Capstone01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goal: If a visitor is going to generate revenue or not\n",
    "# and If yes how much "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We are trying to predict revenue per customer \n",
    "###### After refering to above general overview we need to \n",
    "###### 1 Format the change of data type to reflect actual parameter for e.g Date -mm/dd/yyyy\n",
    "###### 2 Variables with boolean values can be assigned 0 or 1\n",
    "###### 3 NaN fields can be replaced with 0 or True or False depending on type of variable\n",
    "###### 4 fullvisitorId will be considered as independent variable for this analysis, fullvisitorId will be considered as CustomerId\n",
    "###### 5 totals.transactionRevenue will be the target value to be predicted\n",
    "###### 6 Certain variables have constant values hence they can removed from this analysis\n",
    "###### 7 Certain variables are inclusvie datatype for e.g continent -- country -- metro -- city this would allow us to drill down to specific visitorId groups \n",
    "###### 8 We will also eliminate the columns that are not in train and test dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.1 First we should import necessary packages for this analysis (Updated as required)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.io.json import json_normalize\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from plotly import tools\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "from sklearn import model_selection, preprocessing, metrics\n",
    "import lightgbm as lgb\n",
    "\n",
    "pd.options.mode.chained_assignment = None\n",
    "pd.options.display.max_columns = 999\n",
    "import datetime as datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.2 Importing raw data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<string>:2: DtypeWarning:\n",
      "\n",
      "Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 24.4 s, sys: 2.35 s, total: 26.8 s\n",
      "Wall time: 27.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "train=pd.read_csv(\"/Users/amit/Desktop/Big_data/Capstone/codelab/train.csv\")\n",
    "test=pd.read_csv(\"/Users/amit/Desktop/Big_data/Capstone/codelab/test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.3 Initial Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Checking first five row of train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channelGrouping</th>\n",
       "      <th>date</th>\n",
       "      <th>device</th>\n",
       "      <th>fullVisitorId</th>\n",
       "      <th>geoNetwork</th>\n",
       "      <th>sessionId</th>\n",
       "      <th>socialEngagementType</th>\n",
       "      <th>totals</th>\n",
       "      <th>trafficSource</th>\n",
       "      <th>visitId</th>\n",
       "      <th>visitNumber</th>\n",
       "      <th>visitStartTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>{\"browser\": \"Chrome\", \"browserVersion\": \"not a...</td>\n",
       "      <td>1131660440785968503</td>\n",
       "      <td>{\"continent\": \"Asia\", \"subContinent\": \"Western...</td>\n",
       "      <td>1131660440785968503_1472830385</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1472830385</td>\n",
       "      <td>1</td>\n",
       "      <td>1472830385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>{\"browser\": \"Firefox\", \"browserVersion\": \"not ...</td>\n",
       "      <td>377306020877927890</td>\n",
       "      <td>{\"continent\": \"Oceania\", \"subContinent\": \"Aust...</td>\n",
       "      <td>377306020877927890_1472880147</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1472880147</td>\n",
       "      <td>1</td>\n",
       "      <td>1472880147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>{\"browser\": \"Chrome\", \"browserVersion\": \"not a...</td>\n",
       "      <td>3895546263509774583</td>\n",
       "      <td>{\"continent\": \"Europe\", \"subContinent\": \"South...</td>\n",
       "      <td>3895546263509774583_1472865386</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1472865386</td>\n",
       "      <td>1</td>\n",
       "      <td>1472865386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>{\"browser\": \"UC Browser\", \"browserVersion\": \"n...</td>\n",
       "      <td>4763447161404445595</td>\n",
       "      <td>{\"continent\": \"Asia\", \"subContinent\": \"Southea...</td>\n",
       "      <td>4763447161404445595_1472881213</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1472881213</td>\n",
       "      <td>1</td>\n",
       "      <td>1472881213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>{\"browser\": \"Chrome\", \"browserVersion\": \"not a...</td>\n",
       "      <td>27294437909732085</td>\n",
       "      <td>{\"continent\": \"Europe\", \"subContinent\": \"North...</td>\n",
       "      <td>27294437909732085_1472822600</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1472822600</td>\n",
       "      <td>2</td>\n",
       "      <td>1472822600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  channelGrouping      date  \\\n",
       "0  Organic Search  20160902   \n",
       "1  Organic Search  20160902   \n",
       "2  Organic Search  20160902   \n",
       "3  Organic Search  20160902   \n",
       "4  Organic Search  20160902   \n",
       "\n",
       "                                              device        fullVisitorId  \\\n",
       "0  {\"browser\": \"Chrome\", \"browserVersion\": \"not a...  1131660440785968503   \n",
       "1  {\"browser\": \"Firefox\", \"browserVersion\": \"not ...   377306020877927890   \n",
       "2  {\"browser\": \"Chrome\", \"browserVersion\": \"not a...  3895546263509774583   \n",
       "3  {\"browser\": \"UC Browser\", \"browserVersion\": \"n...  4763447161404445595   \n",
       "4  {\"browser\": \"Chrome\", \"browserVersion\": \"not a...    27294437909732085   \n",
       "\n",
       "                                          geoNetwork  \\\n",
       "0  {\"continent\": \"Asia\", \"subContinent\": \"Western...   \n",
       "1  {\"continent\": \"Oceania\", \"subContinent\": \"Aust...   \n",
       "2  {\"continent\": \"Europe\", \"subContinent\": \"South...   \n",
       "3  {\"continent\": \"Asia\", \"subContinent\": \"Southea...   \n",
       "4  {\"continent\": \"Europe\", \"subContinent\": \"North...   \n",
       "\n",
       "                        sessionId  socialEngagementType  \\\n",
       "0  1131660440785968503_1472830385  Not Socially Engaged   \n",
       "1   377306020877927890_1472880147  Not Socially Engaged   \n",
       "2  3895546263509774583_1472865386  Not Socially Engaged   \n",
       "3  4763447161404445595_1472881213  Not Socially Engaged   \n",
       "4    27294437909732085_1472822600  Not Socially Engaged   \n",
       "\n",
       "                                              totals  \\\n",
       "0  {\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...   \n",
       "1  {\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...   \n",
       "2  {\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...   \n",
       "3  {\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...   \n",
       "4  {\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...   \n",
       "\n",
       "                                       trafficSource     visitId  visitNumber  \\\n",
       "0  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1472830385            1   \n",
       "1  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1472880147            1   \n",
       "2  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1472865386            1   \n",
       "3  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1472881213            1   \n",
       "4  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1472822600            2   \n",
       "\n",
       "   visitStartTime  \n",
       "0      1472830385  \n",
       "1      1472880147  \n",
       "2      1472865386  \n",
       "3      1472881213  \n",
       "4      1472822600  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking first five row of test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channelGrouping</th>\n",
       "      <th>date</th>\n",
       "      <th>device</th>\n",
       "      <th>fullVisitorId</th>\n",
       "      <th>geoNetwork</th>\n",
       "      <th>sessionId</th>\n",
       "      <th>socialEngagementType</th>\n",
       "      <th>totals</th>\n",
       "      <th>trafficSource</th>\n",
       "      <th>visitId</th>\n",
       "      <th>visitNumber</th>\n",
       "      <th>visitStartTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20171016</td>\n",
       "      <td>{\"browser\": \"Chrome\", \"browserVersion\": \"not a...</td>\n",
       "      <td>6167871330617112363</td>\n",
       "      <td>{\"continent\": \"Asia\", \"subContinent\": \"Southea...</td>\n",
       "      <td>6167871330617112363_1508151024</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"4\", \"pageviews\": \"4\"}</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1508151024</td>\n",
       "      <td>2</td>\n",
       "      <td>1508151024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20171016</td>\n",
       "      <td>{\"browser\": \"Chrome\", \"browserVersion\": \"not a...</td>\n",
       "      <td>0643697640977915618</td>\n",
       "      <td>{\"continent\": \"Europe\", \"subContinent\": \"South...</td>\n",
       "      <td>0643697640977915618_1508175522</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"5\", \"pageviews\": \"5\",...</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1508175522</td>\n",
       "      <td>1</td>\n",
       "      <td>1508175522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20171016</td>\n",
       "      <td>{\"browser\": \"Chrome\", \"browserVersion\": \"not a...</td>\n",
       "      <td>6059383810968229466</td>\n",
       "      <td>{\"continent\": \"Europe\", \"subContinent\": \"Weste...</td>\n",
       "      <td>6059383810968229466_1508143220</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"7\", \"pageviews\": \"7\",...</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1508143220</td>\n",
       "      <td>1</td>\n",
       "      <td>1508143220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20171016</td>\n",
       "      <td>{\"browser\": \"Safari\", \"browserVersion\": \"not a...</td>\n",
       "      <td>2376720078563423631</td>\n",
       "      <td>{\"continent\": \"Americas\", \"subContinent\": \"Nor...</td>\n",
       "      <td>2376720078563423631_1508193530</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"8\", \"pageviews\": \"4\",...</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1508193530</td>\n",
       "      <td>1</td>\n",
       "      <td>1508193530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20171016</td>\n",
       "      <td>{\"browser\": \"Safari\", \"browserVersion\": \"not a...</td>\n",
       "      <td>2314544520795440038</td>\n",
       "      <td>{\"continent\": \"Americas\", \"subContinent\": \"Nor...</td>\n",
       "      <td>2314544520795440038_1508217442</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"9\", \"pageviews\": \"4\",...</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1508217442</td>\n",
       "      <td>1</td>\n",
       "      <td>1508217442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  channelGrouping      date  \\\n",
       "0  Organic Search  20171016   \n",
       "1  Organic Search  20171016   \n",
       "2  Organic Search  20171016   \n",
       "3  Organic Search  20171016   \n",
       "4  Organic Search  20171016   \n",
       "\n",
       "                                              device        fullVisitorId  \\\n",
       "0  {\"browser\": \"Chrome\", \"browserVersion\": \"not a...  6167871330617112363   \n",
       "1  {\"browser\": \"Chrome\", \"browserVersion\": \"not a...  0643697640977915618   \n",
       "2  {\"browser\": \"Chrome\", \"browserVersion\": \"not a...  6059383810968229466   \n",
       "3  {\"browser\": \"Safari\", \"browserVersion\": \"not a...  2376720078563423631   \n",
       "4  {\"browser\": \"Safari\", \"browserVersion\": \"not a...  2314544520795440038   \n",
       "\n",
       "                                          geoNetwork  \\\n",
       "0  {\"continent\": \"Asia\", \"subContinent\": \"Southea...   \n",
       "1  {\"continent\": \"Europe\", \"subContinent\": \"South...   \n",
       "2  {\"continent\": \"Europe\", \"subContinent\": \"Weste...   \n",
       "3  {\"continent\": \"Americas\", \"subContinent\": \"Nor...   \n",
       "4  {\"continent\": \"Americas\", \"subContinent\": \"Nor...   \n",
       "\n",
       "                        sessionId  socialEngagementType  \\\n",
       "0  6167871330617112363_1508151024  Not Socially Engaged   \n",
       "1  0643697640977915618_1508175522  Not Socially Engaged   \n",
       "2  6059383810968229466_1508143220  Not Socially Engaged   \n",
       "3  2376720078563423631_1508193530  Not Socially Engaged   \n",
       "4  2314544520795440038_1508217442  Not Socially Engaged   \n",
       "\n",
       "                                              totals  \\\n",
       "0     {\"visits\": \"1\", \"hits\": \"4\", \"pageviews\": \"4\"}   \n",
       "1  {\"visits\": \"1\", \"hits\": \"5\", \"pageviews\": \"5\",...   \n",
       "2  {\"visits\": \"1\", \"hits\": \"7\", \"pageviews\": \"7\",...   \n",
       "3  {\"visits\": \"1\", \"hits\": \"8\", \"pageviews\": \"4\",...   \n",
       "4  {\"visits\": \"1\", \"hits\": \"9\", \"pageviews\": \"4\",...   \n",
       "\n",
       "                                       trafficSource     visitId  visitNumber  \\\n",
