Logistic_Regression_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Data preparation and analysis ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Import the needed libaries and set some things for later visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rc(\"font\", size=14)\n",
    "sns.set(style=\"white\")\n",
    "sns.set(style=\"whitegrid\", color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Read sample-data from csv-file and print the shape (datasets and fields) and the columns' values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true
   },
   "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(\"src/banking.csv\", header=0)\n",
    "data = data.dropna()\n",
    "print(data.shape)\n",
    "print(list(data.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Show the first five rows of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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]\n"
     ]
    }
   ],
   "source": [
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Show the unique values of the column \"education\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "hidden": true
   },
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"education\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The column \"education\" has three values, which are quite similar: \n",
    "    - basic.9y\n",
    "    - basic.6y\n",
    "    - basic.4y\n",
    "The following lines replace them with the value <i>Basic</i> to group them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true
   },
   "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": {
    "hidden": true
   },
   "source": [
    "Having another look at the unique values of the \"education\" column to see the changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Basic', 'unknown', 'university.degree', 'high.school',\n",
       "       'professional.course', 'illiterate'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"education\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Count the values of the \"y\" column (in this case the depended variable) and display it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    36548\n",
       "1     4640\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"y\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Visualize the last line of code using a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116d4ffd0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1169bdf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"y\", data=data, palette=\"hls\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Group the datasets by the values of the \"y\" column (0, 1) and calculate the mean for each column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "hidden": true
   },
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"y\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Doing the same as before, but this time with the \"job\" column instead of the \"y\" column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "hidden": true
   },
   "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",
       "      <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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"job\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Data Visualization ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Displaying the frequencies of purchase for each job and the number of term deposits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d5f518>"
      ]
     },
     "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')\n",
    "plt.savefig('purchase_fre_job')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "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": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The next couple of rows will oppose the martial status values and the purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1167572b0>"
