Targeted_Marketing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training data is pulled from raw logs. It has three columns that record user activity over time:\n",
    "user_id: A hash that uniquely identifies the user.\n",
    "activity_date: The date of the activity\n",
    "activity_type: The type of activity\n",
    "\n",
    "Predict which users will purchase given the user's activities. \n",
    "\n",
    "Using the training data, build a model that predicts which user_id's will make a purchase in the future. Use the model to score the test data from most likely to least likely to purchase.\n",
    "\n",
    "Please submit the following:\n",
    "\n",
    "(1) Descibe which activity types you believe are most useful in predicting which user will purchase in the future.\n",
    "\n",
    "(2) Provide a file containing the 1000 user_id's most likely to convert.\n",
    "\n",
    "(3) [optional] Any work used to generate the list and/or notes you'd like to include."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SUMMARY\n",
    "\n",
    "Before any modeling was done, I aggregated all the times a person completed an action such as email click, website visit, etc. Instead of many IDs, each row represented a many times a person completed an action.\n",
    "\n",
    "For the model, I chose the Gradient Boosted Classifier( 0.915 accuracy)  to identify who would make a purchase and Gradient Boosted Regression to estimate how many a person would purchase.  I chose this for its ensemble method, ability to improve weak learners, and being able to handle imbalanced classes.  Based on feature importance, Logs, which I aggregated as how many times a person has logged into the website is the most important feature in determining a purchase.  That is Following by Email open and afterwards the feature importance significantly drops.\n",
    "\n",
    "Customer Support was not used as a feature of prediction since it did not map to the test set.  Although I do believe could be an important feature since everyone who purchased before at one time used Customer Support.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "from datetime import datetime\n",
    "from __future__ import division\n",
    "from six.moves import cPickle as pickle\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import GradientBoostingClassifier , GradientBoostingRegressor , RandomForestClassifier , AdaBoostClassifier\n",
    "from sklearn.cross_validation import train_test_split , KFold\n",
    "from sklearn.metrics import f1_score, recall_score,precision_score , confusion_matrix, roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n\\nwith open('training.tsv','rb') as fin:\\n    cr = csv.reader(fin, delimiter='\\t')\\n    filecontents = [line for line in cr]\\nwith open('training.csv','wb') as fou:\\n    cw = csv.writer(fou, quotechar='', quoting=csv.QUOTE_NONE)\\n    cw.writerows(filecontents)\\n\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting tsv data to csv\n",
    "'''\n",
    "\n",
    "with open('training.tsv','rb') as fin:\n",
    "    cr = csv.reader(fin, delimiter='\\t')\n",
    "    filecontents = [line for line in cr]\n",
    "with open('training.csv','wb') as fou:\n",
    "    cw = csv.writer(fou, quotechar='', quoting=csv.QUOTE_NONE)\n",
    "    cw.writerows(filecontents)\n",
    "'''\n",
    "# went inside the csv file and gave the columns names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Action</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00002acbe042d69</td>\n",
       "      <td>7/11/13</td>\n",
       "      <td>FormSubmit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00002acbe042d69</td>\n",
       "      <td>7/15/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00002acbe042d69</td>\n",
       "      <td>7/17/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00002acbe042d69</td>\n",
       "      <td>7/18/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00002acbe042d69</td>\n",
       "      <td>7/25/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>00002acbe042d69</td>\n",
       "      <td>7/26/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00002acbe042d69</td>\n",
       "      <td>8/1/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>00002acbe042d69</td>\n",
       "      <td>8/5/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00002acbe042d69</td>\n",
       "      <td>8/12/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0000a3a34bd07e6</td>\n",
       "      <td>7/4/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0000a3a34bd07e6</td>\n",
       "      <td>7/5/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0000a3a34bd07e6</td>\n",
       "      <td>11/1/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0000a3a34bd07e6</td>\n",
       "      <td>11/9/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0000a3a34bd07e6</td>\n",
       "      <td>11/20/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0000a3a34bd07e6</td>\n",
       "      <td>12/24/13</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0000a3a34bd07e6</td>\n",