       "0  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1508151024            2   \n",
       "1  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1508175522            1   \n",
       "2  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1508143220            1   \n",
       "3  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1508193530            1   \n",
       "4  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1508217442            1   \n",
       "\n",
       "   visitStartTime  \n",
       "0      1508151024  \n",
       "1      1508175522  \n",
       "2      1508143220  \n",
       "3      1508193530  \n",
       "4      1508217442  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### As we can see there are four columns both in train and test set which has json format which we will try to transform in next step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As we can see in above snapshot of datset that four out of 12 columns have json format data which we will try to flatten and list them in individual columns "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleansing part-1\n",
    "#### Parsing the all the columns having json format data \n",
    "\n",
    "#### I found a function written a kaggler Julian reference to his work is in link below \n",
    "\n",
    "https://www.kaggle.com/julian3833/1-quick-start-read-csv-and-flatten-json-fields/notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_df(csv_path='../input/train.csv', nrows=None):\n",
    "    JSON_COLUMNS = ['device', 'geoNetwork', 'totals', 'trafficSource']\n",
    "    \n",
    "    df = pd.read_csv(csv_path, \n",
    "                     converters={column: json.loads for column in JSON_COLUMNS}, \n",
    "                     dtype={'fullVisitorId': 'str'}, # Important!!\n",
    "                     nrows=nrows)\n",
    "    \n",
    "    for column in JSON_COLUMNS:\n",
    "        column_as_df = json_normalize(df[column])\n",
    "        column_as_df.columns = [f\"{column}.{subcolumn}\" for subcolumn in column_as_df.columns]\n",
    "        df = df.drop(column, axis=1).merge(column_as_df, right_index=True, left_index=True)\n",
    "    print(f\"Loaded {os.path.basename(csv_path)}. Shape: {df.shape}\")\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# json_normalize: python has nice example for what it does"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](normalizeparser.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying the above created function on raw data to clean json format columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded train.csv. Shape: (903653, 55)\n",
      "Loaded test.csv. Shape: (804684, 53)\n",
      "CPU times: user 6min 19s, sys: 1min 54s, total: 8min 14s\n",
      "Wall time: 8min 47s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train01 = load_df(\"/Users/amit/Desktop/Big_data/Capstone/codelab/train.csv\")\n",
    "test01 = load_df(\"/Users/amit/Desktop/Big_data/Capstone/codelab/test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channelGrouping</th>\n",
       "      <th>date</th>\n",
       "      <th>fullVisitorId</th>\n",
       "      <th>sessionId</th>\n",
       "      <th>socialEngagementType</th>\n",
       "      <th>visitId</th>\n",
       "      <th>visitNumber</th>\n",
       "      <th>visitStartTime</th>\n",
       "      <th>device.browser</th>\n",
       "      <th>device.browserSize</th>\n",
       "      <th>device.browserVersion</th>\n",
       "      <th>device.deviceCategory</th>\n",
       "      <th>device.flashVersion</th>\n",
       "      <th>device.isMobile</th>\n",
       "      <th>device.language</th>\n",
       "      <th>device.mobileDeviceBranding</th>\n",
       "      <th>device.mobileDeviceInfo</th>\n",
       "      <th>device.mobileDeviceMarketingName</th>\n",
       "      <th>device.mobileDeviceModel</th>\n",
       "      <th>device.mobileInputSelector</th>\n",
       "      <th>device.operatingSystem</th>\n",
       "      <th>device.operatingSystemVersion</th>\n",
       "      <th>device.screenColors</th>\n",
       "      <th>device.screenResolution</th>\n",
       "      <th>geoNetwork.city</th>\n",
       "      <th>geoNetwork.cityId</th>\n",
       "      <th>geoNetwork.continent</th>\n",
       "      <th>geoNetwork.country</th>\n",
       "      <th>geoNetwork.latitude</th>\n",
       "      <th>geoNetwork.longitude</th>\n",
       "      <th>geoNetwork.metro</th>\n",
       "      <th>geoNetwork.networkDomain</th>\n",
       "      <th>geoNetwork.networkLocation</th>\n",
       "      <th>geoNetwork.region</th>\n",
       "      <th>geoNetwork.subContinent</th>\n",
       "      <th>totals.bounces</th>\n",
       "      <th>totals.hits</th>\n",
       "      <th>totals.newVisits</th>\n",
       "      <th>totals.pageviews</th>\n",
       "      <th>totals.transactionRevenue</th>\n",
       "      <th>totals.visits</th>\n",
       "      <th>trafficSource.adContent</th>\n",
       "      <th>trafficSource.adwordsClickInfo.adNetworkType</th>\n",
       "      <th>trafficSource.adwordsClickInfo.criteriaParameters</th>\n",
       "      <th>trafficSource.adwordsClickInfo.gclId</th>\n",
       "      <th>trafficSource.adwordsClickInfo.isVideoAd</th>\n",
       "      <th>trafficSource.adwordsClickInfo.page</th>\n",
       "      <th>trafficSource.adwordsClickInfo.slot</th>\n",
       "      <th>trafficSource.campaign</th>\n",
       "      <th>trafficSource.campaignCode</th>\n",
       "      <th>trafficSource.isTrueDirect</th>\n",
       "      <th>trafficSource.keyword</th>\n",
       "      <th>trafficSource.medium</th>\n",
       "      <th>trafficSource.referralPath</th>\n",
       "      <th>trafficSource.source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>1131660440785968503</td>\n",
       "      <td>1131660440785968503_1472830385</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>1472830385</td>\n",
       "      <td>1</td>\n",
       "      <td>1472830385</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>desktop</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>False</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Windows</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Izmir</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>ttnet.com.tr</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Izmir</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not provided)</td>\n",
       "      <td>organic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>377306020877927890</td>\n",
       "      <td>377306020877927890_1472880147</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>1472880147</td>\n",
       "      <td>1</td>\n",
       "      <td>1472880147</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>desktop</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>False</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Macintosh</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>dodo.net.au</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Australasia</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not provided)</td>\n",
       "      <td>organic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>3895546263509774583</td>\n",
       "      <td>3895546263509774583_1472865386</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>1472865386</td>\n",
       "      <td>1</td>\n",
       "      <td>1472865386</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>desktop</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>False</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Windows</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Spain</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>unknown.unknown</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Community of Madrid</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not provided)</td>\n",
       "      <td>organic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>4763447161404445595</td>\n",
       "      <td>4763447161404445595_1472881213</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>1472881213</td>\n",
       "      <td>1</td>\n",
       "      <td>1472881213</td>\n",
       "      <td>UC Browser</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>desktop</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>False</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Linux</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>unknown.unknown</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Southeast Asia</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>google + online</td>\n",
       "      <td>organic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>27294437909732085</td>\n",
       "      <td>27294437909732085_1472822600</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>1472822600</td>\n",
       "      <td>2</td>\n",
       "      <td>1472822600</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>mobile</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>True</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Android</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Europe</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>unknown.unknown</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>(not provided)</td>\n",
       "      <td>organic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>google</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  channelGrouping      date        fullVisitorId  \\\n",
       "0  Organic Search  20160902  1131660440785968503   \n",
       "1  Organic Search  20160902   377306020877927890   \n",
       "2  Organic Search  20160902  3895546263509774583   \n",
       "3  Organic Search  20160902  4763447161404445595   \n",
       "4  Organic Search  20160902    27294437909732085   \n",
       "\n",
       "                        sessionId  socialEngagementType     visitId  \\\n",
       "0  1131660440785968503_1472830385  Not Socially Engaged  1472830385   \n",
       "1   377306020877927890_1472880147  Not Socially Engaged  1472880147   \n",
       "2  3895546263509774583_1472865386  Not Socially Engaged  1472865386   \n",
       "3  4763447161404445595_1472881213  Not Socially Engaged  1472881213   \n",
       "4    27294437909732085_1472822600  Not Socially Engaged  1472822600   \n",
       "\n",
       "   visitNumber  visitStartTime device.browser             device.browserSize  \\\n",
       "0            1      1472830385         Chrome  not available in demo dataset   \n",
       "1            1      1472880147        Firefox  not available in demo dataset   \n",
       "2            1      1472865386         Chrome  not available in demo dataset   \n",
       "3            1      1472881213     UC Browser  not available in demo dataset   \n",
       "4            2      1472822600         Chrome  not available in demo dataset   \n",
       "\n",
       "           device.browserVersion device.deviceCategory  \\\n",
       "0  not available in demo dataset               desktop   \n",
       "1  not available in demo dataset               desktop   \n",
       "2  not available in demo dataset               desktop   \n",
       "3  not available in demo dataset               desktop   \n",
       "4  not available in demo dataset                mobile   \n",
       "\n",
       "             device.flashVersion  device.isMobile  \\\n",
       "0  not available in demo dataset            False   \n",
       "1  not available in demo dataset            False   \n",
       "2  not available in demo dataset            False   \n",
       "3  not available in demo dataset            False   \n",
       "4  not available in demo dataset             True   \n",
       "\n",
       "                 device.language    device.mobileDeviceBranding  \\\n",
       "0  not available in demo dataset  not available in demo dataset   \n",
       "1  not available in demo dataset  not available in demo dataset   \n",
       "2  not available in demo dataset  not available in demo dataset   \n",
       "3  not available in demo dataset  not available in demo dataset   \n",
       "4  not available in demo dataset  not available in demo dataset   \n",
       "\n",
       "         device.mobileDeviceInfo device.mobileDeviceMarketingName  \\\n",
       "0  not available in demo dataset    not available in demo dataset   \n",
       "1  not available in demo dataset    not available in demo dataset   \n",
       "2  not available in demo dataset    not available in demo dataset   \n",
       "3  not available in demo dataset    not available in demo dataset   \n",
       "4  not available in demo dataset    not available in demo dataset   \n",
       "\n",
       "        device.mobileDeviceModel     device.mobileInputSelector  \\\n",
       "0  not available in demo dataset  not available in demo dataset   \n",
       "1  not available in demo dataset  not available in demo dataset   \n",
       "2  not available in demo dataset  not available in demo dataset   \n",
       "3  not available in demo dataset  not available in demo dataset   \n",
       "4  not available in demo dataset  not available in demo dataset   \n",
       "\n",
       "  device.operatingSystem  device.operatingSystemVersion  \\\n",
       "0                Windows  not available in demo dataset   \n",
       "1              Macintosh  not available in demo dataset   \n",
       "2                Windows  not available in demo dataset   \n",
       "3                  Linux  not available in demo dataset   \n",
       "4                Android  not available in demo dataset   \n",
       "\n",
       "             device.screenColors        device.screenResolution  \\\n",
