{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook was constructed using Python 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Raw data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following data set contains various customer information including whether the customer responded to a marketing campaign or not (the `responded` column). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>custAge</th>\n",
       "      <th>profession</th>\n",
       "      <th>marital</th>\n",
       "      <th>schooling</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>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>pmonths</th>\n",
       "      <th>pastEmail</th>\n",
       "      <th>responded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.0</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>mon</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>999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jul</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>4.960</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.0</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</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>mon</td>\n",
       "      <td>1</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>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55.0</td>\n",
       "      <td>management</td>\n",
       "      <td>divorced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jul</td>\n",
       "      <td>wed</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>4.962</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>admin.</td>\n",
       "      <td>divorced</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</td>\n",
       "      <td>tue</td>\n",
       "      <td>5</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.291</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>999.0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   custAge   profession   marital          schooling  default housing loan  \\\n",
       "0     55.0       admin.    single  university.degree  unknown      no   no   \n",
       "1      NaN  blue-collar   married                NaN       no      no   no   \n",
       "2     42.0   technician   married        high.school       no      no   no   \n",
       "3     55.0   management  divorced                NaN  unknown     yes  yes   \n",
       "4      NaN       admin.  divorced  university.degree       no     yes   no   \n",
       "\n",
       "     contact month day_of_week  campaign  pdays  previous     poutcome  \\\n",
       "0   cellular   nov         mon         1    999         0  nonexistent   \n",
       "1   cellular   jul         mon         1    999         0  nonexistent   \n",
       "2  telephone   may         mon         1    999         0  nonexistent   \n",
       "3   cellular   jul         wed         2    999         0  nonexistent   \n",
       "4   cellular   may         tue         5    999         1      failure   \n",
       "\n",
       "   emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \\\n",
       "0          -0.1          93.200          -42.0      4.191       5195.8   \n",
       "1           1.4          93.918          -42.7      4.960       5228.1   \n",
       "2           1.1          93.994          -36.4      4.857       5191.0   \n",
       "3           1.4          93.918          -42.7      4.962       5228.1   \n",
       "4          -1.8          92.893          -46.2      1.291       5099.1   \n",
       "\n",
       "   pmonths  pastEmail responded  \n",
       "0    999.0          0        no  \n",
       "1    999.0          0        no  \n",
       "2    999.0          0        no  \n",
       "3    999.0          0        no  \n",
       "4    999.0          1        no  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('max_columns', 200)\n",
    "def read_csv(filename): \n",
    "    return pd.read_csv(filename)\n",
    "df = read_csv('marketing_training.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the descriptions of each column: \n",
    "![title](column_descriptions.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data cleaning\n",
    "### Unknown and nonexistent\n",
    "I noticed some predictors took on the values `unknown` and `nonexistent`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['admin.', 'blue-collar', 'technician', 'management', 'services',\n",
       "       'unemployed', 'housemaid', 'unknown', 'self-employed', 'retired',\n",
       "       'student', 'entrepreneur'], dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.profession.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['nonexistent', 'failure', 'success'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.poutcome.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These values are equivalent to missing data, which is represented by `NaN` in pandas. I therefore replaced these special values with `NaN`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df = df.replace('unknown', np.nan)\n",
    "df = df.replace('nonexistent', np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Redundant columns (collinearity) \n",
    "\n",
    "The variables `pdays` and `pmonths` are perfectly correlated so one should be removed. I find it easier to count days in whole numbers than in fractions of a month, so I dropped the `pmonths` column: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.drop('pmonths',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contact in a previous campaign\n",
    "\n",
    "I noticed that customers who were not contacted in a previous campaign were assigned the number 999 in the `pdays` column. In reality, values for this predictor variable are only defined for customers who were previously contacted. \n",
    "\n",
