bank_marketing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sklearn\n",
    "import pickle\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import preprocessing\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "import pandas as pd\n",
    "#import pylab as plb\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Dataset\n",
    "The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls.  Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>0</th>\n",
       "      <td>44</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</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>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jun</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>apr</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>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>1</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",
       "0   44  blue-collar  married           basic.4y  unknown     yes   no   \n",
       "1   53   technician  married            unknown       no      no   no   \n",
       "2   28   management   single  university.degree       no     yes   no   \n",
       "3   39     services  married        high.school       no      no   no   \n",
       "4   55      retired  married           basic.4y       no     yes   no   \n",
       "\n",
       "    contact month day_of_week ...  campaign  pdays  previous     poutcome  \\\n",
       "0  cellular   aug         thu ...         1    999         0  nonexistent   \n",
       "1  cellular   nov         fri ...         1    999         0  nonexistent   \n",
       "2  cellular   jun         thu ...         3      6         2      success   \n",
       "3  cellular   apr         fri ...         2    999         0  nonexistent   \n",
       "4  cellular   aug         fri ...         1      3         1      success   \n",
       "\n",
       "  emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  nr_employed  y  \n",
       "0          1.4          93.444          -36.1      4.963       5228.1  0  \n",
       "1         -0.1          93.200          -42.0      4.021       5195.8  0  \n",
       "2         -1.7          94.055          -39.8      0.729       4991.6  1  \n",
       "3         -1.8          93.075          -47.1      1.405       5099.1  0  \n",
       "4         -2.9          92.201          -31.4      0.869       5076.2  1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('banking_updated.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Input variables:**\n",
    "\n",
    "1 - age (numeric)  \n",
    "2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')  \n",
    "3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)  \n",
    "4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')  \n",
    "5 - default: has credit in default? (categorical: 'no','yes','unknown')  \n",
    "6 - housing: has housing loan? (categorical: 'no','yes','unknown')  \n",
    "7 - loan: has personal loan? (categorical: 'no','yes','unknown')  \n",
    "8 - contact: contact communication type (categorical: 'cellular','telephone')  \n",
    "9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')  \n",
    "10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')  \n",
    "11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no')  \n",
    "12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)  \n",
    "13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)  \n",
    "14 - previous: number of contacts performed before this campaign and for this client (numeric)  \n",
    "15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')  \n",
    "16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)  \n",
    "17 - cons.price.idx: consumer price index - monthly indicator (numeric)  \n",
    "18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)  \n",
    "19 - euribor3m: euribor 3 month rate - daily indicator (numeric)  \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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <th>y</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41188.00000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.02406</td>\n",
       "      <td>258.285010</td>\n",
       "      <td>2.567593</td>\n",
       "      <td>962.475454</td>\n",
       "      <td>0.172963</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>93.575664</td>\n",
       "      <td>-40.502600</td>\n",
       "      <td>3.621291</td>\n",
       "      <td>5167.035911</td>\n",
       "      <td>0.112654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.42125</td>\n",
       "      <td>259.279249</td>\n",
       "      <td>2.770014</td>\n",
       "      <td>186.910907</td>\n",
       "      <td>0.494901</td>\n",
       "      <td>1.570960</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>4.628198</td>\n",
       "      <td>1.734447</td>\n",
       "      <td>72.251528</td>\n",
       "      <td>0.316173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>92.201000</td>\n",
       "      <td>-50.800000</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>4963.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.00000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>93.075000</td>\n",
       "      <td>-42.700000</td>\n",
       "      <td>1.344000</td>\n",
       "      <td>5099.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.00000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.749000</td>\n",