       "0  not available in demo dataset  not available in demo dataset   \n",
       "1  not available in demo dataset  not available in demo dataset   \n",
       "2  not available in demo dataset  not available in demo dataset   \n",
       "3  not available in demo dataset  not available in demo dataset   \n",
       "4  not available in demo dataset  not available in demo dataset   \n",
       "\n",
       "                 geoNetwork.city              geoNetwork.cityId  \\\n",
       "0                          Izmir  not available in demo dataset   \n",
       "1  not available in demo dataset  not available in demo dataset   \n",
       "2                         Madrid  not available in demo dataset   \n",
       "3  not available in demo dataset  not available in demo dataset   \n",
       "4  not available in demo dataset  not available in demo dataset   \n",
       "\n",
       "  geoNetwork.continent geoNetwork.country            geoNetwork.latitude  \\\n",
       "0                 Asia             Turkey  not available in demo dataset   \n",
       "1              Oceania          Australia  not available in demo dataset   \n",
       "2               Europe              Spain  not available in demo dataset   \n",
       "3                 Asia          Indonesia  not available in demo dataset   \n",
       "4               Europe     United Kingdom  not available in demo dataset   \n",
       "\n",
       "            geoNetwork.longitude               geoNetwork.metro  \\\n",
       "0  not available in demo dataset                      (not set)   \n",
       "1  not available in demo dataset  not available in demo dataset   \n",
       "2  not available in demo dataset                      (not set)   \n",
       "3  not available in demo dataset  not available in demo dataset   \n",
       "4  not available in demo dataset  not available in demo dataset   \n",
       "\n",
       "  geoNetwork.networkDomain     geoNetwork.networkLocation  \\\n",
       "0             ttnet.com.tr  not available in demo dataset   \n",
       "1              dodo.net.au  not available in demo dataset   \n",
       "2          unknown.unknown  not available in demo dataset   \n",
       "3          unknown.unknown  not available in demo dataset   \n",
       "4          unknown.unknown  not available in demo dataset   \n",
       "\n",
       "               geoNetwork.region geoNetwork.subContinent totals.bounces  \\\n",
       "0                          Izmir            Western Asia              1   \n",
       "1  not available in demo dataset             Australasia              1   \n",
       "2            Community of Madrid         Southern Europe              1   \n",
       "3  not available in demo dataset          Southeast Asia              1   \n",
       "4  not available in demo dataset         Northern Europe              1   \n",
       "\n",
       "  totals.hits totals.newVisits totals.pageviews totals.transactionRevenue  \\\n",
       "0           1                1                1                       NaN   \n",
       "1           1                1                1                       NaN   \n",
       "2           1                1                1                       NaN   \n",
       "3           1                1                1                       NaN   \n",
       "4           1              NaN                1                       NaN   \n",
       "\n",
       "  totals.visits trafficSource.adContent  \\\n",
       "0             1                     NaN   \n",
       "1             1                     NaN   \n",
       "2             1                     NaN   \n",
       "3             1                     NaN   \n",
       "4             1                     NaN   \n",
       "\n",
       "  trafficSource.adwordsClickInfo.adNetworkType  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3                                          NaN   \n",
       "4                                          NaN   \n",
       "\n",
       "  trafficSource.adwordsClickInfo.criteriaParameters  \\\n",
       "0                     not available in demo dataset   \n",
       "1                     not available in demo dataset   \n",
       "2                     not available in demo dataset   \n",
       "3                     not available in demo dataset   \n",
       "4                     not available in demo dataset   \n",
       "\n",
       "  trafficSource.adwordsClickInfo.gclId  \\\n",
       "0                                  NaN   \n",
       "1                                  NaN   \n",
       "2                                  NaN   \n",
       "3                                  NaN   \n",
       "4                                  NaN   \n",
       "\n",
       "  trafficSource.adwordsClickInfo.isVideoAd  \\\n",
       "0                                      NaN   \n",
       "1                                      NaN   \n",
       "2                                      NaN   \n",
       "3                                      NaN   \n",
       "4                                      NaN   \n",
       "\n",
       "  trafficSource.adwordsClickInfo.page trafficSource.adwordsClickInfo.slot  \\\n",
       "0                                 NaN                                 NaN   \n",
       "1                                 NaN                                 NaN   \n",
       "2                                 NaN                                 NaN   \n",
       "3                                 NaN                                 NaN   \n",
       "4                                 NaN                                 NaN   \n",
       "\n",
       "  trafficSource.campaign trafficSource.campaignCode  \\\n",
       "0              (not set)                        NaN   \n",
       "1              (not set)                        NaN   \n",
       "2              (not set)                        NaN   \n",
       "3              (not set)                        NaN   \n",
       "4              (not set)                        NaN   \n",
       "\n",
       "  trafficSource.isTrueDirect trafficSource.keyword trafficSource.medium  \\\n",
       "0                        NaN        (not provided)              organic   \n",
       "1                        NaN        (not provided)              organic   \n",
       "2                        NaN        (not provided)              organic   \n",
       "3                        NaN       google + online              organic   \n",
       "4                       True        (not provided)              organic   \n",
       "\n",
       "  trafficSource.referralPath trafficSource.source  \n",
       "0                        NaN               google  \n",
       "1                        NaN               google  \n",
       "2                        NaN               google  \n",
       "3                        NaN               google  \n",
       "4                        NaN               google  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train01.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As we can see the data is transformed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channelGrouping</th>\n",
       "      <th>date</th>\n",
       "      <th>fullVisitorId</th>\n",
       "      <th>sessionId</th>\n",
       "      <th>socialEngagementType</th>\n",
       "      <th>visitId</th>\n",
       "      <th>visitNumber</th>\n",
       "      <th>visitStartTime</th>\n",
       "      <th>device.browser</th>\n",
       "      <th>device.browserSize</th>\n",
       "      <th>device.browserVersion</th>\n",
       "      <th>device.deviceCategory</th>\n",
       "      <th>device.flashVersion</th>\n",
       "      <th>device.isMobile</th>\n",
       "      <th>device.language</th>\n",
       "      <th>device.mobileDeviceBranding</th>\n",
       "      <th>device.mobileDeviceInfo</th>\n",
       "      <th>device.mobileDeviceMarketingName</th>\n",
       "      <th>device.mobileDeviceModel</th>\n",
       "      <th>device.mobileInputSelector</th>\n",
       "      <th>device.operatingSystem</th>\n",
       "      <th>device.operatingSystemVersion</th>\n",
       "      <th>device.screenColors</th>\n",
       "      <th>device.screenResolution</th>\n",
       "      <th>geoNetwork.city</th>\n",
       "      <th>geoNetwork.cityId</th>\n",
       "      <th>geoNetwork.continent</th>\n",
       "      <th>geoNetwork.country</th>\n",
       "      <th>geoNetwork.latitude</th>\n",
       "      <th>geoNetwork.longitude</th>\n",
       "      <th>geoNetwork.metro</th>\n",
       "      <th>geoNetwork.networkDomain</th>\n",
       "      <th>geoNetwork.networkLocation</th>\n",
       "      <th>geoNetwork.region</th>\n",
       "      <th>geoNetwork.subContinent</th>\n",
       "      <th>totals.bounces</th>\n",
       "      <th>totals.hits</th>\n",
       "      <th>totals.newVisits</th>\n",
       "      <th>totals.pageviews</th>\n",
       "      <th>totals.visits</th>\n",
       "      <th>trafficSource.adContent</th>\n",
       "      <th>trafficSource.adwordsClickInfo.adNetworkType</th>\n",
       "      <th>trafficSource.adwordsClickInfo.criteriaParameters</th>\n",
       "      <th>trafficSource.adwordsClickInfo.gclId</th>\n",
       "      <th>trafficSource.adwordsClickInfo.isVideoAd</th>\n",
       "      <th>trafficSource.adwordsClickInfo.page</th>\n",
       "      <th>trafficSource.adwordsClickInfo.slot</th>\n",
       "      <th>trafficSource.campaign</th>\n",
       "      <th>trafficSource.isTrueDirect</th>\n",
       "      <th>trafficSource.keyword</th>\n",
       "      <th>trafficSource.medium</th>\n",
       "      <th>trafficSource.referralPath</th>\n",
       "      <th>trafficSource.source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20171016</td>\n",
       "      <td>6167871330617112363</td>\n",
       "      <td>6167871330617112363_1508151024</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>1508151024</td>\n",
       "      <td>2</td>\n",
       "      <td>1508151024</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>desktop</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>False</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Macintosh</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>myrepublic.com.sg</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>Southeast Asia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>True</td>\n",
       "      <td>(not provided)</td>\n",
       "      <td>organic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20171016</td>\n",
       "      <td>0643697640977915618</td>\n",
       "      <td>0643697640977915618_1508175522</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>1508175522</td>\n",
       "      <td>1</td>\n",
       "      <td>1508175522</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>desktop</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>False</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Windows</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Zaragoza</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Spain</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>rima-tde.net</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Aragon</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not provided)</td>\n",
       "      <td>organic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20171016</td>\n",
       "      <td>6059383810968229466</td>\n",
       "      <td>6059383810968229466_1508143220</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>1508143220</td>\n",
       "      <td>1</td>\n",
       "      <td>1508143220</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>desktop</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>False</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Macintosh</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Europe</td>\n",
       "      <td>France</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>sfr.net</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not provided)</td>\n",
       "      <td>organic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20171016</td>\n",
       "      <td>2376720078563423631</td>\n",
       "      <td>2376720078563423631_1508193530</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>1508193530</td>\n",
       "      <td>1</td>\n",
       "      <td>1508193530</td>\n",
       "      <td>Safari</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>mobile</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>True</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>iOS</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Americas</td>\n",
       "      <td>United States</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>San Francisco-Oakland-San Jose CA</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>California</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not provided)</td>\n",
       "      <td>organic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20171016</td>\n",
       "      <td>2314544520795440038</td>\n",
       "      <td>2314544520795440038_1508217442</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>1508217442</td>\n",
       "      <td>1</td>\n",
       "      <td>1508217442</td>\n",
       "      <td>Safari</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>desktop</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>False</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Macintosh</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>Americas</td>\n",