       "      <td>2/15/14</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0000a3a34bd07e6</td>\n",
       "      <td>3/3/14</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0000a3a34bd07e6</td>\n",
       "      <td>3/4/14</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0000a3a34bd07e6</td>\n",
       "      <td>3/5/14</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0000a3a34bd07e6</td>\n",
       "      <td>3/8/14</td>\n",
       "      <td>EmailOpen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID      Date      Action\n",
       "0   00002acbe042d69   7/11/13  FormSubmit\n",
       "1   00002acbe042d69   7/15/13   EmailOpen\n",
       "2   00002acbe042d69   7/17/13   EmailOpen\n",
       "3   00002acbe042d69   7/18/13   EmailOpen\n",
       "4   00002acbe042d69   7/25/13   EmailOpen\n",
       "5   00002acbe042d69   7/26/13   EmailOpen\n",
       "6   00002acbe042d69    8/1/13   EmailOpen\n",
       "7   00002acbe042d69    8/5/13   EmailOpen\n",
       "8   00002acbe042d69   8/12/13   EmailOpen\n",
       "9   0000a3a34bd07e6    7/4/13   EmailOpen\n",
       "10  0000a3a34bd07e6    7/5/13   EmailOpen\n",
       "11  0000a3a34bd07e6   11/1/13   EmailOpen\n",
       "12  0000a3a34bd07e6   11/9/13   EmailOpen\n",
       "13  0000a3a34bd07e6  11/20/13   EmailOpen\n",
       "14  0000a3a34bd07e6  12/24/13   EmailOpen\n",
       "15  0000a3a34bd07e6   2/15/14   EmailOpen\n",
       "16  0000a3a34bd07e6    3/3/14   EmailOpen\n",
       "17  0000a3a34bd07e6    3/4/14   EmailOpen\n",
       "18  0000a3a34bd07e6    3/5/14   EmailOpen\n",
       "19  0000a3a34bd07e6    3/8/14   EmailOpen"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading training data\n",
    "\n",
    "train = pd.read_csv('training.csv')\n",
    "train.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# After Multiple duplicate IDs, I wanted to consolidate them "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def organize_data(ID,df,string1,string2)\n",
    "'''\n",
    "Given rows of multiple log ins from the same ID,\n",
    "this is a way to count their actions as one ID number \n",
    "\n",
    "The information is then stored in nested dictionary.\n",
    "'''\n",
    "    d = {} \n",
    "    for i in ID:\n",
    "        try:\n",
    "            a= df[df[string1]==i]\n",
    "            d[i] = dict(Counter(a[string2]))   \n",
    "        except:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "info = organize_data(ID,train,'ID','Action')\n",
    "\n",
    "with open('d_train.pickle', 'wb') as f: # save for later use\n",
    "    pickle.dump(info, f, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('d_train.pickle','rb') as f: # open saved information\n",
    "    training_info = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_column(ID_list,string,dictionary,df):\n",
    "    \n",
    "'''\n",
    "\n",
    "Making multi-categorical variables into their own column \n",
    "\n",
    "Given the counts, from the nested dictionary, it \n",
    "adds columns to an empty DataFrame to store results for\n",
    "machine learning i.e classification / regression\n",
    "df: the dataframe you want to add to\n",
    "string: name of column\n",
    "ID_list: list of ids\n",
    "\n",
    "\n",
    "'''\n",
    "    lst = []\n",
    "    for i in ID_list:\n",
    "        try:\n",
    "            lst.append(dictionary[i][string])\n",
    "        except:\n",
    "            lst.append(0)\n",
    "    df[string] = lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# creating a new data frame\n",
    "\n",
    "training_set = pd.DataFrame(columns=['ID'])\n",
    "\n",
    "# adding unique IDs into the dataframe\n",
    "\n",
    "training_set['ID'] = list(training_info.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in list(set(train['Action'])): \n",
    "    # adding the collected info from the nested dictionary into the training set\n",
    "    make_column(list(training_set['ID']),i,training_info,training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "count = dict(Counter(train['ID'])) # counting IDs \n",
    "count = [count[i] for i in training_info.keys()] \n",
    "# referring ID to number of times logged into\n",
    "training_set['Logs'] = count # saving how many people logged in\n",
    "purchased = [0 if i == 0 else 1 for i in training_set['Purchase'] ] \n",
    "# If someone purchased or not, creating binary outcome\n",
    "training_set['Purchases'] = purchased # saving how many purchases per ID"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# There is also a Test Set, where I followed the same procedure as above except there were no purchases or Customer Support\n",
    "\n",
    "# The training set will predict which customers will buy and how much"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_metrics(y_actual, y_predict):\n",
    "    \"\"\"Prints multiple metrics\"\"\"\n",
    "    print \"Accuracy:\", (y_predict == y_actual).mean()\n",
    "    print \"Precision:\", precision_score(y_actual, y_predict)\n",
