Conversion Rate Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Conversion Rate Optimization using Classification Model\n",
    "\n",
    "The data revolution enable us to collect all sorts of data about customers who purchase on a website as well as potential customers who had not yet purchase. These data can reveal insights on what's working well (and potentially scale it even further) and what's not working well (and fix it).\n",
    "\n",
    "The goal of this project is to build a model that predicts conversion rate and, based on the model, come up with ideas to improve revenue.\n",
    "\n",
    "\n",
    "## Goal\n",
    "* Predict conversion rate\n",
    "* Come up with recommendations for the product team and the marketing team to improve conversion rate\n",
    "\n",
    "\n",
    "## Description of data\n",
    "Website users information: \n",
    "* Whether they converted or not ; \n",
    "* User characteristics: Country; The marketing channel; Age; Repeat users or not; The number of pages visited during that session (as a proxy for site activity/time spent on site).\n",
    "\n",
    "### Columns:\n",
    "* country : user country based on the IP address\n",
    "* age : user age. Self-reported at sign-in step\n",
    "* new_user : whether the user created the account during this session or had already an account and simply came back to the site\n",
    "* source : marketing channel source\n",
    "* Ads: came to the site by clicking on an advertisement\n",
    "* Seo: came to the site by clicking on search results\n",
    "* Direct: came to the site by directly typing the URL on the browser\n",
    "* total_pages_visited: number of total pages visited during the session. This is a proxy for time spent on site and engagement during the session.\n",
    "* converted: this is our label. 1 means they converted within the session, 0 means they left without buying anything. The company goal is to increase conversion rate: # conversions / total sessions.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 1. Exploratory Data Analysis and Feature Generation \n",
    "### 1.1 Read data from csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import necessary pacakges\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn import base\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV \n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(316200, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>source</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UK</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>Ads</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  age  new_user source  total_pages_visited  converted\n",
       "0      UK   25         1    Ads                    1          0\n",
       "1      US   23         1    Seo                    5          0\n",
       "2      US   28         1    Seo                    4          0\n",
       "3   China   39         1    Seo                    5          0\n",
       "4      US   30         1    Seo                    6          0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data as a pandas dataframe\n",
    "data = pd.read_csv(\"conversion_data.csv\")\n",
    "print(data.shape)    # check size of the data, there are 31.6K user records\n",
    "data.head()          # look at the first 5 rows of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Data cleaning\n",
    "Look at the data to check any usual values. First we get some summary on numeric data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>316200.000000</td>\n",
       "      <td>316200.000000</td>\n",
       "      <td>316200.000000</td>\n",
       "      <td>316200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.569858</td>\n",
       "      <td>0.685465</td>\n",
       "      <td>4.872966</td>\n",
       "      <td>0.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.271802</td>\n",
       "      <td>0.464331</td>\n",
       "      <td>3.341104</td>\n",
       "      <td>0.176685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>123.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age       new_user  total_pages_visited      converted\n",
       "count  316200.000000  316200.000000        316200.000000  316200.000000\n",
       "mean       30.569858       0.685465             4.872966       0.032258\n",
       "std         8.271802       0.464331             3.341104       0.176685\n",
       "min        17.000000       0.000000             1.000000       0.000000\n",
       "25%        24.000000       0.000000             2.000000       0.000000\n",
       "50%        30.000000       1.000000             4.000000       0.000000\n",
       "75%        36.000000       1.000000             7.000000       0.000000\n",
       "max       123.000000       1.000000            29.000000       1.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some findings:\n",
    "* 68.5% of the users are new users;\n",
    "* Overall conversion rate is 0.0323;\n",
    "* The maximum age looks odd and need futher investigation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>source</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90928</th>\n",
       "      <td>Germany</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>Seo</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295581</th>\n",
