WOE_IV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import packages\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_excel('bank.xlsx') #Read excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>oct</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>may</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital  education default  balance housing loan  \\\n",
       "0   30   unemployed  married    primary      no     1787      no   no   \n",
       "1   33     services  married  secondary      no     4789     yes  yes   \n",
       "2   35   management   single   tertiary      no     1350     yes   no   \n",
       "3   30   management  married   tertiary      no     1476     yes  yes   \n",
       "4   59  blue-collar  married  secondary      no        0     yes   no   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  cellular   19   oct        79         1     -1         0  unknown  no  \n",
       "1  cellular   11   may       220         1    339         4  failure  no  \n",
       "2  cellular   16   apr       185         1    330         1  failure  no  \n",
       "3   unknown    3   jun       199         4     -1         0  unknown  no  \n",
       "4   unknown    5   may       226         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4521 entries, 0 to 4520\n",
      "Data columns (total 17 columns):\n",
      "age          4521 non-null int64\n",
      "job          4521 non-null object\n",
      "marital      4521 non-null object\n",
      "education    4521 non-null object\n",
      "default      4521 non-null object\n",
      "balance      4521 non-null int64\n",
      "housing      4521 non-null object\n",
      "loan         4521 non-null object\n",
      "contact      4521 non-null object\n",
      "day          4521 non-null int64\n",
      "month        4521 non-null object\n",
      "duration     4521 non-null int64\n",
      "campaign     4521 non-null int64\n",
      "pdays        4521 non-null int64\n",
      "previous     4521 non-null int64\n",
      "poutcome     4521 non-null object\n",
      "y            4521 non-null object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 600.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     4000\n",
       "yes     521\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['target'] = df['y'].apply(lambda x : 1 if x == 'yes' else 0)  # Convert to numeric\n",
    "df = df.drop('y',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WOE and IV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas.core.algorithms as algos\n",
    "from pandas import Series\n",
    "import scipy.stats.stats as stats\n",
    "import re\n",
    "import traceback\n",
    "import string\n",
    "\n",
    "max_bin = 20\n",
    "force_bin = 3\n",
    "\n",
    "# define a binning function\n",
    "def mono_bin(Y, X, n = max_bin):\n",
    "    \n",
    "    df1 = pd.DataFrame({\"X\": X, \"Y\": Y})\n",
    "    justmiss = df1[['X','Y']][df1.X.isnull()]\n",
    "    notmiss = df1[['X','Y']][df1.X.notnull()]\n",
    "    r = 0\n",
    "    while np.abs(r) < 1:\n",
    "        try:\n",
    "            d1 = pd.DataFrame({\"X\": notmiss.X, \"Y\": notmiss.Y, \"Bucket\": pd.qcut(notmiss.X, n)})\n",
    "            d2 = d1.groupby('Bucket', as_index=True)\n",
    "            r, p = stats.spearmanr(d2.mean().X, d2.mean().Y)\n",
    "            n = n - 1 \n",
    "        except Exception as e:\n",
    "            n = n - 1\n",
    "\n",
    "    if len(d2) == 1:\n",
    "        n = force_bin         \n",
    "        bins = algos.quantile(notmiss.X, np.linspace(0, 1, n))\n",
    "        if len(np.unique(bins)) == 2:\n",
    "            bins = np.insert(bins, 0, 1)\n",
    "            bins[1] = bins[1]-(bins[1]/2)\n",
    "        d1 = pd.DataFrame({\"X\": notmiss.X, \"Y\": notmiss.Y, \"Bucket\": pd.cut(notmiss.X, np.unique(bins),include_lowest=True)}) \n",
    "        d2 = d1.groupby('Bucket', as_index=True)\n",
    "    \n",
    "    d3 = pd.DataFrame({},index=[])\n",
    "    d3[\"MIN_VALUE\"] = d2.min().X\n",
    "    d3[\"MAX_VALUE\"] = d2.max().X\n",
    "    d3[\"COUNT\"] = d2.count().Y\n",
    "    d3[\"EVENT\"] = d2.sum().Y\n",
    "    d3[\"NONEVENT\"] = d2.count().Y - d2.sum().Y\n",
    "    d3=d3.reset_index(drop=True)\n",
    "    \n",
    "    if len(justmiss.index) > 0:\n",
    "        d4 = pd.DataFrame({'MIN_VALUE':np.nan},index=[0])\n",
    "        d4[\"MAX_VALUE\"] = np.nan\n",
    "        d4[\"COUNT\"] = justmiss.count().Y\n",
    "        d4[\"EVENT\"] = justmiss.sum().Y\n",
    "        d4[\"NONEVENT\"] = justmiss.count().Y - justmiss.sum().Y\n",
    "        d3 = d3.append(d4,ignore_index=True)\n",
    "    \n",
    "    d3[\"EVENT_RATE\"] = d3.EVENT/d3.COUNT\n",
    "    d3[\"NON_EVENT_RATE\"] = d3.NONEVENT/d3.COUNT\n",
    "    d3[\"DIST_EVENT\"] = d3.EVENT/d3.sum().EVENT\n",
    "    d3[\"DIST_NON_EVENT\"] = d3.NONEVENT/d3.sum().NONEVENT\n",
    "    d3[\"WOE\"] = np.log(d3.DIST_EVENT/d3.DIST_NON_EVENT)\n",
    "    d3[\"IV\"] = (d3.DIST_EVENT-d3.DIST_NON_EVENT)*np.log(d3.DIST_EVENT/d3.DIST_NON_EVENT)\n",
    "    d3[\"VAR_NAME\"] = \"VAR\"\n",
    "    d3 = d3[['VAR_NAME','MIN_VALUE', 'MAX_VALUE', 'COUNT', 'EVENT', 'EVENT_RATE', 'NONEVENT', 'NON_EVENT_RATE', 'DIST_EVENT','DIST_NON_EVENT','WOE', 'IV']]       \n",
    "    d3 = d3.replace([np.inf, -np.inf], 0)\n",
    "    d3.IV = d3.IV.sum()\n",
    "    \n",
    "    return(d3)\n",
    "\n",
    "def char_bin(Y, X):\n",
    "        \n",
    "    df1 = pd.DataFrame({\"X\": X, \"Y\": Y})\n",
    "    justmiss = df1[['X','Y']][df1.X.isnull()]\n",
    "    notmiss = df1[['X','Y']][df1.X.notnull()]    \n",
    "    df2 = notmiss.groupby('X',as_index=True)\n",
    "    \n",
    "    d3 = pd.DataFrame({},index=[])\n",
    "    d3[\"COUNT\"] = df2.count().Y\n",
    "    d3[\"MIN_VALUE\"] = df2.sum().Y.index\n",
    "    d3[\"MAX_VALUE\"] = d3[\"MIN_VALUE\"]\n",
    "    d3[\"EVENT\"] = df2.sum().Y\n",
    "    d3[\"NONEVENT\"] = df2.count().Y - df2.sum().Y\n",
    "    \n",
    "    if len(justmiss.index) > 0:\n",
    "        d4 = pd.DataFrame({'MIN_VALUE':np.nan},index=[0])\n",
    "        d4[\"MAX_VALUE\"] = np.nan\n",
    "        d4[\"COUNT\"] = justmiss.count().Y\n",
    "        d4[\"EVENT\"] = justmiss.sum().Y\n",
    "        d4[\"NONEVENT\"] = justmiss.count().Y - justmiss.sum().Y\n",