       "      <td>-41.800000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>5191.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.00000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>4.961000</td>\n",
       "      <td>5228.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.00000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>94.767000</td>\n",
       "      <td>-26.900000</td>\n",
       "      <td>5.045000</td>\n",
       "      <td>5228.100000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age      duration      campaign         pdays      previous  \\\n",
       "count  41188.00000  41188.000000  41188.000000  41188.000000  41188.000000   \n",
       "mean      40.02406    258.285010      2.567593    962.475454      0.172963   \n",
       "std       10.42125    259.279249      2.770014    186.910907      0.494901   \n",
       "min       17.00000      0.000000      1.000000      0.000000      0.000000   \n",
       "25%       32.00000    102.000000      1.000000    999.000000      0.000000   \n",
       "50%       38.00000    180.000000      2.000000    999.000000      0.000000   \n",
       "75%       47.00000    319.000000      3.000000    999.000000      0.000000   \n",
       "max       98.00000   4918.000000     56.000000    999.000000      7.000000   \n",
       "\n",
       "       emp_var_rate  cons_price_idx  cons_conf_idx     euribor3m  \\\n",
       "count  41188.000000    41188.000000   41188.000000  41188.000000   \n",
       "mean       0.081886       93.575664     -40.502600      3.621291   \n",
       "std        1.570960        0.578840       4.628198      1.734447   \n",
       "min       -3.400000       92.201000     -50.800000      0.634000   \n",
       "25%       -1.800000       93.075000     -42.700000      1.344000   \n",
       "50%        1.100000       93.749000     -41.800000      4.857000   \n",
       "75%        1.400000       93.994000     -36.400000      4.961000   \n",
       "max        1.400000       94.767000     -26.900000      5.045000   \n",
       "\n",
       "        nr_employed             y  \n",
       "count  41188.000000  41188.000000  \n",
       "mean    5167.035911      0.112654  \n",
       "std       72.251528      0.316173  \n",
       "min     4963.600000      0.000000  \n",
       "25%     5099.100000      0.000000  \n",
       "50%     5191.000000      0.000000  \n",
       "75%     5228.100000      0.000000  \n",
       "max     5228.100000      1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.drop(['duration','contact','month','day_of_week','default','pdays',],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>campaign</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
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       "      <td>93.444</td>\n",
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       "      <td>4.963</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital          education housing loan  campaign  \\\n",
       "0   44  blue-collar  married           basic.4y     yes   no         1   \n",
       "1   53   technician  married            unknown      no   no         1   \n",
       "2   28   management   single  university.degree     yes   no         3   \n",
       "3   39     services  married        high.school      no   no         2   \n",
       "4   55      retired  married           basic.4y     yes   no         1   \n",
       "\n",
       "   previous     poutcome  emp_var_rate  cons_price_idx  cons_conf_idx  \\\n",
       "0         0  nonexistent           1.4          93.444          -36.1   \n",
       "1         0  nonexistent          -0.1          93.200          -42.0   \n",
       "2         2      success          -1.7          94.055          -39.8   \n",
       "3         0  nonexistent          -1.8          93.075          -47.1   \n",
       "4         1      success          -2.9          92.201          -31.4   \n",
       "\n",
       "   euribor3m  nr_employed  y  \n",
       "0      4.963       5228.1  0  \n",
       "1      4.021       5195.8  0  \n",
       "2      0.729       4991.6  1  \n",
       "3      1.405       5099.1  0  \n",
       "4      0.869       5076.2  1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "job               0\n",
       "marital           0\n",
       "education         0\n",
       "housing           0\n",
       "loan              0\n",
       "campaign          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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.replace(['basic.6y','basic.4y', 'basic.9y'], 'basic', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x4fd3ce2f60>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OXtuv9e9NVVUVVVVVABQXF/c4z41qGfBIiWU3c02JxJJ+B8zEiROZOHHiTZ3s8uXL7Ny5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\nRETEEgoYERGxhAJGREQsoYARERFL/F/splKLJMH/VgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4fd3acb0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='y', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x4fd3d40be0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m8qYn/fz88Pb25ueff+bQoUOMGzfOtn90dDRubm7XHHOzc4mIiP1Um2lPb29v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\n2CicP8wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4fd2c2dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='job', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x4fd400ad68>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1agWMtMvc1z4KdAldxsaHfhjoEkQM1+6T/CaTCa/X26LN6/Xi8/k6vCgREen+2h0wI0aM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\nEP8P5zsXbxizK2UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4fd3e5fc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='marital', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "basic                  12513\n",
