Logistic Regression balanced.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt \n",
    "plt.rc(\"font\", size=14)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "sns.set(style=\"whitegrid\", color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data\n",
    "\n",
    "The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe (1/0) a term deposit (variable y).\n",
    "\n",
    "This dataset provides the customer information. It includes 41188 records and 21 fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41188, 21)\n",
      "['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp_var_rate', 'cons_price_idx', 'cons_conf_idx', 'euribor3m', 'nr_employed', 'y']\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('banking.csv', header=0)\n",
    "data = data.dropna()\n",
    "print(data.shape)\n",
    "print(list(data.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>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",
       "    </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>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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Input variables\n",
    "\n",
    "1 - age (numeric)\n",
    "\n",
    "2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')\n",
    "\n",
    "3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)\n",
    "\n",
    "4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')\n",
    "\n",
    "5 - default: has credit in default? (categorical: 'no','yes','unknown')\n",
    "\n",
    "6 - housing: has housing loan? (categorical: 'no','yes','unknown')\n",
    "\n",
    "7 - loan: has personal loan? (categorical: 'no','yes','unknown')\n",
    "\n",
    "8 - contact: contact communication type (categorical: 'cellular','telephone')\n",
    "\n",
    "9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
    "\n",
    "10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')\n",
    "\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'). 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 (numeric, includes last contact)\n",
    "\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",
    "\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",
    "\n",
    "16 - emp.var.rate: employment variation rate - (numeric)\n",
    "\n",
    "17 - cons.price.idx: consumer price index - (numeric)\n",
    "\n",
    "18 - cons.conf.idx: consumer confidence index - (numeric)\n",
    "\n",
    "19 - euribor3m: euribor 3 month rate - (numeric)\n",
    "\n",
    "20 - nr.employed: number of employees - (numeric)\n",
    "\n",
    "#### Predict variable (desired target):\n",
    "\n",
    "y - has the client subscribed a term deposit? (binary: '1','0')\n",
    "\n",
    "The education column of the dataset has many categories and we need to reduce the categories for a better modelling. The education column has the following categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['basic.4y', 'unknown', 'university.degree', 'high.school',\n",
       "       'basic.9y', 'professional.course', 'basic.6y', 'illiterate'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['education'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us group \"basic.4y\", \"basic.9y\" and \"basic.6y\" together and call them \"basic\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['education']=np.where(data['education'] =='basic.9y', 'Basic', data['education'])\n",
    "data['education']=np.where(data['education'] =='basic.6y', 'Basic', data['education'])\n",
    "data['education']=np.where(data['education'] =='basic.4y', 'Basic', data['education'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After grouping, this is the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Basic', 'unknown', 'university.degree', 'high.school',\n",
       "       'professional.course', 'illiterate'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['education'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    36548\n",
       "1     4640\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1984f1bc358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='y',data=data, palette='hls')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of no subscription is 88.73458288821988\n",
      "percentage of subscription 11.265417111780131\n"
     ]
    }
   ],
   "source": [
    "count_no_sub = len(data[data['y']==0])\n",
    "count_sub = len(data[data['y']==1])\n",
    "pct_of_no_sub = count_no_sub/(count_no_sub+count_sub)\n",
    "print(\"percentage of no subscription is\", pct_of_no_sub*100)\n",
    "pct_of_sub = count_sub/(count_no_sub+count_sub)\n",
