Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing data\n",
    "\n",
    "bank = pd.read_csv(\"bank-additional-full.csv\",sep = ';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 41188 rows and 21 coloumns\n"
     ]
    }
   ],
   "source": [
    "#check the shape of the dataset\n",
    "\n",
    "print(\"There are {} rows and {} coloumns\".format(bank.shape[0],bank.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0          93.994          -36.4      4.857       5191.0  no  \n",
       "1          93.994          -36.4      4.857       5191.0  no  \n",
       "2          93.994          -36.4      4.857       5191.0  no  \n",
       "3          93.994          -36.4      4.857       5191.0  no  \n",
       "4          93.994          -36.4      4.857       5191.0  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see first 5 Observations from the data set\n",
    "\n",
    "bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41183</th>\n",
       "      <td>73</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>46</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>56</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41187</th>\n",
       "      <td>74</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job  marital            education default housing loan  \\\n",
       "41183   73      retired  married  professional.course      no     yes   no   \n",
       "41184   46  blue-collar  married  professional.course      no      no   no   \n",
       "41185   56      retired  married    university.degree      no     yes   no   \n",
       "41186   44   technician  married  professional.course      no      no   no   \n",
       "41187   74      retired  married  professional.course      no     yes   no   \n",
       "\n",
       "        contact month day_of_week ...   campaign  pdays  previous  \\\n",
       "41183  cellular   nov         fri ...          1    999         0   \n",
       "41184  cellular   nov         fri ...          1    999         0   \n",
       "41185  cellular   nov         fri ...          2    999         0   \n",
       "41186  cellular   nov         fri ...          1    999         0   \n",
       "41187  cellular   nov         fri ...          3    999         1   \n",
       "\n",
       "          poutcome emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "41183  nonexistent         -1.1          94.767          -50.8      1.028   \n",
       "41184  nonexistent         -1.1          94.767          -50.8      1.028   \n",
       "41185  nonexistent         -1.1          94.767          -50.8      1.028   \n",
       "41186  nonexistent         -1.1          94.767          -50.8      1.028   \n",
       "41187      failure         -1.1          94.767          -50.8      1.028   \n",
       "\n",
       "       nr.employed    y  \n",
       "41183       4963.6  yes  \n",
       "41184       4963.6   no  \n",
       "41185       4963.6   no  \n",
       "41186       4963.6  yes  \n",
       "41187       4963.6   no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see last 5 observations of the data\n",
    "\n",
    "bank.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      "age               41188 non-null int64\n",
      "job               41188 non-null object\n",
      "marital           41188 non-null object\n",
      "education         41188 non-null object\n",
      "default           41188 non-null object\n",
      "housing           41188 non-null object\n",
      "loan              41188 non-null object\n",
      "contact           41188 non-null object\n",
      "month             41188 non-null object\n",
      "day_of_week       41188 non-null object\n",
      "duration          41188 non-null int64\n",
      "campaign          41188 non-null int64\n",
      "pdays             41188 non-null int64\n",
      "previous          41188 non-null int64\n",
      "poutcome          41188 non-null object\n",
      "emp.var.rate      41188 non-null float64\n",
      "cons.price.idx    41188 non-null float64\n",
      "cons.conf.idx     41188 non-null float64\n",
      "euribor3m         41188 non-null float64\n",
      "nr.employed       41188 non-null float64\n",
      "y                 41188 non-null object\n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# To know the data type of each feature\n",
    "\n",
    "bank.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in bank.columns:\n",
    "    if bank[column].dtype == 'object':\n",
    "        bank[column] = bank[column].astype('category') \n",
    "    else:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      "age               41188 non-null int64\n",
      "job               41188 non-null category\n",
      "marital           41188 non-null category\n",
      "education         41188 non-null category\n",
      "default           41188 non-null category\n",
      "housing           41188 non-null category\n",
      "loan              41188 non-null category\n",
      "contact           41188 non-null category\n",
      "month             41188 non-null category\n",
      "day_of_week       41188 non-null category\n",
      "duration          41188 non-null int64\n",
      "campaign          41188 non-null int64\n",
      "pdays             41188 non-null int64\n",
      "previous          41188 non-null int64\n",
      "poutcome          41188 non-null category\n",
      "emp.var.rate      41188 non-null float64\n",
      "cons.price.idx    41188 non-null float64\n",
      "cons.conf.idx     41188 non-null float64\n",
      "euribor3m         41188 non-null float64\n",
      "nr.employed       41188 non-null float64\n",
      "y                 41188 non-null category\n",
      "dtypes: category(11), float64(5), int64(5)\n",
      "memory usage: 3.6 MB\n"
     ]
    }
   ],
   "source": [
    "bank.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>40.024060</td>\n",
       "      <td>10.421250</td>\n",
       "      <td>17.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>38.000</td>\n",
       "      <td>47.000</td>\n",
       "      <td>98.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>258.285010</td>\n",
       "      <td>259.279249</td>\n",
       "      <td>0.000</td>\n",
       "      <td>102.000</td>\n",
       "      <td>180.000</td>\n",
       "      <td>319.000</td>\n",
       "      <td>4918.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>2.567593</td>\n",
       "      <td>2.770014</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>56.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>962.475454</td>\n",
       "      <td>186.910907</td>\n",
       "      <td>0.000</td>\n",
       "      <td>999.000</td>\n",
       "      <td>999.000</td>\n",
       "      <td>999.000</td>\n",
       "      <td>999.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>0.172963</td>\n",
       "      <td>0.494901</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp.var.rate</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>1.570960</td>\n",
       "      <td>-3.400</td>\n",
       "      <td>-1.800</td>\n",
       "      <td>1.100</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.price.idx</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>93.575664</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>92.201</td>\n",
       "      <td>93.075</td>\n",
       "      <td>93.749</td>\n",
       "      <td>93.994</td>\n",
       "      <td>94.767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>-40.502600</td>\n",
       "      <td>4.628198</td>\n",
       "      <td>-50.800</td>\n",
       "      <td>-42.700</td>\n",
       "      <td>-41.800</td>\n",
       "      <td>-36.400</td>\n",
       "      <td>-26.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euribor3m</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>3.621291</td>\n",
       "      <td>1.734447</td>\n",
       "      <td>0.634</td>\n",
       "      <td>1.344</td>\n",
       "      <td>4.857</td>\n",
       "      <td>4.961</td>\n",
       "      <td>5.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nr.employed</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>5167.035911</td>\n",
       "      <td>72.251528</td>\n",
       "      <td>4963.600</td>\n",
       "      <td>5099.100</td>\n",