       "university.degree      12168\n",
       "high.school             9515\n",
       "professional.course     5243\n",
       "unknown                 1731\n",
       "illiterate                18\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.education.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x4fd4065dd8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EhYXxzTffAJCamkrDhg3x8fGhcePGHD9+HIBjx4451/ulivr18fHBarWyY8cO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4fd4021518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='education', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>campaign</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>44</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital          education housing loan  campaign  \\\n",
       "0   44  blue-collar  married              basic     yes   no         1   \n",
       "1   53   technician  married            unknown      no   no         1   \n",
       "2   28   management   single  university.degree     yes   no         3   \n",
       "3   39     services  married        high.school      no   no         2   \n",
       "4   55      retired  married              basic     yes   no         1   \n",
       "\n",
       "   previous     poutcome  emp_var_rate  cons_price_idx  cons_conf_idx  \\\n",
       "0         0  nonexistent           1.4          93.444          -36.1   \n",
       "1         0  nonexistent          -0.1          93.200          -42.0   \n",
       "2         2      success          -1.7          94.055          -39.8   \n",
       "3         0  nonexistent          -1.8          93.075          -47.1   \n",
       "4         1      success          -2.9          92.201          -31.4   \n",
       "\n",
       "   euribor3m  nr_employed  y  \n",
       "0      4.963       5228.1  0  \n",
       "1      4.021       5195.8  0  \n",
       "2      0.729       4991.6  1  \n",
       "3      1.405       5099.1  0  \n",
       "4      0.869       5076.2  1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre Processing\n",
    "Sklearn provides a very efficient tool for encoding the levels of a categorical features into numeric values. `LabelEncoder` encode labels with value between 0 and n_classes-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "le = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.job = le.fit_transform(df.job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.marital = le.fit_transform(df.marital)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.education = le.fit_transform(df.education)\n",
    "df.housing = le.fit_transform(df.housing)\n",
    "df.loan = le.fit_transform(df.loan)\n",
    "df.poutcome = le.fit_transform(df.poutcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>campaign</th>\n",
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       "      <th>poutcome</th>\n",
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       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  marital  education  housing  loan  campaign  previous  poutcome  \\\n",
       "0   44    1        1          0        2     0         1         0         1   \n",
       "1   53    9        1          5        0     0         1         0         1   \n",
       "2   28    4        2          4        2     0         3         2         2   \n",
       "3   39    7        1          1        0     0         2         0         1   \n",
       "4   55    5        1          0        2     0         1         1         2   \n",
       "\n",
       "   emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  nr_employed  y  \n",
       "0           1.4          93.444          -36.1      4.963       5228.1  0  \n",
       "1          -0.1          93.200          -42.0      4.021       5195.8  0  \n",
       "2          -1.7          94.055          -39.8      0.729       4991.6  1  \n",
       "3          -1.8          93.075          -47.1      1.405       5099.1  0  \n",
       "4          -2.9          92.201          -31.4      0.869       5076.2  1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 15)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "<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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>campaign</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
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       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>4.961</td>\n",
       "      <td>5228.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>37</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.327</td>\n",
       "      <td>5099.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.313</td>\n",
       "      <td>5099.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.963</td>\n",
       "      <td>-40.8</td>\n",
       "      <td>1.266</td>\n",
       "      <td>5076.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.410</td>\n",
       "      <td>5099.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  marital  education  housing  loan  campaign  previous  poutcome  \\\n",
       "0   44    1        1          0        2     0         1         0         1   \n",
       "1   53    9        1          5        0     0         1         0         1   \n",
       "2   28    4        2          4        2     0         3         2         2   \n",