    "print(\"percentage of subscription\", pct_of_sub*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our classes are imbalanced, and the ratio of no-subscription to subscription instances is 89:11. Before we go ahead to balance the classes, Let's do some more exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>0</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>1</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",
       "0  39.911185  220.844807  2.633085  984.113878  0.132374      0.248875   \n",
       "1  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",
       "0       93.603757     -40.593097   3.811491  5176.166600  \n",
       "1       93.354386     -39.789784   2.123135  5095.115991  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('y').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "\n",
    "The average age of customers who bought the term deposit is higher than that of the customers who didn't. The pdays (days since the customer was last contacted) is understandably lower for the customers who bought it. The lower the pdays, the better the memory of the last call and hence the better chances of a sale. Surprisingly, campaigns (number of contacts or calls made during the current campaign) are lower for customers who bought the term deposit.\n",
    "\n",
    "We can calculate categorical means for other categorical variables such as education and marital status to get a more detailed sense of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>admin.</th>\n",
       "      <td>38.187296</td>\n",
       "      <td>254.312128</td>\n",
       "      <td>2.623489</td>\n",
       "      <td>954.319229</td>\n",
       "      <td>0.189023</td>\n",
       "      <td>0.015563</td>\n",
       "      <td>93.534054</td>\n",
       "      <td>-40.245433</td>\n",
       "      <td>3.550274</td>\n",
       "      <td>5164.125350</td>\n",
       "      <td>0.129726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue-collar</th>\n",
       "      <td>39.555760</td>\n",
       "      <td>264.542360</td>\n",
       "      <td>2.558461</td>\n",
       "      <td>985.160363</td>\n",
       "      <td>0.122542</td>\n",
       "      <td>0.248995</td>\n",
       "      <td>93.656656</td>\n",
       "      <td>-41.375816</td>\n",
       "      <td>3.771996</td>\n",
       "      <td>5175.615150</td>\n",
       "      <td>0.068943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entrepreneur</th>\n",
       "      <td>41.723214</td>\n",
       "      <td>263.267857</td>\n",
       "      <td>2.535714</td>\n",
       "      <td>981.267170</td>\n",
       "      <td>0.138736</td>\n",
       "      <td>0.158723</td>\n",
       "      <td>93.605372</td>\n",
       "      <td>-41.283654</td>\n",
       "      <td>3.791120</td>\n",
       "      <td>5176.313530</td>\n",
       "      <td>0.085165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housemaid</th>\n",
       "      <td>45.500000</td>\n",
       "      <td>250.454717</td>\n",
       "      <td>2.639623</td>\n",
       "      <td>960.579245</td>\n",
       "      <td>0.137736</td>\n",
       "      <td>0.433396</td>\n",
       "      <td>93.676576</td>\n",
       "      <td>-39.495283</td>\n",
       "      <td>4.009645</td>\n",
       "      <td>5179.529623</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>42.362859</td>\n",
       "      <td>257.058140</td>\n",
       "      <td>2.476060</td>\n",
       "      <td>962.647059</td>\n",
       "      <td>0.185021</td>\n",
       "      <td>-0.012688</td>\n",
       "      <td>93.522755</td>\n",
       "      <td>-40.489466</td>\n",
       "      <td>3.611316</td>\n",
       "      <td>5166.650513</td>\n",
       "      <td>0.112175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>62.027326</td>\n",
       "      <td>273.712209</td>\n",
       "      <td>2.476744</td>\n",
       "      <td>897.936047</td>\n",
       "      <td>0.327326</td>\n",
       "      <td>-0.698314</td>\n",
       "      <td>93.430786</td>\n",
       "      <td>-38.573081</td>\n",
       "      <td>2.770066</td>\n",
       "      <td>5122.262151</td>\n",
       "      <td>0.252326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>self-employed</th>\n",
       "      <td>39.949331</td>\n",
       "      <td>264.142153</td>\n",
       "      <td>2.660802</td>\n",
       "      <td>976.621393</td>\n",
       "      <td>0.143561</td>\n",
       "      <td>0.094159</td>\n",
       "      <td>93.559982</td>\n",
       "      <td>-40.488107</td>\n",
       "      <td>3.689376</td>\n",
       "      <td>5170.674384</td>\n",
       "      <td>0.104856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>services</th>\n",
