{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Review with bank marketing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "- Data from the UCI Machine Learning Repository: [data](https://github.com/sinanuozdemir/sfdat22/blob/master/data/bank.csv), [data dictionary](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing)\n",
    "- **Goal:** Predict whether a customer will purchase a bank product marketed over the phone\n",
    "- `bank.csv` is already in our repo, so there is no need to download the data from the UCI website"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Read the data into Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>30</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</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>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.313</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>single</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.855</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>wed</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>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.962</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.959</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>mon</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>4.191</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital          education default  housing     loan  \\\n",
       "0   30  blue-collar  married           basic.9y      no      yes       no   \n",
       "1   39     services   single        high.school      no       no       no   \n",
       "2   25     services  married        high.school      no      yes       no   \n",
       "3   38     services  married           basic.9y      no  unknown  unknown   \n",
       "4   47       admin.  married  university.degree      no      yes       no   \n",
       "\n",
       "     contact month day_of_week ...  campaign  pdays  previous     poutcome  \\\n",
       "0   cellular   may         fri ...         2    999         0  nonexistent   \n",
       "1  telephone   may         fri ...         4    999         0  nonexistent   \n",
       "2  telephone   jun         wed ...         1    999         0  nonexistent   \n",
       "3  telephone   jun         fri ...         3    999         0  nonexistent   \n",
       "4   cellular   nov         mon ...         1    999         0  nonexistent   \n",
       "\n",
       "  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0         -1.8          92.893          -46.2      1.313       5099.1  no  \n",
       "1          1.1          93.994          -36.4      4.855       5191.0  no  \n",
       "2          1.4          94.465          -41.8      4.962       5228.1  no  \n",
       "3          1.4          94.465          -41.8      4.959       5228.1  no  \n",
       "4         -0.1          93.200          -42.0      4.191       5195.8  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank = pd.read_csv('../data/bank.csv', sep=';')\n",
    "bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4119, 21)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "job               0\n",
       "marital           0\n",
       "education         0\n",
       "default           0\n",
       "housing           0\n",
       "loan              0\n",
       "contact           0\n",
       "month             0\n",
       "day_of_week       0\n",
       "duration          0\n",
       "campaign          0\n",
       "pdays             0\n",
       "previous          0\n",
       "poutcome          0\n",
       "emp.var.rate      0\n",
       "cons.price.idx    0\n",
       "cons.conf.idx     0\n",
       "euribor3m         0\n",
       "nr.employed       0\n",
       "y                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do we have any null values?\n",
    "bank.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Prepare at least three features\n",
    "\n",
    "- Include both numeric and categorical features\n",
    "- Choose features that you think might be related to the response (based on intuition or exploration)\n",
    "- Think about how to handle missing values (encoded as \"unknown\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'age', u'job', u'marital', u'education', u'default', u'housing',\n",
       "       u'loan', u'contact', u'month', u'day_of_week', u'duration', u'campaign',\n",
       "       u'pdays', u'previous', u'poutcome', u'emp.var.rate', u'cons.price.idx',\n",
       "       u'cons.conf.idx', u'euribor3m', u'nr.employed', u'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list all columns (for reference)\n",
    "bank.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### y (response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: outcome, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the response to numeric values and store as a new column called \"outcome\"\n",
    "bank['outcome'] = bank.y.map({'no':0, 'yes':1})\n",
    "bank['outcome'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore a few columns manually and visually"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Exercise!\n",
    "# create a boxplot of age the data by outcome\n",
    "# TODO\n",
    "print \"\\n\"*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114dd9310>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ERrITp3cBPwS+EhH3AH+Rz7sFqBxOeQj475I2kPX/DwKXSNpENgTzq4C/BeZK\nug/4OrAif/7tXrufIqyPAp+QtEnSA8Bps/omzWbIV9GYmXUoH8GbmXUoJ3gzsw7lBG9m1qGc4M3M\nOpQTvJlZh3KCNzPrUE7wZmYdygnezKxD/X/yr8QFsfUQXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112430550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "bank.boxplot(column='age', by='outcome')\n",
    "# probably not a great feature, why?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117c38fd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c24e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# looks like a useful feature\n",
    "bank.groupby('job').outcome.mean().plot(kind='bar')\n",
    "# Ask yourself, WHY is this going to be useful?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_blue-collar</th>\n",
       "      <th>job_entrepreneur</th>\n",
       "      <th>job_housemaid</th>\n",
       "      <th>job_management</th>\n",
       "      <th>job_retired</th>\n",
       "      <th>job_self-employed</th>\n",
       "      <th>job_services</th>\n",
       "      <th>job_student</th>\n",
       "      <th>job_technician</th>\n",
       "      <th>job_unemployed</th>\n",
       "      <th>job_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   job_blue-collar  job_entrepreneur  job_housemaid  job_management  \\\n",
       "0              1.0               0.0            0.0             0.0   \n",
       "1              0.0               0.0            0.0             0.0   \n",
       "2              0.0               0.0            0.0             0.0   \n",
       "3              0.0               0.0            0.0             0.0   \n",
       "4              0.0               0.0            0.0             0.0   \n",
       "\n",
       "   job_retired  job_self-employed  job_services  job_student  job_technician  \\\n",
       "0          0.0                0.0           0.0          0.0             0.0   \n",
       "1          0.0                0.0           1.0          0.0             0.0   \n",
       "2          0.0                0.0           1.0          0.0             0.0   \n",
       "3          0.0                0.0           1.0          0.0             0.0   \n",
       "4          0.0                0.0           0.0          0.0             0.0   \n",
       "\n",
       "   job_unemployed  job_unknown  \n",
       "0             0.0          0.0  \n",
       "1             0.0          0.0  \n",
       "2             0.0          0.0  \n",
       "3             0.0          0.0  \n",
       "4             0.0          0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create job_dummies (we will add it to the bank DataFrame later)\n",
    "job_dummies = pd.get_dummies(bank.job, prefix='job')\n",
    "# removing the first job from the list (making it k-1 columns) where k is the number of jobs\n",