    "One approach is to partition the data into two sets: customers who were or were not contacted in a previous campaign. The partition containing customers who were contacted would contain a `pdays` column while the other partition wouldn't. One could then train predictive models on each partition and use the appropriate model to make predictions. I decided against this approach, in the first analysis, because the training set size for customers who were previously contacted was so small (not shown). The learning curves that I plot below confirm that such small data sets suffer from high variance. \n",
    "\n",
    "The alternative approach is to introduce a predictor boolean variable to indicate whether a customer was or was not contacted in a previous campaign. Though this approach suffers from ambiguity in the choice of `pdays` for those customers who were not previously contacted, it does alleviate concerns about small training set size. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# True and False are equivalent to 1 and 0 in numerical computations\n",
    "df['previously_contacted'] = df.pdays!=999 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The creation of an additional feature to indicate previous contact is especially important given that the target variable appears to be highly correlated with it. Concretely, customers who were contacted in a previous campaign were more likely to respond well to the current marketing campaign:   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21     yes\n",
       "69     yes\n",
       "108    yes\n",
       "125    yes\n",
       "184    yes\n",
       "190     no\n",
       "196     no\n",
       "238    yes\n",
       "241     no\n",
       "304    yes\n",
       "Name: responded, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def target_variable(was_previously_contacted): \n",
    "    return df[df.previously_contacted==was_previously_contacted].responded.head(10)\n",
    "\n",
    "target_variable(was_previously_contacted=True)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     no\n",
       "1     no\n",
       "2     no\n",
       "3     no\n",
       "4     no\n",
       "5     no\n",
       "6    yes\n",
       "7     no\n",
       "8     no\n",
       "9     no\n",
       "Name: responded, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_variable(was_previously_contacted=False)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The current value of `pdays` for customers who were not contacted in a previous campaign (999) is problematic. 999 is quite distant from the  valid range of values of `pdays`, which is 0 - 22: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([999,   6,   7,   5,   1,   3,  10,  15,   2,  22,  14,   4,   9,\n",
       "        11,  12,  13,   8,  16,   0,  17,  21])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pdays.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This implies that most of the dynamic range is uninformative. \n",
    "\n",
    "A better special value for `pdays` is one that is closer to the valid range but obviously non-valid, such as -1: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.loc[df.pdays==999, 'pdays'] = -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical data \n",
    "\n",
    "* **One-hot encoding**\n",
    "Categorical variables need to be encoded before feeding them into a learning algorithm that expects numerical input. One cannot assign scalars to each categorical value because that would impose an arbitrary and meaningless order to the values. Instead, one should expand the feature space to include features for each possible value of the categorical variable. The usual way to do this is termed one-hot encoding: each categorical value becomes a vector in which the component corresponding to the variable's value is set to 1 (i.e made \"hot\") while the other components are set to 0. \n",
    "* **Missing values**\n",
    "In the first instance I chose to consider missing categorical data as just another value (`NaN`) for the categorical variable. This way, if the data were not missing at random, then I ought to be able to detect this in my model. \n",
    "\n",
    "* **Collinearity**\n",
    "I dropped the first column in each one-hot encoding of a categorical variable in order to prevent collinearity (column dependency). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>custAge</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>pastEmail</th>\n",
       "      <th>previously_contacted</th>\n",
       "      <th>profession_blue-collar</th>\n",
       "      <th>profession_entrepreneur</th>\n",
       "      <th>profession_housemaid</th>\n",
       "      <th>profession_management</th>\n",
       "      <th>profession_retired</th>\n",
       "      <th>profession_self-employed</th>\n",
       "      <th>profession_services</th>\n",
       "      <th>profession_student</th>\n",
       "      <th>profession_technician</th>\n",
       "      <th>profession_unemployed</th>\n",
       "      <th>profession_nan</th>\n",
       "      <th>marital_married</th>\n",
       "      <th>marital_single</th>\n",
       "      <th>marital_nan</th>\n",
       "      <th>schooling_basic.6y</th>\n",
       "      <th>schooling_basic.9y</th>\n",
       "      <th>schooling_high.school</th>\n",
       "      <th>schooling_illiterate</th>\n",
       "      <th>schooling_professional.course</th>\n",
       "      <th>schooling_university.degree</th>\n",
       "      <th>schooling_nan</th>\n",
       "      <th>default_yes</th>\n",
       "      <th>default_nan</th>\n",
       "      <th>housing_yes</th>\n",
       "      <th>housing_nan</th>\n",
       "      <th>loan_yes</th>\n",
       "      <th>loan_nan</th>\n",
       "      <th>contact_telephone</th>\n",