    "        d3 = d3.append(d4,ignore_index=True)\n",
    "    \n",
    "    d3[\"EVENT_RATE\"] = d3.EVENT/d3.COUNT\n",
    "    d3[\"NON_EVENT_RATE\"] = d3.NONEVENT/d3.COUNT\n",
    "    d3[\"DIST_EVENT\"] = d3.EVENT/d3.sum().EVENT\n",
    "    d3[\"DIST_NON_EVENT\"] = d3.NONEVENT/d3.sum().NONEVENT\n",
    "    d3[\"WOE\"] = np.log(d3.DIST_EVENT/d3.DIST_NON_EVENT)\n",
    "    d3[\"IV\"] = (d3.DIST_EVENT-d3.DIST_NON_EVENT)*np.log(d3.DIST_EVENT/d3.DIST_NON_EVENT)\n",
    "    d3[\"VAR_NAME\"] = \"VAR\"\n",
    "    d3 = d3[['VAR_NAME','MIN_VALUE', 'MAX_VALUE', 'COUNT', 'EVENT', 'EVENT_RATE', 'NONEVENT', 'NON_EVENT_RATE', 'DIST_EVENT','DIST_NON_EVENT','WOE', 'IV']]      \n",
    "    d3 = d3.replace([np.inf, -np.inf], 0)\n",
    "    d3.IV = d3.IV.sum()\n",
    "    d3 = d3.reset_index(drop=True)\n",
    "    \n",
    "    return(d3)\n",
    "\n",
    "def data_vars(df1, target):\n",
    "    \n",
    "    stack = traceback.extract_stack()\n",
    "    filename, lineno, function_name, code = stack[-2]\n",
    "    vars_name = re.compile(r'\\((.*?)\\).*$').search(code).groups()[0]\n",
    "    final = (re.findall(r\"[\\w']+\", vars_name))[-1]\n",
    "    \n",
    "    x = df1.dtypes.index\n",
    "    count = -1\n",
    "    \n",
    "    for i in x:\n",
    "        if i.upper() not in (final.upper()):\n",
    "            if np.issubdtype(df1[i], np.number) and len(Series.unique(df1[i])) > 2:\n",
    "                conv = mono_bin(target, df1[i])\n",
    "                conv[\"VAR_NAME\"] = i\n",
    "                count = count + 1\n",
    "            else:\n",
    "                conv = char_bin(target, df1[i])\n",
    "                conv[\"VAR_NAME\"] = i            \n",
    "                count = count + 1\n",
    "                \n",
    "            if count == 0:\n",
    "                iv_df = conv\n",
    "            else:\n",
    "                iv_df = iv_df.append(conv,ignore_index=True)\n",
    "    \n",
    "    iv = pd.DataFrame({'IV':iv_df.groupby('VAR_NAME').IV.max()})\n",
    "    iv = iv.reset_index()\n",
    "    return(iv_df,iv) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_iv, IV = data_vars(df,df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VAR_NAME</th>\n",
       "      <th>MIN_VALUE</th>\n",
       "      <th>MAX_VALUE</th>\n",
       "      <th>COUNT</th>\n",
       "      <th>EVENT</th>\n",
       "      <th>EVENT_RATE</th>\n",
       "      <th>NONEVENT</th>\n",
       "      <th>NON_EVENT_RATE</th>\n",
       "      <th>DIST_EVENT</th>\n",
       "      <th>DIST_NON_EVENT</th>\n",
       "      <th>WOE</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>19</td>\n",
       "      <td>39</td>\n",
       "      <td>2290</td>\n",
       "      <td>259</td>\n",
       "      <td>0.113100</td>\n",
       "      <td>2031</td>\n",
       "      <td>0.886900</td>\n",
       "      <td>0.497121</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>-0.021156</td>\n",
       "      <td>0.000452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age</td>\n",
       "      <td>40</td>\n",
       "      <td>87</td>\n",
       "      <td>2231</td>\n",
       "      <td>262</td>\n",
       "      <td>0.117436</td>\n",
       "      <td>1969</td>\n",
       "      <td>0.882564</td>\n",
       "      <td>0.502879</td>\n",
       "      <td>0.49225</td>\n",
       "      <td>0.021363</td>\n",
       "      <td>0.000452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>job</td>\n",
       "      <td>admin.</td>\n",
       "      <td>admin.</td>\n",
       "      <td>478</td>\n",
       "      <td>58</td>\n",
       "      <td>0.121339</td>\n",
       "      <td>420</td>\n",
       "      <td>0.878661</td>\n",
       "      <td>0.111324</td>\n",
       "      <td>0.10500</td>\n",
       "      <td>0.058488</td>\n",
       "      <td>0.132519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>job</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>946</td>\n",
       "      <td>69</td>\n",
       "      <td>0.072939</td>\n",
       "      <td>877</td>\n",
       "      <td>0.927061</td>\n",
       "      <td>0.132438</td>\n",
       "      <td>0.21925</td>\n",
       "      <td>-0.504101</td>\n",
       "      <td>0.132519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>job</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>168</td>\n",
       "      <td>15</td>\n",
       "      <td>0.089286</td>\n",
       "      <td>153</td>\n",
       "      <td>0.910714</td>\n",
       "      <td>0.028791</td>\n",
       "      <td>0.03825</td>\n",
       "      <td>-0.284088</td>\n",
       "      <td>0.132519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>job</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>112</td>\n",
       "      <td>14</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>98</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.026871</td>\n",
       "      <td>0.02450</td>\n",
       "      <td>0.092389</td>\n",
       "      <td>0.132519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>job</td>\n",
       "      <td>management</td>\n",
       "      <td>management</td>\n",
       "      <td>969</td>\n",
       "      <td>131</td>\n",
       "      <td>0.135191</td>\n",
       "      <td>838</td>\n",
       "      <td>0.864809</td>\n",
       "      <td>0.251440</td>\n",
       "      <td>0.20950</td>\n",
       "      <td>0.182479</td>\n",
       "      <td>0.132519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>job</td>\n",
       "      <td>retired</td>\n",
       "      <td>retired</td>\n",
       "      <td>230</td>\n",
       "      <td>54</td>\n",
       "      <td>0.234783</td>\n",
       "      <td>176</td>\n",
       "      <td>0.765217</td>\n",
       "      <td>0.103647</td>\n",
       "      <td>0.04400</td>\n",
       "      <td>0.856800</td>\n",
       "      <td>0.132519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>job</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>183</td>\n",
       "      <td>20</td>\n",
       "      <td>0.109290</td>\n",
       "      <td>163</td>\n",
       "      <td>0.890710</td>\n",
       "      <td>0.038388</td>\n",
       "      <td>0.04075</td>\n",
       "      <td>-0.059718</td>\n",
       "      <td>0.132519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>job</td>\n",
       "      <td>services</td>\n",