    "job_dummies.drop(job_dummies.columns[0], axis=1, inplace=True)\n",
    "job_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1180e21d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEyCAYAAAABVZAhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJDXIcJekBhnuktSg/w88trMh3B3YAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180ccd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# looks like a useful feature\n",
    "bank.groupby('default').outcome.mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Exercise! \n",
    "# How many people have a value of no, yes, and unknown?\n",
    "# TODO\n",
    "print \"\\n\"*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no         3315\n",
       "unknown     803\n",
       "yes           1\n",
       "Name: default, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution!\n",
    "bank.default.value_counts()\n",
    "# only one person in the dataset has a status of yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.313</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>single</td>\n",
       "      <td>high.school</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.855</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>wed</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.962</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.959</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</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>4.191</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital          education  default  housing     loan  \\\n",
       "0   30  blue-collar  married           basic.9y        0      yes       no   \n",
       "1   39     services   single        high.school        0       no       no   \n",
       "2   25     services  married        high.school        0      yes       no   \n",
       "3   38     services  married           basic.9y        0  unknown  unknown   \n",
       "4   47       admin.  married  university.degree        0      yes       no   \n",
       "\n",
       "     contact month day_of_week   ...     pdays  previous     poutcome  \\\n",
       "0   cellular   may         fri   ...       999         0  nonexistent   \n",
       "1  telephone   may         fri   ...       999         0  nonexistent   \n",
       "2  telephone   jun         wed   ...       999         0  nonexistent   \n",
       "3  telephone   jun         fri   ...       999         0  nonexistent   \n",
       "4   cellular   nov         mon   ...       999         0  nonexistent   \n",
       "\n",
       "   emp.var.rate cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \\\n",
       "0          -1.8         92.893          -46.2      1.313       5099.1  no   \n",
       "1           1.1         93.994          -36.4      4.855       5191.0  no   \n",
       "2           1.4         94.465          -41.8      4.962       5228.1  no   \n",
       "3           1.4         94.465          -41.8      4.959       5228.1  no   \n",
       "4          -0.1         93.200          -42.0      4.191       5195.8  no   \n",
       "\n",
       "   outcome  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So, let's treat this as a 2-class feature rather than a 3-class feature\n",
    "bank['default'] = bank.default.map({'no':0, 'unknown':1, 'yes':1})\n",
    "bank.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### contact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "contact\n",
       "cellular     0.141403\n",
       "telephone    0.051806\n",
       "Name: outcome, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks like a useful feature\n",
    "bank.groupby('contact').outcome.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Exercise! \n",
    "# Convert the feature to numeric values, 0 for cellular, 1 for telephone\n",
    "# TODO\n",
    "print \"\\n\"*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Solution!\n",
    "bank['contact'] = bank.contact.map({'cellular':0, 'telephone':1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "apr    0.167442\n",
       "aug    0.100629\n",
       "dec    0.545455\n",
       "jul    0.082982\n",
       "jun    0.128302\n",
       "mar    0.583333\n",
       "may    0.065312\n",
       "nov    0.096413\n",
       "oct    0.362319\n",
       "sep    0.406250\n",
       "Name: outcome, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks like a useful feature at first glance\n",
    "bank.groupby('month').outcome.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dec</th>\n",
       "      <td>22</td>\n",
       "      <td>0.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>48</td>\n",
       "      <td>0.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>64</td>\n",
       "      <td>0.406250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>69</td>\n",
       "      <td>0.362319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apr</th>\n",
       "      <td>215</td>\n",
       "      <td>0.167442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nov</th>\n",
       "      <td>446</td>\n",
       "      <td>0.096413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jun</th>\n",
       "      <td>530</td>\n",
       "      <td>0.128302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aug</th>\n",
       "      <td>636</td>\n",
       "      <td>0.100629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jul</th>\n",
       "      <td>711</td>\n",
       "      <td>0.082982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>may</th>\n",
       "      <td>1378</td>\n",
       "      <td>0.065312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count      mean\n",
       "month                 \n",
       "dec       22  0.545455\n",
       "mar       48  0.583333\n",
       "sep       64  0.406250\n",
       "oct       69  0.362319\n",
       "apr      215  0.167442\n",
       "nov      446  0.096413\n",
       "jun      530  0.128302\n",
       "aug      636  0.100629\n",
       "jul      711  0.082982\n",
       "may     1378  0.065312"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# but, it looks like their success rate is actually just correlated with number of calls\n",
    "# thus, the month feature is unlikely to generalize\n",
    "bank.groupby('month').outcome.agg(['count', 'mean']).sort('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.766364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.766364</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean\n",
       "count  1.000000 -0.766364\n",
       "mean  -0.766364  1.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.groupby('month').outcome.agg(['count', 'mean']).sort('count').corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11847a610>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D0qOS1kg6Onf8MkmbJW2S9MbRZt7MzMbWqNoUJP0UODUiduXSLgN+GRGfkvRx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\niv8P5vk0kfqoXJsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118677410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# looks like an excellent feature, can you think of a reason why duration isn't helpful in practice?\n",
    "bank.boxplot(column='duration', by='outcome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Answer: You can't know the duration of a call beforehand, thus it can't be used in your model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### previous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "previous\n",
       "0    0.082884\n",
       "1    0.208421\n",
       "2    0.410256\n",
       "3    0.600000\n",
       "4    0.714286\n",
       "5    1.000000\n",
       "6    0.500000\n",
       "Name: outcome, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks like a useful feature\n",
    "bank.groupby('previous').outcome.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### poutcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poutcome\n",
       "failure        0.147577\n",
       "nonexistent    0.082884\n",
       "success        0.647887\n",
       "Name: outcome, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks like a useful feature\n",