       "      <th>month_aug</th>\n",
       "      <th>month_dec</th>\n",
       "      <th>month_jul</th>\n",
       "      <th>month_jun</th>\n",
       "      <th>month_mar</th>\n",
       "      <th>month_may</th>\n",
       "      <th>month_nov</th>\n",
       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
       "      <th>day_of_week_mon</th>\n",
       "      <th>day_of_week_thu</th>\n",
       "      <th>day_of_week_tue</th>\n",
       "      <th>day_of_week_wed</th>\n",
       "      <th>day_of_week_nan</th>\n",
       "      <th>poutcome_success</th>\n",
       "      <th>poutcome_nan</th>\n",
       "      <th>responded_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>4.960</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\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",
       "      <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>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>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>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</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>1</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",
       "      <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",
       "      <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>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55.0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>4.962</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.291</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</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",
       "      <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>1</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",
       "      <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",
       "      <td>0</td>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   custAge  campaign  pdays  previous  emp.var.rate  cons.price.idx  \\\n",
       "0     55.0         1     -1         0          -0.1          93.200   \n",
       "1      NaN         1     -1         0           1.4          93.918   \n",
       "2     42.0         1     -1         0           1.1          93.994   \n",
       "3     55.0         2     -1         0           1.4          93.918   \n",
       "4      NaN         5     -1         1          -1.8          92.893   \n",
       "\n",
       "   cons.conf.idx  euribor3m  nr.employed  pastEmail  previously_contacted  \\\n",
       "0          -42.0      4.191       5195.8          0                 False   \n",
       "1          -42.7      4.960       5228.1          0                 False   \n",
       "2          -36.4      4.857       5191.0          0                 False   \n",
       "3          -42.7      4.962       5228.1          0                 False   \n",
       "4          -46.2      1.291       5099.1          1                 False   \n",
       "\n",
       "   profession_blue-collar  profession_entrepreneur  profession_housemaid  \\\n",
       "0                       0                        0                     0   \n",
       "1                       1                        0                     0   \n",
       "2                       0                        0                     0   \n",
       "3                       0                        0                     0   \n",
       "4                       0                        0                     0   \n",
       "\n",
       "   profession_management  profession_retired  profession_self-employed  \\\n",
       "0                      0                   0                         0   \n",
       "1                      0                   0                         0   \n",
       "2                      0                   0                         0   \n",
       "3                      1                   0                         0   \n",
       "4                      0                   0                         0   \n",
       "\n",
       "   profession_services  profession_student  profession_technician  \\\n",
       "0                    0                   0                      0   \n",
       "1                    0                   0                      0   \n",
       "2                    0                   0                      1   \n",
       "3                    0                   0                      0   \n",
       "4                    0                   0                      0   \n",
       "\n",
       "   profession_unemployed  profession_nan  marital_married  marital_single  \\\n",
       "0                      0               0                0               1   \n",
       "1                      0               0                1               0   \n",
       "2                      0               0                1               0   \n",
       "3                      0               0                0               0   \n",
       "4                      0               0                0               0   \n",
       "\n",
       "   marital_nan  schooling_basic.6y  schooling_basic.9y  schooling_high.school  \\\n",
       "0            0                   0                   0                      0   \n",
       "1            0                   0                   0                      0   \n",
       "2            0                   0                   0                      1   \n",
       "3            0                   0                   0                      0   \n",
       "4            0                   0                   0                      0   \n",
       "\n",
       "   schooling_illiterate  schooling_professional.course  \\\n",
       "0                     0                              0   \n",
       "1                     0                              0   \n",