       "      <td>37.926430</td>\n",
       "      <td>258.398085</td>\n",
       "      <td>2.587805</td>\n",
       "      <td>979.974049</td>\n",
       "      <td>0.154951</td>\n",
       "      <td>0.175359</td>\n",
       "      <td>93.634659</td>\n",
       "      <td>-41.290048</td>\n",
       "      <td>3.699187</td>\n",
       "      <td>5171.600126</td>\n",
       "      <td>0.081381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>25.894857</td>\n",
       "      <td>283.683429</td>\n",
       "      <td>2.104000</td>\n",
       "      <td>840.217143</td>\n",
       "      <td>0.524571</td>\n",
       "      <td>-1.408000</td>\n",
       "      <td>93.331613</td>\n",
       "      <td>-40.187543</td>\n",
       "      <td>1.884224</td>\n",
       "      <td>5085.939086</td>\n",
       "      <td>0.314286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>38.507638</td>\n",
       "      <td>250.232241</td>\n",
       "      <td>2.577339</td>\n",
       "      <td>964.408127</td>\n",
       "      <td>0.153789</td>\n",
       "      <td>0.274566</td>\n",
       "      <td>93.561471</td>\n",
       "      <td>-39.927569</td>\n",
       "      <td>3.820401</td>\n",
       "      <td>5175.648391</td>\n",
       "      <td>0.108260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployed</th>\n",
       "      <td>39.733728</td>\n",
       "      <td>249.451677</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>935.316568</td>\n",
       "      <td>0.199211</td>\n",
       "      <td>-0.111736</td>\n",
       "      <td>93.563781</td>\n",
       "      <td>-40.007594</td>\n",
       "      <td>3.466583</td>\n",
       "      <td>5157.156509</td>\n",
       "      <td>0.142012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>45.563636</td>\n",
       "      <td>239.675758</td>\n",
       "      <td>2.648485</td>\n",
       "      <td>938.727273</td>\n",
       "      <td>0.154545</td>\n",
       "      <td>0.357879</td>\n",
       "      <td>93.718942</td>\n",
       "      <td>-38.797879</td>\n",
       "      <td>3.949033</td>\n",
       "      <td>5172.931818</td>\n",
       "      <td>0.112121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age    duration  campaign       pdays  previous  \\\n",
       "job                                                                    \n",
       "admin.         38.187296  254.312128  2.623489  954.319229  0.189023   \n",
       "blue-collar    39.555760  264.542360  2.558461  985.160363  0.122542   \n",
       "entrepreneur   41.723214  263.267857  2.535714  981.267170  0.138736   \n",
       "housemaid      45.500000  250.454717  2.639623  960.579245  0.137736   \n",
       "management     42.362859  257.058140  2.476060  962.647059  0.185021   \n",
       "retired        62.027326  273.712209  2.476744  897.936047  0.327326   \n",
       "self-employed  39.949331  264.142153  2.660802  976.621393  0.143561   \n",
       "services       37.926430  258.398085  2.587805  979.974049  0.154951   \n",
       "student        25.894857  283.683429  2.104000  840.217143  0.524571   \n",
       "technician     38.507638  250.232241  2.577339  964.408127  0.153789   \n",
       "unemployed     39.733728  249.451677  2.564103  935.316568  0.199211   \n",
       "unknown        45.563636  239.675758  2.648485  938.727273  0.154545   \n",
       "\n",
       "               emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  \\\n",
       "job                                                                     \n",
       "admin.             0.015563       93.534054     -40.245433   3.550274   \n",
       "blue-collar        0.248995       93.656656     -41.375816   3.771996   \n",
       "entrepreneur       0.158723       93.605372     -41.283654   3.791120   \n",
       "housemaid          0.433396       93.676576     -39.495283   4.009645   \n",
       "management        -0.012688       93.522755     -40.489466   3.611316   \n",
       "retired           -0.698314       93.430786     -38.573081   2.770066   \n",
       "self-employed      0.094159       93.559982     -40.488107   3.689376   \n",
       "services           0.175359       93.634659     -41.290048   3.699187   \n",
       "student           -1.408000       93.331613     -40.187543   1.884224   \n",
       "technician         0.274566       93.561471     -39.927569   3.820401   \n",
       "unemployed        -0.111736       93.563781     -40.007594   3.466583   \n",
       "unknown            0.357879       93.718942     -38.797879   3.949033   \n",
       "\n",
       "               nr_employed         y  \n",