       "      <td>services</td>\n",
       "      <td>417</td>\n",
       "      <td>38</td>\n",
       "      <td>0.091127</td>\n",
       "      <td>379</td>\n",
       "      <td>0.908873</td>\n",
       "      <td>0.072937</td>\n",
       "      <td>0.09475</td>\n",
       "      <td>-0.261650</td>\n",
       "      <td>0.132519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>job</td>\n",
       "      <td>student</td>\n",
       "      <td>student</td>\n",
       "      <td>84</td>\n",
       "      <td>19</td>\n",
       "      <td>0.226190</td>\n",
       "      <td>65</td>\n",
       "      <td>0.773810</td>\n",
       "      <td>0.036468</td>\n",
       "      <td>0.01625</td>\n",
       "      <td>0.808351</td>\n",
       "      <td>0.132519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>job</td>\n",
       "      <td>technician</td>\n",
       "      <td>technician</td>\n",
       "      <td>768</td>\n",
       "      <td>83</td>\n",
       "      <td>0.108073</td>\n",
       "      <td>685</td>\n",
       "      <td>0.891927</td>\n",
       "      <td>0.159309</td>\n",
       "      <td>0.17125</td>\n",
       "      <td>-0.072279</td>\n",
       "      <td>0.132519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>job</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>128</td>\n",
       "      <td>13</td>\n",
       "      <td>0.101562</td>\n",
       "      <td>115</td>\n",
       "      <td>0.898438</td>\n",
       "      <td>0.024952</td>\n",
       "      <td>0.02875</td>\n",
       "      <td>-0.141683</td>\n",
       "      <td>0.132519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>job</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "      <td>0.184211</td>\n",
       "      <td>31</td>\n",
       "      <td>0.815789</td>\n",
       "      <td>0.013436</td>\n",
       "      <td>0.00775</td>\n",
       "      <td>0.550223</td>\n",
       "      <td>0.132519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>marital</td>\n",
       "      <td>divorced</td>\n",
       "      <td>divorced</td>\n",
       "      <td>528</td>\n",
       "      <td>77</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>451</td>\n",
       "      <td>0.854167</td>\n",
       "      <td>0.147793</td>\n",
       "      <td>0.11275</td>\n",
       "      <td>0.270638</td>\n",
       "      <td>0.040090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>marital</td>\n",
       "      <td>married</td>\n",
       "      <td>married</td>\n",
       "      <td>2797</td>\n",
       "      <td>277</td>\n",
       "      <td>0.099035</td>\n",
       "      <td>2520</td>\n",
       "      <td>0.900965</td>\n",
       "      <td>0.531670</td>\n",
       "      <td>0.63000</td>\n",
       "      <td>-0.169697</td>\n",
       "      <td>0.040090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>marital</td>\n",
       "      <td>single</td>\n",
       "      <td>single</td>\n",
       "      <td>1196</td>\n",
       "      <td>167</td>\n",
       "      <td>0.139632</td>\n",
       "      <td>1029</td>\n",
       "      <td>0.860368</td>\n",
       "      <td>0.320537</td>\n",
       "      <td>0.25725</td>\n",
       "      <td>0.219951</td>\n",
       "      <td>0.040090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>education</td>\n",
       "      <td>primary</td>\n",
       "      <td>primary</td>\n",
       "      <td>678</td>\n",
       "      <td>64</td>\n",
       "      <td>0.094395</td>\n",
       "      <td>614</td>\n",
       "      <td>0.905605</td>\n",
       "      <td>0.122841</td>\n",
       "      <td>0.15350</td>\n",
       "      <td>-0.222812</td>\n",
       "      <td>0.031812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>education</td>\n",
       "      <td>secondary</td>\n",
       "      <td>secondary</td>\n",
       "      <td>2306</td>\n",
       "      <td>245</td>\n",
       "      <td>0.106245</td>\n",
       "      <td>2061</td>\n",
       "      <td>0.893755</td>\n",
       "      <td>0.470250</td>\n",
       "      <td>0.51525</td>\n",
       "      <td>-0.091389</td>\n",
       "      <td>0.031812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>education</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>1350</td>\n",
       "      <td>193</td>\n",
       "      <td>0.142963</td>\n",
       "      <td>1157</td>\n",
       "      <td>0.857037</td>\n",
       "      <td>0.370441</td>\n",
       "      <td>0.28925</td>\n",
       "      <td>0.247404</td>\n",
       "      <td>0.031812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>education</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>187</td>\n",
       "      <td>19</td>\n",
       "      <td>0.101604</td>\n",
       "      <td>168</td>\n",
       "      <td>0.898396</td>\n",
       "      <td>0.036468</td>\n",
       "      <td>0.04200</td>\n",
       "      <td>-0.141225</td>\n",
       "      <td>0.031812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>default</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4445</td>\n",
       "      <td>512</td>\n",
       "      <td>0.115186</td>\n",
       "      <td>3933</td>\n",
       "      <td>0.884814</td>\n",
       "      <td>0.982726</td>\n",
       "      <td>0.98325</td>\n",
       "      <td>-0.000534</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>default</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>0.118421</td>\n",
       "      <td>67</td>\n",
       "      <td>0.881579</td>\n",
       "      <td>0.017274</td>\n",
       "      <td>0.01675</td>\n",
       "      <td>0.030832</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>balance</td>\n",
       "      <td>-3313</td>\n",
       "      <td>69</td>\n",
       "      <td>1133</td>\n",
       "      <td>94</td>\n",
       "      <td>0.082966</td>\n",
       "      <td>1039</td>\n",
       "      <td>0.917034</td>\n",
       "      <td>0.180422</td>\n",
       "      <td>0.25975</td>\n",
       "      <td>-0.364420</td>\n",
       "      <td>0.076208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>balance</td>\n",
       "      <td>70</td>\n",
       "      <td>444</td>\n",
       "      <td>1128</td>\n",
       "      <td>106</td>\n",
       "      <td>0.093972</td>\n",
       "      <td>1022</td>\n",
       "      <td>0.906028</td>\n",
       "      <td>0.203455</td>\n",