       "2                     0                              0   \n",
       "3                     0                              0   \n",
       "4                     0                              0   \n",
       "\n",
       "   schooling_university.degree  schooling_nan  default_yes  default_nan  \\\n",
       "0                            1              0            0            1   \n",
       "1                            0              1            0            0   \n",
       "2                            0              0            0            0   \n",
       "3                            0              1            0            1   \n",
       "4                            1              0            0            0   \n",
       "\n",
       "   housing_yes  housing_nan  loan_yes  loan_nan  contact_telephone  month_aug  \\\n",
       "0            0            0         0         0                  0          0   \n",
       "1            0            0         0         0                  0          0   \n",
       "2            0            0         0         0                  1          0   \n",
       "3            1            0         1         0                  0          0   \n",
       "4            1            0         0         0                  0          0   \n",
       "\n",
       "   month_dec  month_jul  month_jun  month_mar  month_may  month_nov  \\\n",
       "0          0          0          0          0          0          1   \n",
       "1          0          1          0          0          0          0   \n",
       "2          0          0          0          0          1          0   \n",
       "3          0          1          0          0          0          0   \n",
       "4          0          0          0          0          1          0   \n",
       "\n",
       "   month_oct  month_sep  day_of_week_mon  day_of_week_thu  day_of_week_tue  \\\n",
       "0          0          0                1                0                0   \n",
       "1          0          0                1                0                0   \n",
       "2          0          0                1                0                0   \n",
       "3          0          0                0                0                0   \n",
       "4          0          0                0                0                1   \n",
       "\n",
       "   day_of_week_wed  day_of_week_nan  poutcome_success  poutcome_nan  \\\n",
       "0                0                0                 0             1   \n",
       "1                0                0                 0             1   \n",
       "2                0                0                 0             1   \n",
       "3                1                0                 0             1   \n",
       "4                0                0                 0             0   \n",
       "\n",
       "   responded_yes  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def one_hot_encode(data_frame): \n",
    "    data_frame = pd.get_dummies(data_frame, dummy_na=True, drop_first=True)\n",
    "    # https://stackoverflow.com/questions/21164910/delete-column-in-pandas-if-it-is-all-zeros:  \n",
    "    return data_frame.loc[:, (data_frame != 0).any(axis=0)] \n",
    "\n",
    "df = one_hot_encode(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputing missing numerical values\n",
    "\n",
    "I first added an indicator variable to keep track of whether I imputed `custAge` for the record or not. If the fact that it is missing has a meaning (i.e. customer age is not missing at random), then its impact can be assessed by that indicator variable. Bonus advantage: others who use my pre-processed data are made aware of which variables I imputed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def track_missing_ages(data_frame): \n",
    "    data_frame['impute_age'] = data_frame['custAge'].isnull()\n",
    "    return data_frame\n",
    "\n",
    "df = track_missing_ages(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I next used the column mean to impute missing customer ages:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def impute_age(data_frame): \n",
    "    imputed_age = data_frame.mean().custAge\n",
    "    data_frame['custAge'] = data_frame['custAge'].fillna(imputed_age)\n",
    "    return data_frame\n",
    "\n",
    "df = impute_age(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Main advantage: really simple, really fast, but not that nuanced. If time had permitted, I would have inferred the missing ages from the values of the other predictor variables using SVD plus EM, e.g. see [here](https://stackoverflow.com/questions/35577553/how-to-fill-nan-values-in-numeric-array-to-apply-svd) and [here](https://github.com/hammerlab/fancyimpute)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature scaling\n",
    "\n",
    "To prevent oscillations during gradient descent arising from non-spherical level surfaces of the loss function, I made those surfaces more spherical by scaling features to lie in the interval $(0,1)$:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "def extract_features_target(data_frame): \n",
    "    X = data_frame.drop('responded_yes', axis=1).values.astype(float)\n",
    "    y = data_frame.responded_yes.values.astype(float)\n",
    "\n",
    "    X_scaler = MinMaxScaler(feature_range=(0,1))\n",
    "    X = X_scaler.fit_transform(X)\n",
    "\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model training and ROC validation\n",
    "\n",
    "I created a function `roc_analysis` that fits a classifer to a subset of the data and then cross-validates it on the remainder of the data. This fit-and-validate cycle is repeated k times (k-fold cross validation). \n",
    "\n",