       "      <td>UK</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>Ads</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265167</th>\n",
       "      <td>US</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>Direct</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192644</th>\n",
       "      <td>US</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>Direct</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154217</th>\n",
       "      <td>US</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  age  new_user  source  total_pages_visited  converted\n",
       "90928   Germany  123         0     Seo                   15          1\n",
       "295581       UK  111         0     Ads                   10          1\n",
       "265167       US   79         1  Direct                    1          0\n",
       "192644       US   77         0  Direct                    4          0\n",
       "154217       US   73         1     Seo                    5          0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by='age', ascending = False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's not likely that a customer's age is >100 and we have a large sample, so I just drop those 2 records. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.loc[data['age']<100].reset_index().drop('index', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 1.3 Exploratory analysis\n",
    "### 1.3.1 Histogram of numeric variables\n",
    "First, plot the histogram of each continous variable age and total_pages_visited by 'converted'. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10ea972e8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RbwAGZ+YLmfnjlkUnSZLaY9ssSVIXNHvP8HXAeg3zbyxlkiSpd9g2S5LUBc0m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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f2def98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16, 5))\n",
    "\n",
    "data.loc[data['converted']==1 ,'age'].plot.hist( bins= 30, ax=axes[0], alpha=.3, title= 'Age', label='Converted')\n",
    "data.loc[data['converted']==0 ,'age'].plot.hist( bins= 30, ax=axes[0], alpha=.3, label='Not converted')\n",
    "\n",
    "data.loc[data['converted']==1 ,'total_pages_visited'].plot.hist( bins= 30, ax=axes[1], alpha=.3, \n",
    "                                                                title= 'Total pages visited', label='Converted')\n",
    "data.loc[data['converted']==0 ,'total_pages_visited'].plot.hist( bins= 30, ax=axes[1], alpha=.3,  label='Not converted')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Findings:\n",
    "* There are more younger users (age 20-40) than older users (age >40); Hard to observe difference in age between  converted users and users who did not convert.\n",
    "* Conversion is associated with more total pages visited. \n",
    "\n",
    "Explore relationship between total_pages_visited and age:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     total_pages_visited       age\n",
      "total_pages_visited             1.000000 -0.000465\n",
      "age                            -0.000465  1.000000\n",
      "                     total_pages_visited       age\n",
      "total_pages_visited             1.000000  0.001367\n",
      "age                             0.001367  1.000000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1114d2d68>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hhBDrnyRT65gUjgshhBDrnyRT65htWWi9kFDNFY7blvy1CSGEEOuFvCuvY1I4LoQQQqx/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\nn50a6KZqS3geGAZ4horHBYVZ3YJ4HjRPTcklEuCbKi6xWYgZQFp1yVxHn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qZpP3u5B/Mm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\nJKmBxZQkSVIDiylJkqQG/wF0YVS9xe0R3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f2deba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute correlation between age and total_pages_visted:\n",
    "print(data.loc[data['converted']==1][['total_pages_visited','age']].corr())\n",
    "print(data.loc[data['converted']==0][['total_pages_visited','age']].corr())\n",
    "\n",
    "# visualized the relationship by converted or not\n",
    "\n",
    "plt.figure(figsize= (10,6))\n",
    "plt.scatter(data.loc[data['converted']==0, 'total_pages_visited'], data.loc[data['converted']==0, 'age'], \n",
    "            label = 'Converted', alpha=0.02)\n",
    "plt.scatter(data.loc[data['converted']==1, 'total_pages_visited'], data.loc[data['converted']==1, 'age'], \n",
    "            label =  'Not converted', alpha=0.02)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.2 Frequency table of categorical features by Converted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f2de320>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sDg0zM8vm0DAzs2z/H+15ls4DicncAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ffb748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby([ 'new_user', 'converted']).size().unstack().plot.bar(stacked= True, title =\"Conversion by new user or not\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_user</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.071983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.014021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          converted\n",
       "new_user           \n",
       "0          0.071983\n",