       "job                                   \n",
       "admin.         5164.125350  0.129726  \n",
       "blue-collar    5175.615150  0.068943  \n",
       "entrepreneur   5176.313530  0.085165  \n",
       "housemaid      5179.529623  0.100000  \n",
       "management     5166.650513  0.112175  \n",
       "retired        5122.262151  0.252326  \n",
       "self-employed  5170.674384  0.104856  \n",
       "services       5171.600126  0.081381  \n",
       "student        5085.939086  0.314286  \n",
       "technician     5175.648391  0.108260  \n",
       "unemployed     5157.156509  0.142012  \n",
       "unknown        5172.931818  0.112121  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('job').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <th></th>\n",
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       "      <th>divorced</th>\n",
       "      <td>44.899393</td>\n",
       "      <td>253.790330</td>\n",
       "      <td>2.61340</td>\n",
       "      <td>968.639853</td>\n",
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       "      <td>0.103209</td>\n",
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       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>42.307165</td>\n",
       "      <td>257.438623</td>\n",
       "      <td>2.57281</td>\n",
       "      <td>967.247673</td>\n",
       "      <td>0.155608</td>\n",
       "      <td>0.183625</td>\n",
       "      <td>93.597367</td>\n",
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       "      <td>3.745832</td>\n",
       "      <td>5171.848772</td>\n",
       "      <td>0.101573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>single</th>\n",
       "      <td>33.158714</td>\n",
       "      <td>261.524378</td>\n",
       "      <td>2.53380</td>\n",
       "      <td>949.909578</td>\n",
       "      <td>0.211359</td>\n",
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       "      <td>93.517300</td>\n",
       "      <td>-40.918698</td>\n",
       "      <td>3.317447</td>\n",
       "      <td>5155.199265</td>\n",
       "      <td>0.140041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>40.275000</td>\n",
       "      <td>312.725000</td>\n",
       "      <td>3.18750</td>\n",
       "      <td>937.100000</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>-0.221250</td>\n",
       "      <td>93.471250</td>\n",
       "      <td>-40.820000</td>\n",
       "      <td>3.313038</td>\n",
       "      <td>5157.393750</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                age    duration  campaign       pdays  previous  emp_var_rate  \\\n",
       "marital                                                                         \n",
       "divorced  44.899393  253.790330   2.61340  968.639853  0.168690      0.163985   \n",
       "married   42.307165  257.438623   2.57281  967.247673  0.155608      0.183625   \n",
       "single    33.158714  261.524378   2.53380  949.909578  0.211359     -0.167989   \n",
       "unknown   40.275000  312.725000   3.18750  937.100000  0.275000     -0.221250   \n",
       "\n",
       "          cons_price_idx  cons_conf_idx  euribor3m  nr_employed         y  \n",
       "marital                                                                    \n",
       "divorced       93.606563     -40.707069   3.715603  5170.878643  0.103209  \n",
       "married        93.597367     -40.270659   3.745832  5171.848772  0.101573  \n",
       "single         93.517300     -40.918698   3.317447  5155.199265  0.140041  \n",
       "unknown        93.471250     -40.820000   3.313038  5157.393750  0.150000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('marital').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Basic</th>\n",
       "      <td>42.163910</td>\n",
       "      <td>263.043874</td>\n",
       "      <td>2.559498</td>\n",
       "      <td>974.877967</td>\n",
       "      <td>0.141053</td>\n",
       "      <td>0.191329</td>\n",
       "      <td>93.639933</td>\n",
       "      <td>-40.927595</td>\n",
       "      <td>3.729654</td>\n",
       "      <td>5172.014113</td>\n",
       "      <td>0.087029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high.school</th>\n",
       "      <td>37.998213</td>\n",
       "      <td>260.886810</td>\n",
       "      <td>2.568576</td>\n",
       "      <td>964.358382</td>\n",
       "      <td>0.185917</td>\n",
       "      <td>0.032937</td>\n",
       "      <td>93.584857</td>\n",
       "      <td>-40.940641</td>\n",
       "      <td>3.556157</td>\n",
       "      <td>5164.994735</td>\n",
       "      <td>0.108355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>illiterate</th>\n",