       "      <td>5191.000</td>\n",
       "      <td>5228.100</td>\n",
       "      <td>5228.100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count         mean         std       min       25%  \\\n",
       "age             41188.0    40.024060   10.421250    17.000    32.000   \n",
       "duration        41188.0   258.285010  259.279249     0.000   102.000   \n",
       "campaign        41188.0     2.567593    2.770014     1.000     1.000   \n",
       "pdays           41188.0   962.475454  186.910907     0.000   999.000   \n",
       "previous        41188.0     0.172963    0.494901     0.000     0.000   \n",
       "emp.var.rate    41188.0     0.081886    1.570960    -3.400    -1.800   \n",
       "cons.price.idx  41188.0    93.575664    0.578840    92.201    93.075   \n",
       "cons.conf.idx   41188.0   -40.502600    4.628198   -50.800   -42.700   \n",
       "euribor3m       41188.0     3.621291    1.734447     0.634     1.344   \n",
       "nr.employed     41188.0  5167.035911   72.251528  4963.600  5099.100   \n",
       "\n",
       "                     50%       75%       max  \n",
       "age               38.000    47.000    98.000  \n",
       "duration         180.000   319.000  4918.000  \n",
       "campaign           2.000     3.000    56.000  \n",
       "pdays            999.000   999.000   999.000  \n",
       "previous           0.000     0.000     7.000  \n",
       "emp.var.rate       1.100     1.400     1.400  \n",
       "cons.price.idx    93.749    93.994    94.767  \n",
       "cons.conf.idx    -41.800   -36.400   -26.900  \n",
       "euribor3m          4.857     4.961     5.045  \n",
       "nr.employed     5191.000  5228.100  5228.100  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**Input variables:**\n",
    "\n",
    "- 1 - age (numeric)\n",
    "\n",
    "- 2 - job : \n",
    "Type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')\n",
    "\n",
    "- 3 - marital :\n",
    "marital status (categorical: 'divorced','married','single','unknown';) \n",
    "                note: 'divorced' means divorced or widowed)\n",
    "\n",
    "- 4 - education : \n",
    "(categorical: 'tertiary', 'secondary', 'unknown', 'primary')\n",
    "\n",
    "- 5 - default: has credit in default? \n",
    "(categorical: 'no','yes','unknown')\n",
    "\n",
    "- 6 - housing: has housing loan? \n",
    "(categorical: 'no','yes','unknown')\n",
    "\n",
    "- 7 - loan: has personal loan? \n",
    "(categorical: 'no','yes','unknown')\n",
    "\n",
    "- 8 - contact: contact communication type \n",
    "(categorical: 'cellular','telephone')\n",
    "\n",
    "- 9 - month: last contact month of year \n",
    "(categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
    "\n",
    "- 10 - day: last contact day of the month \n",
    "(numeric: 1,2,3,....)\n",
    "\n",
    "- 11 - duration: last contact duration, in seconds (numeric).\n",
    "\n",
    "        Important note: This attribute highly affects the output Target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.\n",
    "\n",
    "- 12 - campaign: number of contacts performed during this campaign and for this client \n",
    "(numeric, includes last contact)\n",
    "\n",
    "- 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign \n",
    "(numeric; 999 means client was not previously contacted)\n",
    "\n",
    "- 14 - previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "\n",
    "- 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')\n",
    " social and economic context attributes\n",
    "\n",
    "- 16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)\n",
    "\n",
    "- 17 - cons.price.idx: consumer price index - monthly indicator (numeric) \n",
    "\n",
    "- 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) \n",
    "\n",
    "- 19 - euribor3m: euribor 3 month rate - daily indicator (numeric)\n",
    "\n",
    "- 20 - nr.employed: number of employees - quarterly indicator (numeric)\n",
    "\n",
    "Output variable (desired target):\n",
    "- 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A term deposit is a cash investment held at a bank.Money is invested for an agreed rate of interest over a fixed amount of time, or term. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "job               0\n",
       "marital           0\n",
       "education         0\n",
       "default           0\n",
       "housing           0\n",
       "loan              0\n",
       "contact           0\n",
       "month             0\n",
       "day_of_week       0\n",
       "duration          0\n",
       "campaign          0\n",
       "pdays             0\n",
       "previous          0\n",
       "poutcome          0\n",
       "emp.var.rate      0\n",
       "cons.price.idx    0\n",
       "cons.conf.idx     0\n",
       "euribor3m         0\n",
       "nr.employed       0\n",
       "y                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check whether Null values is there or not in the dataset\n",
    "\n",
    "bank.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **There are no Null values in the data.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     36548\n",
       "yes     4640\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='y',data=bank, palette='hls')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 36548 no's and 4640 yes's in the outcome variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "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>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>39.911185</td>\n",
       "      <td>220.844807</td>\n",
       "      <td>2.633085</td>\n",
       "      <td>984.113878</td>\n",
       "      <td>0.132374</td>\n",
       "      <td>0.248875</td>\n",
       "      <td>93.603757</td>\n",
       "      <td>-40.593097</td>\n",
       "      <td>3.811491</td>\n",
       "      <td>5176.166600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>40.913147</td>\n",
       "      <td>553.191164</td>\n",
       "      <td>2.051724</td>\n",
       "      <td>792.035560</td>\n",
       "      <td>0.492672</td>\n",
       "      <td>-1.233448</td>\n",
       "      <td>93.354386</td>\n",
       "      <td>-39.789784</td>\n",
       "      <td>2.123135</td>\n",
       "      <td>5095.115991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age    duration  campaign       pdays  previous  emp.var.rate  \\\n",
       "y                                                                          \n",
       "no   39.911185  220.844807  2.633085  984.113878  0.132374      0.248875   \n",
       "yes  40.913147  553.191164  2.051724  792.035560  0.492672     -1.233448   \n",
       "\n",
       "     cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \n",
       "y                                                           \n",
       "no        93.603757     -40.593097   3.811491  5176.166600  \n",
       "yes       93.354386     -39.789784   2.123135  5095.115991  "
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's get a sense of the numbers across the two classes\n",
    "\n",
    "bank.groupby('y').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "1.The average age of customers who bought the term deposit is higher than that of the customers who didn't.\n",
    "\n",
    "2.Surprisingly, campaigns (number of contacts or calls made during the current campaign) are lower for customers who bought the term deposit.\n",
    "\n",
    "3.The Average duration of the calls(in seconds) is more for customers who bought the term deposit than who didn't\n",
    "\n",
    "4.The Average number of contacts performed before this campaign influencying the customers to bought the term deposit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x23be7524278>"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(bank.age,bank.y,marker='+',color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "pd.crosstab(bank.job,bank.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Job Title')\n",
    "plt.xlabel('Job')\n",
    "plt.ylabel('Frequency of Purchase')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The frequency of purchase of the deposit depends a great deal on the job title. Thus, the job title can be a good predictor of the outcome variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Proportion of Customers')"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(bank.marital,bank.y)\n",