       "      <td>United States</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>San Francisco-Oakland-San Jose CA</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>California</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(not provided)</td>\n",
       "      <td>organic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>google</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  channelGrouping      date        fullVisitorId  \\\n",
       "0  Organic Search  20171016  6167871330617112363   \n",
       "1  Organic Search  20171016  0643697640977915618   \n",
       "2  Organic Search  20171016  6059383810968229466   \n",
       "3  Organic Search  20171016  2376720078563423631   \n",
       "4  Organic Search  20171016  2314544520795440038   \n",
       "\n",
       "                        sessionId  socialEngagementType     visitId  \\\n",
       "0  6167871330617112363_1508151024  Not Socially Engaged  1508151024   \n",
       "1  0643697640977915618_1508175522  Not Socially Engaged  1508175522   \n",
       "2  6059383810968229466_1508143220  Not Socially Engaged  1508143220   \n",
       "3  2376720078563423631_1508193530  Not Socially Engaged  1508193530   \n",
       "4  2314544520795440038_1508217442  Not Socially Engaged  1508217442   \n",
       "\n",
       "   visitNumber  visitStartTime device.browser             device.browserSize  \\\n",
       "0            2      1508151024         Chrome  not available in demo dataset   \n",
       "1            1      1508175522         Chrome  not available in demo dataset   \n",
       "2            1      1508143220         Chrome  not available in demo dataset   \n",
       "3            1      1508193530         Safari  not available in demo dataset   \n",
       "4            1      1508217442         Safari  not available in demo dataset   \n",
       "\n",
       "           device.browserVersion device.deviceCategory  \\\n",
       "0  not available in demo dataset               desktop   \n",
       "1  not available in demo dataset               desktop   \n",
       "2  not available in demo dataset               desktop   \n",
       "3  not available in demo dataset                mobile   \n",
       "4  not available in demo dataset               desktop   \n",
       "\n",
       "             device.flashVersion  device.isMobile  \\\n",
       "0  not available in demo dataset            False   \n",
       "1  not available in demo dataset            False   \n",
       "2  not available in demo dataset            False   \n",
       "3  not available in demo dataset             True   \n",
       "4  not available in demo dataset            False   \n",
       "\n",
       "                 device.language    device.mobileDeviceBranding  \\\n",
       "0  not available in demo dataset  not available in demo dataset   \n",
       "1  not available in demo dataset  not available in demo dataset   \n",
       "2  not available in demo dataset  not available in demo dataset   \n",
       "3  not available in demo dataset  not available in demo dataset   \n",
       "4  not available in demo dataset  not available in demo dataset   \n",
       "\n",
       "         device.mobileDeviceInfo device.mobileDeviceMarketingName  \\\n",
       "0  not available in demo dataset    not available in demo dataset   \n",
       "1  not available in demo dataset    not available in demo dataset   \n",
       "2  not available in demo dataset    not available in demo dataset   \n",
       "3  not available in demo dataset    not available in demo dataset   \n",
       "4  not available in demo dataset    not available in demo dataset   \n",
       "\n",
       "        device.mobileDeviceModel     device.mobileInputSelector  \\\n",
       "0  not available in demo dataset  not available in demo dataset   \n",
       "1  not available in demo dataset  not available in demo dataset   \n",
       "2  not available in demo dataset  not available in demo dataset   \n",
       "3  not available in demo dataset  not available in demo dataset   \n",
       "4  not available in demo dataset  not available in demo dataset   \n",
       "\n",
       "  device.operatingSystem  device.operatingSystemVersion  \\\n",
       "0              Macintosh  not available in demo dataset   \n",
       "1                Windows  not available in demo dataset   \n",
       "2              Macintosh  not available in demo dataset   \n",
       "3                    iOS  not available in demo dataset   \n",
       "4              Macintosh  not available in demo dataset   \n",
       "\n",
       "             device.screenColors        device.screenResolution  \\\n",
       "0  not available in demo dataset  not available in demo dataset   \n",
       "1  not available in demo dataset  not available in demo dataset   \n",
       "2  not available in demo dataset  not available in demo dataset   \n",
       "3  not available in demo dataset  not available in demo dataset   \n",
       "4  not available in demo dataset  not available in demo dataset   \n",
       "\n",
       "                 geoNetwork.city              geoNetwork.cityId  \\\n",
       "0                      (not set)  not available in demo dataset   \n",
       "1                       Zaragoza  not available in demo dataset   \n",
       "2  not available in demo dataset  not available in demo dataset   \n",
       "3                  Mountain View  not available in demo dataset   \n",
       "4                       San Jose  not available in demo dataset   \n",
       "\n",
       "  geoNetwork.continent geoNetwork.country            geoNetwork.latitude  \\\n",
       "0                 Asia          Singapore  not available in demo dataset   \n",
       "1               Europe              Spain  not available in demo dataset   \n",
       "2               Europe             France  not available in demo dataset   \n",
       "3             Americas      United States  not available in demo dataset   \n",
       "4             Americas      United States  not available in demo dataset   \n",
       "\n",
       "            geoNetwork.longitude                   geoNetwork.metro  \\\n",
       "0  not available in demo dataset                          (not set)   \n",
       "1  not available in demo dataset                          (not set)   \n",
       "2  not available in demo dataset      not available in demo dataset   \n",
       "3  not available in demo dataset  San Francisco-Oakland-San Jose CA   \n",
       "4  not available in demo dataset  San Francisco-Oakland-San Jose CA   \n",
       "\n",
       "  geoNetwork.networkDomain     geoNetwork.networkLocation  \\\n",
       "0        myrepublic.com.sg  not available in demo dataset   \n",
       "1             rima-tde.net  not available in demo dataset   \n",
       "2                  sfr.net  not available in demo dataset   \n",
       "3                (not set)  not available in demo dataset   \n",
       "4                (not set)  not available in demo dataset   \n",
       "\n",
       "               geoNetwork.region geoNetwork.subContinent totals.bounces  \\\n",
       "0                      (not set)          Southeast Asia            NaN   \n",
       "1                         Aragon         Southern Europe            NaN   \n",
       "2  not available in demo dataset          Western Europe            NaN   \n",
       "3                     California        Northern America            NaN   \n",
       "4                     California        Northern America            NaN   \n",
       "\n",
       "  totals.hits totals.newVisits totals.pageviews totals.visits  \\\n",
       "0           4              NaN                4             1   \n",
       "1           5                1                5             1   \n",
       "2           7                1                7             1   \n",
       "3           8                1                4             1   \n",
       "4           9                1                4             1   \n",
       "\n",
       "  trafficSource.adContent trafficSource.adwordsClickInfo.adNetworkType  \\\n",
       "0                     NaN                                          NaN   \n",
       "1                     NaN                                          NaN   \n",
       "2                     NaN                                          NaN   \n",
       "3                     NaN                                          NaN   \n",
       "4                     NaN                                          NaN   \n",
       "\n",
       "  trafficSource.adwordsClickInfo.criteriaParameters  \\\n",
       "0                     not available in demo dataset   \n",
       "1                     not available in demo dataset   \n",
       "2                     not available in demo dataset   \n",
       "3                     not available in demo dataset   \n",
       "4                     not available in demo dataset   \n",
       "\n",
       "  trafficSource.adwordsClickInfo.gclId  \\\n",
       "0                                  NaN   \n",
       "1                                  NaN   \n",
       "2                                  NaN   \n",
       "3                                  NaN   \n",
       "4                                  NaN   \n",
       "\n",
       "  trafficSource.adwordsClickInfo.isVideoAd  \\\n",
       "0                                      NaN   \n",
       "1                                      NaN   \n",
       "2                                      NaN   \n",
       "3                                      NaN   \n",
       "4                                      NaN   \n",
       "\n",
       "  trafficSource.adwordsClickInfo.page trafficSource.adwordsClickInfo.slot  \\\n",
       "0                                 NaN                                 NaN   \n",
       "1                                 NaN                                 NaN   \n",
       "2                                 NaN                                 NaN   \n",
       "3                                 NaN                                 NaN   \n",
       "4                                 NaN                                 NaN   \n",
       "\n",
       "  trafficSource.campaign trafficSource.isTrueDirect trafficSource.keyword  \\\n",
       "0              (not set)                       True        (not provided)   \n",
       "1              (not set)                        NaN        (not provided)   \n",
       "2              (not set)                        NaN        (not provided)   \n",
       "3              (not set)                        NaN        (not provided)   \n",
       "4              (not set)                        NaN        (not provided)   \n",
       "\n",
       "  trafficSource.medium trafficSource.referralPath trafficSource.source  \n",
       "0              organic                        NaN               google  \n",
       "1              organic                        NaN               google  \n",
       "2              organic                        NaN               google  \n",
       "3              organic                        NaN               google  \n",
       "4              organic                        NaN               google  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test01.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### After processing parser function the size of train and test csv files are half the size now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](filesize.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will make a copy of the transformed data and reload it because jupyter notebook doesn't save the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 18s, sys: 19.9 s, total: 1min 38s\n",
      "Wall time: 1min 51s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train01.to_csv(\"train1.csv\",index=False)\n",
    "test01.to_csv(\"test1.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading processed train and test files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<string>:2: DtypeWarning:\n",
      "\n",
      "Columns (2,49) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "\n",
      "<string>:2: DtypeWarning:\n",
      "\n",
      "Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 34 s, sys: 6.24 s, total: 40.3 s\n",
      "Wall time: 41.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train02=pd.read_csv(\"/Users/amit/Desktop/Big_data/Capstone/codelab/train1.csv\")\n",
    "test02=pd.read_csv(\"/Users/amit/Desktop/Big_data/Capstone/codelab/test1.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization part-1 \n",
    "### 2.4 Nice package for looking at missing values in dataset\n",
    "###  Package is called   ==>    \"\" missingno\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bfea2c208>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(train02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bfc2f7240>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(test02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 903653 entries, 0 to 903652\n",
      "Data columns (total 55 columns):\n",
      "channelGrouping                                      903653 non-null object\n",
      "date                                                 903653 non-null object\n",
      "fullVisitorId                                        903653 non-null object\n",
      "sessionId                                            903653 non-null object\n",
      "socialEngagementType                                 903653 non-null object\n",
      "visitId                                              903653 non-null int64\n",
      "visitNumber                                          903653 non-null int64\n",