      ]
     },
     "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')\n",
    "plt.savefig('mariral_vs_pur_stack')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The marital status does not seem a strong predictor for the outcome variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The following lines create a bar chart comparing education and purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1166fb9b0>"
      ]
     },
     "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')\n",
    "plt.savefig('edu_vs_pur_stack')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Education seems a good predictor of the outcome variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now, let's have a look if the day of the purchase has an impact on our depended variable y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11938f5c0>"
      ]
     },
     "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')\n",
    "plt.savefig('pur_dayofweek_bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Day of week may not be a good predictor of the outcome."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "After looking at the day of order, let's have a look at the month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11643d160>"
      ]
     },
     "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')\n",
    "plt.savefig('pur_fre_month_bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Month might be a good predictor of the outcome variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The following lines will create a histogram showing which age the people have with the highest frequency of purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116538f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.age.hist()\n",
    "plt.title('Histogram of Age')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('hist_age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Most of the customers of the bank in this dataset are in the age range of 30–40."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Last but not least we have a look at the purchase frequency for the previous outcome (from the last campaign)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VWB+4U9J1wEO1DcvMzBpRnqTxNeAMYHhEzAOuSsvMzKzF5GmeWonsvokRktqAgcAXgS/XMjAzM2s8eWoaNwKbk3WELwfsyYcviTUzsxaRJ2msEhFfAX5NlkBGApvWMigzM2tMeZLGP9LvADaLiDeBwbULyczMGlWePo27JF0PfA+YkgYOfL+2YZmZWSOqWNOIiOOAH0TEc2TDiQTwn7UOzMzMGk+eYUQGAxtLOgT4JNmggaNqHZiZmTWePM1T1wNrkA3n0TnybAdwZa2CMjOzxpQnaWwSEZvUPBIzM2t4ea6emp1GlTUzsxbXY01D0t1kzVCrAY9KeoTsCXxtQEdE7FhMiGZm1ih6a54aV1QQZmbWHHpsnoqIeyPiXuBp4PNp+nng68CTBcVnZmYNJE+fxi+AZ9L0S2RP0buqZhGZmVnDypM0VoqIiwEiYm5EjAdWqW1YZmbWiPIkjfck7do5I2kn4J3ahWRmZo0qz30a3wR+KamzSeoFsmHSzcysxeRJGttExCclrQzMj4g5tQ7KzMwaU56kcRRwUUS8VutgzMysseVJGi9Iugt4EHivc2FEnFSzqMzMrCHlSRoPlE231SoQMzNrfBWTRkT8uNqNpuHUJwDrAUsBpwCPA5eTDU0yCxgTEYsknQjsRjZEyTERMUPShnnLVhubmZn1XcWkIWkRHwyJ3umliFi7l9UOBl6LiEMkrQTMTD/HR8Q9ki4C9pL0HDAC2BpYG7gB+AxwdhVlzcysIHlqGv+8lyPVIPYGtqmw2vXApDTdRlYzaAfuTcsmA6PJngI4JSI6gOclDZK0ajVlI+KVyodpZmZLQp4+jX+KiPnA9ZKOq1DubQBJQ8mSx/HAmekDH+AtYEVgBbInAdJleVsVZSsmjVKpVKlIU+vvx9ef+dw1t1Y8f3map75cNtsGbArMy7He2sBNwAURMVHSGWUvDwXeAOak6a7LF1VRtqL29vY8xZpSqVTq18dXqIkvFr5Ln7vm1d//93pKiHmGEdmh7GdEWrZ/bytIWh2YAhwbERPS4ocljUzTu5INfDgd2EXSgPSgpwER8WqVZc3MrCC91jQkDQS+14cb+34EfBQ4QdIJadnRwLmShpA9b3xSRCyUNA24nyyBjUllxwLjc5Y1M7OC9PbkvpHAtcAqkv4MfDEiHs2z0Yg4mixJdDWim7Lj6PLAp4h4Km9ZMzMrTm/NUz8FDgGWI7sE9vRCIjIzs4bVW/PU4IiYkqYvkdRdzcHMzFpIbzWNRV3m59YyEDMza3y91TSGpMtm27qbj4jnax2cmZk1lt6SxvJkd2WXD1I4Nf3uANavVVBmZtaYekwaEbFegXGYmVkTyHNzn5mZGeCkYWZmVegxaUg6Jv3+VHHhmJlZI+utI/woSbcCEyXtSpen9vnqKTOz1tNb0vglcAcwjA+umurkq6fMzFpQb1dPnQicKOnCiDiiwJjMzKxB5XkI0xhJRwA7pfJ3AT+LiK53jJuZWT+XJ2mcDmwETCDr1ziUrGnqmBrGZWZmDShP0hgNbNFZs5B0G5BriHQzM+tf8tynMYgPJ5dBwMLahGNmZo0sT03jl8A9kq5O818CJtYuJDMza1QVaxoRcSpwMrAOsB7wk7TMzMxaTJ6aBhExGZhc41jMzKzBeewpMzPLzUnDzMxyq9g8Jel24OfAzRExv/YhmZlZo8pT0zgN+A/gz5LOl/SZGsdkZmYNqmJNIyKmAlMlLQPsC9wgaQ5wKXBhRMytcYxmZtYgcl09JWkkcAjZ3eGTgWuAUcCvgF16WW9r4PSIGClpC+BW4M/p5Qsj4lpJJwK7AQuAYyJihqQNgcvJRtOdBYyJiEXdla3yeM3MbDHk6dN4DniGrF/jqIh4Ly2/B3iol/W+T5Zo3kmL2oGzI+KssjJbAiOArYG1gRuAzwBnA8dHxD2SLgL2SnF0V9bMzAqSp09jR2D/iLgSINUCiIhFEbFlL+vNBvYpm28HdpM0VdJlkoYC2wFTIqIjPdRpkKRVU9l703qTgZ17KWtmZgXJ0zy1G/BVYEtgNeDXkv4nIi7pbaWIuEHSemWLZgCXRkRJ0nHAicAbwGtlZd4CVgTaIqKjy7IVeij7SqUDKJVKlYo0tf5+fP2Zz11za8XzlydpHE7WJEREPCepHXgQ6DVpdOOmiHijcxo4D7gFGFpWZihZIlnUzbI5PZStqL29vcpQm0epVOrXx1eoiS8Wvkufu+bV3//3ekqIeZqnBgPlV0jNI+ugrtYdkrZK0zsBJWA6sIukAZLWAQZExKvAw6nzHWBXYFovZc3MrCB5aho3A3dJui7N70N21VS1jgDOkzQf+CtweETMkTQNuJ8sgY1JZccC4yUNAZ4AJkXEwh7KmplZQfLcp3GspH3JrlyaD5wbETfn2XhEPAsMT9N/BLbtpsw4YFyXZU+l/VUsa2Zmxck79tQTwHVktY7XJX2udiGZmVmjynOfxvnAHmSX0HbqILsU18zMWkjeZ4Sr86Y+MzNrXXmap54B2modiJmZNb48NY3Xgccl3Qe837kwIr5Ws6jMzKwh5Ukav0k/ZmbW4vJccntFGg5kU+AOYO2I+EutAzMzs8ZTsU9D0v7Ar4FzgJWA+yUdXOvAzMys8eTpCD8W+CzwVkT8HdgC+GFNozIzs4aUJ2ksjIi3Omci4mU+PKCgmZm1iDwd4Y9JOgoYLGlz4EhgZm3DMjOzRpSnpjEGWAt4D5hANkT5kbUMyszMGlOeq6feIevDcD+GmVmLyzP21CL+9fkZL0fEsNqEZGZmjSpPTeOfTViSBgN7A9vUMigzM2tMeYdGByAi5kfE9XiEWzOzlpSneerLZbNtZHeGz6tZRGZm1rDyXHK7Q9l0B/AqsH9twjEzs0aWp0/j0CICMTOzxpeneeov/OvVU5A1VXVExPpLPCozM2tIeZqnJgJzgfHAfOAg4DPAcTWMy8zMGlCepLFLRHy6bP4cSaWIeK5WQZmZWWPKc8ltm6SdO2ck7U42lIiZmbWYPDWNw4ErJf0bWd/Gk8BXahqVmZk1pDxXT5WATSWtArwfEW/n3bikrYHTI2KkpA2By8kSzyxgTEQsknQisBuwADgmImZUU7aKYzUzs8WU58l960r6LXA/sLyku9LjXyut933gUmDptOhs4PiI2J7syqu9JG0JjAC2Bg4Azu9DWTMzK0iePo2LgZ8CbwN/A64Grsyx3mxgn7L5duDeND0Z2BnYDpgSER0R8TwwSNKqVZY1M7OC5OnTWCUipkg6PSI6gPGSxlRaKSJu6FIjaUvrA7wFrAisALxWVqZzeTVlX6kUS6lUqlSkqfX34+vPfO6aWyuevzxJ4z1Jw0g3+Enajuy+jWqVPyJ2KPAG2VVYQ7tZXk3Zitrb2/sQbnMolUr9+vgKNfHFwnfpc9e8+vv/Xk8JMU/z1HeAW4GNJM0ku9nv232I4WFJI9P0rsA0YDqwi6QBktYBBkTEq1WWNTOzguSpaaxOdgf4xsBA4MmI6Msot2PJmraGAE8AkyJioaRpZJ3sA8geLVttWTMzK0iepHFGRNwGPFbtxiPiWWB4mn6K7OqnrmXGAeO6LMtd1szMipMnacyWNAF4EHivc2FE5LmCyszM+pE8SeM1snslhpct6yDfZbdmZtaP9Jg0JK0VEf/n52mYmVmn3q6e+nXnhKSxBcRiZmYNrrek0VY2fVCtAzEzs8bXW9Iof1pfW4+lzMysZeS5uQ+6f9yrmZm1mN6untpU0jNpeq2yaT8b3MysRfWWNDYuLAozM2sKPSYNPwPczMy6ytunYWZm5qRhZmb5OWmYmVluThpmZpabk4aZmeXmpGFmZrk5aZiZWW5OGmZmlpuThpmZ5eakYWZmuTlpmJlZbk4aZmaWm5OGmZnl5qRhZma59fY8jZqQ9EdgTpr9C3AxcA6wAJgSET+WNAC4ANgMmAscFhFPSxretWzR8ZuZtbJCk4akpYG2iBhZtmwm8AXgGeA2SVsAHwOWjohtUqI4C9gLuKhr2Yh4uMhjMDNrZUXXNDYDlpU0Je17HLBURMwGkHQHsDOwBvAbgIh4QNKnJa3QQ1knDTOzghSdNN4FzgQuBTYCJgNvlL3+FrA+sALwZtnyhWnZnG7KVlQqlfoecRPo78fXn/ncNbdWPH9FJ42ngKcjogN4StKbwEplrw8lSyLLpulOA8gSxtBuylbU3t6+ODE3tFKp1K+Pr1ATXyx8lz53zau//+/1lBCLvnrqa2T9E0hakyw5vCNpA0ltwC7ANGA68PlUbjjwaETMAeZ1U9bMzApSdE3jMuBySb8HOsiSyCLgl8BAsiuiHpT0B2CUpPuANuDQtP63upYtOH4zs5ZWaNKIiHnAgd28NLxLuUVkCaLr+g90LWtmZsXxzX1mZpabk4aZmeXmpGFmZrk5aZiZWW6Fjz3V3+0x9pZC9zfuwGGF7s/MWptrGmZmlpuThpmZ5ebmKbM6Of3pS+HpSwvb33X7X1jYvqz/ck3DzMxyc9IwM7PcnDTMzCw3Jw0zM8vNScPMzHJz0jAzs9ycNMzMLDcnDTMzy81Jw8zMcnPSMDOz3Jw0zMwsNycNMzPLzUnDzMxy8yi3ZmZ90KqjFLumYWZmubmm0eRa9duOmdVH0yUNSQOAC4DNgLnAYRHxdH2jMrN622PsLYXub5mtCt1dw2jG5qm9gaUjYhvgB8BZdY7HzKxlNGPS2A74DUBEPAB8ur7hmJm1jraOjo56x1AVSZcCN0TE5DT/PLB+RCzornypVGquAzQzaxDt7e1tXZc1XZ8GMAcYWjY/oKeEAd0ftJmZ9U0zNk9NBz4PIGk48Gh9wzEzax3NWNO4CRgl6T6gDTi0zvGYmbWMpuvTMDOz+mnG5ikzM6sTJw0zM8vNScPMzHJz0jAzs9ycNJqUpJXqHYNVT9LuXeb3q1cstnjSOHgtx1dPNRlJI4DzgYHA9cBzEXFZfaOySlKy2Bb4EjAxLR4I7BkRH69bYFYVSQcBC4GlgJ8CZ0TEmfWNqlgtmSmb3MnA54C/AqcCR9Y3HMvpEeBJ4D0g0s8ssiRizeNo4LfAwcDawB71Dad4zXhzX6tbFBGvS+qIiPclvVXvgKyyiHgBuELSVRGxqN7xWJ+9l36/FRFzJbXcZ2jLHXA/8LSk/wZWlvQD4Ll6B2RVOVbSscC7ZCMadETEmnWOyfJ7BngA+I6