ad_click_pred_user_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Generation for Ad Click Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This document includes the code and exploratory analysis for generating the features which can be used in predicting the click for an ad."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Business Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate the features for Ad Click Prediction Model. Such as highly_active, weekday_biz and any other feature. i.e. properties of a user, that might be predictive of clicking, and could be used in a predictive model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One month of logs from the advertising platform for a subset of UUIDs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "logs.csv – each row represents an ad impression event."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* uuid-hashed cookie ID (you can think of it as a user ID)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* ts-timestamp of the event"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* useragent-String containing info about browser, os etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* hashed_ip-hash of the IP address"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code for EDA and Feature Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import the libraries required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import date\n",
    "from datetime import time\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_location='C:/Users/cram/Desktop/logs.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(file_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>2017-07-11 11:29:43</td>\n",
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       "      <td>2017-07-11 03:45:53</td>\n",
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       "      <td>2017-07-31 21:10:31</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...</td>\n",
       "      <td>BF4112278BB1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669477</th>\n",
       "      <td>C07625509E1D55D6D5911B0AE764239A</td>\n",
       "      <td>2017-07-31 21:10:32</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...</td>\n",
       "      <td>BF4112278BB1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669478</th>\n",
       "      <td>79F2AC5F5303D1EAD6F58D2CE744A434</td>\n",
       "      <td>2017-07-31 21:10:34</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7...</td>\n",
       "      <td>5AE405AD6B70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669479</th>\n",
       "      <td>9BE678655B247F3A6880FAE8517332BA</td>\n",
       "      <td>2017-07-31 21:10:34</td>\n",
       "      <td>Mozilla/5.0 (X11; CrOS armv7l 9460.60.0) Apple...</td>\n",
       "      <td>588776A5BB27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669480</th>\n",
       "      <td>47EB797189F898F28D730E2B1E29F06E</td>\n",
       "      <td>2017-07-31 21:10:35</td>\n",
       "      <td>Mozilla/5.0 (Linux; Android 7.0; EVA-L09 Build...</td>\n",
       "      <td>4D994F770FCD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669481</th>\n",
       "      <td>7CDC5900FC6E5916273EAE2E19CE2A6E</td>\n",
       "      <td>2017-07-31 21:10:35</td>\n",
       "      <td>Mozilla/5.0 (iPad; CPU OS 10_3_3 like Mac OS X...</td>\n",
       "      <td>775E47E10B22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669482</th>\n",
       "      <td>7CDC5900FC6E5916273EAE2E19CE2A6E</td>\n",
       "      <td>2017-07-31 21:10:33</td>\n",
       "      <td>Mozilla/5.0 (iPad; CPU OS 10_3_3 like Mac OS X...</td>\n",
       "      <td>775E47E10B22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669483</th>\n",
       "      <td>657E08C17C2AD8D73FFF3F34578F01BC</td>\n",
       "      <td>2017-07-31 21:10:34</td>\n",
       "      <td>Mozilla/5.0 (Linux; Android 7.0; SAMSUNG SM-T8...</td>\n",
       "      <td>F8E6A2CD85FB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669484</th>\n",
       "      <td>09A32CF65721CC6DBC0C8EE6F2F5C50C</td>\n",
       "      <td>2017-07-31 21:10:37</td>\n",
       "      <td>Mozilla/5.0 (iPhone; CPU iPhone OS 10_3_2 like...</td>\n",
       "      <td>FDCB623A00AC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669485</th>\n",
       "      <td>3D6FA6D79C1EF48D9B489A3DEBE2B2DE</td>\n",
       "      <td>2017-07-31 21:10:37</td>\n",
       "      <td>Mozilla/5.0 (Linux; Android 6.0; ALE-L21 Build...</td>\n",
       "      <td>922CEF60FA0E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669486</th>\n",
       "      <td>47EB797189F898F28D730E2B1E29F06E</td>\n",
       "      <td>2017-07-31 21:10:39</td>\n",
       "      <td>Mozilla/5.0 (Linux; Android 7.0; EVA-L09 Build...</td>\n",
       "      <td>4D994F770FCD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669487</th>\n",
       "      <td>E8EBDDFD40BAFF203FB08B014F9C590D</td>\n",
       "      <td>2017-07-31 21:10:39</td>\n",
       "      <td>Mozilla/5.0 (iPad; CPU OS 10_3_3 like Mac OS X...</td>\n",
       "      <td>D47DD1851ED1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669488</th>\n",
       "      <td>5C8924BED376E31AADFE5DD7250F0C87</td>\n",
       "      <td>2017-07-31 21:10:38</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...</td>\n",
       "      <td>5829AE550814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669489</th>\n",
       "      <td>50378F4DDC47FD654FBC9422098FEF2E</td>\n",
       "      <td>2017-07-31 21:10:40</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...</td>\n",
       "      <td>E1FA065ED7EF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669490</th>\n",
       "      <td>2EFD36AEFC7E94BA819819795F2153F8</td>\n",
       "      <td>2017-07-31 21:10:44</td>\n",
       "      <td>Mozilla/5.0 (iPad; CPU OS 9_0 like Mac OS X) A...</td>\n",
       "      <td>3FEF22DA6A46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>669491 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    uuid                   ts  \\\n",
       "0       313908E1F6825D28ADF3FCE451E5B5E5  2017-07-11 09:31:44   \n",
       "1       C7F60E6140A59120D9C9854CA87758DF  2017-07-11 23:24:43   \n",
       "2       2DC20DA3585AEDFD846E8679AE5C14C7  2017-07-11 11:29:40   \n",
       "3       1C8B0E355480105C5C5B8B466399155F  2017-07-11 16:58:49   \n",
       "4       86F243798AE16A55AFC1D3293279CCD5  2017-07-11 15:26:57   \n",
       "5       42F14D0B0C6D827EDE671491904B5EBD  2017-07-11 07:31:35   \n",
       "6       8DFC1B8495A5EF100CEA682AFB8C7D7C  2017-07-11 00:08:21   \n",
       "7       9D758258C16B9A597D9E31249B2DF25C  2017-07-11 19:00:12   \n",
       "8       BDBF7B79FF78AA95EBF7211B22E7E97D  2017-07-11 23:10:22   \n",
       "9       154FCB27942F7789163E67693E600D7B  2017-07-11 23:10:19   \n",
       "10      C9CDFCD161BE142946E900CBE386BEA0  2017-07-11 20:32:50   \n",
       "11      A9983854A2F5A71B23F0D0842F8E4193  2017-07-11 20:33:23   \n",
       "12      2DC20DA3585AEDFD846E8679AE5C14C7  2017-07-11 11:29:43   \n",
       "13      5854B9C8D42B04682FEE9A1F57D9ED47  2017-07-11 09:31:47   \n",
       "14      D1CFB3CA2E710A4FAB3A17ACDCB9AF98  2017-07-11 09:31:47   \n",
       "15      F63A4D95AB2EEE388E47CE229205344D  2017-07-11 09:31:50   \n",
       "16      FFB07019857CF4EF51E33108A4DB2817  2017-07-11 15:26:56   \n",
       "17      5E27565BE2AD6377E216D7D4F6DAE8FB  2017-07-11 15:26:59   \n",
       "18      E1C05A1FB3C7E32E710E5EA80E42664E  2017-07-11 00:08:28   \n",
       "19      4C4C7CF0072241417563FA33DBA6E6F9  2017-07-11 03:52:54   \n",
       "20      2FE4E590650646E23F0EC122B5542BA2  2017-07-11 19:00:14   \n",
       "21      49D150255B0DBF8985935E141DDB4269  2017-07-11 19:00:15   \n",
       "22      8EC2E46DDE863A285706AC3074E0B437  2017-07-11 19:00:18   \n",
       "23      36DA5D3D311F2AF942D1F2EA52358B30  2017-07-11 23:10:29   \n",
       "24      8299E6676CC62B67C4AB11A1A24DFC04  2017-07-11 20:34:53   \n",
       "25      D8CC6A67F37B0613FCB19949EDC69100  2017-07-11 16:58:53   \n",
       "26      DAFD4523E3FFD44B14A6BB32C98368B9  2017-07-11 16:55:53   \n",
       "27      F12DF33E4280E690C11F5DA065B691FB  2017-07-11 16:55:52   \n",
       "28      A807BAC6482EBB7BE49AC9D93B4CD17D  2017-07-11 03:45:53   \n",
       "29      ADAA82FDF68F5737210EB6638DB13406  2017-07-11 19:00:18   \n",
       "...                                  ...                  ...   \n",
       "669461  2A99326F899D0B822B7C74BEA047928A  2017-07-31 21:10:05   \n",
       "669462  D23E96081170C2565092916555ED3327  2017-07-31 21:10:05   \n",
       "669463  2EFD36AEFC7E94BA819819795F2153F8  2017-07-31 21:10:06   \n",