    "The function also produces ROC curves to quantify model performance. In the presence of skewed classes, ROC curves better characterize algorithm performance than classification accuracy/error. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from scipy import interp\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def parse_function_name(s):\n",
    "    end = s.find( '(' )\n",
    "    if end != -1:\n",
    "        return s[:end]\n",
    "    return 'error parsing function name'\n",
    "\n",
    "def roc_analysis(classifier, data_frame, number_splits=5): \n",
    "    \n",
    "    X, y = extract_features_target(data_frame)\n",
    "    cross_validator = StratifiedKFold(n_splits=number_splits, \n",
    "                                      shuffle=True)\n",
    "\n",
    "    tprs = []\n",
    "    aucs = []\n",
    "    mean_fpr = np.linspace(0, 1, 100)\n",
    "\n",
    "    for i, (train, test) in enumerate(cross_validator.split(X, y)):\n",
    "        probabilities = classifier.fit(X[train], y[train]).predict_proba(X[test])\n",
    "        fpr, tpr, thresholds = roc_curve(y[test], probabilities[:, 1])\n",
    "        tprs.append(interp(mean_fpr, fpr, tpr))\n",
    "        tprs[-1][0] = 0.0\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        aucs.append(roc_auc)\n",
    "        plt.plot(fpr, tpr, lw=1, alpha=0.3,\n",
    "                 label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n",
    "\n",
    "    plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n",
    "             label='classifier that ignores features', alpha=.8)\n",
    "\n",
    "    mean_tpr = np.mean(tprs, axis=0)\n",
    "    mean_tpr[-1] = 1.0\n",
    "    mean_auc = auc(mean_fpr, mean_tpr)\n",
    "    std_auc = np.std(aucs)\n",
    "    plt.plot(mean_fpr, mean_tpr, color='b',\n",
    "             label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % \n",
    "             (mean_auc, std_auc),\n",
    "             lw=2, alpha=.8)\n",
    "\n",
    "    std_tpr = np.std(tprs, axis=0)\n",
    "    tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
    "    tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n",
    "    plt.fill_between(mean_fpr, tprs_lower, tprs_upper, \n",
    "                     color='grey', alpha=.2,\n",
    "                     label=r'$\\pm$ 1 std. dev.')\n",
    "\n",
    "    plt.xlim([-0.05, 1.05])\n",
    "    plt.ylim([-0.05, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title(parse_function_name(classifier.__str__()))\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic regression and neural networks \n",
    "\n",
    "It is always wise to start with a simple algorithm. The simplest classifier is logistic regression: \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LtGk6GMG2hF3KImpZuLIV5b51uJtdpKam4nV7UVEFUfB7ddXMIaWlxSlL4fM5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\nBY1Go9HY0E5Bo9FoNDb+P3XRVV91xs7AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1044256d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "roc_analysis(LogisticRegression(), df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean area under the ROC curve (AUC) is 0.79. That's pretty good performance: it's about half-way between a classifier that ignores features (AUC = 0.5) and a perfect classifier (AUC = 1). \n",
    "\n",
    "Moreover, the fact that there is little variation in the ROC curves as the \"fold\" is varied strongly suggests that the model has low variance. Low variance can be demonstrated more formally via learning curves: \n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VKioCYpKU1Lh2LJYGMHDgQLKzswGTDXjChAmICNnZ2f5U6UVFRVx++eV8++23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\nz+fmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ea7bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "def majority_class_predictor_accuracy(data_frame):\n",
    "    counts = dict(data_frame.responded_yes.value_counts())\n",
    "    min_counts = min(counts.values())\n",
    "    max_counts = max(counts.values())\n",
    "    return max_counts/float(min_counts+max_counts)\n",
    "\n",
    "def plot_learning_curve(classifier, data_frame, ylim=None):  \n",
    "    plt.title(parse_function_name(classifier.__str__()))\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training set size\")\n",
    "    plt.ylabel(\"Classification Score\")\n",
    "\n",
    "    X, y = extract_features_target(data_frame)\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        classifier, X, y)\n",
    "    # average over cross validation folds:\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "    plt.fill_between(train_sizes, \n",
    "                     train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, \n",
    "                     alpha=0.1, color=\"r\")\n",
    "    plt.fill_between(train_sizes, \n",
    "                     test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, \n",
    "                     alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, \n",
    "             'o-', color=\"r\", label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, \n",
    "             'o-', color=\"g\", label=\"Cross-validation score\")\n",
    "\n",
    "    baseline_score = majority_class_predictor_accuracy(data_frame)\n",
    "    \n",
    "    plt.plot([min(train_sizes), max(train_sizes)], \n",
    "             [baseline_score, baseline_score], \n",
    "             '--', color=\"black\", label=\"majority class predictor\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    plt.show()\n",
    "\n",
    "plot_learning_curve(LogisticRegression(), df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After using all our training data, the model does about the same on the CV set as on the training set - that means we don't have  overfitting (the model generalized very well). \n",
