaecC8lHPHJzx+h+SOZhER6SG5O1NZRESSKCCIiAiggCAiIq6+umNaAJwxtTtrV57bV6nMuUFlzg07U+aE5wTSpffVDXLGAa/2dCZERPqo8cBrqSf7akAoAA7BWe4i3MG1IiLiCOBMAn4LaE5N7KsBQUREupg6lUVEBFBAEBERlwKCiIgACggiIuJSQBAREUABQUREXAoIIiIC9N2lK7JijDkTuAHIB2631s5IST8QmAUMAl4BLrXWhro7n10pizKfBPwcZwvTj4HzrbV13Z7RLtRRmROuOwH4vbV2z+7MnxeyeJ8NcA9QDmwAzujv77Mx5iCcMucDq4HvW2vruzufXckYMxB4HTjRWrsqJe1Auvjzq9/WEIwxuwM34yxzMRq42Bizb8plfwautNaOwvmAvKh7c9m1Oiqz+8d1N3CCtXY08C5wUw9ktctk+T5jjPky8D+k38u7T8niffYBTwC/ct/nJcD1PZHXrpLl+3wHcKNbZgv8qHtz2bWMMWNxlpcY1c4lXf751W8DAnAs8IK1drO1thF4BDg1lmiMGQEUWWsXuafuAyZ1ey67VsYyA3nA5e5uduAEhD26OY9draMyx8zGqRn1Bx2V+SCg0Vob27L2FiBtrakPyeZ9DgAD3cfFwPZuzJ8XLgKmkGavea8+v/pzk9EwnLWOYtaTvDtbuvSqbsiXlzKW2VpbCzwGYIwpwvnWeFc35s8LHb3PGGOuAt4GFtE/dFTmrwIbjDH3A2OA94Aruy97nujwfQauAZ4zxvwOaATGdk/WvGGtnQzgtP614cnnV3+uIaRrGoh0Ir0vyqpMxphBwALgHWvt/Z7nylsZy2yM2R+YCPyy23LkvY7e5yBwNHCXtfZrwL+B33ZDvrzU0ftcBMwBvmGtHQrMBB7oprz1BE8+v/pzQFgLVCYcDyW56tVRel/UYZmMMUNxlg5/B5jcfVnzTEdlnuSeW4wTBIcZY/r60ukdlXkDsNJau9g9/gt9f+/yjsq8P7DdWvume3wPTlDsrzz5/OrPAeF54BvGmApjTDHOt8RYmyrW2k+AJmPMke6pc4Cnuj+bXSpjmY0xAeBJ4G/W2quttf1hqduO3uefWWtHWWsPBI4H1llrx/dMVrtMxjLjjEqpMMaMdo+/DdR0cx67Wkdl/hcw3OxoXzkJZ4nnfsmrz69+GxDcjtPpwIvAUmCetfZNY8wCY8zB7mVnAbcbYz4ABgB39khmu0gWZf4OTpvyqcaYpe7P7J7L8a7L8n3uVzoqs7V2O/BdYJYxZjnwn8C1PZbhLpBFmeuA84C/GWPeBS4Azu+p/HrF688v7YcgIiJAP64hiIhI5yggiIgIoIAgIiIuBQQREQEUEERExNWfl64Q2WnGmCiwDAgDUZy1cbYAlyVM+Oqqe40ElllrS4wxNwFfstZe0ZX3EMmGAoJI+46x1n4eOzDG/Ahn7afDey5LIt5RQBDJgjEmiLMy7OaEc9NxZsz6gVU4K8muM8ZUAn8A9sFZX+YP1to7jTGHAb8GCnCWGnjOWnthtxZEJAP1IYi070VjzDvGmHXACvfc+QDGmHOAA4BD3WUxFuAssQ3OwmorrLX74NQmLjbGfBX4Ac56/WOBfYHvGGOqu600Ih1QDUGkfcdYaz83xozBWSfmdWvtRjftRJwF4xa7y+cEcPoZwFm7/8cA1tovcBZewxhzLnC8MWYaTu2hGCgBarunOCKZqYYg0gFr7RLgh8BstwMYnABwm7X2QLeGcDAQW2gshNMRDYAx5ivubnWv4iyw9yHwC2AN/WAHN+k/FBBEsmCt/QuwEPide+oZYLL7QQ/OB/yD7uPn2dG0NAj4B7A3TtCYaq2dD+yOs5FNoDvyL5INNRmJZO8K4F1jzDdx+gt2Bxa5Q1Q/xVltM3bd3e6qm37gVmttjTHmVuBtY0wt8DnwT5yg8FH3FkMkPa12KiIigJqMRETEpYAgIiKAAoKIiLgUEEREBFBAEBERlwKCiIgACggiIuJSQBAREQD+PyXjXM3kRlYpAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "# draw a PR curve\n",
    "# calculate the no skill line as the proportion of the positive class\n",
    "no_skill = len(y_test[y_test == 1]) / len(y)\n",
    "# plot the no skill precision-recall curve\n",
    "plt.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')\n",
    "\n",
    "# calculate inputs for the PR curve\n",
    "precision, recall, thresholds = precision_recall_curve(y_test_proba['y_test_class_actual'], y_test_proba['y_hat_test_proba'])\n",
    "# plot PR curve\n",
    "plt.plot(recall, precision, marker='.', label='Logistic')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.legend()\n",
    "plt.title('PR curve');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.43585441032140426"
      ]
     },
     "metadata": {},
     "execution_count": 78
    }
   ],
   "source": [
    "# calculate PR AUC\n",
    "auc_pr = auc(recall, precision)\n",
    "auc_pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}