       "669464  79580E6A74B3DC0C8C2FC00BAD2B26FF  2017-07-31 21:10:05   \n",
       "669465  CDE8D50618D0784CDB9AB32EB7AAFFB6  2017-07-31 21:10:07   \n",
       "669466  B9CD05797F741A1E2E93F50BE108F95E  2017-07-31 21:10:10   \n",
       "669467  9BE678655B247F3A6880FAE8517332BA  2017-07-31 21:10:09   \n",
       "669468  20B915A3C4321EB41796755E5CCDD330  2017-07-31 21:10:09   \n",
       "669469  47EB797189F898F28D730E2B1E29F06E  2017-07-31 21:10:18   \n",
       "669470  79F2AC5F5303D1EAD6F58D2CE744A434  2017-07-31 21:10:19   \n",
       "669471  464C311C280B9EB50E57DEA481693C71  2017-07-31 21:10:25   \n",
       "669472  A3A44F20E7115F47A175463C8981589D  2017-07-31 21:10:26   \n",
       "669473  74DFDEBF91FCE4F4A8A6B6E44202D59A  2017-07-31 21:10:26   \n",
       "669474  47EB797189F898F28D730E2B1E29F06E  2017-07-31 21:10:29   \n",
       "669475  B8F1EAA96278EBA8C29F8C96D31B063D  2017-07-31 21:10:31   \n",
       "669476  C07625509E1D55D6D5911B0AE764239A  2017-07-31 21:10:31   \n",
       "669477  C07625509E1D55D6D5911B0AE764239A  2017-07-31 21:10:32   \n",
       "669478  79F2AC5F5303D1EAD6F58D2CE744A434  2017-07-31 21:10:34   \n",
       "669479  9BE678655B247F3A6880FAE8517332BA  2017-07-31 21:10:34   \n",
       "669480  47EB797189F898F28D730E2B1E29F06E  2017-07-31 21:10:35   \n",
       "669481  7CDC5900FC6E5916273EAE2E19CE2A6E  2017-07-31 21:10:35   \n",
       "669482  7CDC5900FC6E5916273EAE2E19CE2A6E  2017-07-31 21:10:33   \n",
       "669483  657E08C17C2AD8D73FFF3F34578F01BC  2017-07-31 21:10:34   \n",
       "669484  09A32CF65721CC6DBC0C8EE6F2F5C50C  2017-07-31 21:10:37   \n",
       "669485  3D6FA6D79C1EF48D9B489A3DEBE2B2DE  2017-07-31 21:10:37   \n",
       "669486  47EB797189F898F28D730E2B1E29F06E  2017-07-31 21:10:39   \n",
       "669487  E8EBDDFD40BAFF203FB08B014F9C590D  2017-07-31 21:10:39   \n",
       "669488  5C8924BED376E31AADFE5DD7250F0C87  2017-07-31 21:10:38   \n",
       "669489  50378F4DDC47FD654FBC9422098FEF2E  2017-07-31 21:10:40   \n",
       "669490  2EFD36AEFC7E94BA819819795F2153F8  2017-07-31 21:10:44   \n",
       "\n",
       "                                                useragent     hashed_ip  \n",
       "0       Mozilla/5.0 (Windows NT 6.3; Trident/7.0; Touc...  7C222A735EE6  \n",
       "1       Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...  A4ABB70D677A  \n",
       "2       Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.3...  4F32D980A86D  \n",
       "3       Mozilla/5.0 (Windows NT 6.1; WOW64; rv:54.0) G...  848DDB6C94DE  \n",
       "4       Mozilla/5.0 (Windows NT 6.1; WOW64; rv:52.0) G...  BA557225B261  \n",
       "5       Mozilla/5.0 (Linux; Android 6.0.1; SM-G925F Bu...  77FD0601D10A  \n",
       "6       Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...  EE5EB85EEB47  \n",
       "7       Mozilla/5.0 (Windows NT 6.1; Win64; x64) Apple...  94538BF81103  \n",
       "8       Mozilla/5.0 (iPhone; CPU iPhone OS 10_2_1 like...  D00B1CDC184B  \n",
       "9       Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...  E81DC286398C  \n",
       "10      Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKi...  B7662F838F37  \n",
       "11      Mozilla/5.0 (Linux; Android 5.1; HUAWEI CUN-L0...  4666C432E410  \n",
       "12      Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.3...  4F32D980A86D  \n",
       "13      Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...  3C6B0CE32D08  \n",
       "14      Mozilla/5.0 (Windows NT 6.1; Win64; x64) Apple...  E3C99284F98D  \n",
       "15      Mozilla/5.0 (Windows NT 6.0; rv:52.0) Gecko/20...  0022201AEE1B  \n",
       "16      Mozilla/5.0 (iPhone; CPU iPhone OS 10_3_2 like...  4488D158251F  \n",
       "17      Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...  FBF676F8F576  \n",
       "18      Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...  BEE5BCACC359  \n",
       "19      Mozilla/5.0 (Linux; Android 7.0; SM-A310F Buil...  ECFC86E9805E  \n",
       "20      Mozilla/5.0 (Linux; Android 5.1.1; LIFETAB_S10...  BA9516D221CF  \n",
       "21      Mozilla/5.0 (Windows NT 10.0; WOW64; rv:54.0) ...  109F4923E907  \n",
       "22      Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebK...  5A2D90809EF0  \n",
       "23      Mozilla/5.0 (Linux; Android 6.0.1; Lenovo YT-X...  6E7E3BD1A86E  \n",
       "24      Mozilla/5.0 (Windows NT 6.1; WOW64; rv:52.0) G...  6F6E805188E5  \n",
       "25      Mozilla/5.0 (Windows NT 6.1; Win64; x64) Apple...  CB7F4E3B4A45  \n",
       "26      Mozilla/5.0 (Linux; Android 7.0; SM-G930F Buil...  AB77BAB77E5D  \n",
       "27      Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...  BADBEEDB31BB  \n",
       "28      Mozilla/5.0 (Linux; Android 6.0.1; SM-T555 Bui...  FD7967A03A73  \n",
       "29      Mozilla/5.0 (Windows NT 6.1; Win64; x64) Apple...  1E86C2654B08  \n",
       "...                                                   ...           ...  \n",
       "669461  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6...  52549D9B2E74  \n",
       "669462  Mozilla/5.0 (X11; CrOS x86_64 9460.73.0) Apple...  6DE350519F60  \n",
       "669463  Mozilla/5.0 (iPad; CPU OS 9_0 like Mac OS X) A...  3FEF22DA6A46  \n",
       "669464  Mozilla/5.0 (iPhone; CPU iPhone OS 10_3_3 like...  275522A3CCDB  \n",
       "669465  Mozilla/5.0 (Windows NT 6.3; Trident/7.0; Touc...  66D5B7C60C81  \n",
       "669466  Mozilla/5.0 (iPad; CPU OS 10_0_2 like Mac OS X...  0FA954CBF20C  \n",
       "669467  Mozilla/5.0 (X11; CrOS armv7l 9460.60.0) Apple...  588776A5BB27  \n",
       "669468  Mozilla/5.0 (iPhone; CPU iPhone OS 10_3_3 like...  2E01EA8E22DD  \n",
       "669469  Mozilla/5.0 (Linux; Android 7.0; EVA-L09 Build...  4D994F770FCD  \n",
       "669470  Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7...  5AE405AD6B70  \n",
       "669471  Mozilla/5.0 (Windows NT 10.0; WOW64; rv:54.0) ...  185BB9214379  \n",
       "669472  Mozilla/5.0 (iPad; CPU OS 10_3_2 like Mac OS X...  CCD1063895AF  \n",
       "669473  Mozilla/5.0 (Linux; Android 7.0; SAMSUNG SM-G9...  8C9E9D067026  \n",
       "669474  Mozilla/5.0 (Linux; Android 7.0; EVA-L09 Build...  4D994F770FCD  \n",
       "669475  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...  94DA622987B0  \n",
       "669476  Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...  BF4112278BB1  \n",
       "669477  Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...  BF4112278BB1  \n",
       "669478  Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7...  5AE405AD6B70  \n",
       "669479  Mozilla/5.0 (X11; CrOS armv7l 9460.60.0) Apple...  588776A5BB27  \n",
       "669480  Mozilla/5.0 (Linux; Android 7.0; EVA-L09 Build...  4D994F770FCD  \n",
       "669481  Mozilla/5.0 (iPad; CPU OS 10_3_3 like Mac OS X...  775E47E10B22  \n",
       "669482  Mozilla/5.0 (iPad; CPU OS 10_3_3 like Mac OS X...  775E47E10B22  \n",
       "669483  Mozilla/5.0 (Linux; Android 7.0; SAMSUNG SM-T8...  F8E6A2CD85FB  \n",
       "669484  Mozilla/5.0 (iPhone; CPU iPhone OS 10_3_2 like...  FDCB623A00AC  \n",
       "669485  Mozilla/5.0 (Linux; Android 6.0; ALE-L21 Build...  922CEF60FA0E  \n",
       "669486  Mozilla/5.0 (Linux; Android 7.0; EVA-L09 Build...  4D994F770FCD  \n",
       "669487  Mozilla/5.0 (iPad; CPU OS 10_3_3 like Mac OS X...  D47DD1851ED1  \n",
       "669488  Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...  5829AE550814  \n",
       "669489  Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...  E1FA065ED7EF  \n",
       "669490  Mozilla/5.0 (iPad; CPU OS 9_0 like Mac OS X) A...  3FEF22DA6A46  \n",
       "\n",
       "[669491 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(669491, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uuid</th>\n",
       "      <th>ts</th>\n",
       "      <th>useragent</th>\n",
       "      <th>hashed_ip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>669491</td>\n",
       "      <td>669491</td>\n",
       "      <td>669473</td>\n",
       "      <td>669491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>257354</td>\n",
       "      <td>561881</td>\n",
       "      <td>17664</td>\n",
       "      <td>270797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>58360C618E66720D851BCD72D41B2746</td>\n",
       "      <td>2017-07-16 12:14:44</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...</td>\n",
       "      <td>010BB6D747D7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>923</td>\n",
       "      <td>13</td>\n",
       "      <td>52544</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    uuid                   ts  \\\n",
       "count                             669491               669491   \n",
       "unique                            257354               561881   \n",