       "      <td>0.25550</td>\n",
       "      <td>-0.227778</td>\n",
       "      <td>0.076208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>balance</td>\n",
       "      <td>445</td>\n",
       "      <td>1480</td>\n",
       "      <td>1131</td>\n",
       "      <td>149</td>\n",
       "      <td>0.131742</td>\n",
       "      <td>982</td>\n",
       "      <td>0.868258</td>\n",
       "      <td>0.285988</td>\n",
       "      <td>0.24550</td>\n",
       "      <td>0.152655</td>\n",
       "      <td>0.076208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>balance</td>\n",
       "      <td>1482</td>\n",
       "      <td>71188</td>\n",
       "      <td>1129</td>\n",
       "      <td>172</td>\n",
       "      <td>0.152347</td>\n",
       "      <td>957</td>\n",
       "      <td>0.847653</td>\n",
       "      <td>0.330134</td>\n",
       "      <td>0.23925</td>\n",
       "      <td>0.321991</td>\n",
       "      <td>0.076208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>housing</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>1962</td>\n",
       "      <td>301</td>\n",
       "      <td>0.153415</td>\n",
       "      <td>1661</td>\n",
       "      <td>0.846585</td>\n",
       "      <td>0.577735</td>\n",
       "      <td>0.41525</td>\n",
       "      <td>0.330235</td>\n",
       "      <td>0.106556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>housing</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2559</td>\n",
       "      <td>220</td>\n",
       "      <td>0.085971</td>\n",
       "      <td>2339</td>\n",
       "      <td>0.914029</td>\n",
       "      <td>0.422265</td>\n",
       "      <td>0.58475</td>\n",
       "      <td>-0.325552</td>\n",
       "      <td>0.106556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>loan</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>3830</td>\n",
       "      <td>478</td>\n",
       "      <td>0.124804</td>\n",
       "      <td>3352</td>\n",
       "      <td>0.875196</td>\n",
       "      <td>0.917466</td>\n",
       "      <td>0.83800</td>\n",
       "      <td>0.090598</td>\n",
       "      <td>0.060791</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>month</td>\n",
       "      <td>dec</td>\n",
       "      <td>dec</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.017274</td>\n",
       "      <td>0.00275</td>\n",
       "      <td>1.837629</td>\n",
       "      <td>0.379533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>month</td>\n",
       "      <td>feb</td>\n",
       "      <td>feb</td>\n",
       "      <td>222</td>\n",
       "      <td>38</td>\n",
       "      <td>0.171171</td>\n",
       "      <td>184</td>\n",
       "      <td>0.828829</td>\n",
       "      <td>0.072937</td>\n",
       "      <td>0.04600</td>\n",
       "      <td>0.460950</td>\n",
       "      <td>0.379533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>month</td>\n",
       "      <td>jan</td>\n",
       "      <td>jan</td>\n",
       "      <td>148</td>\n",
       "      <td>16</td>\n",
       "      <td>0.108108</td>\n",
       "      <td>132</td>\n",
       "      <td>0.891892</td>\n",
       "      <td>0.030710</td>\n",
       "      <td>0.03300</td>\n",
       "      <td>-0.071914</td>\n",
       "      <td>0.379533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>month</td>\n",
       "      <td>jul</td>\n",
       "      <td>jul</td>\n",
       "      <td>706</td>\n",
       "      <td>61</td>\n",
       "      <td>0.086402</td>\n",
       "      <td>645</td>\n",
       "      <td>0.913598</td>\n",
       "      <td>0.117083</td>\n",
       "      <td>0.16125</td>\n",
       "      <td>-0.320077</td>\n",
       "      <td>0.379533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>month</td>\n",
       "      <td>jun</td>\n",
       "      <td>jun</td>\n",
       "      <td>531</td>\n",
       "      <td>55</td>\n",
       "      <td>0.103578</td>\n",
       "      <td>476</td>\n",
       "      <td>0.896422</td>\n",
       "      <td>0.105566</td>\n",
       "      <td>0.11900</td>\n",
       "      <td>-0.119785</td>\n",
       "      <td>0.379533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>month</td>\n",
       "      <td>mar</td>\n",
       "      <td>mar</td>\n",
       "      <td>49</td>\n",
       "      <td>21</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>28</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.040307</td>\n",
       "      <td>0.00700</td>\n",
       "      <td>1.750618</td>\n",
       "      <td>0.379533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>month</td>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "      <td>1398</td>\n",
       "      <td>93</td>\n",
       "      <td>0.066524</td>\n",
       "      <td>1305</td>\n",
       "      <td>0.933476</td>\n",
       "      <td>0.178503</td>\n",
       "      <td>0.32625</td>\n",
       "      <td>-0.603059</td>\n",
       "      <td>0.379533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>month</td>\n",
       "      <td>nov</td>\n",
       "      <td>nov</td>\n",
       "      <td>389</td>\n",
       "      <td>39</td>\n",
       "      <td>0.100257</td>\n",
       "      <td>350</td>\n",
       "      <td>0.899743</td>\n",
       "      <td>0.074856</td>\n",
       "      <td>0.08750</td>\n",
       "      <td>-0.156072</td>\n",
       "      <td>0.379533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>month</td>\n",
       "      <td>oct</td>\n",
       "      <td>oct</td>\n",
       "      <td>80</td>\n",
       "      <td>37</td>\n",
       "      <td>0.462500</td>\n",
       "      <td>43</td>\n",
       "      <td>0.537500</td>\n",
       "      <td>0.071017</td>\n",
       "      <td>0.01075</td>\n",
       "      <td>1.888017</td>\n",
       "      <td>0.379533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>month</td>\n",
       "      <td>sep</td>\n",
       "      <td>sep</td>\n",
       "      <td>52</td>\n",
       "      <td>17</td>\n",
       "      <td>0.326923</td>\n",
       "      <td>35</td>\n",
       "      <td>0.673077</td>\n",
       "      <td>0.032630</td>\n",
       "      <td>0.00875</td>\n",
       "      <td>1.316165</td>\n",
       "      <td>0.379533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>duration</td>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>510</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001961</td>\n",