    "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "plt.title('Stacked Bar Chart of Marital Status vs Purchase')\n",
    "plt.xlabel('Marital Status')\n",
    "plt.ylabel('Proportion of Customers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The marital status does not seem a strong predictor for the outcome variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Proportion of Customers')"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(bank.education,bank.y)\n",
    "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "plt.title('Stacked Bar Chart of Education vs Purchase')\n",
    "plt.xlabel('Education')\n",
    "plt.ylabel('Proportion of Customers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Education seems a good predictor of the outcome variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency of Purchase')"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(bank.day_of_week,bank.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Day of Week')\n",
    "plt.xlabel('Day of Week')\n",
    "plt.ylabel('Frequency of Purchase')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency of Purchase')"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(bank.month,bank.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Month')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Frequency of Purchase')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency of Purchase')"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(bank.poutcome,bank.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Poutcome')\n",
    "plt.xlabel('Poutcome')\n",
    "plt.ylabel('Frequency of Purchase')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      "age               41188 non-null int64\n",
      "job               41188 non-null object\n",
      "marital           41188 non-null object\n",
      "education         41188 non-null object\n",
      "default           41188 non-null object\n",
      "housing           41188 non-null object\n",
      "loan              41188 non-null object\n",
      "contact           41188 non-null object\n",
      "month             41188 non-null object\n",
      "day_of_week       41188 non-null object\n",
      "duration          41188 non-null int64\n",
      "campaign          41188 non-null int64\n",
      "pdays             41188 non-null int64\n",
      "previous          41188 non-null int64\n",
      "poutcome          41188 non-null object\n",
      "emp.var.rate      41188 non-null float64\n",
      "cons.price.idx    41188 non-null float64\n",
      "cons.conf.idx     41188 non-null float64\n",
      "euribor3m         41188 non-null float64\n",
      "nr.employed       41188 non-null float64\n",
      "y                 41188 non-null object\n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "bank.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41188, 11)\n",
      "(41188, 10)\n"
     ]
    }
   ],
   "source": [
    "categorical_data = bank.select_dtypes(include='category')\n",
    "numerical_data = bank.select_dtypes(include=('int64','float64'))\n",
    "\n",
    "print(categorical_data.shape)\n",
    "print(numerical_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "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>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000866</td>\n",
       "      <td>0.004594</td>\n",
       "      <td>-0.034369</td>\n",
       "      <td>0.024365</td>\n",
       "      <td>-0.000371</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.129372</td>\n",
       "      <td>0.010767</td>\n",
       "      <td>-0.017725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>-0.000866</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.071699</td>\n",
       "      <td>-0.047577</td>\n",
       "      <td>0.020640</td>\n",
       "      <td>-0.027968</td>\n",
       "      <td>0.005312</td>\n",
       "      <td>-0.008173</td>\n",
       "      <td>-0.032897</td>\n",
       "      <td>-0.044703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>0.004594</td>\n",
       "      <td>-0.071699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052584</td>\n",
       "      <td>-0.079141</td>\n",
       "      <td>0.150754</td>\n",
       "      <td>0.127836</td>\n",
       "      <td>-0.013733</td>\n",
       "      <td>0.135133</td>\n",
       "      <td>0.144095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>-0.034369</td>\n",
       "      <td>-0.047577</td>\n",
       "      <td>0.052584</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.587514</td>\n",
       "      <td>0.271004</td>\n",
       "      <td>0.078889</td>\n",
       "      <td>-0.091342</td>\n",
       "      <td>0.296899</td>\n",
       "      <td>0.372605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>0.024365</td>\n",
       "      <td>0.020640</td>\n",
       "      <td>-0.079141</td>\n",
       "      <td>-0.587514</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420489</td>\n",
       "      <td>-0.203130</td>\n",
       "      <td>-0.050936</td>\n",
       "      <td>-0.454494</td>\n",
       "      <td>-0.501333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp.var.rate</th>\n",
       "      <td>-0.000371</td>\n",
       "      <td>-0.027968</td>\n",
       "      <td>0.150754</td>\n",
       "      <td>0.271004</td>\n",
       "      <td>-0.420489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.775334</td>\n",
       "      <td>0.196041</td>\n",
       "      <td>0.972245</td>\n",
       "      <td>0.906970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.price.idx</th>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.005312</td>\n",
       "      <td>0.127836</td>\n",
       "      <td>0.078889</td>\n",
       "      <td>-0.203130</td>\n",
       "      <td>0.775334</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058986</td>\n",
       "      <td>0.688230</td>\n",
       "      <td>0.522034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <td>0.129372</td>\n",
       "      <td>-0.008173</td>\n",
       "      <td>-0.013733</td>\n",
       "      <td>-0.091342</td>\n",
       "      <td>-0.050936</td>\n",
       "      <td>0.196041</td>\n",
       "      <td>0.058986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.277686</td>\n",
       "      <td>0.100513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euribor3m</th>\n",
       "      <td>0.010767</td>\n",
       "      <td>-0.032897</td>\n",
       "      <td>0.135133</td>\n",
       "      <td>0.296899</td>\n",
       "      <td>-0.454494</td>\n",
       "      <td>0.972245</td>\n",
       "      <td>0.688230</td>\n",
       "      <td>0.277686</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.945154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nr.employed</th>\n",
       "      <td>-0.017725</td>\n",
       "      <td>-0.044703</td>\n",
       "      <td>0.144095</td>\n",
       "      <td>0.372605</td>\n",
       "      <td>-0.501333</td>\n",
       "      <td>0.906970</td>\n",
       "      <td>0.522034</td>\n",
       "      <td>0.100513</td>\n",
       "      <td>0.945154</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age  duration  campaign     pdays  previous  \\\n",
       "age             1.000000 -0.000866  0.004594 -0.034369  0.024365   \n",
       "duration       -0.000866  1.000000 -0.071699 -0.047577  0.020640   \n",
       "campaign        0.004594 -0.071699  1.000000  0.052584 -0.079141   \n",
       "pdays          -0.034369 -0.047577  0.052584  1.000000 -0.587514   \n",
       "previous        0.024365  0.020640 -0.079141 -0.587514  1.000000   \n",
       "emp.var.rate   -0.000371 -0.027968  0.150754  0.271004 -0.420489   \n",
       "cons.price.idx  0.000857  0.005312  0.127836  0.078889 -0.203130   \n",
       "cons.conf.idx   0.129372 -0.008173 -0.013733 -0.091342 -0.050936   \n",
       "euribor3m       0.010767 -0.032897  0.135133  0.296899 -0.454494   \n",
       "nr.employed    -0.017725 -0.044703  0.144095  0.372605 -0.501333   \n",
       "\n",
       "                emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "age                -0.000371        0.000857       0.129372   0.010767   \n",