    "\n",
    "But does the model have high bias? After all, the classification score is not that much higher than what you'd expect from a classifier that simply predicts the majority class all the time (see dashed horizontal line in the plot above). \n",
    "\n",
    "The only way to address the possibility that there is high bias is to increase the model's complexity and see whether CV performance goes up. Logistic regression can be viewed as a neural network with no hidden layers. I therefore next considered a net with one hidden layer. With that increase in model complexity comes an increase in variance, which I corrected by cranking up the regularization ($\\alpha$ term in the code that follows): \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UlWswZUqQqVNDDB9uxWLJA/IIckB5FMCWJQjmHtAdivYkjpfBjr7iicYMANLS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\nKWg0Go3Gzv8DcDnpYZfTyEAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11263b6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "roc_analysis(MLPClassifier(hidden_layer_sizes=(100), alpha=1), df) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! The area under the curve hardly changed. This suggests that the original logistic regression model is not suffering from high bias. \n",
    "\n",
    "# Other models\n",
    "\n",
    "I wondered whether topologically different models might out-perform the simple logistic regression model. Neither of the two models I tried (support vector machine and decision tree) did: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1XG5nABs8xGbGjAQzZ6YYP94gWLOB5CkXdHQam2Zm6ci2bWKxGPn5+ZSWlmZs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\n8g7ygRiGQXV1NYmESr7HNJ3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R4cwQuvg++3w+HnnkET7+8Y9nHM0339yxePHgwYP5wQ9+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\nGjvaKWg0Go3GjnYKGo1Go7Hz/wPNVCXdWKTisAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112733e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "roc_analysis(SVC(probability=True), df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rBWdNaGTWuF1kJBVSG+Vgf8pA8jNSyM5I69xZziL1+efwxz9CRQWYzXDjjcYY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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1125ff278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "roc_analysis(DecisionTreeClassifier(), df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Putting it all together \n",
    "\n",
    "I collected all of the sanitizing operations I describe above into a single function, `sanitize`, in order to sanitize the training and test data in a similar way. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sanitize(data_frame):\n",
    "    data_frame = data_frame.replace('unknown', np.nan)\n",
    "    data_frame = data_frame.replace('nonexistent', np.nan)\n",
    "    data_frame = data_frame.drop('pmonths',axis=1)\n",
    "    data_frame['previously_contacted'] = data_frame.pdays!=999 \n",
    "    data_frame.loc[data_frame.pdays==999, 'pdays'] = -1\n",
    "    data_frame = track_missing_ages(data_frame)\n",
    "    data_frame = impute_age(data_frame)\n",
    "    return data_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list of values that a categorical variable assumed in the training and test sets were not perfectly coincident. I dealt with this by concatenating the sets, applying one-hot encoding on the concatenation, and then unpacking the concatenation into one-hot-encoded training and test sets. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_values(data_frame): \n",
    "    return data_frame.values.astype(float)\n",
    "\n",
    "def encode_train_test(): \n",
    "    df_train = sanitize(read_csv('marketing_training.csv'))\n",
    "    df_train_X = df_train.drop('responded', axis=1)\n",
    "    df_train_y = one_hot_encode(pd.DataFrame(df_train['responded']))\n",
    "\n",
    "    df_test_X = sanitize(read_csv('marketing_test.csv')).drop('Unnamed: 0', axis=1)\n",
    "\n",
    "    df_X = pd.concat([df_train_X, df_test_X])\n",
    "    df_X = one_hot_encode(df_X)\n",
    "    split = len(df_train_X)\n",
    "    df_train_X = df_X[:split]\n",
    "    df_test_X = df_X[split:]\n",
    "        \n",
    "    return (get_values(df_train_X), \n",
    "            get_values(df_train_y), \n",
    "            get_values(df_test_X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I re-trained the logistic regression model on the entire training set to predict whether customers in the test set responded favorably to the current marketing campaign. I then saved the data in a csv file in the same format as the training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compute_normalized_X_y(): \n",
    "    X_train, y_train, X_test = encode_train_test() \n",
    "    X_scaler = MinMaxScaler(feature_range=(0,1))\n",
    "    X_train = X_scaler.fit_transform(X_train)\n",
    "    X_test = X_scaler.transform(X_test)\n",
    "    return X_train, y_train, X_test\n",
    "\n",
    "def convert_to_yes_or_no(x): \n",
    "    if int(x) == 0: \n",
    "        return 'no'\n",
    "    else: \n",
    "        return 'yes'\n",
    "\n",
    "def predict_with_final_model():\n",
    "    X_train, y_train, X_test = compute_normalized_X_y()\n",
    "    final_model = LogisticRegression()\n",
    "    predictions = final_model.fit(X_train, y_train.ravel()).predict(X_test)\n",
    "    predictions = list(map(convert_to_yes_or_no, predictions))\n",
    "\n",
    "    df_test = read_csv('marketing_test.csv')\n",
    "    df_test['responded'] = predictions\n",
    "    df_test.to_csv('marketing_test_with_predictions.csv')\n",
    "    \n",
    "predict_with_final_model()"
   ]
  }
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