       "top     58360C618E66720D851BCD72D41B2746  2017-07-16 12:14:44   \n",
       "freq                                 923                   13   \n",
       "\n",
       "                                                useragent     hashed_ip  \n",
       "count                                              669473        669491  \n",
       "unique                                              17664        270797  \n",
       "top     Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...  010BB6D747D7  \n",
       "freq                                                52544           801  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert ts column into a timestamp or date format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df[\"ts\"]=df.ts.apply(lambda x:datetime.strptime(x,'%Y-%m-%d %H:%M:%S'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "uuid                 object\n",
       "ts           datetime64[ns]\n",
       "useragent            object\n",
       "hashed_ip            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the Date Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df[\"dt\"]=df.ts.apply(lambda x:x.date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "uuid                 object\n",
       "ts           datetime64[ns]\n",
       "useragent            object\n",
       "hashed_ip            object\n",
       "dt                   object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the Frequency of Events per user (All events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_events_per_user=df.groupby([\"uuid\"]).size().sort_values(ascending=False).reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>257354.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.601440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.223889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>923.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count\n",
       "count  257354.000000\n",
       "mean        2.601440\n",
       "std         8.223889\n",
       "min         1.000000\n",
       "25%         1.000000\n",
       "50%         1.000000\n",
       "75%         2.000000\n",
       "max       923.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_events_per_user.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Look at the distribution of the event counts per user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "events_per_user_distribution_df=all_events_per_user.groupby(\"count\").size().sort_values(ascending=False).reset_index(name='usersCount').sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>usersCount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eventCountsByUser</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>185374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   usersCount\n",
       "eventCountsByUser            \n",
       "1                      185374\n",
       "2                       24342\n",
       "3                       13835\n",
       "4                        7542\n",
       "5                        4996\n",
       "6                        3767\n",
       "7                        2673\n",
       "8                        2108\n",
       "9                        1704\n",
       "10                       1410"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_per_user_distribution_df.columns = ['eventCountsByUser', 'usersCount']\n",
    "events_per_user_distribution_df = events_per_user_distribution_df.set_index('eventCountsByUser').sort_index()\n",
    "events_per_user_distribution_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (12,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c646a0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gOxGxX8MxDmQcpwOnA+yxxx61r06SJEkasEEf4Y6IWcB/Bj5cpomQma9m5sqyfh/wBLAP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\nSVJFBm5JkiSpIgO3JEmSVNH/B0ZuatKyK4jPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf6b7198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "events_per_user_distribution_df.head(30).plot.bar(y='usersCount')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Get the 90th percentile value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "val_90_percentile=all_events_per_user.quantile(0.90)[\"count\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_90_percentile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the Average Frequency of Events per user at Daily Level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "daily_events_per_user=df.groupby([\"uuid\",\"dt\"]).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "avg_daily_events_per_user=daily_events_per_user.groupby(\"uuid\").mean().reset_index().sort_values(['count'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "avg_daily_events_per_user_distribution_df=avg_daily_events_per_user.groupby(\"count\").size().sort_values(ascending=False).reset_index(name='usersCount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>usersCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>195856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>20104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>9269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>4731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>1666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.333333</td>\n",
       "      <td>1605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>1508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.666667</td>\n",
       "      <td>1164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.250000</td>\n",
       "      <td>587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.666667</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.750000</td>\n",
       "      <td>397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.250000</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.200000</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3.333333</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.600000</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.800000</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>8.200000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>8.142857</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>8.133333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>8.100000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>8.066667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>9.285714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>7.947368</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>7.727273</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>7.625000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>7.363636</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>7.307692</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>8.269231</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>8.285714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>8.307692</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>8.350000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>8.444444</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>8.466667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>8.545455</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>8.631579</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>8.636364</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>8.692308</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>8.700000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>8.769231</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>8.857143</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>8.888889</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>8.909091</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>9.111111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>9.222222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>6.611111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>808 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         count  usersCount\n",
       "0     1.000000      195856\n",
       "1     2.000000       20104\n",
       "2     3.000000        9269\n",
       "3     1.500000        4731\n",
       "4     4.000000        3937\n",
       "5     5.000000        2385\n",
       "6     6.000000        1666\n",
       "7     1.333333        1605\n",
       "8     2.500000        1508\n",
       "9     1.666667        1164\n",
       "10    7.000000        1025\n",
       "11    8.000000         789\n",
       "12    3.500000         676\n",
       "13    9.000000         617\n",
       "14    1.250000         587\n",
       "15    2.333333         548\n",