    "bank.groupby('poutcome').outcome.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create poutcome_dummies\n",
    "poutcome_dummies = pd.get_dummies(bank.poutcome, prefix='poutcome')\n",
    "poutcome_dummies.drop(poutcome_dummies.columns[0], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# concatenate bank DataFrame with job_dummies and poutcome_dummies\n",
    "bank = pd.concat([bank, job_dummies, poutcome_dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### euribor3m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1186bf150>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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858wbNTTWbPds5JI0cGbkHTJv1NBYs/1hRi5JM8xG3nPmjRoaa7Z7NnJJGjgz8g6ZN2po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\nkjRw/x+eddzbMyidbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11847a950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# looks like an excellent feature\n",
    "bank.boxplot(column='euribor3m', by='outcome')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Model building\n",
    "\n",
    "- Use cross-validation to evaluate the AUC of a logistic regression model with your chosen features\n",
    "- Try to increase the AUC by selecting different sets of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'age', u'job', u'marital', u'education', u'default', u'housing',\n",
       "       u'loan', u'contact', u'month', u'day_of_week', u'duration', u'campaign',\n",
       "       u'pdays', u'previous', u'poutcome', u'emp.var.rate', u'cons.price.idx',\n",
       "       u'cons.conf.idx', u'euribor3m', u'nr.employed', u'y', u'outcome',\n",
       "       u'job_blue-collar', u'job_entrepreneur', u'job_housemaid',\n",
       "       u'job_management', u'job_retired', u'job_self-employed',\n",
       "       u'job_services', u'job_student', u'job_technician', u'job_unemployed',\n",
       "       u'job_unknown', u'poutcome_nonexistent', u'poutcome_success'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new list of columns (including dummy columns)\n",
    "bank.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# create X (including 13 dummy columns)\n",
    "feature_cols = ['default', 'contact', 'previous', 'euribor3m'] + list(bank.columns[-13:])\n",
    "X = bank[feature_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create y\n",
    "y = bank.outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Exercise! \n",
    "# calculate cross-validated Accuracy for logisic regression, and knn with 5 neighbors\n",
    "# TODO\n",
    "print \"\\n\"*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90215602496498271"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution!\n",
    "logreg = LogisticRegression()\n",
    "cross_val_score(logreg, X, y, cv=10, scoring='accuracy').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90046229940191136"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(5)\n",
    "cross_val_score(knn, X, y, cv=10, scoring='accuracy').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Exercise! \n",
    "# use grid search to try k in range from 1-29 (odd only) for knn, and graph the accuracy from a cross val score vs the k\n",
    "# TODO\n",
    "print \"\\n\"*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'n_neighbors': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution!\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "knn = KNeighborsClassifier()\n",
    "param_grid = {'n_neighbors': range(1, 29, 2)}\n",
    "grid = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy')\n",
    "grid.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11b643b10>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JxwJXAmdFxBnAUGBGJXegr9zTN7M8KqWnPwVoi4gNEfE6cA8wvds6ATRlz5uAbRGxO5s+\nBBgpaShwGNDe/2b3n0PfzPKolNA/DthYNL0pm1dsHnCqpHZgOXA1QES0A7cAzwCbgRci4uf9bXR/\ndXTAjh0wblytW2JmNrCGVmg7U4GlEfF+SScCiyV1lnOmA+OBncACSR+LiO/3tJHm5uY3nhcKBQqF\nQoWa11VbG0yaBENyexrbzOpRS0sLLS0t/dpGrxdnSToHaI6Iadn0dUBExE1F6zwI3BgRj2TTDwFz\ngBOAqRFxWTb/k8C7I2J2D+8zYBdn3Xcf3Hsv/PCHA/J2ZmZVUa2Ls5YAkySNz0bezAAWdltnA3BB\n1oixwEnAelJZ5xxJh0oScD6wupwGVkNrq0fumFk+9Rr6EbEHmA0sAlYB90TEakmzJF2erXY98F8k\nrQAWA9dGxPaIeBRYACwl1foF3F6F/SiLT+KaWV7l8t4773kPfPnLcN55A/J2ZmZV4XvvlMjlHTPL\nq9yF/rZtsHt3+m1cM7O8yV3ot7Wler7K+kJkZtYYchf6Lu2YWZ7lMvQ9csfM8ip3od9Z3jEzy6Pc\nhb7LO2aWZ7kapx8BTU2waRMccURV38rMrOo8Tr8XW7bAyJEOfDPLr1yFvks7ZpZ3uQt9n8Q1szzL\nVeh75I6Z5V2uQt/lHTPLu9yFvnv6ZpZnuRmyuWdPGrmzYweMGFG1tzEzGzAesnkQGzakO2s68M0s\nz3IT+i7tmJnlKPQ9csfMLEeh75E7ZmY5C3339M0s73IT+i7vmJnlZMjmq6/C6NHQ0QHDhlXlLczM\nBpyHbB7AunUwfrwD38yspNCXNE3SGkmtkub0sHy0pIWSlklaKemSbP5JkpZKeiJ73CnpqgrvQ69c\n2jEzS4b2toKkIcA84HygHVgi6YGIWFO02hXAqoi4UNIYYK2k70ZEK/DOou1sAn5c6Z3ojUfumJkl\npfT0pwBtEbEhIl4H7gGmd1sngKbseROwLSJ2d1vnAmBdRGzsT4P7wiN3zMySUkL/OKA4qDdl84rN\nA06V1A4sB67uYTt/BfygL43sL5d3zMySXss7JZoKLI2I90s6EVgs6YyI6ACQNAy4ELjuYBtpbm5+\n43mhUKBQKFSkcS7vmFkjaGlpoaWlpV/b6HXIpqRzgOaImJZNXwdERNxUtM6DwI0R8Ug2/RAwJyIe\ny6YvBD7fuY0DvE9Vhmx2dKQbrXV0wJBcjFUys7yo1pDNJcAkSeMlDQdmAAu7rbOBVLNH0ljgJGB9\n0fKZ1LC0M2mSA9/MDEoo70TEHkmzgUWkg8T8iFgtaVZaHLcD1wN3SVqRvezaiNgOIOkw0gHh8qrs\nQS9c2jEz26ekmn5E/Adwcrd53yx6voVU1+/ptS8Db+lHG/vFJ3HNzPZp+KKHh2uame2Ti9B3ecfM\nLGn40Hd5x8xsn4YO/W3b0g+iv6VmZxTMzAaXhg79ztKOyhrFambWuBo69F3aMTPrqqFD3yN3zMy6\navjQ98gdM7N9Gjr0Xd4xM+uqYX8jNwKammDzZjj88Ipt1sxs0PBv5BZpb4eRIx34ZmbFGjb0Xdox\nM9tfw4a+R+6Yme2voUPfI3fMzLpq2NB3ecfMbH8NG/ou75iZ7a8hh2zu3g2jRsGOHTBiREU2aWY2\n6HjIZuaZZ2DsWAe+mVl3DRn6Lu2YmfWsYUPfI3fMzPbXkKHvkTtmZj1ryNB3ecfMrGcNG/ou75iZ\n7a+k0Jc0TdIaSa2S5vSwfLSkhZKWSVop6ZKiZYdLul/SakmrJL27gu3fz6uvwpYtcMIJ1XwXM7P6\n1GvoSxoCzAOmAqcBMyWd0m21K4BVEXEm8D7gFklDs2W3Av83It4GvANYXanG92TdOhg/HoYNq+a7\nmJnVp1J6+lOAtojYEBGvA/cA07utE0BT9rwJ2BYRuyWNBt4bEXcCRMTuiHixQm3vkUs7ZmYHVkro\nHwdsLJrelM0rNg84VVI7sBy4Ops/AfiDpDslPSHpdklVvWTKI3fMzA5saO+rlGQqsDQi3i/pRGCx\npDOy7Z8FXBERj0n6OnAdMLenjTQ3N7/xvFAoUCgUym5IayucfXbZLzMzG/RaWlpoaWnp1zZ6vfeO\npHOA5oiYlk1fB0RE3FS0zoPAjRHxSDb9EDCH9A3h/0XExGz+ecCciPhwD+9TkXvvFArwD/8A55/f\n702ZmQ1q1br3zhJgkqTxkoYDM4CF3dbZAFyQNWIscBKwPiK2AhsldRZczgeeKqeB5fIYfTOzA+u1\nvBMReyTNBhaRDhLzI2K1pFlpcdwOXA/cJWlF9rJrI2J79vwq4HuShgHrgUsrvheZXbvghRfguO5n\nHMzMDGiwWysvXQoXXwwrVvS+rplZvcv9rZVd2jEzOziHvplZjjRU6Le1+cIsM7ODaajQd0/fzOzg\nHPpmZjnSMKG/bRvs3QtjxtS6JWZmg1fDhH5nL19lDV4yM8uXhgt9MzM7sIYJfY/cMTPrXcOEvnv6\nZma9c+ibmeVIQ9x7JwKamqC9HUaPrnDDzMwGqdzee6e9HUaNcuCbmfWmIULfpR0zs9I0ROh75I6Z\nWWkaIvTd0zczK41D38wsRxoi9F3eMTMrTd0P2dy9O43c2bEDRoyoQsPMzAapXA7Z3LABjjnGgW9m\nVoq6D32XdszMSlf3oe+TuGZmpXPom5nlSEmhL2mapDWSWiXN6WH5aEkLJS2TtFLSJUXLfi9puaSl\nkh6tYNsBl3fMzMoxtLcVJA0B5gHnA+3AEkkPRMSaotWuAFZFxIWSxgBrJX03InYDe4FCROyoQvvd\n0zczK0MpPf0pQFtEbIiI14F7gOnd1gmgKXveBGzLAh9AJb5P2V55BbZsgRNOqMbWzcwaTylhfByw\nsWh6Uzav2DzgVEntwHLg6qJlASyWtETSZf1pbHfr18P48TC01+8rZmYGJZR3SjQVWBoR75d0Iink\nz4iIDuDciNgi6S3Z/NUR8XBPG2lubn7jeaFQoFAoHPRNXdoxszxpaWmhpaWlX9vo9YpcSecAzREx\nLZu+DoiIuKlonQeBGyPikWz6IWBORDzWbVtzgV0R8dUe3qfsK3K//GV49lm45ZayXmZm1hCqdUXu\nEmCSpPGShgMzgIXd1tkAXJA1YixwErBe0mGSRmXzRwIfAJ4sp4EH09rqkTtmZuXotbwTEXskzQYW\nkQ4S8yNitaRZaXHcDlwP3CVpRfayayNiu6QJwI8lRfZe34uIRZVqfGsrzJxZqa2ZmTW+ur7h2rHH\nwqOPwrhxVWqUmdkglqsbru3aBTt3puA3M7PS1G3ot7XBpEkwpG73wMxs4NVtZLa1ebimmVm56jb0\nPXLHzKx8dR367umbmZWnbkPfd9c0MytfXYZ+BKxd656+mVm56jL0t21Lj2PG1LYdZmb1pi5Dv7O0\no7IuSTAzs7oMfZ/ENTPrG4e+mVmO1GXoe+SOmVnf1GXou6dvZtY3dXeXzQgYNSr9Nu7o0QPQMDOz\nQSoXd9lsb4emJge+mVlf1F3ou7RjZtZ3Dn0zsxypu9D3yB0zs76ru9B3T9/MrO/qLvQ/+EE4++xa\nt8LMrD7V3ZBNMzNLcjFk08zM+q6k0Jc0TdIaSa2S5vSwfLSkhZKWSVop6ZJuy4dIekLSwgq128zM\n+qDX0Jc0BJgHTAVOA2ZKOqXbalcAqyLiTOB9wC2ShhYtvxp4qjJNrj8tLS21bkJVef/qm/cvX0rp\n6U8B2iJiQ0S8DtwDTO+2TgBN2fMmYFtE7AaQNA74EPCvlWly/Wn0f3Tev/rm/cuXUkL/OGBj0fSm\nbF6xecCpktqB5aSefaevAf+LdGAwM7MaqtSJ3KnA0og4FngncJukUZL+O7A1IpYByv7MzKxGeh2y\nKekcoDkipmXT1wERETcVrfMgcGNEPJJNPwTMAS4CPgHsBkaQSj8/iohP9fA+/iZgZlamcodslhL6\nhwBrgfOBLcCjwMyIWF20zm3AcxHxvyWNBR4D3hER24vW+a/ANRFxYTkNNDOzyhna2woRsUfSbGAR\nqRw0PyJWS5qVFsftwPXAXZJWZC+7tjjwzcxscBg0V+SamVn11fyK3N4u/Kp3kn4vabmkpZIerXV7\n+kvSfElbi77VIelISYskrZX0M0mH17KN/XGA/ZsraVN2geETkqbVso19JWmcpF9IWpVdRHlVNr8h\nPr8e9u/KbH6jfH5vkvTbLEtWSpqbzS/r86tpTz+78KuVdL6gHVgCzIiINTVrVIVJWg+cHRE7at2W\nSpB0HtABfDsizsjm3US6NuPm7MB9ZERcV8t29tUB9m8usCsivlrTxvWTpGOAYyJimaRRwOOka24u\npQE+v4Ps31/RAJ8fgKTDIuLl7FzrI8BVwJ9TxudX655+KRd+1TtR+//OFRMRDwPdD2DTgbuz53cD\nHxnQRlXQAfYPGmC4cUQ8mw2fJiI6gNXAOBrk8zvA/nVeU1T3nx9ARLycPX0T6ZxsUObnV+swKuXC\nr3oXwGJJSyRdVuvGVMnREbEV0v94wNE1bk81zM7uLfWv9Vr+KCbpBOBM4DfA2Eb7/Ir277fZrIb4\n/LL7mC0FngUWR8QSyvz8ah36eXBuRJxFuhXFFVn5oNE12uiA/wNMzO4t9SxQ12WCrPSxALg66xF3\n/7zq+vPrYf8a5vOLiL0R8U7SN7Qpkk6jzM+v1qG/GTi+aHpcNq9hRMSW7PF54Mekklaj2Zpdn9FZ\nV32uxu2pqIh4vujHHu4AJteyPf2R3QhxAfCdiHggm90wn19P+9dIn1+niHgRaAGmUebnV+vQXwJM\nkjRe0nBgBtAwt1+WdFjW60DSSOADwJO1bVVFdL+lxkLgkuz5xcAD3V9QZ7rsX/Y/UqeLqO/P8FvA\nUxFxa9G8Rvr89tu/Rvn8JI3pLE1JGgH8N9J5i7I+v5qP08+GT93Kvgu/vlTTBlWQpAmk3n2QTrp8\nr973T9L3gQLwZmArMBf4CXA/8FZgA/A/IuKFWrWxPw6wf+8j1Yf3Ar8HZnXWUOuJpHOBXwErSf8m\nA/h70lX291Hnn99B9u9jNMbndzrpRO2Q7O/eiLhB0lGU8fnVPPTNzGzg1Lq8Y2ZmA8ihb2aWIw59\nM7McceibmeWIQ9/MLEcc+mZmOeLQNzPLEYe+mVmO/H+9mqk4PogpfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1188da690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_mean_scores = [result[1] for result in grid.grid_scores_]\n",