       "duration           -0.027968        0.005312      -0.008173  -0.032897   \n",
       "campaign            0.150754        0.127836      -0.013733   0.135133   \n",
       "pdays               0.271004        0.078889      -0.091342   0.296899   \n",
       "previous           -0.420489       -0.203130      -0.050936  -0.454494   \n",
       "emp.var.rate        1.000000        0.775334       0.196041   0.972245   \n",
       "cons.price.idx      0.775334        1.000000       0.058986   0.688230   \n",
       "cons.conf.idx       0.196041        0.058986       1.000000   0.277686   \n",
       "euribor3m           0.972245        0.688230       0.277686   1.000000   \n",
       "nr.employed         0.906970        0.522034       0.100513   0.945154   \n",
       "\n",
       "                nr.employed  \n",
       "age               -0.017725  \n",
       "duration          -0.044703  \n",
       "campaign           0.144095  \n",
       "pdays              0.372605  \n",
       "previous          -0.501333  \n",
       "emp.var.rate       0.906970  \n",
       "cons.price.idx     0.522034  \n",
       "cons.conf.idx      0.100513  \n",
       "euribor3m          0.945154  \n",
       "nr.employed        1.000000  "
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = numerical_data.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "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>columns</th>\n",
       "      <th>vif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>16.047296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>duration</td>\n",
       "      <td>2.011044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>campaign</td>\n",
       "      <td>1.921499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pdays</td>\n",
       "      <td>44.413175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>previous</td>\n",
       "      <td>2.001464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>emp.var.rate</td>\n",
       "      <td>28.910219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cons.price.idx</td>\n",
       "      <td>22561.123124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cons.conf.idx</td>\n",
       "      <td>120.086975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>euribor3m</td>\n",
       "      <td>226.237349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nr.employed</td>\n",
       "      <td>26746.634212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          columns           vif\n",
       "0             age     16.047296\n",
       "1        duration      2.011044\n",
       "2        campaign      1.921499\n",
       "3           pdays     44.413175\n",
       "4        previous      2.001464\n",
       "5    emp.var.rate     28.910219\n",
       "6  cons.price.idx  22561.123124\n",
       "7   cons.conf.idx    120.086975\n",
       "8       euribor3m    226.237349\n",
       "9     nr.employed  26746.634212"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "def VIFfunc(x):\n",
    "    data_mat = x.as_matrix()\n",
    "    vif = [variance_inflation_factor(data_mat,i) for i in range(data_mat.shape[1])]\n",
    "    vif_factors = pd.DataFrame()\n",
    "    vif_factors['columns'] = x.columns\n",
    "    vif_factors['vif'] = vif\n",
    "    return vif_factors\n",
    "\n",
    "VIFfunc(numerical_data)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_data.drop(['nr.employed','cons.price.idx','euribor3m'],axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "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>columns</th>\n",
       "      <th>vif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>13.023272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>duration</td>\n",
       "      <td>1.984789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>campaign</td>\n",
       "      <td>1.911105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pdays</td>\n",
       "      <td>34.257941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>previous</td>\n",
       "      <td>1.773642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>emp.var.rate</td>\n",
       "      <td>1.270128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cons.conf.idx</td>\n",
       "      <td>39.323864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns        vif\n",
       "0            age  13.023272\n",
       "1       duration   1.984789\n",
       "2       campaign   1.911105\n",
       "3          pdays  34.257941\n",
       "4       previous   1.773642\n",
       "5   emp.var.rate   1.270128\n",
       "6  cons.conf.idx  39.323864"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VIFfunc(numerical_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_data.drop(['cons.conf.idx','pdays'],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "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>columns</th>\n",
       "      <th>vif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>2.806385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>duration</td>\n",
       "      <td>1.876292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>campaign</td>\n",
       "      <td>1.807322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>previous</td>\n",
       "      <td>1.357120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>emp.var.rate</td>\n",
       "      <td>1.238962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        columns       vif\n",
       "0           age  2.806385\n",
       "1      duration  1.876292\n",
       "2      campaign  1.807322\n",
       "3      previous  1.357120\n",
       "4  emp.var.rate  1.238962"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VIFfunc(numerical_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use hist to create histogram\n",
    "\n",
    "numerical_data.age.plot(kind='hist', title='histogram for Age', color='c');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness for age : 0.78\n"
     ]
    }
   ],
   "source": [
    "print('skewness for age : {0:.2f}'.format(numerical_data.age.skew()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23be7d10c88>"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_data.age.plot(kind = 'box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- If your data distribution is Substantially positive skewness Use  Logarithmic (Log 10) transformation method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try some transformations to reduce the skewness\n",
    "\n",
    "numerical_data.age = np.log(bank.age) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23be7d39780>"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank.age.plot(kind = 'box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skewness for age : -0.04\n"
     ]
    }
   ],
   "source": [
    "print('skewness for age : {0:.2f}'.format(numerical_data.age.skew()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_data.age.plot(kind='hist', title='histogram for Age', color='c');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "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>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.392612</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.397000</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.283962</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.305323</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.392612</td>\n",
       "      <td>307</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  duration  campaign  previous  emp.var.rate\n",
       "0  1.392612       261         1         0           1.1\n",
       "1  1.397000       149         1         0           1.1\n",
       "2  1.283962       226         1         0           1.1\n",
       "3  1.305323       151         1         0           1.1\n",
       "4  1.392612       307         1         0           1.1"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         job  marital    education  default housing loan    contact month  \\\n",
       "0  housemaid  married     basic.4y       no      no   no  telephone   may   \n",
       "1   services  married  high.school  unknown      no   no  telephone   may   \n",
       "2   services  married  high.school       no     yes   no  telephone   may   \n",
       "3     admin.  married     basic.6y       no      no   no  telephone   may   \n",