       "16   10.000000         501\n",
       "17    2.666667         402\n",
       "18    1.750000         397\n",
       "19   11.000000         373\n",
       "20    4.500000         346\n",
       "21   12.000000         278\n",
       "22    1.400000         263\n",
       "23    2.250000         261\n",
       "24    1.200000         242\n",
       "25    3.333333         237\n",
       "26   14.000000         233\n",
       "27   13.000000         228\n",
       "28    1.600000         220\n",
       "29    1.800000         192\n",
       "..         ...         ...\n",
       "778   8.200000           1\n",
       "779   8.142857           1\n",
       "780   8.133333           1\n",
       "781   8.100000           1\n",
       "782   8.066667           1\n",
       "783   9.285714           1\n",
       "784   7.947368           1\n",
       "785   7.727273           1\n",
       "786   7.625000           1\n",
       "787   7.400000           1\n",
       "788   7.363636           1\n",
       "789   7.307692           1\n",
       "790   8.269231           1\n",
       "791   8.285714           1\n",
       "792   8.307692           1\n",
       "793   8.350000           1\n",
       "794   8.444444           1\n",
       "795   8.466667           1\n",
       "796   8.545455           1\n",
       "797   8.631579           1\n",
       "798   8.636364           1\n",
       "799   8.692308           1\n",
       "800   8.700000           1\n",
       "801   8.769231           1\n",
       "802   8.857143           1\n",
       "803   8.888889           1\n",
       "804   8.909091           1\n",
       "805   9.111111           1\n",
       "806   9.222222           1\n",
       "807   6.611111           1\n",
       "\n",
       "[808 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_daily_events_per_user_distribution_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>usersCount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AvgDailyEventCountByUser</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.000000</th>\n",
       "      <td>195856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.066667</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.100000</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1.111111</th>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>1.125000</th>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>1.133333</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1.142857</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.166667</th>\n",
       "      <td>119</td>\n",
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       "    <tr>\n",
       "      <th>1.181818</th>\n",
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       "    <tr>\n",
       "      <th>1.200000</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          usersCount\n",
       "AvgDailyEventCountByUser            \n",
       "1.000000                      195856\n",
       "1.066667                           1\n",
       "1.100000                           2\n",
       "1.111111                           7\n",
       "1.125000                          13\n",
       "1.133333                           1\n",
       "1.142857                          51\n",
       "1.166667                         119\n",
       "1.181818                           2\n",
       "1.200000                         242"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_daily_events_per_user_distribution_df.columns = ['AvgDailyEventCountByUser', 'usersCount']\n",
    "avg_daily_events_per_user_distribution_df = avg_daily_events_per_user_distribution_df.set_index('AvgDailyEventCountByUser').sort_index()\n",
    "avg_daily_events_per_user_distribution_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12019e80>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i4Ot9P+8vA/sl2azvsrJfXyZJkiRNjA3W9IAknwb2AR6XZCndyCH7JJkPFHA98BqAqvpe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\n8pLkNoS5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12016588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_daily_events_per_user_distribution_df.head(30).plot.bar(y='usersCount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "val_90_percentile1=avg_daily_events_per_user.quantile(0.90)[\"count\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_90_percentile1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note : As per above analysis, it's difficult to define a cutoff for the average daily event counts for each user. Hence we proceed with the overall event counts for the highly_active feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### highly_active user definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* highly_active: Whether the user is highly active, i.e. has a large number of events (True/False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* If a user is having event counts greater than the 90th percentile (or 95th percentile) of the all users' overall event counts, we can call it as a highly active user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_events_per_user[\"highly_active\"]=all_events_per_user[\"count\"].apply(lambda x:x>=val_90_percentile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>257354 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    uuid highly_active\n",
       "0       58360C618E66720D851BCD72D41B2746          True\n",
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       "3       C3BF3AF36A95244CE112A523FD91508C          True\n",
       "4       483979DB7BD64575B8E73E451D6B1B2D          True\n",
       "5       3D6FA6D79C1EF48D9B489A3DEBE2B2DE          True\n",
       "6       C4D6ED9AF4EE2FDD80A01DEB50E96ECB          True\n",
       "7       F4E9C4C42660EF660AC5910329D1CC88          True\n",
       "8       40D4543E015574F1D6151FF453C0A976          True\n",
       "9       E73762C0AAF6E1E20EB04DFDE95EFB90          True\n",
       "10      132BEA873D8B2AC256644DB8AAF8E150          True\n",
       "11      4C8A81CD05A8FAA89F11A3F81E6A59E4          True\n",
       "12      9D758258C16B9A597D9E31249B2DF25C          True\n",
       "13      D5793FA9DA52A2F557ED7E0434F3CCDA          True\n",
       "14      1BE2B1EDF3A0B232ADA08C993B9EF6EC          True\n",
       "15      8558BAE43B59D4FE1786036413F8DDEA          True\n",
       "16      F2E8A5ED81952B1489DCC8006356C1B9          True\n",
       "17      18289054D5FFA67C7BC563A2989FB788          True\n",
       "18      193F1758C823BCD14EBF99EC7BD22714          True\n",
       "19      C02196FA16C4D36DC80D39574EE2C06B          True\n",
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       "21      6514EA87A741FD931D3A5E2BA71ED724          True\n",
       "22      5E0234B3DF906957A2BFD04C1B038CBE          True\n",
       "23      2E999D5A0A9765E0D7CBC9D91F4C21A2          True\n",
       "24      E5EA79DB3FDA38ED0834A42261E706CB          True\n",
       "25      B1AB582A1B909C46BC3F55B583DF4B51          True\n",
       "26      F9EA84862E6836DA64309593702A0F34          True\n",
       "27      A63531C1944724158A3E8039164405EA          True\n",
       "28      72EBE7AE241301AFCFD53F73A338B3CB          True\n",
       "29      E6F26D6D5366BD4BECB03845A76ECAD1          True\n",
       "...                                  ...           ...\n",
       "257324  9CB7770F4E985EBF1E90C5D4D748FB31         False\n",
       "257325  9CB713812450A38C1C1A0AAAF677B077         False\n",
       "257326  9CB6FF8DDF6497BA39314EE03E952E0D         False\n",
       "257327  9CBC85E8568AD8E2579294ADD4EC6FFE         False\n",
       "257328  9CBD357B3FEFF9F1D4EF2B19C7948118         False\n",
       "257329  9CC4667E6AC6BBED3F808C73198A3F2C         False\n",
       "257330  9CC10999D378133EFF8C00F4CBE397EE         False\n",
       "257331  9CC41BC9B7785A31BB1371C62565B563         False\n",
       "257332  9CC399018F57BE654D2956561BEC53A0         False\n",
       "257333  9CC38894F9FBC3702ADFDA3885290C2C         False\n",
       "257334  9CC32C77B709041D8464735D22F91F02         False\n",
       "257335  9CC2B89E6A94EB05D5237D1506EDACD4         False\n",
       "257336  9CC2373916C2A5EF11AF3B84B3BC8B5E         False\n",
       "257337  9CC227987987D298C3411F940B7B786C         False\n",
       "257338  9CC2123007C60BA65873EACF9FED2C9E         False\n",
       "257339  9CC1C223F4E628FEB0F3F84C02D8A862         False\n",
       "257340  9CC1904E47878950A2D09D01DEC1252F         False\n",
       "257341  9CC0F179322C589C0F25D149AE6A7801         False\n",
       "257342  9CBD5D3175611D8178B569769E85BD5B         False\n",
       "257343  9CC02E3813F30832CE56C83D76C21049         False\n",