       "      <td>48.500000</td>\n",
       "      <td>276.777778</td>\n",
       "      <td>2.277778</td>\n",
       "      <td>943.833333</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>93.317333</td>\n",
       "      <td>-39.950000</td>\n",
       "      <td>3.516556</td>\n",
       "      <td>5171.777778</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>professional.course</th>\n",
       "      <td>40.080107</td>\n",
       "      <td>252.533855</td>\n",
       "      <td>2.586115</td>\n",
       "      <td>960.765974</td>\n",
       "      <td>0.163075</td>\n",
       "      <td>0.173012</td>\n",
       "      <td>93.569864</td>\n",
       "      <td>-40.124108</td>\n",
       "      <td>3.710457</td>\n",
       "      <td>5170.155979</td>\n",
       "      <td>0.113485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>university.degree</th>\n",
       "      <td>38.879191</td>\n",
       "      <td>253.223373</td>\n",
       "      <td>2.563527</td>\n",
       "      <td>951.807692</td>\n",
       "      <td>0.192390</td>\n",
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       "      <td>-39.975805</td>\n",
       "      <td>3.529663</td>\n",
       "      <td>5163.226298</td>\n",
       "      <td>0.137245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>43.481225</td>\n",
       "      <td>262.390526</td>\n",
       "      <td>2.596187</td>\n",
       "      <td>942.830734</td>\n",
       "      <td>0.226459</td>\n",
       "      <td>0.059099</td>\n",
       "      <td>93.658615</td>\n",
       "      <td>-39.877816</td>\n",
       "      <td>3.571098</td>\n",
       "      <td>5159.549509</td>\n",
       "      <td>0.145003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           age    duration  campaign       pdays  previous  \\\n",
       "education                                                                    \n",
       "Basic                42.163910  263.043874  2.559498  974.877967  0.141053   \n",
       "high.school          37.998213  260.886810  2.568576  964.358382  0.185917   \n",
       "illiterate           48.500000  276.777778  2.277778  943.833333  0.111111   \n",
       "professional.course  40.080107  252.533855  2.586115  960.765974  0.163075   \n",
       "university.degree    38.879191  253.223373  2.563527  951.807692  0.192390   \n",
       "unknown              43.481225  262.390526  2.596187  942.830734  0.226459   \n",
       "\n",
       "                     emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  \\\n",
       "education                                                                     \n",
       "Basic                    0.191329       93.639933     -40.927595   3.729654   \n",
       "high.school              0.032937       93.584857     -40.940641   3.556157   \n",
       "illiterate              -0.133333       93.317333     -39.950000   3.516556   \n",
       "professional.course      0.173012       93.569864     -40.124108   3.710457   \n",
       "university.degree       -0.028090       93.493466     -39.975805   3.529663   \n",
       "unknown                  0.059099       93.658615     -39.877816   3.571098   \n",
       "\n",
       "                     nr_employed         y  \n",
       "education                                   \n",
       "Basic                5172.014113  0.087029  \n",
       "high.school          5164.994735  0.108355  \n",
       "illiterate           5171.777778  0.222222  \n",
       "professional.course  5170.155979  0.113485  \n",
       "university.degree    5163.226298  0.137245  \n",
       "unknown              5159.549509  0.145003  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('education').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1985a470940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "pd.crosstab(data.job,data.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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Proportion of Customers')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1985c1d22e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(data.marital,data.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": [
    "Hard to see, but the marital status does not seem a strong predictor for the outcome variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Proportion of Customers')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1985c3050f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(data.education,data.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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency of Purchase')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1985e6c1198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data.day_of_week,data.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": "markdown",