       "4   services  married  high.school       no      no  yes  telephone   may   \n",
       "\n",
       "  day_of_week     poutcome   y  \n",
       "0         mon  nonexistent  no  \n",
       "1         mon  nonexistent  no  \n",
       "2         mon  nonexistent  no  \n",
       "3         mon  nonexistent  no  \n",
       "4         mon  nonexistent  no  "
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_data = bank.select_dtypes(include='uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "#Auto encodes any dataframe column of type category or object.\n",
    "def dummyEncode(df):\n",
    "        columnsToEncode = list(bank.select_dtypes(include=['category','object']))\n",
    "        le = LabelEncoder()\n",
    "        for feature in columnsToEncode:\n",
    "            try:\n",
    "                bank[feature] = le.fit_transform(bank[feature])\n",
    "            except:\n",
    "                print('Error encoding '+feature)\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>57</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>54</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>39</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>55</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41158</th>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.035</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41159</th>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.035</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41160</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.035</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41161</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.035</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41162</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.035</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41163</th>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.035</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41164</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.035</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41165</th>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.030</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41166</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.030</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41167</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.030</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41168</th>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.030</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41169</th>\n",
       "      <td>62</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.030</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41170</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.030</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41171</th>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.031</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41172</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.031</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41173</th>\n",
       "      <td>62</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.031</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41174</th>\n",
       "      <td>62</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.031</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41175</th>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.031</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41176</th>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.031</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41177</th>\n",
       "      <td>57</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.031</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41178</th>\n",
       "      <td>62</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.031</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41179</th>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41180</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41181</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41182</th>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41183</th>\n",
       "      <td>73</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>56</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41187</th>\n",
       "      <td>74</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41188 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  job  marital  education  default  housing  loan  contact  month  \\\n",
       "0       56    3        1          0        0        0     0        1      6   \n",
       "1       57    7        1          3        1        0     0        1      6   \n",
       "2       37    7        1          3        0        2     0        1      6   \n",
       "3       40    0        1          1        0        0     0        1      6   \n",
       "4       56    7        1          3        0        0     2        1      6   \n",
       "5       45    7        1          2        1        0     0        1      6   \n",
       "6       59    0        1          5        0        0     0        1      6   \n",
       "7       41    1        1          7        1        0     0        1      6   \n",
       "8       24    9        2          5        0        2     0        1      6   \n",
       "9       25    7        2          3        0        2     0        1      6   \n",
       "10      41    1        1          7        1        0     0        1      6   \n",
       "11      25    7        2          3        0        2     0        1      6   \n",
       "12      29    1        2          3        0        0     2        1      6   \n",
       "13      57    3        0          0        0        2     0        1      6   \n",
       "14      35    1        1          1        0        2     0        1      6   \n",
       "15      54    5        1          2        1        2     2        1      6   \n",
       "16      35    1        1          1        0        2     0        1      6   \n",
       "17      46    1        1          1        1        2     2        1      6   \n",
       "18      50    1        1          2        0        2     2        1      6   \n",
       "19      39    4        2          2        1        0     0        1      6   \n",
       "20      30   10        1          3        0        0     0        1      6   \n",
       "21      55    1        1          0        1        2     0        1      6   \n",
       "22      55    5        2          3        0        2     0        1      6   \n",
       "23      41    9        2          3        0        2     0        1      6   \n",
       "24      37    0        1          3        0        2     0        1      6   \n",
       "25      35    9        1          6        0        0     2        1      6   \n",
       "26      59    9        1          7        0        2     0        1      6   \n",
       "27      39    6        1          2        1        0     0        1      6   \n",
       "28      54    9        2          6        1        0     0        1      6   \n",
       "29      55   11        1          6        1        1     1        1      6   \n",
       "...    ...  ...      ...        ...      ...      ...   ...      ...    ...   \n",
       "41158   35    9        0          0        0        0     0        0      7   \n",
       "41159   35    9        0          0        0        2     0        0      7   \n",
       "41160   33    0        1          6        0        0     0        0      7   \n",
       "41161   33    0        1          6        0        2     0        0      7   \n",
       "41162   60    1        1          0        0        2     0        0      7   \n",
       "41163   35    9        0          0        0        2     0        0      7   \n",
       "41164   54    0        1          5        0        0     0        0      7   \n",
       "41165   38    3        0          6        0        0     0        0      7   \n",
       "41166   32    0        1          6        0        0     0        1      7   \n",
       "41167   32    0        1          6        0        2     0        0      7   \n",