      "visitStartTime                                       903653 non-null int64\n",
      "device.browser                                       903653 non-null object\n",
      "device.browserSize                                   903653 non-null object\n",
      "device.browserVersion                                903653 non-null object\n",
      "device.deviceCategory                                903653 non-null object\n",
      "device.flashVersion                                  903653 non-null object\n",
      "device.isMobile                                      903653 non-null bool\n",
      "device.language                                      903653 non-null object\n",
      "device.mobileDeviceBranding                          903653 non-null object\n",
      "device.mobileDeviceInfo                              903653 non-null object\n",
      "device.mobileDeviceMarketingName                     903653 non-null object\n",
      "device.mobileDeviceModel                             903653 non-null object\n",
      "device.mobileInputSelector                           903653 non-null object\n",
      "device.operatingSystem                               903653 non-null object\n",
      "device.operatingSystemVersion                        903653 non-null object\n",
      "device.screenColors                                  903653 non-null object\n",
      "device.screenResolution                              903653 non-null object\n",
      "geoNetwork.city                                      903653 non-null object\n",
      "geoNetwork.cityId                                    903653 non-null object\n",
      "geoNetwork.continent                                 903653 non-null object\n",
      "geoNetwork.country                                   903653 non-null object\n",
      "geoNetwork.latitude                                  903653 non-null object\n",
      "geoNetwork.longitude                                 903653 non-null object\n",
      "geoNetwork.metro                                     903653 non-null object\n",
      "geoNetwork.networkDomain                             903653 non-null object\n",
      "geoNetwork.networkLocation                           903653 non-null object\n",
      "geoNetwork.region                                    903653 non-null object\n",
      "geoNetwork.subContinent                              903653 non-null object\n",
      "totals.bounces                                       450630 non-null float64\n",
      "totals.hits                                          903653 non-null int64\n",
      "totals.newVisits                                     703060 non-null float64\n",
      "totals.pageviews                                     903553 non-null float64\n",
      "totals.transactionRevenue                            11515 non-null float64\n",
      "totals.visits                                        903653 non-null int64\n",
      "trafficSource.adContent                              10946 non-null object\n",
      "trafficSource.adwordsClickInfo.adNetworkType         21460 non-null object\n",
      "trafficSource.adwordsClickInfo.criteriaParameters    903653 non-null object\n",
      "trafficSource.adwordsClickInfo.gclId                 21561 non-null object\n",
      "trafficSource.adwordsClickInfo.isVideoAd             21460 non-null object\n",
      "trafficSource.adwordsClickInfo.page                  21460 non-null float64\n",
      "trafficSource.adwordsClickInfo.slot                  21460 non-null object\n",
      "trafficSource.campaign                               903653 non-null object\n",
      "trafficSource.campaignCode                           1 non-null object\n",
      "trafficSource.isTrueDirect                           274005 non-null object\n",
      "trafficSource.keyword                                400724 non-null object\n",
      "trafficSource.medium                                 903653 non-null object\n",
      "trafficSource.referralPath                           330941 non-null object\n",
      "trafficSource.source                                 903653 non-null object\n",
      "dtypes: bool(1), float64(5), int64(5), object(44)\n",
      "memory usage: 373.2+ MB\n"
     ]
    }
   ],
   "source": [
    "train02.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 804684 entries, 0 to 804683\n",
      "Data columns (total 53 columns):\n",
      "channelGrouping                                      804684 non-null object\n",
      "date                                                 804684 non-null object\n",
      "fullVisitorId                                        804684 non-null object\n",
      "sessionId                                            804684 non-null object\n",
      "socialEngagementType                                 804684 non-null object\n",
      "visitId                                              804684 non-null int64\n",
      "visitNumber                                          804684 non-null int64\n",
      "visitStartTime                                       804684 non-null int64\n",
      "device.browser                                       804684 non-null object\n",
      "device.browserSize                                   804684 non-null object\n",
      "device.browserVersion                                804684 non-null object\n",
      "device.deviceCategory                                804684 non-null object\n",
      "device.flashVersion                                  804684 non-null object\n",
      "device.isMobile                                      804684 non-null bool\n",
      "device.language                                      804684 non-null object\n",
      "device.mobileDeviceBranding                          804684 non-null object\n",
      "device.mobileDeviceInfo                              804684 non-null object\n",
      "device.mobileDeviceMarketingName                     804684 non-null object\n",
      "device.mobileDeviceModel                             804684 non-null object\n",
      "device.mobileInputSelector                           804684 non-null object\n",
      "device.operatingSystem                               804684 non-null object\n",
      "device.operatingSystemVersion                        804684 non-null object\n",
      "device.screenColors                                  804684 non-null object\n",
      "device.screenResolution                              804684 non-null object\n",
      "geoNetwork.city                                      804684 non-null object\n",
      "geoNetwork.cityId                                    804684 non-null object\n",
      "geoNetwork.continent                                 804684 non-null object\n",
      "geoNetwork.country                                   804684 non-null object\n",
      "geoNetwork.latitude                                  804684 non-null object\n",
      "geoNetwork.longitude                                 804684 non-null object\n",
      "geoNetwork.metro                                     804684 non-null object\n",
      "geoNetwork.networkDomain                             804684 non-null object\n",
      "geoNetwork.networkLocation                           804684 non-null object\n",
      "geoNetwork.region                                    804684 non-null object\n",
      "geoNetwork.subContinent                              804684 non-null object\n",
      "totals.bounces                                       420948 non-null float64\n",
      "totals.hits                                          804684 non-null int64\n",
      "totals.newVisits                                     604370 non-null float64\n",
      "totals.pageviews                                     804545 non-null float64\n",
      "totals.visits                                        804684 non-null int64\n",
      "trafficSource.adContent                              53791 non-null object\n",
      "trafficSource.adwordsClickInfo.adNetworkType         53814 non-null object\n",
      "trafficSource.adwordsClickInfo.criteriaParameters    804684 non-null object\n",
      "trafficSource.adwordsClickInfo.gclId                 53862 non-null object\n",
      "trafficSource.adwordsClickInfo.isVideoAd             53814 non-null object\n",
      "trafficSource.adwordsClickInfo.page                  53814 non-null float64\n",
      "trafficSource.adwordsClickInfo.slot                  53814 non-null object\n",
      "trafficSource.campaign                               804684 non-null object\n",
      "trafficSource.isTrueDirect                           260513 non-null object\n",
      "trafficSource.keyword                                413652 non-null object\n",
      "trafficSource.medium                                 804684 non-null object\n",
      "trafficSource.referralPath                           235323 non-null object\n",
      "trafficSource.source                                 804684 non-null object\n",
      "dtypes: bool(1), float64(4), int64(5), object(43)\n",
      "memory usage: 320.0+ MB\n"
     ]
    }
   ],
   "source": [
    "test02.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As we can see there are a 10 columns with missing values which we will try to replace with numeric or logical place holders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will try to see visually how many user who visited website did buy something\n",
    "\n",
    "### Below chart explains the number of visitors actually is generating revenue for gstore(target population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train02[\"totals.transactionRevenue\"] = train02[\"totals.transactionRevenue\"].astype('float')\n",
    "gdf = train02.groupby(\"fullVisitorId\")[\"totals.transactionRevenue\"].sum().reset_index()\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(range(gdf.shape[0]), np.sort(np.log1p(gdf[\"totals.transactionRevenue\"].values)))\n",
    "plt.xlabel('index', fontsize=15)\n",
    "plt.ylabel('TransactionRevenue', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Since the number of customer's generating revenue is low we should check the strength of this target group in train and test dataset (helped us in selecting training and validation split also fine tunning Gradient Boosting Models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here we will explore upon how many non zero transactions were made have and how many were unique from them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of instances in train set with non-zero revenue :  11515  and ratio is :  0.0127427231470487\n",
      "Number of unique customers with non-zero revenue :  10114 and the ratio is :  0.013973415418283704\n"
     ]
    }
   ],
   "source": [
    "nzincome = pd.notnull(train02[\"totals.transactionRevenue\"]).sum()\n",
    "nzrevenue = (gdf[\"totals.transactionRevenue\"]>0).sum()\n",
    "print(\"Number of instances in train set with non-zero revenue : \", nzincome, \" and ratio is : \", nzincome / train02.shape[0])\n",
    "print(\"Number of unique customers with non-zero revenue : \", nzrevenue, \"and the ratio is : \", nzrevenue / gdf.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleansing part-2\n",
    "### Following function will find the constant values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.48 s, sys: 286 ms, total: 3.77 s\n",
      "Wall time: 3.45 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "const_cols = [c for c in train02.columns if train02[c].nunique(dropna=False)==1 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(const_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Above variables have constant value and we are going to drop them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique visitors in train set :  723803  out of rows :  903653\n",
      "Number of unique visitors in test set :  650489  out of rows :  804684\n",
      "Number of common visitors in train and test set :  6300\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of unique visitors in train set : \",train_df.fullVisitorId.nunique(), \" out of rows : \",train_df.shape[0])\n",
    "print(\"Number of unique visitors in test set : \",test_df.fullVisitorId.nunique(), \" out of rows : \",test_df.shape[0])\n",
    "print(\"Number of common visitors in train and test set : \",len(set(train_df.fullVisitorId.unique()).intersection(set(test_df.fullVisitorId.unique())) ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization part-2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### We will try to find the variables that are contributing towards transaction | revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the format of your plot grid:\n",
      "[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]  [ (1,3) x3,y3 ]\n",
      "[ (2,1) x4,y4 ]  [ (2,2) x5,y5 ]  [ (2,3) x6,y6 ]\n",
      "[ (3,1) x7,y7 ]  [ (3,2) x8,y8 ]  [ (3,3) x9,y9 ]\n",
      "\n"
     ]
    },
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       "        ).then(function () {return Plotly.addFrames('18030931-6980-4223-9501-1ab2c35a3fa3',{});}).then(function(){Plotly.animate('18030931-6980-4223-9501-1ab2c35a3fa3');})\n",
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      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def horizontal_bar_chart(cnt_srs, color):\n",
    "    trace = go.Bar(\n",
    "        y=cnt_srs.index[::-1],\n",
    "        x=cnt_srs.values[::-1],\n",
    "        showlegend=False,\n",
    "        orientation = 'h',\n",
    "        marker=dict(\n",
    "            color=color,\n",
    "        ),\n",
    "    )\n",
    "    return trace\n",
    "\n",