    "plt.figure()\n",
    "plt.plot(range(1, 29, 2), grid_mean_scores)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=27, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# look's like adding to k after 5 doesn't even help that much...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Exercise! \n",
    "# Create a confusion matrix from the best estimator in the gridsearch and find the accuracy, sensitivity, and specificity\n",
    "# TODO\n",
    "print \"\\n\"*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3625   43]\n",
      " [ 351  100]]\n"
     ]
    }
   ],
   "source": [
    "# Solution! \n",
    "from sklearn import metrics\n",
    "preds = grid.best_estimator_.predict(X)\n",
    "print metrics.confusion_matrix(y, preds)\n",
    "# Accuracy = (3625  + 100) / 4119  == .904\n",
    "# Sensitivity = 100 / (100 + 351)  == .222\n",
    "# Specificity = 3625 / (3625 + 43) == .988"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Exercise! \n",
    "# Compare the accuracy to the null accuracy rate\n",
    "# TODO\n",
    "print \"\\n\"*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89050740470988099"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution!\n",
    "\n",
    "null_accuracy_rate = bank['outcome'].value_counts()[0] / float(bank.shape[0])\n",
    "null_accuracy_rate\n",
    "# accuracy is greater than the null accuracy rate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# What can we do to make the model better?\n",
    "\n",
    "# Thought Experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we want to regress to the duration of the call so the rep can better plan their day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>default</th>\n",
       "      <th>contact</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>...</th>\n",
       "      <th>job_management</th>\n",
       "      <th>job_retired</th>\n",
       "      <th>job_self-employed</th>\n",
       "      <th>job_services</th>\n",
       "      <th>job_student</th>\n",
       "      <th>job_technician</th>\n",
       "      <th>job_unemployed</th>\n",
       "      <th>job_unknown</th>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <th>poutcome_success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.161427</td>\n",
       "      <td>0.012700</td>\n",
       "      <td>0.041299</td>\n",
       "      <td>-0.014169</td>\n",
       "      <td>-0.043425</td>\n",
       "      <td>0.050931</td>\n",
       "      <td>-0.019192</td>\n",
       "      <td>-0.000482</td>\n",
       "      <td>0.098135</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065606</td>\n",
       "      <td>0.412542</td>\n",
       "      <td>0.010991</td>\n",
       "      <td>-0.050379</td>\n",
       "      <td>-0.185453</td>\n",
       "      <td>-0.064930</td>\n",
       "      <td>-0.009394</td>\n",
       "      <td>0.063831</td>\n",
       "      <td>-0.031205</td>\n",
       "      <td>0.043341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default</th>\n",
       "      <td>0.161427</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150494</td>\n",
       "      <td>-0.020140</td>\n",
       "      <td>0.021239</td>\n",
       "      <td>0.089514</td>\n",
       "      <td>-0.092754</td>\n",
       "      <td>0.184389</td>\n",
       "      <td>0.162098</td>\n",
       "      <td>0.021570</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.043783</td>\n",
       "      <td>0.023665</td>\n",
       "      <td>-0.019191</td>\n",
       "      <td>0.021453</td>\n",
       "      <td>-0.017566</td>\n",
       "      <td>-0.081766</td>\n",
       "      <td>-0.010086</td>\n",
       "      <td>0.065701</td>\n",
       "      <td>0.091124</td>\n",
       "      <td>-0.086343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact</th>\n",
       "      <td>0.012700</td>\n",
       "      <td>0.150494</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028185</td>\n",
       "      <td>0.085654</td>\n",
       "      <td>0.120670</td>\n",
       "      <td>-0.215464</td>\n",
       "      <td>0.383405</td>\n",
       "      <td>0.574452</td>\n",
       "      <td>0.252035</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045940</td>\n",
       "      <td>-0.038983</td>\n",
       "      <td>0.003609</td>\n",
       "      <td>0.024214</td>\n",
       "      <td>-0.018891</td>\n",
       "      <td>-0.021849</td>\n",
       "      <td>0.001462</td>\n",
       "      <td>0.021516</td>\n",
       "      <td>0.251147</td>\n",
       "      <td>-0.109971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>0.041299</td>\n",
       "      <td>-0.020140</td>\n",
       "      <td>-0.028185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.085348</td>\n",
       "      <td>-0.046998</td>\n",
       "      <td>0.025724</td>\n",
       "      <td>-0.028848</td>\n",
       "      <td>0.016672</td>\n",
       "      <td>-0.034745</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011460</td>\n",
       "      <td>0.044257</td>\n",
       "      <td>-0.001466</td>\n",
       "      <td>-0.030936</td>\n",
       "      <td>0.016982</td>\n",
       "      <td>-0.006173</td>\n",
       "      <td>-0.004565</td>\n",
       "      <td>-0.008591</td>\n",
       "      <td>-0.021259</td>\n",
       "      <td>0.046154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>-0.014169</td>\n",
       "      <td>0.021239</td>\n",
       "      <td>0.085654</td>\n",
       "      <td>-0.085348</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058742</td>\n",
       "      <td>-0.091490</td>\n",
       "      <td>0.176079</td>\n",
       "      <td>0.145021</td>\n",
       "      <td>0.007882</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011967</td>\n",
       "      <td>-0.011147</td>\n",
       "      <td>0.021876</td>\n",
       "      <td>0.038893</td>\n",
       "      <td>-0.032527</td>\n",
       "      <td>-0.004871</td>\n",
       "      <td>0.005467</td>\n",
       "      <td>-0.007765</td>\n",
       "      <td>0.098957</td>\n",
       "      <td>-0.054564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>-0.043425</td>\n",
       "      <td>0.089514</td>\n",
       "      <td>0.120670</td>\n",
       "      <td>-0.046998</td>\n",
       "      <td>0.058742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.587941</td>\n",
       "      <td>0.270684</td>\n",
       "      <td>0.058472</td>\n",
       "      <td>-0.092090</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011278</td>\n",
       "      <td>-0.067405</td>\n",
       "      <td>0.007591</td>\n",
       "      <td>0.031058</td>\n",
       "      <td>-0.043072</td>\n",
       "      <td>0.009654</td>\n",
       "      <td>-0.028664</td>\n",
       "      <td>-0.006368</td>\n",
       "      <td>0.488761</td>\n",
       "      <td>-0.940565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>0.050931</td>\n",
       "      <td>-0.092754</td>\n",
       "      <td>-0.215464</td>\n",
       "      <td>0.025724</td>\n",
       "      <td>-0.091490</td>\n",
       "      <td>-0.587941</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.415238</td>\n",
       "      <td>-0.164922</td>\n",
       "      <td>-0.051420</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030516</td>\n",