       "1          0.014021"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['converted','new_user']].groupby(['new_user']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding: There are more new users than users who already have an account, but return user have higher conversion rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1163b3cc0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PqlqT5ADgi0n2Wd/jVVUlqY3I4xQ6Q53Zc889N3R3SZLGzDu7kiT1iSQnAa8H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\nJ9j+dukgsYVngZWSFtNV8Nr+ULlI0U3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11624eda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp1 = data.groupby(['country', 'converted']).size().unstack()\n",
    "tmp2 = data.groupby(['source', 'converted']).size().unstack()\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16, 5))\n",
    "tmp2.plot.bar(stacked= True, ax=axes[0], title = 'Conversion by source')\n",
    "tmp1.plot.bar(stacked= True, ax=axes[1], title = 'Conversion by country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ads</th>\n",
       "      <td>0.034472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct</th>\n",
       "      <td>0.028169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seo</th>\n",
       "      <td>0.032888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        converted\n",
       "source           \n",
       "Ads      0.034472\n",
       "Direct   0.028169\n",
       "Seo      0.032888"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['converted','source']].groupby(['source']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>0.001332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>0.062428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>0.052612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>0.037801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         converted\n",
       "country           \n",
       "China     0.001332\n",
       "Germany   0.062428\n",
       "UK        0.052612\n",
       "US        0.037801"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['converted','country']].groupby(['country']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Build a python class for feature generation - One Hot Encoding\n",
    "use OneHotEncoder, LabelEncoder from sklearn to convert categorical variables (UniqueCarrier, Month and Hour) into binary variables to use for model fitting.\n",
    "\n",
    "* Build a custom transformer that will transform data to features we want:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (base.BaseEstimator, base.TransformerMixin)-> this class will have .fit(),.fit_transform( mothods\n",
    "\n",
    "class ColumnSelectTransformer(base.BaseEstimator, base.TransformerMixin):\n",
    "    \n",
    "    def __init__(self, categorical_variables, numeric_variables): \n",
    "        # parameters taken by the transformer: a list of categorical_variables, a list of numeric_variables\n",
    "        self.categorical_variables = categorical_variables  # We will need these in transform()\n",
    "        self.numeric_variables     = numeric_variables\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        # Create empty list to store encode fit\n",
    "        self.label_encode = [] \n",
    "        self.OneHot_encode = []\n",
    "        \n",
    "        # fit LabelEncoder and OneHotEncoder to data\n",
    "        for i, column in enumerate(self.categorical_variables):\n",
    "            LE = LabelEncoder()                 # encode categorical variables as numeric\n",
    "            OHE = OneHotEncoder( sparse=False)  # do one hot encoding\n",
    "            OHE.fit(LE.fit_transform( X[column]).reshape(-1,1))\n",
    "            self.label_encode.append( LE )    \n",
    "            self.OneHot_encode.append( OHE )  \n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        X = X.reset_index() \n",
    "        df_cat = []  # empty list to store transformation result for each categorical variable\n",
    "        \n",
    "        # use LabelEncoder and OneHotEncoder to transform data\n",
    "        for i, column in enumerate(self.categorical_variables):\n",
    "            df_feature = self.OneHot_encode[i].transform(self.label_encode[i].transform( X[column]).reshape(-1,1))\n",
    "            df_feature = pd.DataFrame(df_feature)\n",
    "            df_feature.columns = [column  +'_'+str(x) for x in list(self.label_encode[i].classes_)]\n",
    "            df_cat.append(df_feature)\n",
    "            \n",
    "        # Merge these dummary variables with numeric features: \n",
    "        df_numeric = X[self.numeric_variables]\n",
    "        X = pd.concat([df_numeric]+ df_cat, axis=1) \n",
    "        \n",
    "        return X "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Transform data, \n",
    "* list feature names\n",
    "* look at the transformed feature matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['total_pages_visited', 'age', 'new_user', 'country_China', 'country_Germany', 'country_UK', 'country_US', 'source_Ads', 'source_Direct', 'source_Seo']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>country_China</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>country_US</th>\n",
       "      <th>source_Ads</th>\n",
       "      <th>source_Direct</th>\n",