    "# Device Browser\n",
    "cnt_srs = train02.groupby('device.browser')['totals.transactionRevenue'].agg(['size', 'count', 'mean'])\n",
    "cnt_srs.columns = [\"count\", \"count of non-zero revenue\", \"mean\"]\n",
    "cnt_srs = cnt_srs.sort_values(by=\"count\", ascending=False)\n",
    "trace1 = horizontal_bar_chart(cnt_srs[\"count\"].head(10), 'rgba(50, 171, 96, 0.6)')\n",
    "trace2 = horizontal_bar_chart(cnt_srs[\"count of non-zero revenue\"].head(10), 'rgba(50, 171, 96, 0.6)')\n",
    "trace3 = horizontal_bar_chart(cnt_srs[\"mean\"].head(10), 'rgba(50, 171, 96, 0.6)')\n",
    "\n",
    "# Device Category\n",
    "cnt_srs = train02.groupby('device.deviceCategory')['totals.transactionRevenue'].agg(['size', 'count', 'mean'])\n",
    "cnt_srs.columns = [\"count\", \"count of non-zero revenue\", \"mean\"]\n",
    "cnt_srs = cnt_srs.sort_values(by=\"count\", ascending=False)\n",
    "trace4 = horizontal_bar_chart(cnt_srs[\"count\"].head(10), 'rgba(71, 58, 131, 0.8)')\n",
    "trace5 = horizontal_bar_chart(cnt_srs[\"count of non-zero revenue\"].head(10), 'rgba(71, 58, 131, 0.8)')\n",
    "trace6 = horizontal_bar_chart(cnt_srs[\"mean\"].head(10), 'rgba(71, 58, 131, 0.8)')\n",
    "\n",
    "# Operating system\n",
    "cnt_srs = train02.groupby('device.operatingSystem')['totals.transactionRevenue'].agg(['size', 'count', 'mean'])\n",
    "cnt_srs.columns = [\"count\", \"count of non-zero revenue\", \"mean\"]\n",
    "cnt_srs = cnt_srs.sort_values(by=\"count\", ascending=False)\n",
    "trace7 = horizontal_bar_chart(cnt_srs[\"count\"].head(10), 'rgba(246, 78, 139, 0.6)')\n",
    "trace8 = horizontal_bar_chart(cnt_srs[\"count of non-zero revenue\"].head(10),'rgba(246, 78, 139, 0.6)')\n",
    "trace9 = horizontal_bar_chart(cnt_srs[\"mean\"].head(10),'rgba(246, 78, 139, 0.6)')\n",
    "\n",
    "# Creating two subplots\n",
    "fig = tools.make_subplots(rows=3, cols=3, vertical_spacing=0.04, \n",
    "                          subplot_titles=[\"Device Browser - Count\", \"Device Browser - Non-zero Revenue Count\", \"Device Browser - Mean Revenue\",\n",
    "                                          \"Device Category - Count\",  \"Device Category - Non-zero Revenue Count\", \"Device Category - Mean Revenue\", \n",
    "                                          \"Device OS - Count\", \"Device OS - Non-zero Revenue Count\", \"Device OS - Mean Revenue\"])\n",
    "\n",
    "fig.append_trace(trace1, 1, 1)\n",
    "fig.append_trace(trace2, 1, 2)\n",
    "fig.append_trace(trace3, 1, 3)\n",
    "fig.append_trace(trace4, 2, 1)\n",
    "fig.append_trace(trace5, 2, 2)\n",
    "fig.append_trace(trace6, 2, 3)\n",
    "fig.append_trace(trace7, 3, 1)\n",
    "fig.append_trace(trace8, 3, 2)\n",
    "fig.append_trace(trace9, 3, 3)\n",
    "\n",
    "fig['layout'].update(height=1200, width=1200, paper_bgcolor='rgb(233,233,233)', title=\"Device Plots\")\n",
    "py.iplot(fig, filename='device-plots')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Above plot for device tells us even though Chrome is most frequently used but Firefox is generating higher revenue "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot for Device category shows that Desktop has highest impact on non zero revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot for device os shows IOS has more percentage of revenue than Android"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Following plots would allow us to check for seasonality in purchase behavior\n",
    "## This will also help us ensure that while splitting train and validation data we are not selecting samples that deviate a lot from actual population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the format of your plot grid:\n",
      "[ (1,1) x1,y1 ]\n",
      "[ (2,1) x2,y2 ]\n",
      "\n"
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       "            {\"annotations\": [{\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Date - Count\", \"x\": 0.5, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 1.0, \"yanchor\": \"bottom\", \"yref\": \"paper\"}, {\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Date - Non-zero Revenue count\", \"x\": 0.5, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 0.46, \"yanchor\": \"bottom\", \"yref\": \"paper\"}], \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.54, 1.0]}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.0, 1.0]}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.0, 0.46]}, \"height\": 800, \"width\": 800, \"paper_bgcolor\": \"rgb(233,233,233)\", \"title\": \"Date Plots\"},\n",
       "            {\"showLink\": true, \"linkText\": \"Export to plot.ly\"}\n",
       "        ).then(function () {return Plotly.addFrames('3823b8c4-c823-46bb-b00a-76dc042ec8a3',{});}).then(function(){Plotly.animate('3823b8c4-c823-46bb-b00a-76dc042ec8a3');})\n",
       "        });</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "go.bar\n",
    "def scatter_plot(cnt_srs, color):\n",
    "    trace = go.Scatter(\n",
    "        x=cnt_srs.index[::-1],\n",
    "        y=cnt_srs.values[::-1],\n",
    "        showlegend=False,\n",
    "        marker=dict(\n",
    "            color=color,\n",
    "        ),\n",
    "    )\n",
    "    return trace\n",
    "\n",
    "train02['date'] = train02['date'].apply(lambda x: datetime.date(int(str(x)[:4]), int(str(x)[4:6]), int(str(x)[6:])))\n",
    "cnt_srs = train02.groupby('date')['totals.transactionRevenue'].agg(['size', 'count'])\n",
    "cnt_srs.columns = [\"count\", \"count of non-zero revenue\"]\n",
    "cnt_srs = cnt_srs.sort_index()\n",
    "#cnt_srs.index = cnt_srs.index.astype('str')\n",
    "trace1 = scatter_plot(cnt_srs[\"count\"], 'red')\n",
    "trace2 = scatter_plot(cnt_srs[\"count of non-zero revenue\"], 'blue')\n",
    "\n",
    "fig = tools.make_subplots(rows=2, cols=1, vertical_spacing=0.08,\n",
    "                          subplot_titles=[\"Date - Count\", \"Date - Non-zero Revenue count\"])\n",
    "fig.append_trace(trace1, 1, 1)\n",
    "fig.append_trace(trace2, 2, 1)\n",
    "fig['layout'].update(height=800, width=800, paper_bgcolor='rgb(233,233,233)', title=\"Date Plots\")\n",
    "py.iplot(fig, filename='date-plots')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As we can see in above plot even though we have increase in number of visitors during month of november it is not impacting revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "            {\"height\": 400, \"paper_bgcolor\": \"rgb(233,233,233)\", \"title\": \"Dates in Test set\", \"width\": 800},\n",
       "            {\"showLink\": true, \"linkText\": \"Export to plot.ly\"}\n",
       "        ).then(function () {return Plotly.addFrames('c3e0ba98-7df7-454c-9f2f-197be3866ae1',{});}).then(function(){Plotly.animate('c3e0ba98-7df7-454c-9f2f-197be3866ae1');})\n",
       "        });</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test02['date'] = test02['date'].apply(lambda x: datetime.date(int(str(x)[:4]), int(str(x)[4:6]), int(str(x)[6:])))\n",
    "cnt_srs = test02.groupby('date')['fullVisitorId'].size()\n",
    "\n",
    "\n",
    "trace = scatter_plot(cnt_srs, 'red')\n",
    "\n",
    "layout = go.Layout(\n",
    "    height=400,\n",
    "    width=800,\n",
    "    paper_bgcolor='rgb(233,233,233)',\n",
    "    title='Dates in Test set'\n",
    ")\n",
    "\n",
    "data = [trace]\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename=\"ActivationDate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see the dates in training and test set are different in above plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geographical plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   "source": [
    "# Continent\n",
    "cnt_srs = train02.groupby('geoNetwork.continent')['totals.transactionRevenue'].agg(['size', 'count', 'mean'])\n",
    "cnt_srs.columns = [\"count\", \"count of non-zero revenue\", \"mean\"]\n",
    "cnt_srs = cnt_srs.sort_values(by=\"count\", ascending=False)\n",
    "trace1 = horizontal_bar_chart(cnt_srs[\"count\"].head(10), 'rgba(58, 71, 80, 0.6)')\n",
    "trace2 = horizontal_bar_chart(cnt_srs[\"count of non-zero revenue\"].head(10), 'rgba(58, 71, 80, 0.6)')\n",
    "trace3 = horizontal_bar_chart(cnt_srs[\"mean\"].head(10), 'rgba(58, 71, 80, 0.6)')\n",
    "\n",
    "# Sub-continent\n",
    "cnt_srs = train02.groupby('geoNetwork.subContinent')['totals.transactionRevenue'].agg(['size', 'count', 'mean'])\n",
    "cnt_srs.columns = [\"count\", \"count of non-zero revenue\", \"mean\"]\n",
    "cnt_srs = cnt_srs.sort_values(by=\"count\", ascending=False)\n",
    "trace4 = horizontal_bar_chart(cnt_srs[\"count\"], 'orange')\n",
    "trace5 = horizontal_bar_chart(cnt_srs[\"count of non-zero revenue\"], 'orange')\n",
    "trace6 = horizontal_bar_chart(cnt_srs[\"mean\"], 'orange')\n",
    "\n",
    "# Network domain\n",
    "cnt_srs = train02.groupby('geoNetwork.networkDomain')['totals.transactionRevenue'].agg(['size', 'count', 'mean'])\n",
    "cnt_srs.columns = [\"count\", \"count of non-zero revenue\", \"mean\"]\n",
    "cnt_srs = cnt_srs.sort_values(by=\"count\", ascending=False)\n",
    "trace7 = horizontal_bar_chart(cnt_srs[\"count\"].head(10), 'blue')\n",
    "trace8 = horizontal_bar_chart(cnt_srs[\"count of non-zero revenue\"].head(10), 'blue')\n",
    "trace9 = horizontal_bar_chart(cnt_srs[\"mean\"].head(10), 'blue')\n",
    "\n",
    "# Creating two subplots\n",
    "fig = tools.make_subplots(rows=3, cols=3, vertical_spacing=0.08, horizontal_spacing=0.15, \n",
    "                          subplot_titles=[\"Continent - Count\", \"Continent - Non-zero Revenue Count\", \"Continent - Mean Revenue\",\n",
    "                                          \"Sub Continent - Count\",  \"Sub Continent - Non-zero Revenue Count\", \"Sub Continent - Mean Revenue\",\n",
    "                                          \"Network Domain - Count\", \"Network Domain - Non-zero Revenue Count\", \"Network Domain - Mean Revenue\"])\n",
    "\n",
    "fig.append_trace(trace1, 1, 1)\n",
    "fig.append_trace(trace2, 1, 2)\n",
    "fig.append_trace(trace3, 1, 3)\n",
    "fig.append_trace(trace4, 2, 1)\n",
    "fig.append_trace(trace5, 2, 2)\n",
    "fig.append_trace(trace6, 2, 3)\n",
    "fig.append_trace(trace7, 3, 1)\n",
    "fig.append_trace(trace8, 3, 2)\n",
    "fig.append_trace(trace9, 3, 3)\n",
    "\n",
    "fig['layout'].update(height=1500, width=1200, paper_bgcolor='rgb(233,233,233)', title=\"Geography Plots\")\n",
    "py.iplot(fig, filename='geo-plots')"
   ]
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   "metadata": {},
   "source": [
    "### We can clearly see that America has highest impact on the revenue eventhough there are a lot of visitors from Asia and Europe"
   ]
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   "source": [
    "# Continent\n",
    "cnt_srs = train02.groupby('trafficSource.source')['totals.transactionRevenue'].agg(['size', 'count', 'mean'])\n",
    "cnt_srs.columns = [\"count\", \"count of non-zero revenue\", \"mean\"]\n",
    "cnt_srs = cnt_srs.sort_values(by=\"count\", ascending=False)\n",
    "trace1 = horizontal_bar_chart(cnt_srs[\"count\"].head(10), 'green')\n",
    "trace2 = horizontal_bar_chart(cnt_srs[\"count of non-zero revenue\"].head(10), 'green')\n",
    "trace3 = horizontal_bar_chart(cnt_srs[\"mean\"].head(10), 'green')\n",
    "\n",
    "# Sub-continent\n",
    "cnt_srs = train02.groupby('trafficSource.medium')['totals.transactionRevenue'].agg(['size', 'count', 'mean'])\n",
    "cnt_srs.columns = [\"count\", \"count of non-zero revenue\", \"mean\"]\n",
    "cnt_srs = cnt_srs.sort_values(by=\"count\", ascending=False)\n",
    "trace4 = horizontal_bar_chart(cnt_srs[\"count\"], 'purple')\n",
    "trace5 = horizontal_bar_chart(cnt_srs[\"count of non-zero revenue\"], 'purple')\n",
    "trace6 = horizontal_bar_chart(cnt_srs[\"mean\"], 'purple')\n",
    "\n",
    "# Creating two subplots\n",
    "fig = tools.make_subplots(rows=2, cols=3, vertical_spacing=0.08, horizontal_spacing=0.15, \n",
    "                          subplot_titles=[\"Traffic Source - Count\", \"Traffic Source - Non-zero Revenue Count\", \"Traffic Source - Mean Revenue\",\n",
    "                                          \"Traffic Source Medium - Count\",  \"Traffic Source Medium - Non-zero Revenue Count\", \"Traffic Source Medium - Mean Revenue\"\n",
    "                                          ])\n",
    "\n",
    "fig.append_trace(trace1, 1, 1)\n",
    "fig.append_trace(trace2, 1, 2)\n",
    "fig.append_trace(trace3, 1, 3)\n",
    "fig.append_trace(trace4, 2, 1)\n",
    "fig.append_trace(trace5, 2, 2)\n",
    "fig.append_trace(trace6, 2, 3)\n",
    "\n",
    "fig['layout'].update(height=1000, width=1200, paper_bgcolor='rgb(233,233,233)', title=\"Traffic Source Plots\")\n",
    "py.iplot(fig, filename='traffic-source-plots')"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## dfa(Double click for advertiser) is a tool campaign management for the advertisers. Which can help in tracking performance of campaign\n",
    "### And as we can see the highest meanconversion is made through the campaign \n",
    "\n",
    "### cpm( Cost-per-thousand impression) A way to bid where you pay per one thousand views. Viewable cpm ensures that you only pay when your ads can be seen. It is also recommended that you update your bids since viewable impressions are potentially more valuable \n",
    "Reference: https://support.google.com/google-ads/answer/6310?hl=en\n",
    "\n",
    "# And we can see that in above plot the cpm medium has generated relatively highest average revenue\n",
    "\n"
   ]