       "257344  9CC02B9638EECA7A06869ACA9186330D         False\n",
       "257345  9CBFE6A76263712ACB28117125CDBADB         False\n",
       "257346  9CBFAFA9EFBABAD430A0D343A9460774         False\n",
       "257347  9CBF3A0F8EC7B4BC8D790A82316683F0         False\n",
       "257348  9CBF2AD649F5D286C44F7E05E2E636E6         False\n",
       "257349  9CBE9C80D0E9C3B3DA8C57E440512F23         False\n",
       "257350  9CBE1B39273FEBB88ADAC8D7912DBAF4         False\n",
       "257351  9CBE106F5E427C83546B2D4F3544D1A9         False\n",
       "257352  9CBDC7BA1D4F67E03366CA659DD28E23         False\n",
       "257353  0000AE02C6B52538D17DFAF66F2ACAF1         False\n",
       "\n",
       "[257354 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highly_active_df=all_events_per_user.loc[:,[\"uuid\",\"highly_active\"]]\n",
    "highly_active_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "highly_active_distribution=highly_active_df.groupby(\"highly_active\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "highly_active\n",
       "False    231093\n",
       "True      26261\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highly_active_distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x185d7160>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J65KcsTzOX8YvTvJQdz8vyVuTvOUYrBcAAI6biS0lFyS5djm+Nskr141f390Pd/c9SXYl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\nBDcAAAwS3AAAMOj/AUaVHvo/mJvEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x133f12b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "highly_active_distribution.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### weekday_biz feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Whether the user's traffic tends to occur during weekday business hours (True/False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Weekdays = Monday to Friday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Business Hours = 9:00 to 18:00 hours ( 9 am to 6 pm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekdays = [0, 1 ,2, 3, 4]\n",
    "bizhours = [9 , 18] # 9 am to 6 pm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First define the weekday_biz_hours features based on time stamp for each event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[\"weekday_biz_hours\"]=df.ts.apply(lambda x:1 if (bizhours[0]<=x.hour<=bizhours[1]) and (x.weekday() in weekdays) else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now Aggregate the data at the user level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekday_biz_df=df.loc[:,[\"uuid\",\"weekday_biz_hours\"]].groupby(\"uuid\").sum().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the weekday_biz feature based on the weekday_biz_hours counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weekday_biz_df[\"weekday_biz\"]=weekday_biz_df.weekday_biz_hours.apply(lambda x:x>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weekday_biz_final_df=weekday_biz_df.loc[:,[\"uuid\",\"weekday_biz\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uuid</th>\n",
       "      <th>weekday_biz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000AE02C6B52538D17DFAF66F2ACAF1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000CD03517160EAEDB23A93A16489AE</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000F516516CD22AC34384D65FE369B6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000FBBCD26E0934C890EF3BE71B3122</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00011B831B460DD8EF00C3214E1F794F</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>000124E7DB33683745C16181B51B9400</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00015D114458660BDCF15F7B4FA9B933</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>00017B671B96A7A28B78F965D325B8C2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00021EA3CBA4E3A56AEA6EFC3F93B723</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>00025C5FB0B4BEC9FB67F431D9BE5ECC</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>00027AC82272DABE3EAB81718C516719</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0002E175B476F44C5CE55FC07DB5E408</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0003CBA9FB298E0D493692411E2E0DB4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0003D8018B46F98E276130C255E09369</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>000405E07D66A819FE105AFE6B62B1BF</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>00043325A6C48A9A2F1B7FC28E77AE2F</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>00045B3B6042416718B4507931AC8CD6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0004616FC20B99B907DB19EF995C987B</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>00047D0BA1F5F43C45DD14685143D7F3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>00047F3C40051C863D6E06E26ACD479A</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>00049BEA4F9C6C4D2C619C50DB347ADE</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0004AD5E71D706B43F6B68DDE686F0CC</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0004C0423DA721891093195D510090FA</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0004CA8300CFF0015BC55C49322B9C2B</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0005E44EE64BC7ADBD9AF534BD46E6E2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0005F6412FF4A4CC9D6DFBCCCF2E875A</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>00060642E9EF758373D907647C53B2F6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>000632293AE9130B12AD7F9A374F008E</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>00064001923839B5B5A0068EE7FA70E6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>00064E04691BA6582FDC3C9CDF7AC2EC</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257324</th>\n",
       "      <td>FFFA75DF84162DF0D488D30BC5FB3FE8</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257325</th>\n",
       "      <td>FFFA9BC8EEB2DCEF06ED8EE22239D148</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257326</th>\n",
       "      <td>FFFA9BF0FF504A4789179DC73105E915</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257327</th>\n",
       "      <td>FFFAB418F9D91FD3940EC8557932E963</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257328</th>\n",
       "      <td>FFFAB72468AEDBF7810D85937E1E89DB</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257329</th>\n",
       "      <td>FFFAD32077540B608A2D80F45EADC9B2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257330</th>\n",
       "      <td>FFFADC672F08CF55B952335DC4A448A5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257331</th>\n",
       "      <td>FFFAE2D22F51870DE282A8C29EFF24F8</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257332</th>\n",
       "      <td>FFFAF5789E8DB9267587AC7FEDA050DE</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257333</th>\n",
       "      <td>FFFB2E4BEF34F7DA7FDADC32A685DC0B</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257334</th>\n",
       "      <td>FFFB84307FF5EACCD81A0C2306129B86</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257335</th>\n",
       "      <td>FFFB9BE5F95072981FDECF241A318DBE</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257336</th>\n",
       "      <td>FFFBF31D096C84E26FAB16397E612630</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257337</th>\n",
       "      <td>FFFC6A1F8EBC0F393044F5B64F0880B1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257338</th>\n",
       "      <td>FFFC7C79BABD1B92F4E1B53292AF41A5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257339</th>\n",
       "      <td>FFFC8869D0A7B2A21189441B45CA4621</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257340</th>\n",
       "      <td>FFFCE9C2B7A8EFA9CFAB286E9800BD6C</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257341</th>\n",
       "      <td>FFFCFD474997EF3589D56B0160F361D2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257342</th>\n",
       "      <td>FFFD497FAEF3B2681E9B676DEBA3D9F6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257343</th>\n",
       "      <td>FFFD49E9646DAB7264614965AD81AACE</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257344</th>\n",
       "      <td>FFFD8A282ABCB6B5584FA0E7312A55E8</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257345</th>\n",
       "      <td>FFFD97F233D6EA94A0F5EA5651CBC393</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257346</th>\n",
       "      <td>FFFD9D533981DB1BBD035CB5515AF05E</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257347</th>\n",
       "      <td>FFFEC667F33D0252C649D08C5E9808AA</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257348</th>\n",