       "      <th>source_Seo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_pages_visited  age  new_user  country_China  country_Germany  \\\n",
       "0                    1   25         1            0.0              0.0   \n",
       "1                    5   23         1            0.0              0.0   \n",
       "2                    4   28         1            0.0              0.0   \n",
       "3                    5   39         1            1.0              0.0   \n",
       "4                    6   30         1            0.0              0.0   \n",
       "\n",
       "   country_UK  country_US  source_Ads  source_Direct  source_Seo  \n",
       "0         1.0         0.0         1.0            0.0         0.0  \n",
       "1         0.0         1.0         0.0            0.0         1.0  \n",
       "2         0.0         1.0         0.0            0.0         1.0  \n",
       "3         0.0         0.0         0.0            0.0         1.0  \n",
       "4         0.0         1.0         0.0            0.0         1.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CT = ColumnSelectTransformer(categorical_variables=['country','source' ], \n",
    "                                numeric_variables=['total_pages_visited','age','new_user'])\n",
    "df_feature = CT.fit_transform(data)\n",
    "columns = list(df_feature.columns)\n",
    "print(columns)\n",
    "df_feature.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Build models to predict whether a user will convert.\n",
    "Build prediction models using features generated above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import roc_curve, auc, f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Split data into training set and test set\n",
    "Split data into traing set(75%) and test set(25%): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X =  data.drop('converted', axis=1)\n",
    "y = data['converted']\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size= 0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Fit different classifiers to training data and plot ROC curve using test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression: Test F1 score 0.769128704113 , Test Accuracy 0.98679316888\n",
      "Confusion matrix of test data:\n",
      "[[76267   286]\n",
      " [  758  1739]]\n",
      "Decision Tree: Test F1 score 0.739216987392 , Test Accuracy 0.985085388994\n",
      "Confusion matrix of test data:\n",
      "[[76200   353]\n",
      " [  826  1671]]\n",
      "Regularized Logistic Regression: Test F1 score 0.769502762431 , Test Accuracy 0.986805819102\n",
      "Confusion matrix of test data:\n",
      "[[76266   287]\n",
      " [  756  1741]]\n",
      "Random Forest: Test F1 score 0.757239277161 , Test Accuracy 0.985895003163\n",
      "Confusion matrix of test data:\n",
      "[[76196   357]\n",
      " [  758  1739]]\n",
      "Naive Bayes: Test F1 score 0.597099800967 , Test Accuracy 0.964149272612\n",
      "Confusion matrix of test data:\n",
      "[[74116  2437]\n",
      " [  397  2100]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x116d52ef0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6zQsWtAffVIrY9Om+YXbrd75N8xNPFrTFDjnEN1A3L3iGpv/8p6At1+Bfj2ki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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1164ba320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#max_depth= 10, n_estimators = 10, max_features= 10\n",
    "# name -> (line format, classifier)\n",
    "from sklearn.metrics import f1_score, confusion_matrix, accuracy_score\n",
    "\n",
    "CLASS_MAP ={\n",
    "    'Logistic Regression': ('-', LogisticRegression() ),\n",
    "    'Regularized Logistic Regression': ('.', GridSearchCV(LogisticRegression(penalty= 'l2'), {'C':np.logspace(-2, 2.0, num=10)}) ),\n",
    "    'Naive Bayes' :('--', GaussianNB() ),\n",
    "    'Decision Tree' :('.-',DecisionTreeClassifier(min_samples_split=10) ) ,\n",
    "    'Random Forest': (':', RandomForestClassifier(min_samples_split=20)),\n",
    "}\n",
    "\n",
    "plt.figure(figsize= (12,8))\n",
    "for name, (line_fmt, clf) in CLASS_MAP.items():\n",
    "    # train model\n",
    "    model = Pipeline([\n",
    "        ('ColSelect', CT ),# ColumnSelectTransformer\n",
    "        ('clf', clf)       # classifier\n",
    "    ])\n",
    "    \n",
    "    model.fit( X_train, y_train)\n",
    "    # predict probability on test data\n",
    "    preds = model.predict_proba(X_test)\n",
    "    pred = pd.Series(preds[:, 1])\n",
    "    \n",
    "    # Calcualte FPR, TPR for plotting ROC curve\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, pred)\n",
    "    auc_score = auc(fpr, tpr)\n",
    "    label = '%s: AUC=%f' %(name, auc_score)\n",
    "    plt.plot(fpr, tpr, line_fmt, linewidth=5, label= label)\n",
    "    \n",
    "    train_pred = model.predict(X_train)\n",
    "    test_pred = model.predict(X_test)\n",
    "\n",
    "    print(name + ': Test F1 score', f1_score(y_test, test_pred), #sum(train_pred== y_train)/len(y_train),\n",
    "          ', Test Accuracy', accuracy_score(y_test, test_pred))\n",
    "\n",
    "    #print('Confusion matrix of train data:')\n",
    "    #print(confusion_matrix(y_train, train_pred))\n",
    "    print('Confusion matrix of test data:')\n",
    "    print(confusion_matrix(y_test, test_pred))\n",