       "      <td>509</td>\n",
       "      <td>0.998039</td>\n",
       "      <td>0.001919</td>\n",
       "      <td>0.12725</td>\n",
       "      <td>-4.194148</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>duration</td>\n",
       "      <td>63</td>\n",
       "      <td>96</td>\n",
       "      <td>502</td>\n",
       "      <td>10</td>\n",
       "      <td>0.019920</td>\n",
       "      <td>492</td>\n",
       "      <td>0.980080</td>\n",
       "      <td>0.019194</td>\n",
       "      <td>0.12300</td>\n",
       "      <td>-1.857594</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>duration</td>\n",
       "      <td>97</td>\n",
       "      <td>128</td>\n",
       "      <td>509</td>\n",
       "      <td>16</td>\n",
       "      <td>0.031434</td>\n",
       "      <td>493</td>\n",
       "      <td>0.968566</td>\n",
       "      <td>0.030710</td>\n",
       "      <td>0.12325</td>\n",
       "      <td>-1.389621</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>duration</td>\n",
       "      <td>129</td>\n",
       "      <td>163</td>\n",
       "      <td>490</td>\n",
       "      <td>20</td>\n",
       "      <td>0.040816</td>\n",
       "      <td>470</td>\n",
       "      <td>0.959184</td>\n",
       "      <td>0.038388</td>\n",
       "      <td>0.11750</td>\n",
       "      <td>-1.118701</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>duration</td>\n",
       "      <td>164</td>\n",
       "      <td>208</td>\n",
       "      <td>505</td>\n",
       "      <td>26</td>\n",
       "      <td>0.051485</td>\n",
       "      <td>479</td>\n",
       "      <td>0.948515</td>\n",
       "      <td>0.049904</td>\n",
       "      <td>0.11975</td>\n",
       "      <td>-0.875304</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>duration</td>\n",
       "      <td>209</td>\n",
       "      <td>261</td>\n",
       "      <td>498</td>\n",
       "      <td>61</td>\n",
       "      <td>0.122490</td>\n",
       "      <td>437</td>\n",
       "      <td>0.877510</td>\n",
       "      <td>0.117083</td>\n",
       "      <td>0.10925</td>\n",
       "      <td>0.069240</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>duration</td>\n",
       "      <td>262</td>\n",
       "      <td>354</td>\n",
       "      <td>505</td>\n",
       "      <td>73</td>\n",
       "      <td>0.144554</td>\n",
       "      <td>432</td>\n",
       "      <td>0.855446</td>\n",
       "      <td>0.140115</td>\n",
       "      <td>0.10800</td>\n",
       "      <td>0.260333</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>duration</td>\n",
       "      <td>355</td>\n",
       "      <td>546</td>\n",
       "      <td>500</td>\n",
       "      <td>101</td>\n",
       "      <td>0.202000</td>\n",
       "      <td>399</td>\n",
       "      <td>0.798000</td>\n",
       "      <td>0.193858</td>\n",
       "      <td>0.09975</td>\n",
       "      <td>0.664459</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>duration</td>\n",
       "      <td>547</td>\n",
       "      <td>3025</td>\n",
       "      <td>502</td>\n",
       "      <td>213</td>\n",
       "      <td>0.424303</td>\n",
       "      <td>289</td>\n",
       "      <td>0.575697</td>\n",
       "      <td>0.408829</td>\n",
       "      <td>0.07225</td>\n",
       "      <td>1.733165</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>campaign</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2998</td>\n",
       "      <td>378</td>\n",
       "      <td>0.126084</td>\n",
       "      <td>2620</td>\n",
       "      <td>0.873916</td>\n",
       "      <td>0.725528</td>\n",
       "      <td>0.65500</td>\n",
       "      <td>0.102264</td>\n",
       "      <td>0.023342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>campaign</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>1523</td>\n",
       "      <td>143</td>\n",
       "      <td>0.093894</td>\n",
       "      <td>1380</td>\n",
       "      <td>0.906106</td>\n",
       "      <td>0.274472</td>\n",
       "      <td>0.34500</td>\n",
       "      <td>-0.228695</td>\n",
       "      <td>0.023342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>pdays</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>3705</td>\n",
       "      <td>337</td>\n",
       "      <td>0.090958</td>\n",
       "      <td>3368</td>\n",
       "      <td>0.909042</td>\n",
       "      <td>0.646833</td>\n",
       "      <td>0.84200</td>\n",
       "      <td>-0.263692</td>\n",
       "      <td>0.203267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>pdays</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003839</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.203267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>pdays</td>\n",
       "      <td>2</td>\n",
       "      <td>871</td>\n",
       "      <td>814</td>\n",
       "      <td>182</td>\n",
       "      <td>0.223587</td>\n",
       "      <td>632</td>\n",
       "      <td>0.776413</td>\n",
       "      <td>0.349328</td>\n",
       "      <td>0.15800</td>\n",
       "      <td>0.793417</td>\n",
       "      <td>0.203267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>previous</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3991</td>\n",
       "      <td>388</td>\n",
       "      <td>0.097219</td>\n",
       "      <td>3603</td>\n",
       "      <td>0.902781</td>\n",
       "      <td>0.744722</td>\n",
       "      <td>0.90075</td>\n",
       "      <td>-0.190217</td>\n",
       "      <td>0.177081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>previous</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>530</td>\n",
       "      <td>133</td>\n",
       "      <td>0.250943</td>\n",
       "      <td>397</td>\n",
       "      <td>0.749057</td>\n",
       "      <td>0.255278</td>\n",
       "      <td>0.09925</td>\n",
       "      <td>0.944712</td>\n",
       "      <td>0.177081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>failure</td>\n",
       "      <td>failure</td>\n",
       "      <td>490</td>\n",
       "      <td>63</td>\n",
       "      <td>0.128571</td>\n",
       "      <td>427</td>\n",
       "      <td>0.871429</td>\n",
       "      <td>0.120921</td>\n",
       "      <td>0.10675</td>\n",
       "      <td>0.124650</td>\n",