       "      <td>0.051054</td>\n",
       "      <td>0.006366</td>\n",
       "      <td>-0.007326</td>\n",
       "      <td>0.078260</td>\n",
       "      <td>-0.017419</td>\n",
       "      <td>0.013485</td>\n",
       "      <td>-0.001958</td>\n",
       "      <td>-0.854241</td>\n",
       "      <td>0.510865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp.var.rate</th>\n",
       "      <td>-0.019192</td>\n",
       "      <td>0.184389</td>\n",
       "      <td>0.383405</td>\n",
       "      <td>-0.028848</td>\n",
       "      <td>0.176079</td>\n",
       "      <td>0.270684</td>\n",
       "      <td>-0.415238</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.755155</td>\n",
       "      <td>0.195022</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.025176</td>\n",
       "      <td>-0.087204</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>-0.003592</td>\n",
       "      <td>-0.116507</td>\n",
       "      <td>0.047053</td>\n",
       "      <td>-0.028425</td>\n",
       "      <td>0.013772</td>\n",
       "      <td>0.482106</td>\n",
       "      <td>-0.256587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.price.idx</th>\n",
       "      <td>-0.000482</td>\n",
       "      <td>0.162098</td>\n",
       "      <td>0.574452</td>\n",
       "      <td>0.016672</td>\n",
       "      <td>0.145021</td>\n",
       "      <td>0.058472</td>\n",
       "      <td>-0.164922</td>\n",
       "      <td>0.755155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045835</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048750</td>\n",
       "      <td>-0.032616</td>\n",
       "      <td>-0.001529</td>\n",
       "      <td>0.025988</td>\n",
       "      <td>-0.032322</td>\n",
       "      <td>-0.002939</td>\n",
       "      <td>-0.003210</td>\n",
       "      <td>0.016884</td>\n",
       "      <td>0.289799</td>\n",
       "      <td>-0.060257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <td>0.098135</td>\n",
       "      <td>0.021570</td>\n",
       "      <td>0.252035</td>\n",
       "      <td>-0.034745</td>\n",
       "      <td>0.007882</td>\n",
       "      <td>-0.092090</td>\n",
       "      <td>-0.051420</td>\n",
       "      <td>0.195022</td>\n",
       "      <td>0.045835</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005887</td>\n",
       "      <td>0.065338</td>\n",
       "      <td>0.007642</td>\n",
       "      <td>-0.073024</td>\n",
       "      <td>0.025850</td>\n",
       "      <td>0.058946</td>\n",
       "      <td>-0.000228</td>\n",
       "      <td>0.032504</td>\n",
       "      <td>0.100217</td>\n",
       "      <td>0.087382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euribor3m</th>\n",
       "      <td>-0.015033</td>\n",
       "      <td>0.176800</td>\n",
       "      <td>0.390313</td>\n",
       "      <td>-0.032329</td>\n",
       "      <td>0.159435</td>\n",
       "      <td>0.301478</td>\n",
       "      <td>-0.458851</td>\n",
       "      <td>0.970308</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.276595</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011447</td>\n",
       "      <td>-0.088148</td>\n",
       "      <td>0.005542</td>\n",
       "      <td>-0.012849</td>\n",
       "      <td>-0.119714</td>\n",
       "      <td>0.046468</td>\n",
       "      <td>-0.023823</td>\n",
       "      <td>0.017662</td>\n",
       "      <td>0.502340</td>\n",
       "      <td>-0.282272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nr.employed</th>\n",
       "      <td>-0.041936</td>\n",
       "      <td>0.167754</td>\n",
       "      <td>0.261496</td>\n",
       "      <td>-0.044218</td>\n",
       "      <td>0.161037</td>\n",
       "      <td>0.381983</td>\n",
       "      <td>-0.514853</td>\n",
       "      <td>0.897173</td>\n",
       "      <td>0.472560</td>\n",
       "      <td>0.107054</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002022</td>\n",
       "      <td>-0.112450</td>\n",
       "      <td>0.001342</td>\n",
       "      <td>-0.002975</td>\n",
       "      <td>-0.144023</td>\n",
       "      <td>0.042848</td>\n",
       "      <td>-0.030453</td>\n",
       "      <td>0.010605</td>\n",
       "      <td>0.508717</td>\n",
       "      <td>-0.354030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outcome</th>\n",
       "      <td>0.060374</td>\n",
       "      <td>-0.076567</td>\n",
       "      <td>-0.137401</td>\n",
       "      <td>0.418565</td>\n",
       "      <td>-0.076091</td>\n",
       "      <td>-0.332012</td>\n",
       "      <td>0.255697</td>\n",
       "      <td>-0.283216</td>\n",
       "      <td>-0.098326</td>\n",
       "      <td>0.054393</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015814</td>\n",
       "      <td>0.078373</td>\n",
       "      <td>-0.017796</td>\n",
       "      <td>-0.021253</td>\n",
       "      <td>0.055781</td>\n",
       "      <td>0.009032</td>\n",
       "      <td>0.032872</td>\n",
       "      <td>-0.002169</td>\n",
       "      <td>-0.207179</td>\n",
       "      <td>0.325804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_blue-collar</th>\n",
       "      <td>-0.042976</td>\n",
       "      <td>0.167771</td>\n",
       "      <td>0.098988</td>\n",
       "      <td>0.010396</td>\n",
       "      <td>-0.021404</td>\n",
       "      <td>0.062240</td>\n",
       "      <td>-0.041762</td>\n",
       "      <td>0.050353</td>\n",
       "      <td>0.080969</td>\n",
       "      <td>-0.122025</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.152741</td>\n",
       "      <td>-0.107122</td>\n",
       "      <td>-0.104747</td>\n",
       "      <td>-0.169771</td>\n",
       "      <td>-0.074502</td>\n",
       "      <td>-0.234697</td>\n",
       "      <td>-0.086993</td>\n",
       "      <td>-0.051108</td>\n",
       "      <td>0.026745</td>\n",
       "      <td>-0.053400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_entrepreneur</th>\n",
       "      <td>0.039110</td>\n",
       "      <td>0.020113</td>\n",
       "      <td>0.028028</td>\n",
       "      <td>-0.005750</td>\n",
       "      <td>-0.024137</td>\n",
       "      <td>0.032033</td>\n",
       "      <td>-0.017265</td>\n",
       "      <td>0.022054</td>\n",
       "      <td>0.011657</td>\n",
       "      <td>-0.014320</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.056409</td>\n",
       "      <td>-0.039561</td>\n",
       "      <td>-0.038684</td>\n",
       "      <td>-0.062698</td>\n",
       "      <td>-0.027514</td>\n",
       "      <td>-0.086676</td>\n",
       "      <td>-0.032128</td>\n",
       "      <td>-0.018875</td>\n",
       "      <td>0.008954</td>\n",
       "      <td>-0.029330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_housemaid</th>\n",
       "      <td>0.089297</td>\n",
       "      <td>0.036203</td>\n",
       "      <td>0.018311</td>\n",
       "      <td>-0.017642</td>\n",
       "      <td>-0.004750</td>\n",
       "      <td>0.017684</td>\n",
       "      <td>-0.027623</td>\n",
       "      <td>0.036182</td>\n",
       "      <td>0.022542</td>\n",
       "      <td>0.042351</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048400</td>\n",
       "      <td>-0.033944</td>\n",
       "      <td>-0.033192</td>\n",
       "      <td>-0.053796</td>\n",
       "      <td>-0.023608</td>\n",
       "      <td>-0.074370</td>\n",
       "      <td>-0.027566</td>\n",
       "      <td>-0.016195</td>\n",
       "      <td>0.042448</td>\n",
       "      <td>-0.014792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_management</th>\n",
       "      <td>0.065606</td>\n",
       "      <td>-0.043783</td>\n",
       "      <td>-0.045940</td>\n",
       "      <td>-0.011460</td>\n",
       "      <td>-0.011967</td>\n",
       "      <td>-0.011278</td>\n",
       "      <td>0.030516</td>\n",
       "      <td>-0.025176</td>\n",
       "      <td>-0.048750</td>\n",
       "      <td>-0.005887</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059877</td>\n",
       "      <td>-0.058549</td>\n",
       "      <td>-0.094895</td>\n",
       "      <td>-0.041643</td>\n",
       "      <td>-0.131185</td>\n",