   "metadata": {},
   "source": [
    "Day of week may not be a good predictor of the outcome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency of Purchase')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1985c1aad30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data.month,data.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Month')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Frequency of Purchase')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Month might be a good predictor of the outcome variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1985bfd5518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.age.hist()\n",
    "plt.title('Histogram of Age')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most customers of the bank in this dataset are in the age range of 30-40."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency of Purchase')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1985e74e208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data.poutcome,data.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": [
    "Poutcome seems to be a good predictor of the outcome variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['age', 'duration', 'campaign', 'pdays', 'previous', 'emp_var_rate',\n",
       "       'cons_price_idx', 'cons_conf_idx', 'euribor3m', 'nr_employed', 'y',\n",
       "       'job_admin.', 'job_blue-collar', 'job_entrepreneur',\n",
       "       'job_housemaid', 'job_management', 'job_retired',\n",
       "       'job_self-employed', 'job_services', 'job_student',\n",
       "       'job_technician', 'job_unemployed', 'job_unknown',\n",
       "       'marital_divorced', 'marital_married', 'marital_single',\n",
       "       'marital_unknown', 'education_basic.4y', 'education_basic.6y',\n",
       "       'education_basic.9y', 'education_high.school',\n",
       "       'education_illiterate', 'education_professional.course',\n",
       "       'education_university.degree', 'education_unknown', 'default_no',\n",
       "       'default_unknown', 'default_yes', 'housing_no', 'housing_unknown',\n",
       "       'housing_yes', 'loan_no', 'loan_unknown', 'loan_yes',\n",
       "       'contact_cellular', 'contact_telephone', 'month_apr', 'month_aug',\n",
       "       'month_dec', 'month_jul', 'month_jun', 'month_mar', 'month_may',\n",
       "       'month_nov', 'month_oct', 'month_sep', 'day_of_week_fri',\n",
       "       'day_of_week_mon', 'day_of_week_thu', 'day_of_week_tue',\n",
       "       'day_of_week_wed', 'poutcome_failure', 'poutcome_nonexistent',\n",
       "       'poutcome_success'], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_vars=['job','marital','education','default','housing','loan','contact','month','day_of_week','poutcome']\n",
    "for var in cat_vars:\n",
    "    cat_list='var'+'_'+var\n",
    "    cat_list = pd.get_dummies(data[var], prefix=var)\n",
    "    data1=data.join(cat_list)\n",
    "    data=data1\n",
    "    \n",
    "cat_vars=['job','marital','education','default','housing','loan','contact','month','day_of_week','poutcome']\n",
    "data_vars=data.columns.values.tolist()\n",
    "to_keep=[i for i in data_vars if i not in cat_vars]\n",
    "data_final=data[to_keep]\n",
    "data_final.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Over-sampling using SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_final.loc[:, data_final.columns != 'y']\n",
    "y = data_final.loc[:, data_final.columns == 'y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of oversampled data is  51134\n",
      "Number of no subscription in oversampled data 25567\n",
      "Number of subscription 25567\n",
      "Proportion of no subscription data in oversampled data is  0.5\n",
      "Proportion of subscription data in oversampled data is  0.5\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "os = SMOTE(random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "columns = X_train.columns\n",
    "\n",
    "os_data_X,os_data_y=os.fit_sample(X_train, y_train)\n",
    "os_data_X = pd.DataFrame(data=os_data_X,columns=columns )\n",
    "os_data_y= pd.DataFrame(data=os_data_y,columns=['y'])\n",
    "# we can Check the numbers of our data\n",
    "print(\"length of oversampled data is \",len(os_data_X))\n",
    "print(\"Number of no subscription in oversampled data\",len(os_data_y[os_data_y['y']==0]))\n",
    "print(\"Number of subscription\",len(os_data_y[os_data_y['y']==1]))\n",
