notebooks/13_bank_exercise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise with bank marketing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "- Data from the UCI Machine Learning Repository: [data](https://github.com/justmarkham/DAT8/blob/master/data/bank-additional.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-additional.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": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.313</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>single</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.855</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>wed</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.962</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.959</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.191</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital          education default  housing     loan  \\\n",
       "0   30  blue-collar  married           basic.9y      no      yes       no   \n",
       "1   39     services   single        high.school      no       no       no   \n",
       "2   25     services  married        high.school      no      yes       no   \n",
       "3   38     services  married           basic.9y      no  unknown  unknown   \n",
       "4   47       admin.  married  university.degree      no      yes       no   \n",
       "\n",
       "     contact month day_of_week ...  campaign  pdays  previous     poutcome  \\\n",
       "0   cellular   may         fri ...         2    999         0  nonexistent   \n",
       "1  telephone   may         fri ...         4    999         0  nonexistent   \n",
       "2  telephone   jun         wed ...         1    999         0  nonexistent   \n",
       "3  telephone   jun         fri ...         3    999         0  nonexistent   \n",
       "4   cellular   nov         mon ...         1    999         0  nonexistent   \n",
       "\n",
       "  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0         -1.8          92.893          -46.2      1.313       5099.1  no  \n",
       "1          1.1          93.994          -36.4      4.855       5191.0  no  \n",
       "2          1.4          94.465          -41.8      4.962       5228.1  no  \n",
       "3          1.4          94.465          -41.8      4.959       5228.1  no  \n",
       "4         -0.1          93.200          -42.0      4.191       5195.8  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "url = 'https://github.com/justmarkham/DAT8/master/data/bank-additional.csv'\n",
    "bank = pd.read_csv(url, sep=';')\n",
    "bank.head()"
   ]
  },
  {
   "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": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'age', u'job', u'marital', u'education', u'default', u'housing',\n",
       "       u'loan', u'contact', u'month', u'day_of_week', u'duration', u'campaign',\n",
       "       u'pdays', u'previous', u'poutcome', u'emp.var.rate', u'cons.price.idx',\n",
       "       u'cons.conf.idx', u'euribor3m', u'nr.employed', u'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "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": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert the response to numeric values and store as a new column\n",
    "bank['outcome'] = bank.y.map({'no':0, 'yes':1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc1c3160>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MlNyh2hmez92KxmWZ9tjjDlVJ+5F1pt5Us/oy4N2S1gOnpraZmXWBhsoyEfEccPCkdVvI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\n7mZmPej/Az06t9SM9b3RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1bfbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# probably not a great feature\n",
    "bank.boxplot(column='age', by='outcome')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job\n",
       "admin.           0.131423\n",
       "blue-collar      0.069005\n",
       "entrepreneur     0.054054\n",
       "housemaid        0.100000\n",
       "management       0.092593\n",
       "retired          0.228916\n",
       "self-employed    0.081761\n",
       "services         0.089059\n",
       "student          0.231707\n",
       "technician       0.115774\n",
       "unemployed       0.171171\n",
       "unknown          0.102564\n",
       "Name: outcome, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks like a useful feature\n",
    "bank.groupby('job').outcome.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create job_dummies (we will add it to the bank DataFrame later)\n",
    "job_dummies = pd.get_dummies(bank.job, prefix='job')\n",
    "job_dummies.drop(job_dummies.columns[0], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default\n",
       "no         0.121267\n",
       "unknown    0.061021\n",
       "yes        0.000000\n",
       "Name: outcome, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks like a useful feature\n",
    "bank.groupby('default').outcome.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no         3315\n",
       "unknown     803\n",
       "yes           1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# but only one person in the dataset has a status of yes\n",
    "bank.default.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### contact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "contact\n",
       "cellular     0.141403\n",
       "telephone    0.051806\n",
       "Name: outcome, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks like a useful feature\n",
    "bank.groupby('contact').outcome.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert the feature to numeric values\n",
    "bank['contact'] = bank.contact.map({'cellular':0, 'telephone':1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 14,
     "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": "markdown",
   "metadata": {},
   "source": [
    "### duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc32e438>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GMKPNmjV2f6oEe/Z0Ph4rL48hdA+PIZiZWUNOCDPY/r+UquPUm3UnjyF03pQS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc545f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# looks like an excellent feature, but you can't know the duration of a call beforehand, thus it can't be used in your model\n",
    "bank.boxplot(column='duration', by='outcome')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### previous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "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": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks like a useful feature\n",
    "bank.groupby('poutcome').outcome.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "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": "markdown",
   "metadata": {},
   "source": [
    "### euribor3m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc553668>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc7ab5f8>"
      ]
     },
     "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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'age', u'job', u'marital', u'education', u'default', u'housing',\n",
       "       u'loan', u'contact', u'month', u'day_of_week', u'duration', u'campaign',\n",
       "       u'pdays', u'previous', u'poutcome', u'emp.var.rate', u'cons.price.idx',\n",
       "       u'cons.conf.idx', u'euribor3m', u'nr.employed', u'y', u'outcome',\n",
       "       u'job_blue-collar', u'job_entrepreneur', u'job_housemaid',\n",
       "       u'job_management', u'job_retired', u'job_self-employed',\n",
       "       u'job_services', u'job_student', u'job_technician', u'job_unemployed',\n",
       "       u'job_unknown', u'poutcome_nonexistent', u'poutcome_success'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new list of columns (including dummy columns)\n",
    "bank.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "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": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create y\n",
    "y = bank.outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75566564072331199"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate cross-validated AUC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "logreg = LogisticRegression(C=1e9)\n",
    "cross_val_score(logreg, X, y, cv=10, scoring='roc_auc').mean()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}