kE4E/1TmewjlpNJ8jga8BvwfeAb5R33CsSgcAa0bEu/UOxKoXEYdKWj4i3pb0h4j4W71jKpqTRvO5NSJG1zsI67O/8EEThzUZSTsDg9KVU+dJOiEiJlZarz9x0mg+/5C0J/AUsAggIp6qb0hWhSHAo5IeBToAIuLA+oZkVfgJcCDZFYzbAtfxwdVwLcFJo/msBnynbL4D2LFOsVj1Tq93ALZY3gX+BiyIiL9Karl7Fpw0mkxE7FDvGGyx/BE4FlgTuJUW7EhtcnPIHjd9iaQxwN/rHE/hnDSajKS/kJo1kjcjYot6xWNVmwBMBkaQ3WtzWZq25rAfsEFEPC7pk8Cl9Q6oaL65r/lsAnwc+ARwCHBvfcOxKq0cEROA+RFxH/4fbDarAD+SNAXYBti8zvEUzjWNJhMRc8tmp6d7NqyJSNok/R4G9Ph8e2tIlwBnAScAU4ErgOF1jahgThpNJiWJzuapNUlXUFnT+Dbwc7La4iTgiPqGY1VaJiLuknR8RISk9+sdUNGcNJrPk2XTj5B1ylnzWC8itumcSaPcPlzHeKw670vaBRgoaTjgpGGNSVLnDX0vd3lpa2BKweFYlcpHuZX02bR4ALAX2bX+1hwOB84k69v4Hi1YU3TSaB49jYbagZNGM3gEWJkPRrmFrGnxmrpFZH3xHnBZRPxW0lHAP+odUNGcNJrHNyJigaQh9Q7Eqlc+ym1aNIDs6pvH6xeV9cE1wDlp+nXgF8DuPRfvf5w0mseVZMMXBB90hLel6fXrFZRV7WzgCWBdYEuyu4u/UteIrBrLRcStABExUdJh9Q6oaE4aTaJzfKKI+Fi9Y7HF8pmIOEbS3RGxg6Tf1Tsgq8o8SaPIhkffiha8etFJo8mkwQrHAIPJahorR8Sn6huVVWGgpHbg2dTUOLTeAVlVDiPrCD+XrGnxm/UNp3i+G7X5nAJL9k/lAAAFlElEQVSMA14gu7Ho0bpGY9W6EriA7IPnDODi+oZjVfoHcH56rvvdwGt1jqdwThrN5+WIuB8gIi4H1qpvOFaNiLggIraOiMci4piIuKzeMVlVrgGWStOdHeEtxUmjSUhaMU3OlfQ5YHC6yWiVOoZlOUmalH6/LOml9POypJfqHZtV5UMd4cCydY6ncO7TaB63AduRDcU8iKyZ6iTg5HoGZflExL5pclhELOxcLmmFOoVkfdPyHeGuaTSP+ZL+AOxLljAuJWuaOrquUVm17pK0BoCkrYD76hyPVecwsgtRZgBH0oId4a5pNI+dyZLEhWR/rNacfgzcLule4NNkXwKsSUTE08De9Y6jnpw0mkRq0nge2K3esdhieYysiXEUcAcwu77hWDUkvUx2Q20bsBLwTLqSqmW4ecqsWNOACyJiU+Al4P46x2NViIg1ImLNiFgD2Jisb6OlOGmYFWvHiLgFICLOpAXbxPuLiHiO7EmaLcXNU2bFWlHS1cBHya7xn1XneKwK6dx1jv22Btlz3luKaxpmxToXOBR4BbiM7O5+ax63AdOBe8mSx0/qG07xnDTMCpauwOmIiFeAt+odj1XlG2RjTo0ie174T+sbTvGcNMyK9bqkbwLLSToAeKPeAVlVFgFTgY9ExDX45j4zq7GvAx8DXiW7T+Pr9Q3HqjSYbKDJqZJ2AFruoWhOGmYFiog5ZA9iOoKsf2P5+kZkVTqU7N6a04FVacEHaLV1dHRULmVmS4SkC4BdgZdJT16MiM/WNyqz/HzJrVmxtgI2iIiWawu3/sHNU2bFehpYut5BmPWVaxpmxVoHeE7S02nezVPWVJw0zIr1pXoHYLY43DxlVqyFZM8Hvx34X7LOcLOm4aRhVqzxwFXAtsAVZEOJmDUNN0+ZFWvpiPhVmr5Z0nfrGo1ZlVzTMCvWIEn/DpB++0YpayquaZgV67+AyyStSfYQpm/UOR6zqrimYVaszYChwHyyYShuqm84ZtVxTcOsWN8H9gBeqHcgZn3hpGFWrGfS8zTMmpKThlmx3pU0GZhJ6gSPiB/VNySz/Jw0zIp1e70DMFscHhrdzMxy89VTZmaWm5OGmZnl5j4Nsx5IWg94CnicrNN6CNkNeYdGxItVbmsPYKOIOHtJx2lWJCcNs969FBGbd85I+m/gPOA/q9xO+xKNyqxOnDTMqjMV2FPScOAcsqfwvQp8MyKelnQPMC4i7kk1lXuAzwPfApD0HHAL2ei2mwBzge9GxF2SdgdOIWs2fiZt82+SngWuBXYHFgA/AsYCGwFjI+I6SasDFwNrA4uAH0bEnbV9K6wVuU/DLCdJg4H9gQeBa4CjImIz4CLg6p7Wi4jHU5mLIuLnwMnA0xHxceAQ4CeSViP70N87Ij4FTAd+VraZlyJiU+CPwA+A0cDBwA/T6+cAEyKiHdgTuFjS0CVz5GYfcNIw692akmZKmgn8ieyhSZcD/4iIPwBExPXAhpJWzLnNEWTP1CAiHo2IbYCtgBkR8WwqcwmwU9k6k9Pv54B7I2JBmv5oWr4zcFKKczIwGNigymM1q8jNU2a9+1CfBoCkT3VTrg0YSNZh3vk0vsE9bHN+l+1twr9+gWvjw/+f88qmF3SzzYHAjhHxetrmmsDfeti/WZ+5pmFWvQBWlvQZAEn7Ac+lD+xXgU1Tub3L1lnAB0lgKnBAWncT4DdkTV7DUz8IwOHA3VXEdBdwZNrmJ8hqRctWdVRmObimYValiJgraX/gZ5KWA14n6+sAOAO4QtLXgJvLVpualv8NOBEYL+kRsmRySOrwPhy4SdIQsqanr1cR1n8Bl0jqbEI7JCLeWozDNOuWhxExM7Pc3DxlZma5OWmYmVluThpmZpabk4aZmeXmpGFmZrk5aZiZWW5OGmZmlpuThpmZ5fb/ARmZRvJiy5dbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116538a58>"
      ]
     },
     "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')\n",
    "plt.savefig('pur_fre_pout_bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Poutcome (previous outcome) seems to be a good predictor of the outcome variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Create dummy variables ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "That are variables with only two values, zero and one. They are created by categorial variables. It's hard to analyse categorial variables, that's why we create for each possible value of a categorial variable a new (dummy) variable, which then has only the possible values of one and zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "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",