       "      <td>0.461890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>197</td>\n",
       "      <td>38</td>\n",
       "      <td>0.192893</td>\n",
       "      <td>159</td>\n",
       "      <td>0.807107</td>\n",
       "      <td>0.072937</td>\n",
       "      <td>0.03975</td>\n",
       "      <td>0.606982</td>\n",
       "      <td>0.461890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>success</td>\n",
       "      <td>success</td>\n",
       "      <td>129</td>\n",
       "      <td>83</td>\n",
       "      <td>0.643411</td>\n",
       "      <td>46</td>\n",
       "      <td>0.356589</td>\n",
       "      <td>0.159309</td>\n",
       "      <td>0.01150</td>\n",
       "      <td>2.628499</td>\n",
       "      <td>0.461890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3705</td>\n",
       "      <td>337</td>\n",
       "      <td>0.090958</td>\n",
       "      <td>3368</td>\n",
       "      <td>0.909042</td>\n",
       "      <td>0.646833</td>\n",
       "      <td>0.84200</td>\n",
       "      <td>-0.263692</td>\n",
       "      <td>0.461890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     VAR_NAME      MIN_VALUE      MAX_VALUE  COUNT  EVENT  EVENT_RATE  \\\n",
       "0         age             19             39   2290    259    0.113100   \n",
       "1         age             40             87   2231    262    0.117436   \n",
       "2         job         admin.         admin.    478     58    0.121339   \n",
       "3         job    blue-collar    blue-collar    946     69    0.072939   \n",
       "4         job   entrepreneur   entrepreneur    168     15    0.089286   \n",
       "5         job      housemaid      housemaid    112     14    0.125000   \n",
       "6         job     management     management    969    131    0.135191   \n",
       "7         job        retired        retired    230     54    0.234783   \n",
       "8         job  self-employed  self-employed    183     20    0.109290   \n",
       "9         job       services       services    417     38    0.091127   \n",
       "10        job        student        student     84     19    0.226190   \n",
       "11        job     technician     technician    768     83    0.108073   \n",
       "12        job     unemployed     unemployed    128     13    0.101562   \n",
       "13        job        unknown        unknown     38      7    0.184211   \n",
       "14    marital       divorced       divorced    528     77    0.145833   \n",
       "15    marital        married        married   2797    277    0.099035   \n",
       "16    marital         single         single   1196    167    0.139632   \n",
       "17  education        primary        primary    678     64    0.094395   \n",
       "18  education      secondary      secondary   2306    245    0.106245   \n",
       "19  education       tertiary       tertiary   1350    193    0.142963   \n",
       "20  education        unknown        unknown    187     19    0.101604   \n",
       "21    default             no             no   4445    512    0.115186   \n",
       "22    default            yes            yes     76      9    0.118421   \n",
       "23    balance          -3313             69   1133     94    0.082966   \n",
       "24    balance             70            444   1128    106    0.093972   \n",
       "25    balance            445           1480   1131    149    0.131742   \n",
       "26    balance           1482          71188   1129    172    0.152347   \n",
       "27    housing             no             no   1962    301    0.153415   \n",
       "28    housing            yes            yes   2559    220    0.085971   \n",
       "29       loan             no             no   3830    478    0.124804   \n",
       "..        ...            ...            ...    ...    ...         ...   \n",
       "38      month            dec            dec     20      9    0.450000   \n",
       "39      month            feb            feb    222     38    0.171171   \n",
       "40      month            jan            jan    148     16    0.108108   \n",
       "41      month            jul            jul    706     61    0.086402   \n",
       "42      month            jun            jun    531     55    0.103578   \n",
       "43      month            mar            mar     49     21    0.428571   \n",
       "44      month            may            may   1398     93    0.066524   \n",
       "45      month            nov            nov    389     39    0.100257   \n",
       "46      month            oct            oct     80     37    0.462500   \n",
       "47      month            sep            sep     52     17    0.326923   \n",
       "48   duration              4             62    510      1    0.001961   \n",
       "49   duration             63             96    502     10    0.019920   \n",
       "50   duration             97            128    509     16    0.031434   \n",
       "51   duration            129            163    490     20    0.040816   \n",
       "52   duration            164            208    505     26    0.051485   \n",
       "53   duration            209            261    498     61    0.122490   \n",
       "54   duration            262            354    505     73    0.144554   \n",
       "55   duration            355            546    500    101    0.202000   \n",
       "56   duration            547           3025    502    213    0.424303   \n",
       "57   campaign              1              2   2998    378    0.126084   \n",
       "58   campaign              3             50   1523    143    0.093894   \n",
       "59      pdays             -1             -1   3705    337    0.090958   \n",
       "60      pdays              1              1      2      2    1.000000   \n",
       "61      pdays              2            871    814    182    0.223587   \n",
       "62   previous              0              1   3991    388    0.097219   \n",
       "63   previous              2             25    530    133    0.250943   \n",
       "64   poutcome        failure        failure    490     63    0.128571   \n",
       "65   poutcome          other          other    197     38    0.192893   \n",
       "66   poutcome        success        success    129     83    0.643411   \n",
       "67   poutcome        unknown        unknown   3705    337    0.090958   \n",