       "41168   38    2        1          6        0        0     0        0      7   \n",
       "41169   62    7        1          3        0        2     0        0      7   \n",
       "41170   40    4        0          6        0        2     0        0      7   \n",
       "41171   33    8        1          5        0        2     0        1      7   \n",
       "41172   31    0        2          6        0        2     0        0      7   \n",
       "41173   62    5        1          6        0        2     0        0      7   \n",
       "41174   62    5        1          6        0        2     0        0      7   \n",
       "41175   34    8        2          7        0        2     0        0      7   \n",
       "41176   38    3        0          3        0        2     2        0      7   \n",
       "41177   57    5        1          5        0        2     0        0      7   \n",
       "41178   62    5        1          6        0        0     0        0      7   \n",
       "41179   64    5        0          5        0        2     0        0      7   \n",
       "41180   36    0        1          6        0        0     0        0      7   \n",
       "41181   37    0        1          6        0        2     0        0      7   \n",
       "41182   29   10        2          0        0        2     0        0      7   \n",
       "41183   73    5        1          5        0        2     0        0      7   \n",
       "41184   46    1        1          5        0        0     0        0      7   \n",
       "41185   56    5        1          6        0        2     0        0      7   \n",
       "41186   44    9        1          5        0        0     0        0      7   \n",
       "41187   74    5        1          5        0        2     0        0      7   \n",
       "\n",
       "       day_of_week ...  campaign  pdays  previous  poutcome  emp.var.rate  \\\n",
       "0                1 ...         1    999         0         1           1.1   \n",
       "1                1 ...         1    999         0         1           1.1   \n",
       "2                1 ...         1    999         0         1           1.1   \n",
       "3                1 ...         1    999         0         1           1.1   \n",
       "4                1 ...         1    999         0         1           1.1   \n",
       "5                1 ...         1    999         0         1           1.1   \n",
       "6                1 ...         1    999         0         1           1.1   \n",
       "7                1 ...         1    999         0         1           1.1   \n",
       "8                1 ...         1    999         0         1           1.1   \n",
       "9                1 ...         1    999         0         1           1.1   \n",
       "10               1 ...         1    999         0         1           1.1   \n",
       "11               1 ...         1    999         0         1           1.1   \n",
       "12               1 ...         1    999         0         1           1.1   \n",
       "13               1 ...         1    999         0         1           1.1   \n",
       "14               1 ...         1    999         0         1           1.1   \n",
       "15               1 ...         1    999         0         1           1.1   \n",
       "16               1 ...         1    999         0         1           1.1   \n",
       "17               1 ...         1    999         0         1           1.1   \n",
       "18               1 ...         1    999         0         1           1.1   \n",
       "19               1 ...         1    999         0         1           1.1   \n",
       "20               1 ...         1    999         0         1           1.1   \n",
       "21               1 ...         1    999         0         1           1.1   \n",
       "22               1 ...         1    999         0         1           1.1   \n",
       "23               1 ...         1    999         0         1           1.1   \n",
       "24               1 ...         1    999         0         1           1.1   \n",
       "25               1 ...         1    999         0         1           1.1   \n",
       "26               1 ...         1    999         0         1           1.1   \n",
       "27               1 ...         1    999         0         1           1.1   \n",
       "28               1 ...         2    999         0         1           1.1   \n",
       "29               1 ...         1    999         0         1           1.1   \n",
       "...            ... ...       ...    ...       ...       ...           ...   \n",
       "41158            3 ...         1    999         0         1          -1.1   \n",
       "41159            3 ...         1      9         4         2          -1.1   \n",
       "41160            3 ...         1    999         0         1          -1.1   \n",
       "41161            3 ...         1    999         1         0          -1.1   \n",
       "41162            3 ...         2      4         1         2          -1.1   \n",
       "41163            3 ...         3      4         2         2          -1.1   \n",
       "41164            3 ...         2     10         1         2          -1.1   \n",
       "41165            4 ...         2    999         0         1          -1.1   \n",
       "41166            4 ...         1    999         1         0          -1.1   \n",
       "41167            4 ...         3    999         0         1          -1.1   \n",
       "41168            4 ...         2    999         0         1          -1.1   \n",
       "41169            4 ...         5    999         0         1          -1.1   \n",
       "41170            4 ...         2    999         4         0          -1.1   \n",
       "41171            2 ...         1    999         0         1          -1.1   \n",
       "41172            2 ...         1    999         0         1          -1.1   \n",
       "41173            2 ...         1    999         2         0          -1.1   \n",
       "41174            2 ...         1      1         6         2          -1.1   \n",
       "41175            2 ...         1    999         2         0          -1.1   \n",
       "41176            2 ...         1    999         0         1          -1.1   \n",
       "41177            2 ...         6    999         0         1          -1.1   \n",
       "41178            2 ...         2      6         3         2          -1.1   \n",
       "41179            0 ...         3    999         0         1          -1.1   \n",
       "41180            0 ...         2    999         0         1          -1.1   \n",
       "41181            0 ...         1    999         0         1          -1.1   \n",
       "41182            0 ...         1      9         1         2          -1.1   \n",
       "41183            0 ...         1    999         0         1          -1.1   \n",
       "41184            0 ...         1    999         0         1          -1.1   \n",
       "41185            0 ...         2    999         0         1          -1.1   \n",
       "41186            0 ...         1    999         0         1          -1.1   \n",
       "41187            0 ...         3    999         1         0          -1.1   \n",
       "\n",
       "       cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
       "0              93.994          -36.4      4.857       5191.0  0  \n",
       "1              93.994          -36.4      4.857       5191.0  0  \n",
       "2              93.994          -36.4      4.857       5191.0  0  \n",
       "3              93.994          -36.4      4.857       5191.0  0  \n",
       "4              93.994          -36.4      4.857       5191.0  0  \n",
       "5              93.994          -36.4      4.857       5191.0  0  \n",
       "6              93.994          -36.4      4.857       5191.0  0  \n",
       "7              93.994          -36.4      4.857       5191.0  0  \n",
       "8              93.994          -36.4      4.857       5191.0  0  \n",
       "9              93.994          -36.4      4.857       5191.0  0  \n",
       "10             93.994          -36.4      4.857       5191.0  0  \n",
       "11             93.994          -36.4      4.857       5191.0  0  \n",