    "print(\"Proportion of no subscription data in oversampled data is \",len(os_data_y[os_data_y['y']==0])/len(os_data_X))\n",
    "print(\"Proportion of subscription data in oversampled data is \",len(os_data_y[os_data_y['y']==1])/len(os_data_X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recursive feature elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_final_vars=data_final.columns.values.tolist()\n",
    "y=['y']\n",
    "X=[i for i in data_final_vars if i not in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False False False False False False  True False False  True\n",
      " False  True False False False False False False False False False False\n",
      " False  True False False False False  True False False False  True  True\n",
      " False False False False False False False  True  True  True  True  True\n",
      "  True  True  True  True  True  True False False False False False False\n",
      "  True False  True]\n",
      "[39 38 26 42  9 12 24 36  1 35  8  1  7  1  5 32  2  4 31  3  6 10 23 21\n",
      " 17  1 14 18 15 22  1 20 16 19  1  1 41 28 44 37 33 43 34  1  1  1  1  1\n",
      "  1  1  1  1  1  1 29 30 11 27 40 25  1 13  1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "rfe = RFE(logreg, 20)\n",
    "rfe = rfe.fit(os_data_X, os_data_y.values.ravel())\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Recursive Feature Elimination (RFE) has helped us select the following features: \"previous\", \"euribor3m\", \"job_blue-collar\", \"job_retired\", \"job_services\", \"job_student\", \"default_no\", \"month_aug\", \"month_dec\", \"month_jul\", \"month_nov\", \"month_oct\", \"month_sep\", \"day_of_week_fri\", \"day_of_week_wed\", \"poutcome_failure\", \"poutcome_nonexistent\", \"poutcome_success\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['euribor3m', 'job_blue-collar', 'job_housemaid', 'marital_unknown', 'education_illiterate', 'default_no', 'default_unknown', \n",
    "      'contact_cellular', 'contact_telephone', 'month_apr', 'month_aug', 'month_dec', 'month_jul', 'month_jun', 'month_mar', \n",
    "      'month_may', 'month_nov', 'month_oct', \"poutcome_failure\", \"poutcome_success\"] \n",
    "X=os_data_X[cols]\n",
    "y=os_data_y['y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.545891\n",
      "         Iterations: 35\n",
      "                                Results: Logit\n",
      "==============================================================================\n",
      "Model:                   Logit                No. Iterations:       35.0000   \n",
      "Dependent Variable:      y                    Pseudo R-squared:     0.212     \n",
      "Date:                    2018-09-10 12:16     AIC:                  55867.1778\n",
      "No. Observations:        51134                BIC:                  56044.0219\n",
      "Df Model:                19                   Log-Likelihood:       -27914.   \n",
      "Df Residuals:            51114                LL-Null:              -35443.   \n",
      "Converged:               0.0000               Scale:                1.0000    \n",
      "------------------------------------------------------------------------------\n",
      "                      Coef.    Std.Err.    z     P>|z|     [0.025     0.975]  \n",
      "------------------------------------------------------------------------------\n",
      "euribor3m             -0.4634    0.0091 -50.9471 0.0000     -0.4813    -0.4456\n",
      "job_blue-collar       -0.1736    0.0283  -6.1230 0.0000     -0.2291    -0.1180\n",
      "job_housemaid         -0.3260    0.0778  -4.1912 0.0000     -0.4784    -0.1735\n",
      "marital_unknown        0.7454    0.2253   3.3082 0.0009      0.3038     1.1870\n",
      "education_illiterate   1.3156    0.4373   3.0084 0.0026      0.4585     2.1727\n",
      "default_no            16.1521 5414.0744   0.0030 0.9976 -10595.2387 10627.5429\n",
      "default_unknown       15.8945 5414.0744   0.0029 0.9977 -10595.4963 10627.2853\n",
      "contact_cellular     -13.9393 5414.0744  -0.0026 0.9979 -10625.3302 10597.4515\n",
      "contact_telephone    -14.0065 5414.0744  -0.0026 0.9979 -10625.3973 10597.3843\n",
      "month_apr             -0.8356    0.0913  -9.1490 0.0000     -1.0145    -0.6566\n",
      "month_aug             -0.6882    0.0929  -7.4053 0.0000     -0.8703    -0.5061\n",
      "month_dec             -0.4233    0.1655  -2.5579 0.0105     -0.7477    -0.0990\n",
      "month_jul             -0.4056    0.0935  -4.3391 0.0000     -0.5889    -0.2224\n",
      "month_jun             -0.4817    0.0917  -5.2550 0.0000     -0.6614    -0.3021\n",