       "\n",
       "    NONEVENT  NON_EVENT_RATE  DIST_EVENT  DIST_NON_EVENT       WOE        IV  \n",
       "0       2031        0.886900    0.497121         0.50775 -0.021156  0.000452  \n",
       "1       1969        0.882564    0.502879         0.49225  0.021363  0.000452  \n",
       "2        420        0.878661    0.111324         0.10500  0.058488  0.132519  \n",
       "3        877        0.927061    0.132438         0.21925 -0.504101  0.132519  \n",
       "4        153        0.910714    0.028791         0.03825 -0.284088  0.132519  \n",
       "5         98        0.875000    0.026871         0.02450  0.092389  0.132519  \n",
       "6        838        0.864809    0.251440         0.20950  0.182479  0.132519  \n",
       "7        176        0.765217    0.103647         0.04400  0.856800  0.132519  \n",
       "8        163        0.890710    0.038388         0.04075 -0.059718  0.132519  \n",
       "9        379        0.908873    0.072937         0.09475 -0.261650  0.132519  \n",
       "10        65        0.773810    0.036468         0.01625  0.808351  0.132519  \n",
       "11       685        0.891927    0.159309         0.17125 -0.072279  0.132519  \n",
       "12       115        0.898438    0.024952         0.02875 -0.141683  0.132519  \n",
       "13        31        0.815789    0.013436         0.00775  0.550223  0.132519  \n",
       "14       451        0.854167    0.147793         0.11275  0.270638  0.040090  \n",
       "15      2520        0.900965    0.531670         0.63000 -0.169697  0.040090  \n",
       "16      1029        0.860368    0.320537         0.25725  0.219951  0.040090  \n",
       "17       614        0.905605    0.122841         0.15350 -0.222812  0.031812  \n",
       "18      2061        0.893755    0.470250         0.51525 -0.091389  0.031812  \n",
       "19      1157        0.857037    0.370441         0.28925  0.247404  0.031812  \n",
       "20       168        0.898396    0.036468         0.04200 -0.141225  0.031812  \n",
       "21      3933        0.884814    0.982726         0.98325 -0.000534  0.000016  \n",
       "22        67        0.881579    0.017274         0.01675  0.030832  0.000016  \n",
       "23      1039        0.917034    0.180422         0.25975 -0.364420  0.076208  \n",
       "24      1022        0.906028    0.203455         0.25550 -0.227778  0.076208  \n",
       "25       982        0.868258    0.285988         0.24550  0.152655  0.076208  \n",
       "26       957        0.847653    0.330134         0.23925  0.321991  0.076208  \n",
       "27      1661        0.846585    0.577735         0.41525  0.330235  0.106556  \n",
       "28      2339        0.914029    0.422265         0.58475 -0.325552  0.106556  \n",
       "29      3352        0.875196    0.917466         0.83800  0.090598  0.060791  \n",
       "..       ...             ...         ...             ...       ...       ...  \n",
       "38        11        0.550000    0.017274         0.00275  1.837629  0.379533  \n",
       "39       184        0.828829    0.072937         0.04600  0.460950  0.379533  \n",
       "40       132        0.891892    0.030710         0.03300 -0.071914  0.379533  \n",
       "41       645        0.913598    0.117083         0.16125 -0.320077  0.379533  \n",
       "42       476        0.896422    0.105566         0.11900 -0.119785  0.379533  \n",
       "43        28        0.571429    0.040307         0.00700  1.750618  0.379533  \n",
       "44      1305        0.933476    0.178503         0.32625 -0.603059  0.379533  \n",
       "45       350        0.899743    0.074856         0.08750 -0.156072  0.379533  \n",
       "46        43        0.537500    0.071017         0.01075  1.888017  0.379533  \n",
       "47        35        0.673077    0.032630         0.00875  1.316165  0.379533  \n",
       "48       509        0.998039    0.001919         0.12725 -4.194148  1.651501  \n",
       "49       492        0.980080    0.019194         0.12300 -1.857594  1.651501  \n",
       "50       493        0.968566    0.030710         0.12325 -1.389621  1.651501  \n",
       "51       470        0.959184    0.038388         0.11750 -1.118701  1.651501  \n",
       "52       479        0.948515    0.049904         0.11975 -0.875304  1.651501  \n",
       "53       437        0.877510    0.117083         0.10925  0.069240  1.651501  \n",
       "54       432        0.855446    0.140115         0.10800  0.260333  1.651501  \n",
       "55       399        0.798000    0.193858         0.09975  0.664459  1.651501  \n",
       "56       289        0.575697    0.408829         0.07225  1.733165  1.651501  \n",
       "57      2620        0.873916    0.725528         0.65500  0.102264  0.023342  \n",
       "58      1380        0.906106    0.274472         0.34500 -0.228695  0.023342  \n",
       "59      3368        0.909042    0.646833         0.84200 -0.263692  0.203267  \n",
       "60         0        0.000000    0.003839         0.00000  0.000000  0.203267  \n",
       "61       632        0.776413    0.349328         0.15800  0.793417  0.203267  \n",
       "62      3603        0.902781    0.744722         0.90075 -0.190217  0.177081  \n",
       "63       397        0.749057    0.255278         0.09925  0.944712  0.177081  \n",
       "64       427        0.871429    0.120921         0.10675  0.124650  0.461890  \n",
       "65       159        0.807107    0.072937         0.03975  0.606982  0.461890  \n",
       "66        46        0.356589    0.159309         0.01150  2.628499  0.461890  \n",
       "67      3368        0.909042    0.646833         0.84200 -0.263692  0.461890  \n",
       "\n",
       "[68 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_iv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>VAR_NAME</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>default</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.000452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>day</td>\n",
       "      <td>0.004581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>campaign</td>\n",
       "      <td>0.023342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>education</td>\n",
       "      <td>0.031812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>marital</td>\n",
       "      <td>0.040090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>loan</td>\n",