       "12             93.994          -36.4      4.857       5191.0  0  \n",
       "13             93.994          -36.4      4.857       5191.0  0  \n",
       "14             93.994          -36.4      4.857       5191.0  0  \n",
       "15             93.994          -36.4      4.857       5191.0  0  \n",
       "16             93.994          -36.4      4.857       5191.0  0  \n",
       "17             93.994          -36.4      4.857       5191.0  0  \n",
       "18             93.994          -36.4      4.857       5191.0  0  \n",
       "19             93.994          -36.4      4.857       5191.0  0  \n",
       "20             93.994          -36.4      4.857       5191.0  0  \n",
       "21             93.994          -36.4      4.857       5191.0  0  \n",
       "22             93.994          -36.4      4.857       5191.0  0  \n",
       "23             93.994          -36.4      4.857       5191.0  0  \n",
       "24             93.994          -36.4      4.857       5191.0  0  \n",
       "25             93.994          -36.4      4.857       5191.0  0  \n",
       "26             93.994          -36.4      4.857       5191.0  0  \n",
       "27             93.994          -36.4      4.857       5191.0  0  \n",
       "28             93.994          -36.4      4.857       5191.0  0  \n",
       "29             93.994          -36.4      4.857       5191.0  0  \n",
       "...               ...            ...        ...          ... ..  \n",
       "41158          94.767          -50.8      1.035       4963.6  1  \n",
       "41159          94.767          -50.8      1.035       4963.6  1  \n",
       "41160          94.767          -50.8      1.035       4963.6  1  \n",
       "41161          94.767          -50.8      1.035       4963.6  0  \n",
       "41162          94.767          -50.8      1.035       4963.6  0  \n",
       "41163          94.767          -50.8      1.035       4963.6  1  \n",
       "41164          94.767          -50.8      1.035       4963.6  1  \n",
       "41165          94.767          -50.8      1.030       4963.6  1  \n",
       "41166          94.767          -50.8      1.030       4963.6  1  \n",
       "41167          94.767          -50.8      1.030       4963.6  0  \n",
       "41168          94.767          -50.8      1.030       4963.6  0  \n",
       "41169          94.767          -50.8      1.030       4963.6  0  \n",
       "41170          94.767          -50.8      1.030       4963.6  0  \n",
       "41171          94.767          -50.8      1.031       4963.6  1  \n",
       "41172          94.767          -50.8      1.031       4963.6  1  \n",
       "41173          94.767          -50.8      1.031       4963.6  1  \n",
       "41174          94.767          -50.8      1.031       4963.6  1  \n",
       "41175          94.767          -50.8      1.031       4963.6  0  \n",
       "41176          94.767          -50.8      1.031       4963.6  0  \n",
       "41177          94.767          -50.8      1.031       4963.6  0  \n",
       "41178          94.767          -50.8      1.031       4963.6  1  \n",
       "41179          94.767          -50.8      1.028       4963.6  0  \n",
       "41180          94.767          -50.8      1.028       4963.6  0  \n",
       "41181          94.767          -50.8      1.028       4963.6  1  \n",
       "41182          94.767          -50.8      1.028       4963.6  0  \n",
       "41183          94.767          -50.8      1.028       4963.6  1  \n",
       "41184          94.767          -50.8      1.028       4963.6  0  \n",
       "41185          94.767          -50.8      1.028       4963.6  0  \n",
       "41186          94.767          -50.8      1.028       4963.6  1  \n",
       "41187          94.767          -50.8      1.028       4963.6  0  \n",
       "\n",
       "[41188 rows x 21 columns]"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummyEncode(bank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      "age               41188 non-null int64\n",
      "job               41188 non-null int64\n",
      "marital           41188 non-null int64\n",
      "education         41188 non-null int64\n",
      "default           41188 non-null int64\n",
      "housing           41188 non-null int64\n",
      "loan              41188 non-null int64\n",
      "contact           41188 non-null int64\n",
      "month             41188 non-null int64\n",
      "day_of_week       41188 non-null int64\n",
      "duration          41188 non-null int64\n",
      "campaign          41188 non-null int64\n",
      "pdays             41188 non-null int64\n",
      "previous          41188 non-null int64\n",
      "poutcome          41188 non-null int64\n",
      "emp.var.rate      41188 non-null float64\n",
      "cons.price.idx    41188 non-null float64\n",
      "cons.conf.idx     41188 non-null float64\n",
      "euribor3m         41188 non-null float64\n",
      "nr.employed       41188 non-null float64\n",
      "y                 41188 non-null int64\n",
      "dtypes: float64(5), int64(16)\n",
      "memory usage: 6.6 MB\n"
     ]
    }
   ],
   "source": [
    "bank.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = bank.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "\n",
    "chi2, pval = chi2(categorical_data,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_var = pd.DataFrame(pval < 0.05,categorical_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=1, step=1)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_var.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(cat_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  5,  7,  8, 10, 12, 13, 14, 16, 17, 18, 22, 23, 24, 25,\n",
       "        33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 46, 47, 50, 51, 52],\n",
       "       dtype=int64),)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(cat_var[0] == True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "select_indices = list(np.where(cat_var[0] == True)[0])\n",
    "\n",
    "CAT = cat_var.iloc[select_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = CAT.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['job_admin.', 'job_blue-collar', 'job_entrepreneur', 'job_retired',\n",
       "       'job_services', 'job_student', 'job_unemployed', 'marital_divorced',\n",
       "       'marital_married', 'marital_single', 'education_basic.4y',\n",
       "       'education_basic.6y', 'education_basic.9y',\n",
       "       'education_university.degree', 'education_unknown', 'default_no',\n",
       "       'default_unknown', 'contact_cellular', 'contact_telephone', 'month_apr',\n",
       "       'month_dec', 'month_jul', 'month_mar', 'month_may', 'month_nov',\n",
       "       'month_oct', 'month_sep', 'day_of_week_mon', 'day_of_week_thu',\n",
       "       'poutcome_failure', 'poutcome_nonexistent', 'poutcome_success'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 53)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_vars=categorical_data.columns.values.tolist()\n",
    "to_keep=[i for i in data_vars if i in categorical]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(to_keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['job_admin.', 'job_blue-collar', 'job_entrepreneur', 'job_retired',\n",
       "       'job_services', 'job_student', 'job_unemployed',\n",
       "       'marital_divorced', 'marital_married', 'marital_single',\n",
       "       'education_basic.4y', 'education_basic.6y', 'education_basic.9y',\n",
       "       'education_university.degree', 'education_unknown', 'default_no',\n",
       "       'default_unknown', 'contact_cellular', 'contact_telephone',\n",
       "       'month_apr', 'month_dec', 'month_jul', 'month_mar', 'month_may',\n",
       "       'month_nov', 'month_oct', 'month_sep', 'day_of_week_mon',\n",
       "       'day_of_week_thu', 'poutcome_failure', 'poutcome_nonexistent',\n",
       "       'poutcome_success'], dtype=object)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_final=categorical_data[to_keep]\n",
    "bank_final.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concat Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 32)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 5)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank = pd.concat([numerical_data,bank_final],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>job_admin.</th>\n",