       "      <td>FFFECD47E00776946754F44D506E0B6E</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257349</th>\n",
       "      <td>FFFF2D7C41EC818EE567BD98D9DFBA45</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257350</th>\n",
       "      <td>FFFF423A443D8E0814E65678A91C0F20</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257351</th>\n",
       "      <td>FFFF94353155E55E293224DF7DF4BD17</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257352</th>\n",
       "      <td>FFFFAE3493148B3ADC2101795E016B25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257353</th>\n",
       "      <td>FFFFDC6C5243CDF851C5D09F4CAD0EBE</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>257354 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    uuid weekday_biz\n",
       "0       0000AE02C6B52538D17DFAF66F2ACAF1       False\n",
       "1       0000CD03517160EAEDB23A93A16489AE       False\n",
       "2       0000F516516CD22AC34384D65FE369B6       False\n",
       "3       0000FBBCD26E0934C890EF3BE71B3122       False\n",
       "4       00011B831B460DD8EF00C3214E1F794F       False\n",
       "5       000124E7DB33683745C16181B51B9400        True\n",
       "6       00015D114458660BDCF15F7B4FA9B933        True\n",
       "7       00017B671B96A7A28B78F965D325B8C2       False\n",
       "8       00021EA3CBA4E3A56AEA6EFC3F93B723       False\n",
       "9       00025C5FB0B4BEC9FB67F431D9BE5ECC        True\n",
       "10      00027AC82272DABE3EAB81718C516719       False\n",
       "11      0002E175B476F44C5CE55FC07DB5E408        True\n",
       "12      0003CBA9FB298E0D493692411E2E0DB4        True\n",
       "13      0003D8018B46F98E276130C255E09369       False\n",
       "14      000405E07D66A819FE105AFE6B62B1BF       False\n",
       "15      00043325A6C48A9A2F1B7FC28E77AE2F       False\n",
       "16      00045B3B6042416718B4507931AC8CD6        True\n",
       "17      0004616FC20B99B907DB19EF995C987B       False\n",
       "18      00047D0BA1F5F43C45DD14685143D7F3        True\n",
       "19      00047F3C40051C863D6E06E26ACD479A        True\n",
       "20      00049BEA4F9C6C4D2C619C50DB347ADE        True\n",
       "21      0004AD5E71D706B43F6B68DDE686F0CC        True\n",
       "22      0004C0423DA721891093195D510090FA       False\n",
       "23      0004CA8300CFF0015BC55C49322B9C2B       False\n",
       "24      0005E44EE64BC7ADBD9AF534BD46E6E2        True\n",
       "25      0005F6412FF4A4CC9D6DFBCCCF2E875A        True\n",
       "26      00060642E9EF758373D907647C53B2F6        True\n",
       "27      000632293AE9130B12AD7F9A374F008E       False\n",
       "28      00064001923839B5B5A0068EE7FA70E6        True\n",
       "29      00064E04691BA6582FDC3C9CDF7AC2EC        True\n",
       "...                                  ...         ...\n",
       "257324  FFFA75DF84162DF0D488D30BC5FB3FE8       False\n",
       "257325  FFFA9BC8EEB2DCEF06ED8EE22239D148       False\n",
       "257326  FFFA9BF0FF504A4789179DC73105E915        True\n",
       "257327  FFFAB418F9D91FD3940EC8557932E963        True\n",
       "257328  FFFAB72468AEDBF7810D85937E1E89DB        True\n",
       "257329  FFFAD32077540B608A2D80F45EADC9B2       False\n",
       "257330  FFFADC672F08CF55B952335DC4A448A5        True\n",
       "257331  FFFAE2D22F51870DE282A8C29EFF24F8        True\n",
       "257332  FFFAF5789E8DB9267587AC7FEDA050DE       False\n",
       "257333  FFFB2E4BEF34F7DA7FDADC32A685DC0B        True\n",
       "257334  FFFB84307FF5EACCD81A0C2306129B86        True\n",
       "257335  FFFB9BE5F95072981FDECF241A318DBE        True\n",
       "257336  FFFBF31D096C84E26FAB16397E612630        True\n",
       "257337  FFFC6A1F8EBC0F393044F5B64F0880B1       False\n",
       "257338  FFFC7C79BABD1B92F4E1B53292AF41A5       False\n",
       "257339  FFFC8869D0A7B2A21189441B45CA4621        True\n",
       "257340  FFFCE9C2B7A8EFA9CFAB286E9800BD6C       False\n",
       "257341  FFFCFD474997EF3589D56B0160F361D2        True\n",
       "257342  FFFD497FAEF3B2681E9B676DEBA3D9F6        True\n",
       "257343  FFFD49E9646DAB7264614965AD81AACE        True\n",
       "257344  FFFD8A282ABCB6B5584FA0E7312A55E8       False\n",
       "257345  FFFD97F233D6EA94A0F5EA5651CBC393       False\n",
       "257346  FFFD9D533981DB1BBD035CB5515AF05E        True\n",
       "257347  FFFEC667F33D0252C649D08C5E9808AA        True\n",
       "257348  FFFECD47E00776946754F44D506E0B6E       False\n",
       "257349  FFFF2D7C41EC818EE567BD98D9DFBA45       False\n",
       "257350  FFFF423A443D8E0814E65678A91C0F20       False\n",
       "257351  FFFF94353155E55E293224DF7DF4BD17       False\n",
       "257352  FFFFAE3493148B3ADC2101795E016B25        True\n",
       "257353  FFFFDC6C5243CDF851C5D09F4CAD0EBE        True\n",
       "\n",
       "[257354 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_biz_final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekday_biz_distribution=weekday_biz_final_df.groupby(\"weekday_biz\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekday_biz\n",
       "False    145637\n",
       "True     111717\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_biz_distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1486ca58>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3cv8VAVYfgRtg9fhwku+ajjcneW5VPXuqfTrJzyR5WXe/MAurxT8+nX9rkrO7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13145358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekday_biz_distribution.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other feature - Recency (In terms of : No. of Days since last event for a user)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Recency factor is an important one in the context of ad click, it identifies whether user is still attached or attracted towards the content or the ad we're offering or user has moved or liked something else. If user was active in near past, there are high chances that he will click a related ad. ( This also takes the users' memory factor into consideration. Whether he remembers past transactions/interactions/events or not )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Recency can be defined as numeric or boolean(is_recent or not ). For simplicity, here we'll go with the numeric measure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define/specify End date for Recency look-back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "end_date=\"2017-08-01 00:00:00\" # The date from which the look-back is to be done. Taking 1st August here to look back for July Events\n",
    "end_ts=datetime.strptime(end_date,'%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2017, 8, 1, 0, 0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end_ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First Get the Last Event Date/timestamp for each user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_gb=df.loc[:,[\"uuid\",\"ts\"]].groupby(\"uuid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_last_event=df_gb[\"ts\"].agg({'last_event' : np.max}).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now Calculate the Days since Last Event date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_last_event[\"days_since_last_event\"]=df_last_event.last_event.apply(lambda x:(end_ts-x).days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_last_event_final=df_last_event.loc[:,[\"uuid\",\"days_since_last_event\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uuid</th>\n",