       "      <td>0.060791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>balance</td>\n",
       "      <td>0.076208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>housing</td>\n",
       "      <td>0.106556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>job</td>\n",
       "      <td>0.132519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>previous</td>\n",
       "      <td>0.177081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pdays</td>\n",
       "      <td>0.203267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>contact</td>\n",
       "      <td>0.247762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>month</td>\n",
       "      <td>0.379533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>0.461890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>duration</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     VAR_NAME        IV\n",
       "5     default  0.000016\n",
       "0         age  0.000452\n",
       "4         day  0.004581\n",
       "2    campaign  0.023342\n",
       "7   education  0.031812\n",
       "11    marital  0.040090\n",
       "10       loan  0.060791\n",
       "1     balance  0.076208\n",
       "8     housing  0.106556\n",
       "9         job  0.132519\n",
       "15   previous  0.177081\n",
       "13      pdays  0.203267\n",
       "3     contact  0.247762\n",
       "12      month  0.379533\n",
       "14   poutcome  0.461890\n",
       "6    duration  1.651501"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IV.sort_values('IV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "IV.to_csv('test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply WOE values to your dataframe columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below code snippet can be used to apply the WOE values to your dataframe columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transform_vars_list = df.columns.difference(['target'])\n",
    "transform_prefix = 'new_' # leave this value blank if you need replace the original column values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'balance', 'campaign', 'contact', 'day', 'default', 'duration',\n",
       "       'education', 'housing', 'job', 'loan', 'marital', 'month', 'pdays',\n",
       "       'poutcome', 'previous'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform_vars_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for var in transform_vars_list:\n",
    "    small_df = final_iv[final_iv['VAR_NAME'] == var]\n",
    "    transform_dict = dict(zip(small_df.MAX_VALUE,small_df.WOE))\n",
    "    replace_cmd = ''\n",
    "    replace_cmd1 = ''\n",
    "    for i in sorted(transform_dict.items()):\n",
    "        replace_cmd = replace_cmd + str(i[1]) + str(' if x <= ') + str(i[0]) + ' else '\n",
    "        replace_cmd1 = replace_cmd1 + str(i[1]) + str(' if x == \"') + str(i[0]) + '\" else '\n",
    "    replace_cmd = replace_cmd + '0'\n",
    "    replace_cmd1 = replace_cmd1 + '0'\n",
    "    if replace_cmd != '0':\n",
    "        try:\n",
    "            df[transform_prefix + var] = df[var].apply(lambda x: eval(replace_cmd))\n",
    "        except:\n",
    "            df[transform_prefix + var] = df[var].apply(lambda x: eval(replace_cmd1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cellular     2896\n",
       "unknown      1324\n",
       "telephone     301\n",
       "Name: contact, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['contact'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.252971    2896\n",
       "-0.992072    1324\n",
       " 0.273413     301\n",
       "Name: new_contact, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['new_contact'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "small_df = final_iv[final_iv['VAR_NAME'] == 'contact']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>VAR_NAME</th>\n",
       "      <th>MIN_VALUE</th>\n",
       "      <th>MAX_VALUE</th>\n",
       "      <th>COUNT</th>\n",
       "      <th>EVENT</th>\n",
       "      <th>EVENT_RATE</th>\n",
       "      <th>NONEVENT</th>\n",
       "      <th>NON_EVENT_RATE</th>\n",
       "      <th>DIST_EVENT</th>\n",
       "      <th>DIST_NON_EVENT</th>\n",
       "      <th>WOE</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>contact</td>\n",
       "      <td>cellular</td>\n",
       "      <td>cellular</td>\n",
       "      <td>2896</td>\n",
       "      <td>416</td>\n",
       "      <td>0.143646</td>\n",
       "      <td>2480</td>\n",
       "      <td>0.856354</td>\n",
       "      <td>0.798464</td>\n",
       "      <td>0.62000</td>\n",
       "      <td>0.252971</td>\n",
       "      <td>0.247762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>contact</td>\n",
       "      <td>telephone</td>\n",
       "      <td>telephone</td>\n",
       "      <td>301</td>\n",
       "      <td>44</td>\n",
       "      <td>0.146179</td>\n",
       "      <td>257</td>\n",
       "      <td>0.853821</td>\n",
       "      <td>0.084453</td>\n",
       "      <td>0.06425</td>\n",
       "      <td>0.273413</td>\n",
       "      <td>0.247762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>contact</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1324</td>\n",
       "      <td>61</td>\n",
       "      <td>0.046073</td>\n",
       "      <td>1263</td>\n",
       "      <td>0.953927</td>\n",
       "      <td>0.117083</td>\n",
       "      <td>0.31575</td>\n",
       "      <td>-0.992072</td>\n",
       "      <td>0.247762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VAR_NAME  MIN_VALUE  MAX_VALUE  COUNT  EVENT  EVENT_RATE  NONEVENT  \\\n",
       "31  contact   cellular   cellular   2896    416    0.143646      2480   \n",
       "32  contact  telephone  telephone    301     44    0.146179       257   \n",
       "33  contact    unknown    unknown   1324     61    0.046073      1263   \n",
       "\n",
       "    NON_EVENT_RATE  DIST_EVENT  DIST_NON_EVENT       WOE        IV  \n",
       "31        0.856354    0.798464         0.62000  0.252971  0.247762  \n",
       "32        0.853821    0.084453         0.06425  0.273413  0.247762  \n",
       "33        0.953927    0.117083         0.31575 -0.992072  0.247762  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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