    "    print \"Recall:\", recall_score(y_actual, y_predict)\n",
    "    print \"F1-score:\", f1_score(y_actual, y_predict)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# After breaking out the action items, I created a training set where I can model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>ID</th>\n",
       "      <th>CustomerSupport</th>\n",
       "      <th>EmailClickthrough</th>\n",
       "      <th>EmailOpen</th>\n",
       "      <th>FormSubmit</th>\n",
       "      <th>PageView</th>\n",
       "      <th>WebVisit</th>\n",
       "      <th>Logs</th>\n",
       "      <th>Purchased</th>\n",
       "      <th>Purchases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19f2dba2fe6f1ce</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
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       "      <td>18</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05be946d079510d</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>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1a07e9bd3298a93</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>76</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142e44bf3b4c1e1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID  CustomerSupport  EmailClickthrough  EmailOpen  FormSubmit  \\\n",
       "0  19f2dba2fe6f1ce                0                  1          2           1   \n",
       "1  130c9ac7efcf577                0                  0         18           0   \n",
       "2  05be946d079510d                1                  0          0           0   \n",
       "3  1a07e9bd3298a93                1                  7         76           6   \n",
       "4  142e44bf3b4c1e1                0                  1          2           0   \n",
       "\n",
       "   PageView  WebVisit  Logs  Purchased  Purchases  \n",
       "0         0         0     4          0          0  \n",
       "1         0         0    18          0          0  \n",
       "2         0         0     4          1          3  \n",
       "3         0         0   105          1         15  \n",
       "4         2         2     7          0          0  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data = pd.read_csv('training_set.csv')\n",
    "training_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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IuCsivhoRS6cp88CIeEdE3NottzkinlFfCkmSJElSmzRiOZSIOBIYB3YA0d08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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d53850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_data = training_data[list(training_data)[2:]]\n",
    "training_data.hist(alpha=0.5, bins=50, figsize=(18, 14) , normed = True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>EmailClickthrough</th>\n",
       "      <th>EmailOpen</th>\n",
       "      <th>FormSubmit</th>\n",
       "      <th>PageView</th>\n",
       "      <th>WebVisit</th>\n",
       "      <th>Logs</th>\n",
       "      <th>Purchased</th>\n",
       "      <th>Purchases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EmailClickthrough</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688520</td>\n",
       "      <td>0.769532</td>\n",
       "      <td>0.627488</td>\n",
       "      <td>0.628243</td>\n",
       "      <td>0.810514</td>\n",
       "      <td>0.085819</td>\n",
       "      <td>0.396072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmailOpen</th>\n",
       "      <td>0.688520</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.607651</td>\n",
       "      <td>0.526258</td>\n",
       "      <td>0.526985</td>\n",
       "      <td>0.887660</td>\n",
       "      <td>0.098269</td>\n",
       "      <td>0.422265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FormSubmit</th>\n",
       "      <td>0.769532</td>\n",
       "      <td>0.607651</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.585521</td>\n",
       "      <td>0.588880</td>\n",
       "      <td>0.749905</td>\n",
       "      <td>0.075280</td>\n",
       "      <td>0.447306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PageView</th>\n",
       "      <td>0.627488</td>\n",
       "      <td>0.526258</td>\n",
       "      <td>0.585521</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999852</td>\n",
       "      <td>0.836133</td>\n",
       "      <td>0.096809</td>\n",
       "      <td>0.331459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WebVisit</th>\n",
       "      <td>0.628243</td>\n",
       "      <td>0.526985</td>\n",
       "      <td>0.588880</td>\n",
       "      <td>0.999852</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836823</td>\n",
       "      <td>0.096672</td>\n",
       "      <td>0.331841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logs</th>\n",
       "      <td>0.810514</td>\n",
       "      <td>0.887660</td>\n",
       "      <td>0.749905</td>\n",
       "      <td>0.836133</td>\n",
       "      <td>0.836823</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.153418</td>\n",