       "      <td>-0.048626</td>\n",
       "      <td>-0.028567</td>\n",
       "      <td>-0.007995</td>\n",
       "      <td>0.004104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_retired</th>\n",
       "      <td>0.412542</td>\n",
       "      <td>0.023665</td>\n",
       "      <td>-0.038983</td>\n",
       "      <td>0.044257</td>\n",
       "      <td>-0.011147</td>\n",
       "      <td>-0.067405</td>\n",
       "      <td>0.051054</td>\n",
       "      <td>-0.087204</td>\n",
       "      <td>-0.032616</td>\n",
       "      <td>0.065338</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.059877</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.041062</td>\n",
       "      <td>-0.066553</td>\n",
       "      <td>-0.029206</td>\n",
       "      <td>-0.092005</td>\n",
       "      <td>-0.034103</td>\n",
       "      <td>-0.020035</td>\n",
       "      <td>-0.045550</td>\n",
       "      <td>0.056007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_self-employed</th>\n",
       "      <td>0.010991</td>\n",
       "      <td>-0.019191</td>\n",
       "      <td>0.003609</td>\n",
       "      <td>-0.001466</td>\n",
       "      <td>0.021876</td>\n",
       "      <td>0.007591</td>\n",
       "      <td>0.006366</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>-0.001529</td>\n",
       "      <td>0.007642</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.058549</td>\n",
       "      <td>-0.041062</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.065077</td>\n",
       "      <td>-0.028558</td>\n",
       "      <td>-0.089964</td>\n",
       "      <td>-0.033346</td>\n",
       "      <td>-0.019591</td>\n",
       "      <td>-0.014306</td>\n",
       "      <td>-0.003325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_services</th>\n",
       "      <td>-0.050379</td>\n",
       "      <td>0.021453</td>\n",
       "      <td>0.024214</td>\n",
       "      <td>-0.030936</td>\n",
       "      <td>0.038893</td>\n",
       "      <td>0.031058</td>\n",
       "      <td>-0.007326</td>\n",
       "      <td>-0.003592</td>\n",
       "      <td>0.025988</td>\n",
       "      <td>-0.073024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.094895</td>\n",
       "      <td>-0.066553</td>\n",
       "      <td>-0.065077</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.046286</td>\n",
       "      <td>-0.145812</td>\n",
       "      <td>-0.054047</td>\n",
       "      <td>-0.031752</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>-0.025132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_student</th>\n",
       "      <td>-0.185453</td>\n",
       "      <td>-0.017566</td>\n",
       "      <td>-0.018891</td>\n",
       "      <td>0.016982</td>\n",
       "      <td>-0.032527</td>\n",
       "      <td>-0.043072</td>\n",
       "      <td>0.078260</td>\n",
       "      <td>-0.116507</td>\n",
       "      <td>-0.032322</td>\n",
       "      <td>0.025850</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.041643</td>\n",
       "      <td>-0.029206</td>\n",
       "      <td>-0.028558</td>\n",
       "      <td>-0.046286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.063988</td>\n",
       "      <td>-0.023718</td>\n",
       "      <td>-0.013934</td>\n",
       "      <td>-0.084656</td>\n",
       "      <td>0.011176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_technician</th>\n",
       "      <td>-0.064930</td>\n",
       "      <td>-0.081766</td>\n",
       "      <td>-0.021849</td>\n",
       "      <td>-0.006173</td>\n",
       "      <td>-0.004871</td>\n",
       "      <td>0.009654</td>\n",
       "      <td>-0.017419</td>\n",
       "      <td>0.047053</td>\n",
       "      <td>-0.002939</td>\n",
       "      <td>0.058946</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.131185</td>\n",
       "      <td>-0.092005</td>\n",
       "      <td>-0.089964</td>\n",
       "      <td>-0.145812</td>\n",
       "      <td>-0.063988</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.074716</td>\n",
       "      <td>-0.043896</td>\n",
       "      <td>0.020288</td>\n",
       "      <td>-0.013611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_unemployed</th>\n",
       "      <td>-0.009394</td>\n",
       "      <td>-0.010086</td>\n",
       "      <td>0.001462</td>\n",
       "      <td>-0.004565</td>\n",
       "      <td>0.005467</td>\n",
       "      <td>-0.028664</td>\n",
       "      <td>0.013485</td>\n",
       "      <td>-0.028425</td>\n",
       "      <td>-0.003210</td>\n",
       "      <td>-0.000228</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048626</td>\n",
       "      <td>-0.034103</td>\n",
       "      <td>-0.033346</td>\n",
       "      <td>-0.054047</td>\n",
       "      <td>-0.023718</td>\n",
       "      <td>-0.074716</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016270</td>\n",
       "      <td>-0.012524</td>\n",
       "      <td>0.034295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_unknown</th>\n",
       "      <td>0.063831</td>\n",
       "      <td>0.065701</td>\n",
       "      <td>0.021516</td>\n",
       "      <td>-0.008591</td>\n",
       "      <td>-0.007765</td>\n",
       "      <td>-0.006368</td>\n",
       "      <td>-0.001958</td>\n",
       "      <td>0.013772</td>\n",
       "      <td>0.016884</td>\n",
       "      <td>0.032504</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.028567</td>\n",
       "      <td>-0.020035</td>\n",
       "      <td>-0.019591</td>\n",
       "      <td>-0.031752</td>\n",
       "      <td>-0.013934</td>\n",
       "      <td>-0.043896</td>\n",
       "      <td>-0.016270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004583</td>\n",
       "      <td>0.009007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <td>-0.031205</td>\n",
       "      <td>0.091124</td>\n",
       "      <td>0.251147</td>\n",
       "      <td>-0.021259</td>\n",
       "      <td>0.098957</td>\n",
       "      <td>0.488761</td>\n",
       "      <td>-0.854241</td>\n",
       "      <td>0.482106</td>\n",
       "      <td>0.289799</td>\n",
       "      <td>0.100217</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007995</td>\n",
       "      <td>-0.045550</td>\n",
       "      <td>-0.014306</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>-0.084656</td>\n",
       "      <td>0.020288</td>\n",
       "      <td>-0.012524</td>\n",
       "      <td>0.004583</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.459409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poutcome_success</th>\n",
       "      <td>0.043341</td>\n",
       "      <td>-0.086343</td>\n",
       "      <td>-0.109971</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>-0.054564</td>\n",
       "      <td>-0.940565</td>\n",
       "      <td>0.510865</td>\n",
       "      <td>-0.256587</td>\n",
       "      <td>-0.060257</td>\n",
       "      <td>0.087382</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004104</td>\n",
       "      <td>0.056007</td>\n",
       "      <td>-0.003325</td>\n",
       "      <td>-0.025132</td>\n",
       "      <td>0.011176</td>\n",
       "      <td>-0.013611</td>\n",
       "      <td>0.034295</td>\n",
       "      <td>0.009007</td>\n",
       "      <td>-0.459409</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           age   default   contact  duration  campaign  \\\n",
       "age                   1.000000  0.161427  0.012700  0.041299 -0.014169   \n",
       "default               0.161427  1.000000  0.150494 -0.020140  0.021239   \n",
       "contact               0.012700  0.150494  1.000000 -0.028185  0.085654   \n",
       "duration              0.041299 -0.020140 -0.028185  1.000000 -0.085348   \n",
       "campaign             -0.014169  0.021239  0.085654 -0.085348  1.000000   \n",
       "pdays                -0.043425  0.089514  0.120670 -0.046998  0.058742   \n",
       "previous              0.050931 -0.092754 -0.215464  0.025724 -0.091490   \n",
       "emp.var.rate         -0.019192  0.184389  0.383405 -0.028848  0.176079   \n",
       "cons.price.idx       -0.000482  0.162098  0.574452  0.016672  0.145021   \n",