    "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]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Our final data columns will be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "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', '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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final=data[to_keep]\n",
    "data_final.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "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": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Feature Selection ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Recursive Feature Elimination (RFE) is based on the idea to repeatedly construct a model and choose either the best or worst performing feature, setting the feature aside and then repeating the process with the rest of the features. This process is applied until all features in the dataset are exhausted. The goal of RFE is to select features by recursively considering smaller and smaller sets of features.\n",
    "\n",
    "Let's do some feature selection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/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": [
      "[False False False False  True False False False  True False False  True\n",
      " False False False  True False  True  True False False False False False\n",
      " False False  True False False False False False  True False False False\n",
      " False False False False False  True False  True  True False False False\n",
      "  True  True  True False False False  True False False False  True  True\n",
      "  True]\n",
      "[35 33 13 42  1 17 21 23  1 30 12  1 28 41 38  1 31  1  1 14 24 40  7  8\n",
      "  9 43  1  2 39  3  4  5  1 20 44 36 15 37 22 19 16  1 18  1  1 25 27 26\n",
      "  1  1  1 32 10 11  1 34 29  6  1  1  1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg = LogisticRegression()\n",
    "rfe = RFE(logreg, 18)\n",
    "rfe = rfe.fit(data_final[X], data_final[y] )\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The 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": 54,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cols=[\"previous\", \"euribor3m\", \"job_blue-collar\", \"job_retired\", \"job_services\", \"job_student\", \"default_no\", \n",
    "      \"month_aug\", \"month_dec\", \"month_jul\", \"month_nov\", \"month_oct\", \"month_sep\", \"day_of_week_fri\", \"day_of_week_wed\", \n",
    "      \"poutcome_failure\", \"poutcome_nonexistent\", \"poutcome_success\"] \n",
    "X=data_final[cols]\n",
    "y=data_final['y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Implementing the model ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.287116\n",
      "         Iterations 7\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                41188\n",
      "Model:                          Logit   Df Residuals:                    41170\n",
      "Method:                           MLE   Df Model:                           17\n",
      "Date:                Sat, 03 Mar 2018   Pseudo R-squ.:                  0.1844\n",
      "Time:                        09:01:56   Log-Likelihood:                -11826.\n",
      "converged:                       True   LL-Null:                       -14499.\n",
      "                                        LLR p-value:                     0.000\n",
      "========================================================================================\n",
      "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "previous                 0.2385      0.051      4.642      0.000       0.138       0.339\n",
      "euribor3m               -0.4981      0.012    -40.386      0.000      -0.522      -0.474\n",
      "job_blue-collar         -0.3222      0.049     -6.549      0.000      -0.419      -0.226\n",