    "plt.legend(loc= 'lower right')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC Curve of Different Classfiers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96841239721695127"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy of predicting all flights to be not canceled:\n",
    "sum(y_test==0)/len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Findings: \n",
    "* All models have similar AUC. \n",
    "* Beause conversion rate is low, even using the simplest model predicting all user not to convert, the accuracy is high (0.9684).\n",
    "* So accuracy is not a good metric in this case, F1 score is better.\n",
    "* I choose logistic classifier to be the final prediction model, which is a simple model with high F1 score and high accuracy.\n",
    "\n",
    "Logistic Regression is an excellent algorithm for classification. Even though some of the sexier, black box classification algorithms like SVM and RandomForest can perform better in some cases, it's hard to deny the value in knowing exactly what your model is doing. Often times you can get by using RandomForest to select the features of your model and then rebuild the model with Logistic Regression using the best features.\n",
    "\n",
    "### 2.3 Fit the final model using all data:\n",
    "\n",
    "### Model fitting using scikit-learn LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The pipeline approach is very convenient to comparedifferent models.\n",
    "* However, becasue we include all categories when creating dummy variables in the transformer class, \n",
    "it will have multi-colinearity problem for logistic regression. \n",
    "* A better way is to set a reference category for each categorical variable and remove that before fitting model.\n",
    "* Here I set US as reference for country and direct as a reference for source.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = CT.fit_transform(data).drop(['country_US', 'source_Direct'], axis= 1)\n",
    "clf = LogisticRegression()\n",
    "clf.fit(df, data['converted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-7.20086815]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_pages_visited</th>\n",
       "      <td>0.754838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.075214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_user</th>\n",
       "      <td>-1.735214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_China</th>\n",
       "      <td>-3.179391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_Germany</th>\n",
       "      <td>0.564013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_UK</th>\n",
       "      <td>0.366504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_Ads</th>\n",
       "      <td>0.173303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_Seo</th>\n",
       "      <td>0.150062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0\n",
       "total_pages_visited  0.754838\n",
       "age                 -0.075214\n",
       "new_user            -1.735214\n",
       "country_China       -3.179391\n",
       "country_Germany      0.564013\n",
       "country_UK           0.366504\n",
       "source_Ads           0.173303\n",
       "source_Seo           0.150062"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(clf.intercept_)\n",
    "pd.DataFrame( clf.coef_[0] , list(df.columns))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model fitting using statsmodels\n",
    "* Point estimates given by sklearn is not enough for model interpretation, so I use the package statsmodels which produces p-values and confidence intervals for fitting a logistic model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.052938\n",
      "         Iterations 11\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import pylab as pl\n",
    "logit = sm.Logit(data['converted'], df)\n",
    "# fit the model\n",
    "result = logit.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>converted</td>    <th>  No. Observations:  </th>  <td>316198</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>316190</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 02 Aug 2017</td> <th>  Pseudo R-squ.:     </th>  <td>0.6285</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>23:07:11</td>     <th>  Log-Likelihood:    </th> <td> -16739.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -45053.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th>  <td> 0.000</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>total_pages_visited</th> <td>    0.6021</td> <td>    0.005</td> <td>  128.610</td> <td> 0.000</td> <td>    0.593</td> <td>    0.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>                 <td>   -0.2388</td> <td>    0.002</td> <td> -127.803</td> <td> 0.000</td> <td>   -0.242</td> <td>   -0.235</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>new_user</th>            <td>   -2.1997</td> <td>    0.031</td> <td>  -70.077</td> <td> 0.000</td> <td>   -2.261</td> <td>   -2.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>country_China</th>       <td>   -3.7419</td> <td>    0.112</td> <td>  -33.516</td> <td> 0.000</td> <td>   -3.961</td> <td>   -3.523</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>country_Germany</th>     <td>    0.1850</td> <td>    0.063</td> <td>    2.937</td> <td> 0.003</td> <td>    0.062</td> <td>    0.308</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>country_UK</th>          <td>   -0.0254</td> <td>    0.037</td> <td>   -0.690</td> <td> 0.490</td> <td>   -0.097</td> <td>    0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_Ads</th>          <td>   -0.8836</td> <td>    0.039</td> <td>  -22.664</td> <td> 0.000</td> <td>   -0.960</td> <td>   -0.807</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_Seo</th>          <td>   -0.9245</td> <td>    0.034</td> <td>  -27.110</td> <td> 0.000</td> <td>   -0.991</td> <td>   -0.858</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:              converted   No. Observations:               316198\n",