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       "            {\"annotations\": [{\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Total Pageviews - Count\", \"x\": 0.11666666666666665, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 1.0, \"yanchor\": \"bottom\", \"yref\": \"paper\"}, {\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Total Pageviews - Non-zero Revenue Count\", \"x\": 0.49999999999999994, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 1.0, \"yanchor\": \"bottom\", \"yref\": \"paper\"}, {\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Total Pageviews - Mean Revenue\", \"x\": 0.8833333333333333, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 1.0, \"yanchor\": \"bottom\", \"yref\": \"paper\"}, {\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Total Hits - Count\", \"x\": 0.11666666666666665, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 0.46, \"yanchor\": \"bottom\", \"yref\": \"paper\"}, {\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Total Hits - Non-zero Revenue Count\", \"x\": 0.49999999999999994, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 0.46, \"yanchor\": \"bottom\", \"yref\": \"paper\"}, {\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Total Hits - Mean Revenue\", \"x\": 0.8833333333333333, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 0.46, \"yanchor\": \"bottom\", \"yref\": \"paper\"}], \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.2333333333333333]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.54, 1.0]}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.3833333333333333, 0.6166666666666666]}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.54, 1.0]}, \"xaxis3\": {\"anchor\": \"y3\", \"domain\": [0.7666666666666666, 0.9999999999999999]}, \"yaxis3\": {\"anchor\": \"x3\", \"domain\": [0.54, 1.0]}, \"xaxis4\": {\"anchor\": \"y4\", \"domain\": [0.0, 0.2333333333333333]}, \"yaxis4\": {\"anchor\": \"x4\", \"domain\": [0.0, 0.46]}, \"xaxis5\": {\"anchor\": \"y5\", \"domain\": [0.3833333333333333, 0.6166666666666666]}, \"yaxis5\": {\"anchor\": \"x5\", \"domain\": [0.0, 0.46]}, \"xaxis6\": {\"anchor\": \"y6\", \"domain\": [0.7666666666666666, 0.9999999999999999]}, \"yaxis6\": {\"anchor\": \"x6\", \"domain\": [0.0, 0.46]}, \"height\": 1200, \"width\": 900, \"paper_bgcolor\": \"rgb(233,233,233)\", \"title\": \"Visitor Profile Plots\"},\n",
       "            {\"showLink\": true, \"linkText\": \"Export to plot.ly\"}\n",
       "        ).then(function () {return Plotly.addFrames('a86dc812-2982-43e3-aee6-190ae4d9025b',{});}).then(function(){Plotly.animate('a86dc812-2982-43e3-aee6-190ae4d9025b');})\n",
       "        });</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Page views\n",
    "cnt_srs = train_df.groupby('totals.pageviews')['totals.transactionRevenue'].agg(['size', 'count', 'mean'])\n",
    "cnt_srs.columns = [\"count\", \"count of non-zero revenue\", \"mean\"]\n",
    "cnt_srs = cnt_srs.sort_values(by=\"count\", ascending=False)\n",
    "trace1 = horizontal_bar_chart(cnt_srs[\"count\"].head(60), 'cyan')\n",
    "trace2 = horizontal_bar_chart(cnt_srs[\"count of non-zero revenue\"].head(60), 'cyan')\n",
    "trace5 = horizontal_bar_chart(cnt_srs[\"mean\"].head(60), 'cyan')\n",
    "\n",
    "# Hits\n",
    "cnt_srs = train_df.groupby('totals.hits')['totals.transactionRevenue'].agg(['size', 'count', 'mean'])\n",
    "cnt_srs.columns = [\"count\", \"count of non-zero revenue\", 'mean']\n",
    "cnt_srs = cnt_srs.sort_values(by=\"count\", ascending=False)\n",
    "trace3 = horizontal_bar_chart(cnt_srs[\"count\"].head(60), 'black')\n",
    "trace4 = horizontal_bar_chart(cnt_srs[\"count of non-zero revenue\"].head(60), 'black')\n",
    "trace6 = horizontal_bar_chart(cnt_srs[\"mean\"].head(60), 'black')\n",
    "\n",
    "# Creating two subplots\n",
    "fig = tools.make_subplots(rows=2, cols=3, vertical_spacing=0.08, horizontal_spacing=0.15, \n",
    "                          subplot_titles=[\"Total Pageviews - Count\", \"Total Pageviews - Non-zero Revenue Count\", \"Total Pageviews - Mean Revenue\",\n",
    "                                          \"Total Hits - Count\",  \"Total Hits - Non-zero Revenue Count\", \"Total Hits - Mean Revenue\"])\n",
    "\n",
    "fig.append_trace(trace1, 1, 1)\n",
    "fig.append_trace(trace2, 1, 2)\n",
    "fig.append_trace(trace5, 1, 3)\n",
    "fig.append_trace(trace3, 2, 1)\n",
    "fig.append_trace(trace4, 2, 2)\n",
    "fig.append_trace(trace6, 2, 3)\n",
    "\n",
    "fig['layout'].update(height=1200, width=900, paper_bgcolor='rgb(233,233,233)', title=\"Visitor Profile Plots\")\n",
    "py.iplot(fig, filename='visitor-profile-plots')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Above plot is self explanatory but still I will try to interpret it in my words: We can see the increase in non zero revenue with increase in total pageviews and total hits "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline Model\n",
    "# Boosting: Combines the set of weak learners to form a strong rule through an iterative process. It gives higher importance to the observations that are miss classfied.\n",
    "### LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:\n",
    "\n",
    "### * Faster training speed and higher efficiency. \n",
    "### * Lower memory usage\n",
    "### * Better accuracy\n",
    "### * Support of parallel and GPU learning.\n",
    "### * Capable of handling large-scale data.\n",
    "\n",
    "For further details, please refer to Features.\n",
    "\n",
    "Benefitting from these advantages, LightGBM is being widely-used in many winning solutions of machine learning competitions.\n",
    "\n",
    "Comparison experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, parallel experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Following line of code will find columns that are in training set and not in test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variables not in test but in train :  {'trafficSource.campaignCode', 'totals.transactionRevenue'}\n"
     ]
    }
   ],
   "source": [
    "print(\"Variables not in test but in train : \", set(train_df.columns).difference(set(test_df.columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_drop = const_cols + ['sessionId']\n",
    "\n",
    "train03 = train02.drop(cols_to_drop + [\"trafficSource.campaignCode\"], axis=1)\n",
    "test03 = test02.drop(cols_to_drop, axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LabelEncoder() transforms the string, example: a=0 and ab=1 b=2 \n",
    "### In order to make a model we need to transform all the variables having values in string to number. Because we don't know the impact of string values and impact of it on the target variable this is one way of dealing with string values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "channelGrouping\n",
      "device.browser\n",
      "device.deviceCategory\n",
      "device.operatingSystem\n",
      "geoNetwork.city\n",
      "geoNetwork.continent\n",
      "geoNetwork.country\n",
      "geoNetwork.metro\n",
      "geoNetwork.networkDomain\n",
      "geoNetwork.region\n",
      "geoNetwork.subContinent\n",
      "trafficSource.adContent\n",
      "trafficSource.adwordsClickInfo.adNetworkType\n",
      "trafficSource.adwordsClickInfo.gclId\n",
      "trafficSource.adwordsClickInfo.page\n",
      "trafficSource.adwordsClickInfo.slot\n",
      "trafficSource.campaign\n",
      "trafficSource.keyword\n",
      "trafficSource.medium\n",
      "trafficSource.referralPath\n",
      "trafficSource.source\n",
      "trafficSource.adwordsClickInfo.isVideoAd\n",
      "trafficSource.isTrueDirect\n",
      "CPU times: user 2min 30s, sys: 2.64 s, total: 2min 33s\n",
      "Wall time: 2min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Impute 0 for missing target values\n",
    "train03[\"totals.transactionRevenue\"].fillna(0, inplace=True)\n",
    "train_y = train03[\"totals.transactionRevenue\"].values\n",
    "train_id = train03[\"fullVisitorId\"].values\n",
    "test_id = test03[\"fullVisitorId\"].values\n",
    "\n",
    "\n",
    "# label encode the categorical variables and convert the numerical variables to float\n",
    "cat_cols = [\"channelGrouping\", \"device.browser\", \n",
    "            \"device.deviceCategory\", \"device.operatingSystem\", \n",
    "            \"geoNetwork.city\", \"geoNetwork.continent\", \n",
    "            \"geoNetwork.country\", \"geoNetwork.metro\",\n",
    "            \"geoNetwork.networkDomain\", \"geoNetwork.region\", \n",
    "            \"geoNetwork.subContinent\", \"trafficSource.adContent\", \n",
    "            \"trafficSource.adwordsClickInfo.adNetworkType\", \n",
    "            \"trafficSource.adwordsClickInfo.gclId\", \n",
    "            \"trafficSource.adwordsClickInfo.page\", \n",
    "            \"trafficSource.adwordsClickInfo.slot\", \"trafficSource.campaign\",\n",
    "            \"trafficSource.keyword\", \"trafficSource.medium\", \n",
    "            \"trafficSource.referralPath\", \"trafficSource.source\",\n",
    "            'trafficSource.adwordsClickInfo.isVideoAd', 'trafficSource.isTrueDirect']\n",
    "for col in cat_cols:\n",
    "    print(col)\n",
    "    lbl = preprocessing.LabelEncoder()\n",
    "    lbl.fit(list(train03[col].values.astype('str')) + list(test03[col].values.astype('str')))\n",
    "    train03[col] = lbl.transform(list(train03[col].values.astype('str')))\n",
    "    test03[col] = lbl.transform(list(test03[col].values.astype('str')))\n",
    "\n",
    "\n",
    "num_cols = [\"totals.hits\", \"totals.pageviews\", \"visitNumber\", \"visitStartTime\", 'totals.bounces',  'totals.newVisits']    \n",
    "for col in num_cols:\n",
    "    train03[col] = train03[col].astype(float)\n",
    "    test03[col] = test03[col].astype(float)\n",
    "\n",
    "# Split the train dataset into development and valid based on time \n",
    "dev_df = train03[train03['date'] <= datetime.date(2017,6,30)]\n",
    "val_df = train03[train03['date'] > datetime.date(2017,6,30)]\n",
    "dev_y = np.log1p(dev_df[\"totals.transactionRevenue\"].values)\n",
    "val_y = np.log1p(val_df[\"totals.transactionRevenue\"].values)\n",
    "\n",
    "dev_X = dev_df[cat_cols + num_cols] \n",
    "val_X = val_df[cat_cols + num_cols] \n",
    "test_X = test03[cat_cols + num_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>channelGrouping</th>\n",
       "      <th>date</th>\n",
       "      <th>fullVisitorId</th>\n",
       "      <th>visitId</th>\n",
       "      <th>visitNumber</th>\n",
       "      <th>visitStartTime</th>\n",
       "      <th>device.browser</th>\n",
       "      <th>device.deviceCategory</th>\n",
       "      <th>device.isMobile</th>\n",
       "      <th>device.operatingSystem</th>\n",
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       "      <th>geoNetwork.country</th>\n",
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       "      <th>geoNetwork.networkDomain</th>\n",
       "      <th>geoNetwork.region</th>\n",
       "      <th>geoNetwork.subContinent</th>\n",
       "      <th>totals.bounces</th>\n",
       "      <th>totals.hits</th>\n",
       "      <th>totals.newVisits</th>\n",
       "      <th>totals.pageviews</th>\n",
       "      <th>totals.transactionRevenue</th>\n",
       "      <th>trafficSource.adContent</th>\n",
       "      <th>trafficSource.adwordsClickInfo.adNetworkType</th>\n",
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       "      <th>trafficSource.medium</th>\n",
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       "      <td>27</td>\n",
       "      <td>112</td>\n",
       "      <td>426</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "      <td>54456</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>113</td>\n",
       "      <td>5</td>\n",
       "      <td>2442</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2016-09-02</td>\n",
       "      <td>3895546263509774583</td>\n",
       "      <td>1472865386</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.472865e+09</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>418</td>\n",
       "      <td>4</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>31919</td>\n",
       "      <td>482</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "      <td>54456</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>113</td>\n",
       "      <td>5</td>\n",
       "      <td>2442</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2016-09-02</td>\n",
       "      <td>4763447161404445595</td>\n",
       "      <td>1472881213</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.472881e+09</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>951</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>27</td>\n",
       "      <td>31919</td>\n",
       "      <td>426</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "      <td>54456</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>677</td>\n",
       "      <td>5</td>\n",
       "      <td>2442</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2016-09-02</td>\n",
       "      <td>27294437909732085</td>\n",
       "      <td>1472822600</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.472823e+09</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>951</td>\n",
       "      <td>4</td>\n",
       "      <td>132</td>\n",
       "      <td>27</td>\n",
       "      <td>31919</td>\n",
       "      <td>426</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "      <td>54456</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>113</td>\n",
       "      <td>5</td>\n",
       "      <td>2442</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   channelGrouping        date        fullVisitorId     visitId  visitNumber  \\\n",
       "0                4  2016-09-02  1131660440785968503  1472830385          1.0   \n",