       "3   39    7        1          1        0     0         2         0         1   \n",
       "4   55    5        1          0        2     0         1         1         2   \n",
       "5   30    4        0          0        2     0         8         0         1   \n",
       "6   37    1        1          0        2     0         1         0         1   \n",
       "7   39    1        0          0        2     0         1         0         1   \n",
       "8   36    0        1          4        0     0         1         1         2   \n",
       "9   27    1        2          0        2     0         2         1         0   \n",
       "\n",
       "   emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  nr_employed  \n",
       "0           1.4          93.444          -36.1      4.963       5228.1  \n",
       "1          -0.1          93.200          -42.0      4.021       5195.8  \n",
       "2          -1.7          94.055          -39.8      0.729       4991.6  \n",
       "3          -1.8          93.075          -47.1      1.405       5099.1  \n",
       "4          -2.9          92.201          -31.4      0.869       5076.2  \n",
       "5           1.4          93.918          -42.7      4.961       5228.1  \n",
       "6          -1.8          92.893          -46.2      1.327       5099.1  \n",
       "7          -1.8          92.893          -46.2      1.313       5099.1  \n",
       "8          -2.9          92.963          -40.8      1.266       5076.2  \n",
       "9          -1.8          93.075          -47.1      1.410       5099.1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:,0:14]\n",
    "X[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "5    0\n",
       "6    0\n",
       "7    0\n",
       "8    1\n",
       "9    0\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.iloc[:,14]\n",
    "y[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and Test split\n",
    "A general practice is to split your data into a training and test set. You train/tune your model with your training set and test how well it generalizes to data it has never seen before with your test set. \n",
    "We will make use of scikit learn’s `train_test_split` method to achieve this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=0) #80/20 split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((32950, 14), (32950,))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8238, 14), (8238,))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model\n",
    "We will make use of different classification algorithms to train this data set and will record the accuracy on test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model=LogisticRegression(penalty='l2', max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=1000, 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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90228210730759895"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7270   55]\n",
      " [ 750  163]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, prediction)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89232823500849723"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict = rfc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88820101966496723"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n",
      "WARNING (theano.configdefaults): g++ not available, if using conda: `conda install m2w64-toolchain`\n",
      "WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(10, input_dim=14, activation='tanh'))\n",
    "\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train_keras = np.array(x_train)\n",
    "y_train_keras = np.array(y_train)\n",
    "#print(x_train_keras.shape)\n",
    "y_train_keras = y_train_keras.reshape(y_train_keras.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "32950/32950 [==============================] - 24s - loss: 0.6617 - acc: 0.6011    \n",
      "Epoch 2/10\n",
      "32950/32950 [==============================] - 23s - loss: 0.3743 - acc: 0.8869    \n",
      "Epoch 3/10\n",
      "32950/32950 [==============================] - 22s - loss: 0.3542 - acc: 0.8869    \n",
      "Epoch 4/10\n",
      "32950/32950 [==============================] - 22s - loss: 0.3530 - acc: 0.8869    \n",
      "Epoch 5/10\n",
      "32950/32950 [==============================] - 23s - loss: 0.3530 - acc: 0.8869    \n",
      "Epoch 6/10\n",
      "32950/32950 [==============================] - 23s - loss: 0.3530 - acc: 0.8869    \n",
      "Epoch 7/10\n",
      "32950/32950 [==============================] - 23s - loss: 0.3530 - acc: 0.8869    \n",
      "Epoch 8/10\n",
      "32950/32950 [==============================] - 24s - loss: 0.3530 - acc: 0.8869    \n",
      "Epoch 9/10\n",
      "32950/32950 [==============================] - 22s - loss: 0.3530 - acc: 0.8869    \n",
      "Epoch 10/10\n",
      "32950/32950 [==============================] - 23s - loss: 0.3530 - acc: 0.8869    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x4feaa16780>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(np.array(x_train_keras), np.array(y_train_keras), epochs=10, batch_size=128, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8064/8238 [============================>.] - ETA: 0s"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(np.array(x_test), np.array(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "acc: 88.92%\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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