      "job_retired              0.3821      0.069      5.552      0.000       0.247       0.517\n",
      "job_services            -0.2423      0.065     -3.701      0.000      -0.371      -0.114\n",
      "job_student              0.3540      0.086      4.107      0.000       0.185       0.523\n",
      "default_no               0.3312      0.056      5.943      0.000       0.222       0.440\n",
      "month_aug                0.4272      0.055      7.770      0.000       0.319       0.535\n",
      "month_dec                0.8061      0.163      4.948      0.000       0.487       1.125\n",
      "month_jul                0.7319      0.056     13.094      0.000       0.622       0.841\n",
      "month_nov                0.2706      0.064      4.249      0.000       0.146       0.395\n",
      "month_oct                0.8043      0.087      9.258      0.000       0.634       0.975\n",
      "month_sep                0.5906      0.096      6.160      0.000       0.403       0.778\n",
      "day_of_week_fri         -0.0044      0.046     -0.097      0.923      -0.094       0.085\n",
      "day_of_week_wed          0.1226      0.044      2.771      0.006       0.036       0.209\n",
      "poutcome_failure        -1.8438      0.100    -18.412      0.000      -2.040      -1.647\n",
      "poutcome_nonexistent    -1.1344      0.070    -16.253      0.000      -1.271      -0.998\n",
      "poutcome_success         0.0912      0.114      0.803      0.422      -0.131       0.314\n",
      "========================================================================================\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Workaround, because of error inside of the official released package\n",
    "from scipy import stats\n",
    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)\n",
    "# End of workaround\n",
    "\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The p-value for each term tests the null hypothesis that the coefficient is equal to zero (no effect). A low p-value (< 0.05) indicates that you can reject the null hypothesis. In other words, a predictor that has a low p-value is likely to be a meaningful addition to your model because changes in the predictor's value are related to changes in the response variable.\n",
    "\n",
    "Conversely, a larger (insignificant) p-value suggests that changes in the predictor are not associated with changes in the response.\n",
    "\n",
    "The p-values for most of the variables are smaller than 0.05, therefore, most of them are significant to the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Logistic Regression Model Fitting ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hidden": true
   },
   "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": 56,
     "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": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We used the LogisticRegression() without any parameters. Here are the default values, which are used.\n",
    "\n",
    "*LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, max_iter=100, multi_class=’ovr’, n_jobs=1, penalty=’l2', random_state=None, solver=’liblinear’, tol=0.0001,\n",
    "verbose=0, warm_start=False)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Predicting the test set results and calculating the accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.90\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": {
    "hidden": true
   },
   "source": [
    "Accuracy of logistic regression classifier on test set: 0.90"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Cross Validation ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Cross validation attempts to avoid overfitting while still producing a prediction for each observation dataset. We are using 10-fold Cross-Validation to train our Logistic Regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-fold cross validation average accuracy: 0.897\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "kfold = model_selection.KFold(n_splits=10, random_state=7)\n",