       "1                4  2016-09-02   377306020877927890  1472880147          1.0   \n",
       "2                4  2016-09-02  3895546263509774583  1472865386          1.0   \n",
       "3                4  2016-09-02  4763447161404445595  1472881213          1.0   \n",
       "4                4  2016-09-02    27294437909732085  1472822600          2.0   \n",
       "\n",
       "   visitStartTime  device.browser  device.deviceCategory  device.isMobile  \\\n",
       "0    1.472830e+09              58                      0            False   \n",
       "1    1.472880e+09              67                      0            False   \n",
       "2    1.472865e+09              58                      0            False   \n",
       "3    1.472881e+09             106                      0            False   \n",
       "4    1.472823e+09              58                      1             True   \n",
       "\n",
       "   device.operatingSystem  geoNetwork.city  geoNetwork.continent  \\\n",
       "0                      13              310                     3   \n",
       "1                      21              951                     5   \n",
       "2                      13              418                     4   \n",
       "3                      20              951                     3   \n",
       "4                       1              951                     4   \n",
       "\n",
       "   geoNetwork.country  geoNetwork.metro  geoNetwork.networkDomain  \\\n",
       "0                 125                 0                     30507   \n",
       "1                  24                27                       112   \n",
       "2                  96                 0                     31919   \n",
       "3                 222                27                     31919   \n",
       "4                 132                27                     31919   \n",
       "\n",
       "   geoNetwork.region  geoNetwork.subContinent  totals.bounces  totals.hits  \\\n",
       "0                105                       14             1.0          1.0   \n",
       "1                426                        1             1.0          1.0   \n",
       "2                482                       11             1.0          1.0   \n",
       "3                426                        8             1.0          1.0   \n",
       "4                426                        5             1.0          1.0   \n",
       "\n",
       "   totals.newVisits  totals.pageviews  totals.transactionRevenue  \\\n",
       "0               1.0               1.0                        0.0   \n",
       "1               1.0               1.0                        0.0   \n",
       "2               1.0               1.0                        0.0   \n",
       "3               1.0               1.0                        0.0   \n",
       "4               NaN               1.0                        0.0   \n",
       "\n",
       "   trafficSource.adContent  trafficSource.adwordsClickInfo.adNetworkType  \\\n",
       "0                       59                                             3   \n",
       "1                       59                                             3   \n",
       "2                       59                                             3   \n",
       "3                       59                                             3   \n",
       "4                       59                                             3   \n",
       "\n",
       "   trafficSource.adwordsClickInfo.gclId  \\\n",
       "0                                 54456   \n",
       "1                                 54456   \n",
       "2                                 54456   \n",
       "3                                 54456   \n",
       "4                                 54456   \n",
       "\n",
       "   trafficSource.adwordsClickInfo.isVideoAd  \\\n",
       "0                                         1   \n",
       "1                                         1   \n",
       "2                                         1   \n",
       "3                                         1   \n",
       "4                                         1   \n",
       "\n",
       "   trafficSource.adwordsClickInfo.page  trafficSource.adwordsClickInfo.slot  \\\n",
       "0                                    3                                    3   \n",
       "1                                    3                                    3   \n",
       "2                                    3                                    3   \n",
       "3                                    3                                    3   \n",
       "4                                    3                                    3   \n",
       "\n",
       "   trafficSource.campaign  trafficSource.isTrueDirect  trafficSource.keyword  \\\n",
       "0                      29                           1                    113   \n",
       "1                      29                           1                    113   \n",
       "2                      29                           1                    113   \n",
       "3                      29                           1                    677   \n",
       "4                      29                           0                    113   \n",
       "\n",
       "   trafficSource.medium  trafficSource.referralPath  trafficSource.source  \n",
       "0                     5                        2442                   122  \n",
       "1                     5                        2442                   122  \n",
       "2                     5                        2442                   122  \n",
       "3                     5                        2442                   122  \n",
       "4                     5                        2442                   122  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train03.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model preparation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Type one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds.\n",
      "[100]\tvalid_0's rmse: 1.74065\n",
      "[200]\tvalid_0's rmse: 1.70125\n",
      "[300]\tvalid_0's rmse: 1.68855\n",
      "[400]\tvalid_0's rmse: 1.68417\n",
      "[500]\tvalid_0's rmse: 1.68168\n",
      "[600]\tvalid_0's rmse: 1.68055\n",
      "[700]\tvalid_0's rmse: 1.67916\n",
      "[800]\tvalid_0's rmse: 1.67771\n",
      "[900]\tvalid_0's rmse: 1.67672\n",
      "[1000]\tvalid_0's rmse: 1.67574\n",
      "[1100]\tvalid_0's rmse: 1.67529\n",
      "[1200]\tvalid_0's rmse: 1.67502\n",
      "[1300]\tvalid_0's rmse: 1.67475\n",
      "[1400]\tvalid_0's rmse: 1.67452\n",
      "Early stopping, best iteration is:\n",
      "[1392]\tvalid_0's rmse: 1.67439\n",
      "CPU times: user 6min 4s, sys: 3.62 s, total: 6min 8s\n",
      "Wall time: 1min 38s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# custom function to run light gbm model\n",
    "def run_lgb(train_X, train_y, val_X, val_y, test_X):\n",
    "    params = {\n",
    "        \"objective\" : \"regression\",\n",
    "        \"metric\" : \"rmse\", \n",
    "        \"num_leaves\" : 30,\n",
    "        \"min_child_samples\" : 100,\n",
    "        \"learning_rate\" : 0.02,\n",
    "        \"bagging_fraction\" : 0.65,\n",
    "        \"feature_fraction\" : 0.5,\n",
    "        \"bagging_frequency\" : 5,\n",
    "        \"bagging_seed\" : 2018,\n",
    "        \"verbosity\" : -1\n",
    "    }\n",
    "    \n",
    "    lgtrain = lgb.Dataset(train_X, label=train_y)\n",
    "    lgval = lgb.Dataset(val_X, label=val_y)\n",
    "    model = lgb.train(params, lgtrain, 2000, valid_sets=[lgval], early_stopping_rounds=100, verbose_eval=100)\n",
    "    \n",
    "    pred_test_y = model.predict(test_X, num_iteration=model.best_iteration)\n",
    "    pred_val_y = model.predict(val_X, num_iteration=model.best_iteration)\n",
    "    return pred_test_y, model, pred_val_y\n",
    "\n",
    "# Training the model #\n",
    "pred_test, model, pred_val = run_lgb(dev_X, dev_y, val_X, val_y, test_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Light gbm is added features to Gradient boosting a opensource project by microsoft  and explanation of Gradient Boosting can be found in link below:\n",
    "https://en.wikipedia.org/wiki/Gradient_boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variation in changing the parameter with  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Type 2 (first one has slightly better prediction)\n",
    "#### When running the cells we can run either Type 1 or Type 2 otherwise we will only see the results of Type 2 as it is later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds.\n",
      "[100]\tvalid_0's rmse: 1.68492\n",
      "[200]\tvalid_0's rmse: 1.68053\n",
      "[300]\tvalid_0's rmse: 1.67964\n",
      "Early stopping, best iteration is:\n",
      "[232]\tvalid_0's rmse: 1.67945\n",
      "CPU times: user 1min 9s, sys: 2.35 s, total: 1min 11s\n",
      "Wall time: 19.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# custom function to run light gbm model\n",
    "def run_lgb(train_X, train_y, val_X, val_y, test_X):\n",
    "    params = {\n",
    "        \"objective\" : \"regression\",\n",
    "        \"metric\" : \"rmse\", \n",
    "        \"num_leaves\" : 29,\n",
    "        \"min_child_samples\" :100 ,\n",
    "        \"learning_rate\" : 0.1,\n",
    "        \"bagging_fraction\" : 0.65,\n",
    "        \"feature_fraction\" : 0.5,\n",
    "        \"bagging_frequency\" : 6,\n",
    "        \"bagging_seed\" : 2018,\n",
    "        \"verbosity\" : -1\n",
    "    }\n",
    "    \n",
    "    lgtrain = lgb.Dataset(train_X, label=train_y)\n",
    "    lgval = lgb.Dataset(val_X, label=val_y)\n",
    "    model = lgb.train(params, lgtrain, 1000, valid_sets=[lgval], early_stopping_rounds=100, verbose_eval=100)\n",
    "    \n",
    "    pred_test_y = model.predict(test_X, num_iteration=model.best_iteration)\n",
    "    pred_val_y = model.predict(val_X, num_iteration=model.best_iteration)\n",
    "    return pred_test_y, model, pred_val_y\n",
    "\n",
    "# Training the model #\n",
    "pred_test, model, pred_val = run_lgb(dev_X, dev_y, val_X, val_y, test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6962827305023227\n",
      "CPU times: user 152 ms, sys: 7.28 ms, total: 160 ms\n",
      "Wall time: 158 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn import metrics\n",
    "pred_val[pred_val<0] = 0\n",
    "val_pred_df = pd.DataFrame({\"fullVisitorId\":val_df[\"fullVisitorId\"].values})\n",
    "val_pred_df[\"transactionRevenue\"] = val_df[\"totals.transactionRevenue\"].values\n",
    "val_pred_df[\"PredictedRevenue\"] = np.expm1(pred_val)\n",
    "#print(np.sqrt(metrics.mean_squared_error(np.log1p(val_pred_df[\"transactionRevenue\"].values), np.log1p(val_pred_df[\"PredictedRevenue\"].values))))\n",
    "val_pred_df = val_pred_df.groupby(\"fullVisitorId\")[\"transactionRevenue\", \"PredictedRevenue\"].sum().reset_index()\n",
    "print(np.sqrt(metrics.mean_squared_error(np.log1p(val_pred_df[\"transactionRevenue\"].values), np.log1p(val_pred_df[\"PredictedRevenue\"].values))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predicted_values = pd.DataFrame({\"fullVisitorId\":test_id})\n",
    "pred_test[pred_test<0] = 0\n",
    "Predicted_values[\"PredictedLogRevenue\"] = np.expm1(pred_test)\n",
    "sub_df = Predicted_values.groupby(\"fullVisitorId\")[\"PredictedLogRevenue\"].sum().reset_index()\n",
    "Predicted_values.columns = [\"fullVisitorId\", \"PredictedLogRevenue\"]\n",
    "Predicted_values[\"PredictedLogRevenue\"] = np.log1p(Predicted_values[\"PredictedLogRevenue\"])\n",
    "Predicted_values.to_csv(\"baseline_lgb.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fullVisitorId</th>\n",
       "      <th>PredictedLogRevenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6167871330617112363</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0643697640977915618</td>\n",
       "      <td>0.000883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6059383810968229466</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2376720078563423631</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2314544520795440038</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fullVisitorId  PredictedLogRevenue\n",
       "0  6167871330617112363             0.000000\n",
       "1  0643697640977915618             0.000883\n",
       "2  6059383810968229466             0.000000\n",
       "3  2376720078563423631             0.000000\n",
       "4  2314544520795440038             0.000000"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Predicted_values.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "#### Following plot will show us which are the most important features generated by Lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,18))\n",
    "lgb.plot_importance(model, max_num_features=50, height=0.8, ax=ax)\n",
    "ax.grid(False)\n",
    "plt.title(\"LightGBM - Feature Importance\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "### totals.hits seems to be the most important features followed by visitStartTime\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We were able to predict how much a customer will spend with rmse: 1.6962827305023227 with Type 1 model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reference"
   ]
  },
  {
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   "source": [
    "#### https://www.kaggle.com/sudalairajkumar/simple-exploration-baseline-ga-customer-revenue/notebook"
   ]
  }
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