       "      <th>days_since_last_event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000AE02C6B52538D17DFAF66F2ACAF1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000CD03517160EAEDB23A93A16489AE</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000F516516CD22AC34384D65FE369B6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000FBBCD26E0934C890EF3BE71B3122</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00011B831B460DD8EF00C3214E1F794F</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>000124E7DB33683745C16181B51B9400</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00015D114458660BDCF15F7B4FA9B933</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>00017B671B96A7A28B78F965D325B8C2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00021EA3CBA4E3A56AEA6EFC3F93B723</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>00025C5FB0B4BEC9FB67F431D9BE5ECC</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>00027AC82272DABE3EAB81718C516719</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0002E175B476F44C5CE55FC07DB5E408</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0003CBA9FB298E0D493692411E2E0DB4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0003D8018B46F98E276130C255E09369</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>000405E07D66A819FE105AFE6B62B1BF</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>00043325A6C48A9A2F1B7FC28E77AE2F</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>00045B3B6042416718B4507931AC8CD6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0004616FC20B99B907DB19EF995C987B</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>00047D0BA1F5F43C45DD14685143D7F3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>00047F3C40051C863D6E06E26ACD479A</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>00049BEA4F9C6C4D2C619C50DB347ADE</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0004AD5E71D706B43F6B68DDE686F0CC</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0004C0423DA721891093195D510090FA</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0004CA8300CFF0015BC55C49322B9C2B</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0005E44EE64BC7ADBD9AF534BD46E6E2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0005F6412FF4A4CC9D6DFBCCCF2E875A</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>00060642E9EF758373D907647C53B2F6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>000632293AE9130B12AD7F9A374F008E</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>00064001923839B5B5A0068EE7FA70E6</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>00064E04691BA6582FDC3C9CDF7AC2EC</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257324</th>\n",
       "      <td>FFFA75DF84162DF0D488D30BC5FB3FE8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257325</th>\n",
       "      <td>FFFA9BC8EEB2DCEF06ED8EE22239D148</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257326</th>\n",
       "      <td>FFFA9BF0FF504A4789179DC73105E915</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257327</th>\n",
       "      <td>FFFAB418F9D91FD3940EC8557932E963</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257328</th>\n",
       "      <td>FFFAB72468AEDBF7810D85937E1E89DB</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257329</th>\n",
       "      <td>FFFAD32077540B608A2D80F45EADC9B2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257330</th>\n",
       "      <td>FFFADC672F08CF55B952335DC4A448A5</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257331</th>\n",
       "      <td>FFFAE2D22F51870DE282A8C29EFF24F8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257332</th>\n",
       "      <td>FFFAF5789E8DB9267587AC7FEDA050DE</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257333</th>\n",
       "      <td>FFFB2E4BEF34F7DA7FDADC32A685DC0B</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257334</th>\n",
       "      <td>FFFB84307FF5EACCD81A0C2306129B86</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257335</th>\n",
       "      <td>FFFB9BE5F95072981FDECF241A318DBE</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257336</th>\n",
       "      <td>FFFBF31D096C84E26FAB16397E612630</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257337</th>\n",
       "      <td>FFFC6A1F8EBC0F393044F5B64F0880B1</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257338</th>\n",
       "      <td>FFFC7C79BABD1B92F4E1B53292AF41A5</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257339</th>\n",
       "      <td>FFFC8869D0A7B2A21189441B45CA4621</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257340</th>\n",
       "      <td>FFFCE9C2B7A8EFA9CFAB286E9800BD6C</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257341</th>\n",
       "      <td>FFFCFD474997EF3589D56B0160F361D2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257342</th>\n",
       "      <td>FFFD497FAEF3B2681E9B676DEBA3D9F6</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257343</th>\n",
       "      <td>FFFD49E9646DAB7264614965AD81AACE</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257344</th>\n",
       "      <td>FFFD8A282ABCB6B5584FA0E7312A55E8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257345</th>\n",
       "      <td>FFFD97F233D6EA94A0F5EA5651CBC393</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257346</th>\n",
       "      <td>FFFD9D533981DB1BBD035CB5515AF05E</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257347</th>\n",
       "      <td>FFFEC667F33D0252C649D08C5E9808AA</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257348</th>\n",
       "      <td>FFFECD47E00776946754F44D506E0B6E</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257349</th>\n",
       "      <td>FFFF2D7C41EC818EE567BD98D9DFBA45</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257350</th>\n",
       "      <td>FFFF423A443D8E0814E65678A91C0F20</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257351</th>\n",
       "      <td>FFFF94353155E55E293224DF7DF4BD17</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257352</th>\n",
       "      <td>FFFFAE3493148B3ADC2101795E016B25</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257353</th>\n",
       "      <td>FFFFDC6C5243CDF851C5D09F4CAD0EBE</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>257354 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    uuid  days_since_last_event\n",
       "0       0000AE02C6B52538D17DFAF66F2ACAF1                      5\n",
       "1       0000CD03517160EAEDB23A93A16489AE                      8\n",
       "2       0000F516516CD22AC34384D65FE369B6                      5\n",
       "3       0000FBBCD26E0934C890EF3BE71B3122                     15\n",
       "4       00011B831B460DD8EF00C3214E1F794F                      9\n",
       "5       000124E7DB33683745C16181B51B9400                     21\n",
       "6       00015D114458660BDCF15F7B4FA9B933                     10\n",
       "7       00017B671B96A7A28B78F965D325B8C2                     11\n",
       "8       00021EA3CBA4E3A56AEA6EFC3F93B723                     22\n",
       "9       00025C5FB0B4BEC9FB67F431D9BE5ECC                     14\n",
       "10      00027AC82272DABE3EAB81718C516719                     10\n",
       "11      0002E175B476F44C5CE55FC07DB5E408                      0\n",
       "12      0003CBA9FB298E0D493692411E2E0DB4                     11\n",
       "13      0003D8018B46F98E276130C255E09369                      7\n",
       "14      000405E07D66A819FE105AFE6B62B1BF                      2\n",
       "15      00043325A6C48A9A2F1B7FC28E77AE2F                     11\n",
       "16      00045B3B6042416718B4507931AC8CD6                      4\n",
       "17      0004616FC20B99B907DB19EF995C987B                     26\n",
       "18      00047D0BA1F5F43C45DD14685143D7F3                     17\n",
       "19      00047F3C40051C863D6E06E26ACD479A                      6\n",
       "20      00049BEA4F9C6C4D2C619C50DB347ADE                     18\n",
       "21      0004AD5E71D706B43F6B68DDE686F0CC                     11\n",
       "22      0004C0423DA721891093195D510090FA                      9\n",
       "23      0004CA8300CFF0015BC55C49322B9C2B                     14\n",
       "24      0005E44EE64BC7ADBD9AF534BD46E6E2                     18\n",
       "25      0005F6412FF4A4CC9D6DFBCCCF2E875A                     10\n",
       "26      00060642E9EF758373D907647C53B2F6                     11\n",
       "27      000632293AE9130B12AD7F9A374F008E                      4\n",
       "28      00064001923839B5B5A0068EE7FA70E6                     27\n",
       "29      00064E04691BA6582FDC3C9CDF7AC2EC                     21\n",
       "...                                  ...                    ...\n",
       "257324  FFFA75DF84162DF0D488D30BC5FB3FE8                     16\n",
       "257325  FFFA9BC8EEB2DCEF06ED8EE22239D148                      1\n",
       "257326  FFFA9BF0FF504A4789179DC73105E915                     28\n",
       "257327  FFFAB418F9D91FD3940EC8557932E963                      1\n",
       "257328  FFFAB72468AEDBF7810D85937E1E89DB                      7\n",
       "257329  FFFAD32077540B608A2D80F45EADC9B2                     27\n",
       "257330  FFFADC672F08CF55B952335DC4A448A5                     14\n",
       "257331  FFFAE2D22F51870DE282A8C29EFF24F8                      0\n",
       "257332  FFFAF5789E8DB9267587AC7FEDA050DE                      3\n",
       "257333  FFFB2E4BEF34F7DA7FDADC32A685DC0B                     10\n",
       "257334  FFFB84307FF5EACCD81A0C2306129B86                     12\n",
       "257335  FFFB9BE5F95072981FDECF241A318DBE                      7\n",
       "257336  FFFBF31D096C84E26FAB16397E612630                     10\n",
       "257337  FFFC6A1F8EBC0F393044F5B64F0880B1                     29\n",
       "257338  FFFC7C79BABD1B92F4E1B53292AF41A5                     24\n",
       "257339  FFFC8869D0A7B2A21189441B45CA4621                     11\n",