       "      <td>0.540705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purchased</th>\n",
       "      <td>0.085819</td>\n",
       "      <td>0.098269</td>\n",
       "      <td>0.075280</td>\n",
       "      <td>0.096809</td>\n",
       "      <td>0.096672</td>\n",
       "      <td>0.153418</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.344465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purchases</th>\n",
       "      <td>0.396072</td>\n",
       "      <td>0.422265</td>\n",
       "      <td>0.447306</td>\n",
       "      <td>0.331459</td>\n",
       "      <td>0.331841</td>\n",
       "      <td>0.540705</td>\n",
       "      <td>0.344465</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   EmailClickthrough  EmailOpen  FormSubmit  PageView  \\\n",
       "EmailClickthrough           1.000000   0.688520    0.769532  0.627488   \n",
       "EmailOpen                   0.688520   1.000000    0.607651  0.526258   \n",
       "FormSubmit                  0.769532   0.607651    1.000000  0.585521   \n",
       "PageView                    0.627488   0.526258    0.585521  1.000000   \n",
       "WebVisit                    0.628243   0.526985    0.588880  0.999852   \n",
       "Logs                        0.810514   0.887660    0.749905  0.836133   \n",
       "Purchased                   0.085819   0.098269    0.075280  0.096809   \n",
       "Purchases                   0.396072   0.422265    0.447306  0.331459   \n",
       "\n",
       "                   WebVisit      Logs  Purchased  Purchases  \n",
       "EmailClickthrough  0.628243  0.810514   0.085819   0.396072  \n",
       "EmailOpen          0.526985  0.887660   0.098269   0.422265  \n",
       "FormSubmit         0.588880  0.749905   0.075280   0.447306  \n",
       "PageView           0.999852  0.836133   0.096809   0.331459  \n",
       "WebVisit           1.000000  0.836823   0.096672   0.331841  \n",
       "Logs               0.836823  1.000000   0.153418   0.540705  \n",
       "Purchased          0.096672  0.153418   1.000000   0.344465  \n",
       "Purchases          0.331841  0.540705   0.344465   1.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# customer support was not used in the prediction because future customers\n",
    "# have not yet used this service, therefore cannot be a feature for prediction\n",
    "\n",
    "col = ['EmailClickthrough','EmailOpen','FormSubmit','PageView','WebVisit','Logs']\n",
    "label = 'Purchased'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding the number of purchased customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30572445165067785"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(training_data[training_data['Purchased']==1]) / len(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "purchased = training_data[training_data['Purchased']==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = training_data[col]\n",
    "y = training_data[label]\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.5, random_state=42)\n",
    "GB = GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9159585647800631"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GB.fit(X_train,y_train)\n",
    "GB.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Logs', 0.40337],\n",
       " ['EmailOpen', 0.32932],\n",
       " ['FormSubmit', 0.0939],\n",
       " ['EmailClickthrough', 0.08679],\n",
       " ['WebVisit', 0.05673],\n",
       " ['PageView', 0.02989]]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = [[i,round(j,5)] for i,j in zip(col,GB.feature_importances_)]\n",
    "features_sorted = sorted(features, key= lambda x: x[1] ,reverse=True)\n",
    "features_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_names = [ i[0] for i in features_sorted]\n",
    "feature_importance = [ i[1] for i in features_sorted]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.40337, 0.32932, 0.0939, 0.08679, 0.05673, 0.02989]"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_names.reverse()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The most important features are how many times someone logged in and emails opened which led to a purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CTTegM5PsneSTSa5Jcn2S05Ps05RZlORzTReb65P8R5I9+tT1kqbcz5L8vin/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\nlDLDzmTS3krpsvPmPpv7HiMzfwasMYtQRGxAmY3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119fcb750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#combined features\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.barh(range(len(feature_importance)),sorted(feature_importance),align='center', color = 'whitesmoke')\n",
    "plt.yticks(range(len(feature_names)),feature_names)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.xlabel('Relative importance', fontsize=18)\n",
    "plt.title('Made a Purchase', size = 25)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC: 0.99\n",
      "Accuracy: 0.91595856478\n",
      "Precision: 0.941489361702\n",
      "Recall: 0.775635407537\n",
      "F1-score: 0.850552618933\n"