       "Model:                          Logit   Df Residuals:                   316190\n",
       "Method:                           MLE   Df Model:                            7\n",
       "Date:                Wed, 02 Aug 2017   Pseudo R-squ.:                  0.6285\n",
       "Time:                        23:07:11   Log-Likelihood:                -16739.\n",
       "converged:                       True   LL-Null:                       -45053.\n",
       "                                        LLR p-value:                     0.000\n",
       "=======================================================================================\n",
       "                          coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------\n",
       "total_pages_visited     0.6021      0.005    128.610      0.000       0.593       0.611\n",
       "age                    -0.2388      0.002   -127.803      0.000      -0.242      -0.235\n",
       "new_user               -2.1997      0.031    -70.077      0.000      -2.261      -2.138\n",
       "country_China          -3.7419      0.112    -33.516      0.000      -3.961      -3.523\n",
       "country_Germany         0.1850      0.063      2.937      0.003       0.062       0.308\n",
       "country_UK             -0.0254      0.037     -0.690      0.490      -0.097       0.047\n",
       "source_Ads             -0.8836      0.039    -22.664      0.000      -0.960      -0.807\n",
       "source_Seo             -0.9245      0.034    -27.110      0.000      -0.991      -0.858\n",
       "=======================================================================================\n",
       "\n",
       "Possibly complete quasi-separation: A fraction 0.31 of observations can be\n",
       "perfectly predicted. This might indicate that there is complete\n",
       "quasi-separation. In this case some parameters will not be identified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a great overview of the coefficients of the model, how well those coefficients fit, the overall fit quality, and several other statistical measures.\n",
    "\n",
    "#### Odds ratio\n",
    "\n",
    "Take the exponential of each of the coefficients to generate the odds ratios. This tells you how a 1 unit increase or decrease in a variable affects the odds of being admitted. \n",
    "* For example, we can expect the odds of conversion decrease by about 89% if the user is a new user.\n",
    "\n",
    "We can also do the same calculations using the coefficients estimated using the confidence interval to get a better picture for how uncertainty in variables can impact the conversion rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         2.5%     97.5%        OR\n",
      "total_pages_visited  1.809325  1.842836  1.826004\n",
      "age                  0.784706  0.790474  0.787585\n",
      "new_user             0.104227  0.117874  0.110841\n",
      "country_China        0.019050  0.029509  0.023709\n",
      "country_Germany      1.063483  1.361264  1.203196\n",
      "country_UK           0.907106  1.047850  0.974942\n",
      "source_Ads           0.382897  0.446121  0.413302\n",
      "source_Seo           0.371096  0.424168  0.396746\n"
     ]
    }
   ],
   "source": [
    "# odds ratios and 95% CI\n",
    "params = result.params\n",
    "conf = result.conf_int()\n",
    "conf['OR'] = params\n",
    "conf.columns = ['2.5%', '97.5%', 'OR']\n",
    "print (np.exp(conf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation:\n",
    "* Country effect: The probability of conversion is higher for users in China is lower than that of users in the US, which is again lower than conversion rate in German users. \n",
    "* The odds of conversion for new users are 89% lower than that of a user who already have an account.\n",
    "* For every year increase in age, the odds of conversion descrease by 11%.\n",
    "* The odds of conversion for Ads or SEO are both 60% lower compared with that of direct source."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Recommendations for the product team and the marketing team\n",
    "Recommendations for the product team and the marketing team to improve conversion rate:\n",
    "We want to improve our product and increase marketing effects to target users with lower conversion rate. \n",
    "\n",
    "The groups of users with lower conversion rate right now are:\n",
    "* users older in age, \n",
    "* users with fewer total page visited\n",
    "* new users\n",
    "* Chinese users\n",
    "* users from the source of Ads and search engine optimization(SEO)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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