       "257340  FFFCE9C2B7A8EFA9CFAB286E9800BD6C                     22\n",
       "257341  FFFCFD474997EF3589D56B0160F361D2                     25\n",
       "257342  FFFD497FAEF3B2681E9B676DEBA3D9F6                     12\n",
       "257343  FFFD49E9646DAB7264614965AD81AACE                     11\n",
       "257344  FFFD8A282ABCB6B5584FA0E7312A55E8                      5\n",
       "257345  FFFD97F233D6EA94A0F5EA5651CBC393                     18\n",
       "257346  FFFD9D533981DB1BBD035CB5515AF05E                      4\n",
       "257347  FFFEC667F33D0252C649D08C5E9808AA                     11\n",
       "257348  FFFECD47E00776946754F44D506E0B6E                      9\n",
       "257349  FFFF2D7C41EC818EE567BD98D9DFBA45                     25\n",
       "257350  FFFF423A443D8E0814E65678A91C0F20                     21\n",
       "257351  FFFF94353155E55E293224DF7DF4BD17                     27\n",
       "257352  FFFFAE3493148B3ADC2101795E016B25                     26\n",
       "257353  FFFFDC6C5243CDF851C5D09F4CAD0EBE                     18\n",
       "\n",
       "[257354 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_last_event_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13486710>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J/8/MvSfJBoN1Vb0qq6H69u7+06n8ZFWd3d1PVNXZSZ6a6geTnLdm+LlT7eC0fXR97ZjH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\nrAEAYADBGgAABvh/OSws7lMg7qgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x147ae2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_last_event_final[\"days_since_last_event\"].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine All the Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge the 3 dataframes highly_active_df,weekday_biz_final_df,df_last_event_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_final=highly_active_df.merge(weekday_biz_final_df,on=\"uuid\").merge(df_last_event_final,on=\"uuid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<p>257354 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    uuid highly_active weekday_biz  \\\n",
       "0       58360C618E66720D851BCD72D41B2746          True        True   \n",
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       "16      F2E8A5ED81952B1489DCC8006356C1B9          True        True   \n",
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       "...                                  ...           ...         ...   \n",
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       "257325  9CB713812450A38C1C1A0AAAF677B077         False       False   \n",
       "257326  9CB6FF8DDF6497BA39314EE03E952E0D         False       False   \n",
       "257327  9CBC85E8568AD8E2579294ADD4EC6FFE         False       False   \n",
       "257328  9CBD357B3FEFF9F1D4EF2B19C7948118         False        True   \n",
       "257329  9CC4667E6AC6BBED3F808C73198A3F2C         False       False   \n",
       "257330  9CC10999D378133EFF8C00F4CBE397EE         False       False   \n",
       "257331  9CC41BC9B7785A31BB1371C62565B563         False       False   \n",
       "257332  9CC399018F57BE654D2956561BEC53A0         False       False   \n",
       "257333  9CC38894F9FBC3702ADFDA3885290C2C         False       False   \n",
       "257334  9CC32C77B709041D8464735D22F91F02         False       False   \n",
       "257335  9CC2B89E6A94EB05D5237D1506EDACD4         False        True   \n",
       "257336  9CC2373916C2A5EF11AF3B84B3BC8B5E         False        True   \n",
       "257337  9CC227987987D298C3411F940B7B786C         False       False   \n",
       "257338  9CC2123007C60BA65873EACF9FED2C9E         False       False   \n",
       "257339  9CC1C223F4E628FEB0F3F84C02D8A862         False       False   \n",
       "257340  9CC1904E47878950A2D09D01DEC1252F         False       False   \n",
       "257341  9CC0F179322C589C0F25D149AE6A7801         False        True   \n",
       "257342  9CBD5D3175611D8178B569769E85BD5B         False       False   \n",
       "257343  9CC02E3813F30832CE56C83D76C21049         False       False   \n",
       "257344  9CC02B9638EECA7A06869ACA9186330D         False        True   \n",
       "257345  9CBFE6A76263712ACB28117125CDBADB         False       False   \n",
       "257346  9CBFAFA9EFBABAD430A0D343A9460774         False       False   \n",
       "257347  9CBF3A0F8EC7B4BC8D790A82316683F0         False       False   \n",
       "257348  9CBF2AD649F5D286C44F7E05E2E636E6         False       False   \n",
       "257349  9CBE9C80D0E9C3B3DA8C57E440512F23         False       False   \n",
       "257350  9CBE1B39273FEBB88ADAC8D7912DBAF4         False       False   \n",
       "257351  9CBE106F5E427C83546B2D4F3544D1A9         False       False   \n",
       "257352  9CBDC7BA1D4F67E03366CA659DD28E23         False        True   \n",
       "257353  0000AE02C6B52538D17DFAF66F2ACAF1         False       False   \n",
       "\n",
       "        days_since_last_event  \n",
       "0                           3  \n",
       "1                           0  \n",
       "2                           0  \n",
       "3                           6  \n",
       "4                           0  \n",
       "5                           0  \n",
       "6                           1  \n",
       "7                           4  \n",
       "8                           0  \n",
       "9                           3  \n",
       "10                          0  \n",
       "11                          2  \n",
       "12                          4  \n",
       "13                         21  \n",
       "14                          0  \n",
       "15                          4  \n",
       "16                          2  \n",
       "17                         14  \n",
       "18                          7  \n",
       "19                          1  \n",
       "20                          0  \n",
       "21                          5  \n",
       "22                          0  \n",
       "23                          7  \n",
       "24                          0  \n",
       "25                          8  \n",
       "26                          8  \n",
       "27                          0  \n",
       "28                          4  \n",
       "29                          0  \n",
       "...                       ...  \n",
       "257324                      6  \n",
       "257325                     30  \n",
       "257326                      2  \n",
       "257327                      5  \n",
       "257328                     18  \n",
       "257329                     25  \n",
       "257330                      2  \n",
       "257331                      0  \n",
       "257332                      8  \n",
       "257333                     29  \n",
       "257334                      4  \n",
       "257335                     13  \n",
       "257336                     17  \n",
       "257337                      1  \n",
       "257338                     19  \n",
       "257339                      8  \n",
       "257340                      8  \n",
       "257341                     12  \n",
       "257342                     30  \n",
       "257343                     29  \n",
       "257344                     14  \n",
       "257345                     15  \n",
       "257346                     17  \n",
       "257347                     20  \n",
       "257348                      9  \n",
       "257349                      8  \n",
       "257350                     26  \n",
       "257351                     11  \n",
       "257352                     11  \n",
       "257353                      5  \n",
       "\n",
       "[257354 rows x 4 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Write the output file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_final.to_csv('output.csv', sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other possible Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Tenure of a user ( Time between first and last event)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* No. of distinct IPs ( On which a user has appeared on)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "* No. of Distinct Devices used by a user ( This can be found out by parsing the user agent header string.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Average gaps between events for a user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Preferred time slot of the user ( Weekdays or Weekend, Morning/Afternoon/Evening/Night )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "* What about the click info for these users? Are all the events related to successful ad clicks?"
   ]
  }
 ],
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