       "cons.conf.idx         0.098135  0.021570  0.252035 -0.034745  0.007882   \n",
       "euribor3m            -0.015033  0.176800  0.390313 -0.032329  0.159435   \n",
       "nr.employed          -0.041936  0.167754  0.261496 -0.044218  0.161037   \n",
       "outcome               0.060374 -0.076567 -0.137401  0.418565 -0.076091   \n",
       "job_blue-collar      -0.042976  0.167771  0.098988  0.010396 -0.021404   \n",
       "job_entrepreneur      0.039110  0.020113  0.028028 -0.005750 -0.024137   \n",
       "job_housemaid         0.089297  0.036203  0.018311 -0.017642 -0.004750   \n",
       "job_management        0.065606 -0.043783 -0.045940 -0.011460 -0.011967   \n",
       "job_retired           0.412542  0.023665 -0.038983  0.044257 -0.011147   \n",
       "job_self-employed     0.010991 -0.019191  0.003609 -0.001466  0.021876   \n",
       "job_services         -0.050379  0.021453  0.024214 -0.030936  0.038893   \n",
       "job_student          -0.185453 -0.017566 -0.018891  0.016982 -0.032527   \n",
       "job_technician       -0.064930 -0.081766 -0.021849 -0.006173 -0.004871   \n",
       "job_unemployed       -0.009394 -0.010086  0.001462 -0.004565  0.005467   \n",
       "job_unknown           0.063831  0.065701  0.021516 -0.008591 -0.007765   \n",
       "poutcome_nonexistent -0.031205  0.091124  0.251147 -0.021259  0.098957   \n",
       "poutcome_success      0.043341 -0.086343 -0.109971  0.046154 -0.054564   \n",
       "\n",
       "                         pdays  previous  emp.var.rate  cons.price.idx  \\\n",
       "age                  -0.043425  0.050931     -0.019192       -0.000482   \n",
       "default               0.089514 -0.092754      0.184389        0.162098   \n",
       "contact               0.120670 -0.215464      0.383405        0.574452   \n",
       "duration             -0.046998  0.025724     -0.028848        0.016672   \n",
       "campaign              0.058742 -0.091490      0.176079        0.145021   \n",
       "pdays                 1.000000 -0.587941      0.270684        0.058472   \n",
       "previous             -0.587941  1.000000     -0.415238       -0.164922   \n",
       "emp.var.rate          0.270684 -0.415238      1.000000        0.755155   \n",
       "cons.price.idx        0.058472 -0.164922      0.755155        1.000000   \n",
       "cons.conf.idx        -0.092090 -0.051420      0.195022        0.045835   \n",
       "euribor3m             0.301478 -0.458851      0.970308        0.657159   \n",
       "nr.employed           0.381983 -0.514853      0.897173        0.472560   \n",
       "outcome              -0.332012  0.255697     -0.283216       -0.098326   \n",
       "job_blue-collar       0.062240 -0.041762      0.050353        0.080969   \n",
       "job_entrepreneur      0.032033 -0.017265      0.022054        0.011657   \n",
       "job_housemaid         0.017684 -0.027623      0.036182        0.022542   \n",
       "job_management       -0.011278  0.030516     -0.025176       -0.048750   \n",
       "job_retired          -0.067405  0.051054     -0.087204       -0.032616   \n",
       "job_self-employed     0.007591  0.006366      0.000153       -0.001529   \n",
       "job_services          0.031058 -0.007326     -0.003592        0.025988   \n",
       "job_student          -0.043072  0.078260     -0.116507       -0.032322   \n",
       "job_technician        0.009654 -0.017419      0.047053       -0.002939   \n",
       "job_unemployed       -0.028664  0.013485     -0.028425       -0.003210   \n",
       "job_unknown          -0.006368 -0.001958      0.013772        0.016884   \n",
       "poutcome_nonexistent  0.488761 -0.854241      0.482106        0.289799   \n",
       "poutcome_success     -0.940565  0.510865     -0.256587       -0.060257   \n",
       "\n",
       "                      cons.conf.idx        ...         job_management  \\\n",
       "age                        0.098135        ...               0.065606   \n",
       "default                    0.021570        ...              -0.043783   \n",
       "contact                    0.252035        ...              -0.045940   \n",
       "duration                  -0.034745        ...              -0.011460   \n",
       "campaign                   0.007882        ...              -0.011967   \n",
       "pdays                     -0.092090        ...              -0.011278   \n",
       "previous                  -0.051420        ...               0.030516   \n",
       "emp.var.rate               0.195022        ...              -0.025176   \n",
       "cons.price.idx             0.045835        ...              -0.048750   \n",
       "cons.conf.idx              1.000000        ...              -0.005887   \n",
       "euribor3m                  0.276595        ...              -0.011447   \n",
       "nr.employed                0.107054        ...              -0.002022   \n",
       "outcome                    0.054393        ...              -0.015814   \n",
       "job_blue-collar           -0.122025        ...              -0.152741   \n",
       "job_entrepreneur          -0.014320        ...              -0.056409   \n",
       "job_housemaid              0.042351        ...              -0.048400   \n",
       "job_management            -0.005887        ...               1.000000   \n",
       "job_retired                0.065338        ...              -0.059877   \n",
       "job_self-employed          0.007642        ...              -0.058549   \n",
       "job_services              -0.073024        ...              -0.094895   \n",
       "job_student                0.025850        ...              -0.041643   \n",
       "job_technician             0.058946        ...              -0.131185   \n",
       "job_unemployed            -0.000228        ...              -0.048626   \n",
       "job_unknown                0.032504        ...              -0.028567   \n",
       "poutcome_nonexistent       0.100217        ...              -0.007995   \n",
       "poutcome_success           0.087382        ...               0.004104   \n",
       "\n",
       "                      job_retired  job_self-employed  job_services  \\\n",
       "age                      0.412542           0.010991     -0.050379   \n",
       "default                  0.023665          -0.019191      0.021453   \n",
       "contact                 -0.038983           0.003609      0.024214   \n",
       "duration                 0.044257          -0.001466     -0.030936   \n",
       "campaign                -0.011147           0.021876      0.038893   \n",
       "pdays                   -0.067405           0.007591      0.031058   \n",
       "previous                 0.051054           0.006366     -0.007326   \n",
       "emp.var.rate            -0.087204           0.000153     -0.003592   \n",
       "cons.price.idx          -0.032616          -0.001529      0.025988   \n",
       "cons.conf.idx            0.065338           0.007642     -0.073024   \n",
       "euribor3m               -0.088148           0.005542     -0.012849   \n",
       "nr.employed             -0.112450           0.001342     -0.002975   \n",
       "outcome                  0.078373          -0.017796     -0.021253   \n",
       "job_blue-collar         -0.107122          -0.104747     -0.169771   \n",
       "job_entrepreneur        -0.039561          -0.038684     -0.062698   \n",
       "job_housemaid           -0.033944          -0.033192     -0.053796   \n",
       "job_management          -0.059877          -0.058549     -0.094895   \n",
       "job_retired              1.000000          -0.041062     -0.066553   \n",
       "job_self-employed       -0.041062           1.000000     -0.065077   \n",
       "job_services            -0.066553          -0.065077      1.000000   \n",
       "job_student             -0.029206          -0.028558     -0.046286   \n",
       "job_technician          -0.092005          -0.089964     -0.145812   \n",
       "job_unemployed          -0.034103          -0.033346     -0.054047   \n",
       "job_unknown             -0.020035          -0.019591     -0.031752   \n",
       "poutcome_nonexistent    -0.045550          -0.014306      0.002033   \n",