     ]
    },
    {
     "data": {
      "image/png": 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bwQjIKuD74TpuIujGOiRCBhGRSNK7maqvh0jmohQgTwBjzOxxYH+Cg0ABdiU4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1204e1850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probabilities = GB.predict_proba(X_test)[:, 1]\n",
    "# predicting 1's\n",
    "\n",
    "fpr,tpr,threshold = roc_curve(y_test,probabilities)\n",
    "\n",
    "# activating roc_curve \n",
    "\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print \"ROC AUC: %0.2f\" % roc_auc\n",
    "print_metrics(y_test, GB.predict(X_test))\n",
    "\n",
    "\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.xlabel(\"False Positive Rate (1 - Specificity)\")\n",
    "plt.ylabel(\"True Positive Rate (Sensitivity, Recall)\")\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.title(\"ROC Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[22541   495]\n",
      " [ 2304  7965]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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P/E/duWWBjYDjSQPKVwBOB64GNivkC+DHwLGFor5Sd69LgHGkODoKuBA4B9gf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\nADQk4AGgoW8Pd64zSxAaZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1204e1710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "\n",
    "# Compute confusion matrix\n",
    "cm = confusion_matrix(y_test, GB.predict(X_test))\n",
    "np.set_printoptions(precision=2)\n",
    "print('Confusion matrix, without normalization')\n",
    "print(cm)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Created a Regression to estimate how many someone will purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = np.asarray([training_data['EmailClickthrough'],\n",
    "                training_data['EmailOpen'],\n",
    "                training_data['FormSubmit'],\n",
    "                training_data['PageView'],\n",
    "                training_data['WebVisit'],\n",
    "                training_data['Logs']]).T\n",
    "y = np.asarray(training_data['Purchases'])  # took days out\n",
    "\n",
    "model = GradientBoostingRegressor()\n",
    "\n",
    "X_train , X_test, y_train, y_test = train_test_split(X,y, random_state = 42)\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.05,  0.27,  0.05,  0.13,  0.18,  0.32])"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Logs', 0.32241],\n",
       " ['EmailOpen', 0.26711],\n",
       " ['WebVisit', 0.17837],\n",
       " ['PageView', 0.13375],\n",
       " ['EmailClickthrough', 0.05296],\n",
       " ['FormSubmit', 0.04541]]"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = [[i,round(j,5)] for i,j in zip(col,model.feature_importances_)]\n",
    "features_sorted = sorted(features, key= lambda x: x[1] ,reverse=True)\n",
    "features_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_names = [ i[0] for i in features_sorted]\n",
    "feature_importance = [ i[1] for i in features_sorted]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_names.reverse()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The same feature importance for a purchase also was the same for how many purchased as well.  I estimated on average that a person will purchase 3 items over it’s lifetime versus 4 for the training set.  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4Hx9XZubH+t2IbjJzFXD5MHb5D0rw/zXwisy8obUhIgJYAHwY+HFEzM7MO8ay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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11785da50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.barh(range(len(feature_importance)),sorted(feature_importance),align='center', color = 'whitesmoke')\n",
    "plt.yticks(range(len(feature_names)),feature_names)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.xlabel('Relative importance', fontsize=18)\n",
    "plt.title('Purchases Made', size = 25)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Based on the scatter plot, the Regression model was moderately successful in predictions.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x122719410>"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KKiIiIpJYCioiIiKSWAoqIiIiklgKKiIiIpJYCioiIiKSWAoqIiIiklgKKiIiIpJYCioi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\n1dX3Is95GGMyfyfc/Rl3/7O7/8Hd/5M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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a2103d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predict);\n",
    "plt.title('Y vs Y Predicted')\n",
    "plt.xlabel('Y')\n",
    "plt.ylabel('predicted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "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>ID</th>\n",
       "      <th>FormSubmit</th>\n",
       "      <th>EmailOpen</th>\n",
       "      <th>EmailClickthrough</th>\n",
       "      <th>PageView</th>\n",
       "      <th>WebVisit</th>\n",
       "      <th>Logs</th>\n",
       "      <th>PredictPurchase</th>\n",