    "modelCV = LogisticRegression()\n",
    "scoring = 'accuracy'\n",
    "results = model_selection.cross_val_score(modelCV, X_train, y_train, cv=kfold, scoring=scoring)\n",
    "print(\"10-fold cross validation average accuracy: %.3f\" % (results.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "10-fold cross validation average accuracy: 0.897\n",
    "\n",
    "The average accuracy remains very close to the Logistic Regression model accuracy; hence, we can conclude that our model generalizes well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Confusion Matrix ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one. Each row of the matrix represents the instances in a predicted class while each column represents the instances in an actual class (or vice versa). The name stems from the fact that it makes it easy to see if the system is confusing two classes (i.e. commonly mislabelling one as another).\n",
    "\n",
    "It is a special kind of contingency table, with two dimensions (\"actual\" and \"predicted\"), and identical sets of \"classes\" in both dimensions (each combination of dimension and class is a variable in the contingency table).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10872   109]\n",
      " [ 1122   254]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The result is telling us that we have 10872+254 correct predictions and 1122+109 incorrect predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Compute precision, recall, F-measure and support ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "To quote from Scikit Learn:\n",
    "\n",
    "The precision is the ratio **tp / (tp + fp)** where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier to not label a sample as positive if it is negative.\n",
    "\n",
    "The recall is the ratio **tp / (tp + fn)** where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.\n",
    "\n",
    "The F-beta score can be interpreted as a weighted harmonic mean of the precision and recall, where an F-beta score reaches its best value at 1 and worst score at 0.\n",
    "The F-beta score weights the recall more than the precision by a factor of beta. **beta = 1.0** means recall and precision are equally important.\n",
    "\n",
    "The support is the number of occurrences of each class in y_test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      0.99      0.95     10981\n",
      "          1       0.70      0.18      0.29      1376\n",
      "\n",
      "avg / total       0.88      0.90      0.87     12357\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Interpretation:** Of the entire test set, 88% of the promoted term deposit were the term deposit that the customers liked. Of the entire test set, 90% of the customer’s preferred term deposits that were promoted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## ROC Curve ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The **receiver operating characteristic (ROC)** curve is another common tool used with binary classifiers. \n",
    "\n",
    "The dotted line represents the ROC curve of a purely random classifier; a good classifier stays as far away from that line as possible (toward the top-left corner)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116a401d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1])\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "\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": "3.6.3"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}