      "month_mar              0.6638    0.1229   5.3989 0.0000      0.4228     0.9047\n",
      "month_may             -1.4752    0.0874 -16.8815 0.0000     -1.6465    -1.3039\n",
      "month_nov             -0.8298    0.0942  -8.8085 0.0000     -1.0144    -0.6451\n",
      "month_oct              0.5065    0.1175   4.3111 0.0000      0.2762     0.7367\n",
      "poutcome_failure      -0.5000    0.0363 -13.7706 0.0000     -0.5711    -0.4288\n",
      "poutcome_success       1.5788    0.0618  25.5313 0.0000      1.4576     1.7000\n",
      "==============================================================================\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "print(result.summary2())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-values for four variables are very high, therefore, we will remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['euribor3m', 'job_blue-collar', 'job_housemaid', 'marital_unknown', 'education_illiterate', \n",
    "      'month_apr', 'month_aug', 'month_dec', 'month_jul', 'month_jun', 'month_mar', \n",
    "      'month_may', 'month_nov', 'month_oct', \"poutcome_failure\", \"poutcome_success\"] \n",
    "X=os_data_X[cols]\n",
    "y=os_data_y['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.555865\n",
      "         Iterations 7\n",
      "                           Results: Logit\n",
      "=====================================================================\n",
      "Model:                Logit             No. Iterations:    7.0000    \n",
      "Dependent Variable:   y                 Pseudo R-squared:  0.198     \n",
      "Date:                 2018-09-10 12:38  AIC:               56879.2425\n",
      "No. Observations:     51134             BIC:               57020.7178\n",
      "Df Model:             15                Log-Likelihood:    -28424.   \n",
      "Df Residuals:         51118             LL-Null:           -35443.   \n",
      "Converged:            1.0000            Scale:             1.0000    \n",
      "---------------------------------------------------------------------\n",
      "                      Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
      "---------------------------------------------------------------------\n",
      "euribor3m            -0.4488   0.0074 -60.6837 0.0000 -0.4633 -0.4343\n",
      "job_blue-collar      -0.2060   0.0278  -7.4032 0.0000 -0.2605 -0.1515\n",
      "job_housemaid        -0.2784   0.0762  -3.6519 0.0003 -0.4278 -0.1290\n",
      "marital_unknown       0.7619   0.2244   3.3956 0.0007  0.3221  1.2017\n",
      "education_illiterate  1.3080   0.4346   3.0096 0.0026  0.4562  2.1598\n",
      "month_apr             1.2863   0.0380  33.8180 0.0000  1.2118  1.3609\n",
      "month_aug             1.3959   0.0411  33.9688 0.0000  1.3153  1.4764\n",
      "month_dec             1.8084   0.1441  12.5483 0.0000  1.5259  2.0908\n",
      "month_jul             1.6747   0.0424  39.5076 0.0000  1.5916  1.7578\n",
      "month_jun             1.5574   0.0408  38.1351 0.0000  1.4773  1.6374\n",
      "month_mar             2.8215   0.0908  31.0891 0.0000  2.6437  2.9994\n",
      "month_may             0.5848   0.0304  19.2166 0.0000  0.5251  0.6444\n",
      "month_nov             1.2725   0.0445  28.5720 0.0000  1.1852  1.3598\n",
      "month_oct             2.7279   0.0816  33.4350 0.0000  2.5680  2.8878\n",
      "poutcome_failure     -0.2797   0.0351  -7.9753 0.0000 -0.3485 -0.2110\n",
      "poutcome_success      1.9617   0.0602  32.5939 0.0000  1.8438  2.0797\n",
      "=====================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "print(result.summary2())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Model Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.74\n"
     ]
    }
   ],
   "source": [
    "y_pred = logreg.predict(X_test)\n",
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6124 1542]\n",
      " [2505 5170]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.71      0.80      0.75      7666\n",
      "          1       0.77      0.67      0.72      7675\n",
      "\n",
      "avg / total       0.74      0.74      0.74     15341\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interpretation:\n",
    "\n",
    "Of the entire test set, 74% of the promoted term deposit were the term deposit that the customers liked. Of the entire test set, 74% of the customer's preferred term deposit were promoted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19861c21b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1])\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()"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}