       "      <th>EstimatedPurchases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e6487f04a44444f</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.685508</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c88480d19b2a617</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.027066</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8cc15e801900cc4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.017591</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1a07e9bd3298a93</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>0.251876</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3a9956a86a11033</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.046197</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID  FormSubmit  EmailOpen  EmailClickthrough  PageView  \\\n",
       "0  e6487f04a44444f           0          0                  0         1   \n",
       "1  c88480d19b2a617           0          3                  0         0   \n",
       "2  8cc15e801900cc4           0          2                  0         0   \n",
       "3  1a07e9bd3298a93           2         14                  2         2   \n",
       "4  3a9956a86a11033           0          6                  0         0   \n",
       "\n",
       "   WebVisit  Logs  PredictPurchase  EstimatedPurchases  \n",
       "0         1     2         0.685508                   1  \n",
       "1         0     3         0.027066                   0  \n",
       "2         0     2         0.017591                   0  \n",
       "3         2    22         0.251876                   1  \n",
       "4         0     6         0.046197                   0  "
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data = pd.read_csv('testing_set.csv')\n",
    "testing_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n = testing_data[['EmailClickthrough','EmailOpen','FormSubmit','PageView','WebVisit','Logs']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictions = GB.predict_proba(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_ = testing_data[['EmailClickthrough','EmailOpen','FormSubmit','PageView','WebVisit','Logs']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "estimated_purchase = model.predict(n_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# At max, someone may purchase up to 18 items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.274283587732342"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(estimated_purchase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictions = GB.predict_proba(n)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted = GB.predict(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model predicts that only 2% of the people in the test set will purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026616971613583316"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(predicted) / len(testing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testing_data[\"PredictPurchase\"] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testing_data['EstimatedPurchases'] = [0 if i<=1 else np.round(i) for i in estimated_purchase]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Will this person purchase? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>ID</th>\n",
       "      <th>FormSubmit</th>\n",
       "      <th>EmailOpen</th>\n",
       "      <th>EmailClickthrough</th>\n",
       "      <th>PageView</th>\n",
       "      <th>WebVisit</th>\n",
       "      <th>Logs</th>\n",
       "      <th>PredictPurchase</th>\n",
       "      <th>EstimatedPurchases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>88</td>\n",
       "      <td>0.980943</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>130602</th>\n",
       "      <td>612df76dc1ac3ed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55253</th>\n",
       "      <td>490267b17a2462b</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116710</th>\n",
       "      <td>bb11509dcc926c9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ID  FormSubmit  EmailOpen  EmailClickthrough  PageView  \\\n",
       "74951   8070b641c86681d           0          0                  0        23   \n",
       "39237   323c7284034098b           0          0                  0        44   \n",
       "130602  612df76dc1ac3ed           0          0                  0        11   \n",
       "55253   490267b17a2462b           0          0                  0        11   \n",
       "116710  bb11509dcc926c9           0          0                  0        11   \n",
       "\n",
       "        WebVisit  Logs  PredictPurchase  EstimatedPurchases  \n",
       "74951         23    46         0.982402                 6.0  \n",
       "39237         44    88         0.980943                 6.0  \n",
       "130602        11    22         0.980911                 5.0  \n",
       "55253         11    22         0.980911                 5.0  \n",
       "116710        11    22         0.980911                 5.0  "
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data.sort_values(by=['PredictPurchase'], ascending=[False]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How many will the person purchase? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>FormSubmit</th>\n",