       "      <th>job_blue-collar</th>\n",
       "      <th>job_entrepreneur</th>\n",
       "      <th>job_retired</th>\n",
       "      <th>job_services</th>\n",
       "      <th>...</th>\n",
       "      <th>month_mar</th>\n",
       "      <th>month_may</th>\n",
       "      <th>month_nov</th>\n",
       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
       "      <th>day_of_week_mon</th>\n",
       "      <th>day_of_week_thu</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <th>poutcome_success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>999</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>307</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  campaign  pdays  previous  emp.var.rate  job_admin.  \\\n",
       "0       261         1    999         0           1.1           0   \n",
       "1       149         1    999         0           1.1           0   \n",
       "2       226         1    999         0           1.1           0   \n",
       "3       151         1    999         0           1.1           1   \n",
       "4       307         1    999         0           1.1           0   \n",
       "\n",
       "   job_blue-collar  job_entrepreneur  job_retired  job_services  \\\n",
       "0                0                 0            0             0   \n",
       "1                0                 0            0             1   \n",
       "2                0                 0            0             1   \n",
       "3                0                 0            0             0   \n",
       "4                0                 0            0             1   \n",
       "\n",
       "         ...         month_mar  month_may  month_nov  month_oct  month_sep  \\\n",
       "0        ...                 0          1          0          0          0   \n",
       "1        ...                 0          1          0          0          0   \n",
       "2        ...                 0          1          0          0          0   \n",
       "3        ...                 0          1          0          0          0   \n",
       "4        ...                 0          1          0          0          0   \n",
       "\n",
       "   day_of_week_mon  day_of_week_thu  poutcome_failure  poutcome_nonexistent  \\\n",
       "0                1                0                 0                     1   \n",
       "1                1                0                 0                     1   \n",
       "2                1                0                 0                     1   \n",
       "3                1                0                 0                     1   \n",
       "4                1                0                 0                     1   \n",
       "\n",
       "   poutcome_success  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank.drop(['month_dec','job_admin.','day_of_week_thu','education_basic.9y','education_unknown'],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['duration', 'campaign', 'pdays', 'emp.var.rate', 'job_blue-collar',\n",
       "       'job_entrepreneur', 'job_retired', 'job_services', 'job_student',\n",
       "       'education_university.degree', 'default_no', 'default_unknown',\n",
       "       'month_apr', 'month_jul', 'month_mar', 'month_may', 'month_nov',\n",
       "       'month_oct', 'month_sep', 'day_of_week_mon'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.214353\n",
      "         Iterations 8\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                41188\n",
      "Model:                          Logit   Df Residuals:                    41168\n",
      "Method:                           MLE   Df Model:                           19\n",
      "Date:                Wed, 27 Feb 2019   Pseudo R-squ.:                  0.3911\n",
      "Time:                        16:44:41   Log-Likelihood:                -8828.8\n",
      "converged:                       True   LL-Null:                       -14499.\n",
      "                                        LLR p-value:                     0.000\n",
      "===============================================================================================\n",
      "                                  coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "duration                        0.0047   7.32e-05     63.549      0.000       0.005       0.005\n",
      "campaign                       -0.0507      0.011     -4.429      0.000      -0.073      -0.028\n",
      "pdays                          -0.0019   6.66e-05    -28.385      0.000      -0.002      -0.002\n",
      "emp.var.rate                   -0.5919      0.015    -39.947      0.000      -0.621      -0.563\n",
      "job_blue-collar                -0.3006      0.062     -4.829      0.000      -0.423      -0.179\n",
      "job_entrepreneur               -0.2415      0.119     -2.024      0.043      -0.475      -0.008\n",
      "job_retired                     0.4027      0.079      5.099      0.000       0.248       0.558\n",
      "job_services                   -0.1648      0.078     -2.110      0.035      -0.318      -0.012\n",
      "job_student                     0.4062      0.097      4.180      0.000       0.216       0.597\n",
      "education_university.degree     0.1371      0.047      2.941      0.003       0.046       0.228\n",
      "default_no                     -1.7569      0.086    -20.390      0.000      -1.926      -1.588\n",
      "default_unknown                -2.1312      0.104    -20.471      0.000      -2.335      -1.927\n",
      "month_apr                      -0.3180      0.070     -4.521      0.000      -0.456      -0.180\n",
      "month_jul                       0.1354      0.067      2.011      0.044       0.003       0.267\n",
      "month_mar                       1.3495      0.104     12.981      0.000       1.146       1.553\n",
      "month_may                      -1.0311      0.058    -17.761      0.000      -1.145      -0.917\n",
      "month_nov                      -0.6469      0.075     -8.596      0.000      -0.794      -0.499\n",
      "month_oct                       0.2255      0.101      2.244      0.025       0.029       0.423\n",
      "month_sep                       0.2535      0.110      2.300      0.021       0.037       0.470\n",
      "day_of_week_mon                -0.1670      0.051     -3.306      0.001      -0.266      -0.068\n",
      "===============================================================================================\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "logit_model=sm.Logit(y,bank)\n",
    "result=logit_model.fit()\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The p-values for all the variables are lessthan 0.05 therefore, most of them are significant to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(bank, y, test_size=0.3, random_state=0,stratify = y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Model Fitting :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting the test set results and caculating the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.91\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-fold cross validation average accuracy: 0.910\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "kfold = model_selection.KFold(n_splits=10, random_state=7)\n",
    "modelCV = LogisticRegression()\n",
    "scoring = 'accuracy'\n",
    "results = model_selection.cross_val_score(modelCV, X_train, y_train, cv=kfold, scoring=scoring)\n",
    "print(\"10-fold cross validation average accuracy: %.3f\" % (results.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.90603329, 0.91467222, 0.91050989, 0.91328477, 0.90669442,\n",
       "       0.91259105, 0.907735  , 0.90634755, 0.91085675, 0.91259105])"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10662   303]\n",
      " [  866   526]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.92      0.97      0.95     10965\n",
      "          1       0.63      0.38      0.47      1392\n",
      "\n",
      "avg / total       0.89      0.91      0.89     12357\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1])\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}