       "poutcome_success         0.056007          -0.003325     -0.025132   \n",
       "\n",
       "                      job_student  job_technician  job_unemployed  \\\n",
       "age                     -0.185453       -0.064930       -0.009394   \n",
       "default                 -0.017566       -0.081766       -0.010086   \n",
       "contact                 -0.018891       -0.021849        0.001462   \n",
       "duration                 0.016982       -0.006173       -0.004565   \n",
       "campaign                -0.032527       -0.004871        0.005467   \n",
       "pdays                   -0.043072        0.009654       -0.028664   \n",
       "previous                 0.078260       -0.017419        0.013485   \n",
       "emp.var.rate            -0.116507        0.047053       -0.028425   \n",
       "cons.price.idx          -0.032322       -0.002939       -0.003210   \n",
       "cons.conf.idx            0.025850        0.058946       -0.000228   \n",
       "euribor3m               -0.119714        0.046468       -0.023823   \n",
       "nr.employed             -0.144023        0.042848       -0.030453   \n",
       "outcome                  0.055781        0.009032        0.032872   \n",
       "job_blue-collar         -0.074502       -0.234697       -0.086993   \n",
       "job_entrepreneur        -0.027514       -0.086676       -0.032128   \n",
       "job_housemaid           -0.023608       -0.074370       -0.027566   \n",
       "job_management          -0.041643       -0.131185       -0.048626   \n",
       "job_retired             -0.029206       -0.092005       -0.034103   \n",
       "job_self-employed       -0.028558       -0.089964       -0.033346   \n",
       "job_services            -0.046286       -0.145812       -0.054047   \n",
       "job_student              1.000000       -0.063988       -0.023718   \n",
       "job_technician          -0.063988        1.000000       -0.074716   \n",
       "job_unemployed          -0.023718       -0.074716        1.000000   \n",
       "job_unknown             -0.013934       -0.043896       -0.016270   \n",
       "poutcome_nonexistent    -0.084656        0.020288       -0.012524   \n",
       "poutcome_success         0.011176       -0.013611        0.034295   \n",
       "\n",
       "                      job_unknown  poutcome_nonexistent  poutcome_success  \n",
       "age                      0.063831             -0.031205          0.043341  \n",
       "default                  0.065701              0.091124         -0.086343  \n",
       "contact                  0.021516              0.251147         -0.109971  \n",
       "duration                -0.008591             -0.021259          0.046154  \n",
       "campaign                -0.007765              0.098957         -0.054564  \n",
       "pdays                   -0.006368              0.488761         -0.940565  \n",
       "previous                -0.001958             -0.854241          0.510865  \n",
       "emp.var.rate             0.013772              0.482106         -0.256587  \n",
       "cons.price.idx           0.016884              0.289799         -0.060257  \n",
       "cons.conf.idx            0.032504              0.100217          0.087382  \n",
       "euribor3m                0.017662              0.502340         -0.282272  \n",
       "nr.employed              0.010605              0.508717         -0.354030  \n",
       "outcome                 -0.002169             -0.207179          0.325804  \n",
       "job_blue-collar         -0.051108              0.026745         -0.053400  \n",
       "job_entrepreneur        -0.018875              0.008954         -0.029330  \n",
       "job_housemaid           -0.016195              0.042448         -0.014792  \n",
       "job_management          -0.028567             -0.007995          0.004104  \n",
       "job_retired             -0.020035             -0.045550          0.056007  \n",
       "job_self-employed       -0.019591             -0.014306         -0.003325  \n",
       "job_services            -0.031752              0.002033         -0.025132  \n",
       "job_student             -0.013934             -0.084656          0.011176  \n",
       "job_technician          -0.043896              0.020288         -0.013611  \n",
       "job_unemployed          -0.016270             -0.012524          0.034295  \n",
       "job_unknown              1.000000              0.004583          0.009007  \n",
       "poutcome_nonexistent     0.004583              1.000000         -0.459409  \n",
       "poutcome_success         0.009007             -0.459409          1.000000  \n",
       "\n",
       "[26 rows x 26 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.041299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>0.041299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age  duration\n",
       "age       1.000000  0.041299\n",
       "duration  0.041299  1.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank[['age', 'duration']].corr()\n",
    "# age won't be useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c8b59d0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EgOSkaEREQ8h2/zcqvQ6YbfvA8v5pgG2f2fGc/u84ImIKsK1u7YMK9LWAB4A3AT8BbgPe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\n6BERDZFAj4hoiAR6RERD/A+RFkXuOowvyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cc00b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank.groupby('education').duration.mean().plot(kind='bar')\n",
    "# or education, except that illiterate column is interesting.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ccd5490>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ce37110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank.groupby('marital').duration.mean().plot(kind='bar')\n",
    "# or marital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c75a150>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nUv1wJ1ad8fWpWvG6pMqbvczoPvTBtDZJbwAOdrMTuKSP0mL9g3HcI2lvr7oa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\nJh0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c76e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank.groupby('job').duration.mean().plot(kind='bar')\n",
    "# job maybe.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c8992d0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c89ead0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank.groupby('day_of_week').duration.mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c908590>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JWgWqKmfrXzuh+U4Alw3d3tj1jSxIknR+JrXl8ghwZZLLk1wAbAP2TWguSRITWqFX1feS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\nf4AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c79e510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank.groupby('previous').duration.mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "duration_response = bank['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255.17246331494047"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg = LinearRegression()\n",
    "rmse = np.sqrt(abs(cross_val_score(linreg, X, duration_response, cv = 5, scoring='mean_squared_error').mean()))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Not that great.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d87d510>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nUv1wJ1ad8fWpWvG6pMqbvczoPvTBtDZJbwAOdrMTuKSP0mL9g3HcI2lvr7oa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\nJh0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d6fa710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# job field\n",
    "bank.groupby('job').duration.mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "254.46828669453504"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only with the retired field\n",
    "linreg = LinearRegression()\n",
    "rmse = np.sqrt(abs(cross_val_score(linreg, X[['job_retired']], duration_response, cv = 5, scoring='mean_squared_error').mean()))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# How can we make this a bit better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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