       "      <th>EmailOpen</th>\n",
       "      <th>EmailClickthrough</th>\n",
       "      <th>PageView</th>\n",
       "      <th>WebVisit</th>\n",
       "      <th>Logs</th>\n",
       "      <th>PredictPurchase</th>\n",
       "      <th>EstimatedPurchases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54825</th>\n",
       "      <td>3ff1feb5b674e3a</td>\n",
       "      <td>59</td>\n",
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       "      <td>19</td>\n",
       "      <td>54</td>\n",
       "      <td>69</td>\n",
       "      <td>264</td>\n",
       "      <td>0.754919</td>\n",
       "      <td>18.0</td>\n",
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       "      <th>74774</th>\n",
       "      <td>1137a7de4a56a09</td>\n",
       "      <td>52</td>\n",
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       "      <td>215</td>\n",
       "      <td>0.809713</td>\n",
       "      <td>17.0</td>\n",
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       "      <th>128111</th>\n",
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       "      <td>58</td>\n",
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       "      <td>259</td>\n",
       "      <td>0.754919</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90858</th>\n",
       "      <td>fb2568cc6e4f93a</td>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
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       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>237</td>\n",
       "      <td>0.823669</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7701</th>\n",
       "      <td>61d714a24ee246f</td>\n",
       "      <td>19</td>\n",
       "      <td>74</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>163</td>\n",
       "      <td>0.801019</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ID  FormSubmit  EmailOpen  EmailClickthrough  PageView  \\\n",
       "54825   3ff1feb5b674e3a          59         63                 19        54   \n",
       "74774   1137a7de4a56a09          52         60                 19        29   \n",
       "128111  7c21520dc24c16f          58         65                 24        49   \n",
       "90858   fb2568cc6e4f93a          20         70                 31        58   \n",
       "7701    61d714a24ee246f          19         74                 26        22   \n",
       "\n",
       "        WebVisit  Logs  PredictPurchase  EstimatedPurchases  \n",
       "54825         69   264         0.754919                18.0  \n",
       "74774         55   215         0.809713                17.0  \n",
       "128111        63   259         0.754919                17.0  \n",
       "90858         58   237         0.823669                17.0  \n",
       "7701          22   163         0.801019                16.0  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data.sort_values(by=['EstimatedPurchases'], ascending=[False]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "purchase_test =testing_data[testing_data['PredictPurchase']>.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FormSubmit             2.194342\n",
       "EmailOpen             14.949929\n",
       "EmailClickthrough      3.596888\n",
       "PageView              10.822631\n",
       "WebVisit              10.874399\n",
       "Logs                  42.438190\n",
       "PredictPurchase        0.672159\n",
       "EstimatedPurchases     3.278925\n",
       "dtype: float64"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(purchase_test) # estimated average purchase per user lifetime on test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerSupport       1.000000\n",
       "EmailClickthrough     1.524357\n",
       "EmailOpen            13.884649\n",
       "FormSubmit            0.898841\n",
       "PageView              2.761982\n",
       "WebVisit              2.763308\n",
       "Logs                 26.972697\n",
       "Purchased             1.000000\n",
       "Purchases             4.139560\n",
       "dtype: float64"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(training_data[training_data['Purchased']==1])  # average purchase per user lifetime."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FUTURE WORK:\n",
    "- Feature engineering to reduce the model size for features that are not relevant.\n",
    "- Run more Diagnostics for Regression model to produce more accurate estimates of purchasers.\n",
    "- Further research why the training set lists 30% of people made a purchase, but only predicted -2% will purchase from the actual test set.  It did so well on the train/test split.\n",
    "- Collect Customer Support data and more features,  to see if it makes an impact.\n",
    "- Automatic data aggregation in a SQL database, where as new logs come in, it will either create a new row for a new user, or update a current row.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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