notebooks/10_review-solutions.ipynb

{
 "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": [],
   "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 style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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', u'loan', u'contact', u'month', u'day_of_week', u'duration', u'campaign', u'pdays', u'previous', u'poutcome', u'emp.var.rate', u'cons.price.idx', u'cons.conf.idx', u'euribor3m', u'nr.employed', u'y'], 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 0x10b3ab110>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SrmQXU2+sWX0p8D5J64GTUtvMzLpE07JMRLwA7D1h3TNkCd/aSKr7hgxcgHTipPv505J1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b2e2d10>"
      ]
     },
     "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 0x10b98ced0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GB/bRetXssVtvKLWsZ72JNDiwG2OM6Yc99llEG3zFYdFr87VZrxl69tiNMWYW0eDAPlqv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b2e2d50>"
      ]
     },
     "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 style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 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   \n",
       "1                0                 0              0               0   \n",
       "2                0                 0              0               0   \n",
       "3                0                 0              0               0   \n",
       "4                0                 0              0               0   \n",
       "\n",
       "   job_retired  job_self-employed  job_services  job_student  job_technician  \\\n",
       "0            0                  0             0            0               0   \n",
       "1            0                  0             1            0               0   \n",
       "2            0                  0             1            0               0   \n",
       "3            0                  0             1            0               0   \n",
       "4            0                  0             0            0               0   \n",
       "\n",
       "   job_unemployed  job_unknown  \n",
       "0               0            0  \n",
       "1               0            0  \n",
       "2               0            0  \n",
       "3               0            0  \n",
       "4               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 0x10b9cb890>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bc38ed0>"
      ]
     },
     "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no         3315\n",
       "unknown     803\n",
       "yes           1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 13,
     "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "contact\n",
       "cellular     0.141403\n",
       "telephone    0.051806\n",
       "Name: outcome, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks like a useful feature\n",
    "bank.groupby('contact').outcome.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": 15,
   "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": 16,
   "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": 16,
     "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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 17,
     "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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 18,
     "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": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10bcaad90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gk9mUKY37UCXYubPz8Vg5ecygu3jMwMzMmnIymMT2/JVUbVJv1n08ZtB5+5QM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b9c1dd0>"
      ]
     },
     "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": null,
   "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": 20,
   "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": 20,
     "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": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks like a useful feature\n",
    "bank.groupby('poutcome').outcome.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "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": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10be95a50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+qKFxzPaHmbgkzSibeI+ZL2poHLPds4lL0oCZiXfEfFFD45jtDzNxSZpRNvEeM1/U0Dhm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\nkgbs/wG5G+NHWhCgTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be9d150>"
      ]
     },
     "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": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'age', u'job', u'marital', u'education', u'default', u'housing', u'loan', u'contact', u'month', u'day_of_week', u'duration', u'campaign', u'pdays', u'previous', u'poutcome', u'emp.var.rate', u'cons.price.idx', u'cons.conf.idx', u'euribor3m', u'nr.employed', u'y', u'outcome', u'job_blue-collar', u'job_entrepreneur', u'job_housemaid', u'job_management', u'job_retired', u'job_self-employed', u'job_services', u'job_student', u'job_technician', u'job_unemployed', u'job_unknown', u'poutcome_nonexistent', u'poutcome_success'], dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new list of columns (including dummy columns)\n",
    "bank.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create y\n",
    "y = bank.outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": 28,
   "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": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90215602496498271"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90046229940191136"
      ]
     },
     "execution_count": 30,
     "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_neighbors=5, p=2, weights='uniform'),\n",
       "       fit_params={}, iid=True, loss_func=None, 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, score_func=None,\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10dfe3750>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wJw3+/mUDeD8B7omIn2aLm+Y9LDq+e7uOr9new4h4ASiQTlwdI6krww8inQvV\nq3qG/WsnbEkaSTph6/46tqeqJO0maXR2f3fgfcATfT+r4dzP9vMqpgE/7WPbhpOFX5cP0sDvX3YJ\nk7uAhRHx9aJVTfEe9nZ8zfAeStq3q/wkaRRpUssi4CHgI9lmZd+7us6zV7r2/deB4cBdEXF93RpT\nZZIOJfXmIZ2p/F+NfHySfgicDOxLqhv+b+BnwI+AccAq4CMRsbFujdwJPRzfNUAL6et/AE8CF3bV\ntxuNpHcBDwOPs/3r/lXAozTBe9jL8f0b6Yz/hn4PJR1DGoAdTuqg3xcR12UZMx3YB5gLnJtNdul5\nPz6pysys+Q21AVozM6sBh72ZWQ447M3McsBhb2aWAw57M7MccNibmeWAw97MLAcc9mZmOfD/AUQd\ndDFi3eNdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dd72b50>"
      ]
     },
     "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": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_neighbors=27, p=2, weights='uniform')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# look's like adding to k after 5 doesn't even help that much...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": 34,
   "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Exercise! \n",
    "# Compare the accuracy to the null accuracy rate\n",
    "# TODO\n",
    "print \"\\n\"*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89050740470988099"
      ]
     },
     "execution_count": 35,
     "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": null,
   "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": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank[['age', 'duration']].corr()\n",
    "# age won't be useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c1359d0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LtOicZVt0zrItukZerkXXyM3MbBymaCBPtQNkSrUDZEi1A2RKtQNkSrUDZEq1A2RKtQNk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\niLeFeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e0950d0>"
      ]
     },
     "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": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e444dd0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R0dkbSSzmPXJA0lsHfT8i3jeqLKWQdDvwTOBu4JF6dfj0QxsH9QXydqbaSes9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\n3MjNzDru/wNT48UVz7WMGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e0b2ad0>"
      ]
     },
     "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": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e4c0910>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0XtKU8pcD/0EKeF+r+nUNApJeRpqq/2RS/aP3RUSlHeWSDiDNc7iM9CW1C/De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\nbowxDceB3BhjGo4DuTHGNJz/Dz7raa4Hz46TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e4d3590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank.groupby('job').duration.mean().plot(kind='bar')\n",
    "# job maybe.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e58e7d0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v2QEqUrIDVKRkB6hIyQ5QkZIdYKIBN3Izs/mw7oxc0luBXwK+FRHHtesOAc4G7gPsA54a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\nx+jS4vwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e5980d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank.groupby('day_of_week').duration.mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e584490>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SSmzfvh3g2fVyOSNfECTpOcAngT+JiKvbY48C8xGxT9IJwI6IeIWk64EUETcPnHf20tX7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\n5GZmlfNCbmZWOS/kZmaV+/8BeuEUuD/T2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e642390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank.groupby('previous').duration.mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "duration_response = bank['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255.1724633149405"
      ]
     },
     "execution_count": 45,
     "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# job field\n",
    "bank.groupby('job').duration.mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
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