1. 1
Bisen Vikrantsingh
Kodamasimham Pridhvi
Vaibhav Singh Rajput

2.  Intro
 Dataset
 Analysis
 Approach
2

3.  The goal
 To design models
 To support web-site personalization and
 To improve the profitability of the site by increasing
customer response.
3

4. SET - I
SET - II
 Session, cookies
 Session
 ID, date, time
 Customer
 ID, visit count, gender, age, martial
status,Income, occupation, home market
value , howDidYouHearAboutUs,
HowDidYouFindUs, U.S. state
 Page View
 View count of 80+ different
pages/product, last page view,
assortment path with level
 ID, date, time
 Customer
 ID, visit count, gender, age, martial
status,Income, occupation, home market
value , howDidYouHearAboutUs,
HowDidYouFindUs, U.S. state
 Order
 Date,time, amount, tax, discount,
shipping amount, promotion code
4

5.  Questions - When given a set of page views,
 will the visitor view another page on the site or leave?
 which product brand will the visitor view in the
remainder of the session?
 characterize heavy spenders
 characterize killer pages
5

6.  Stage-I
 Data cleaning
Technology Stack:
• Python
• MYSQL
 Filtering attributes
 Load into RDBMS
 Stage-II
 Classification of users
 RFV analysis
 Classify users into potential or not
 Decision tree algorithm
 Clustering of products
 Dimension to be consider Product, age group, location, purchase amount
 Fuzzy clustering
 Find correlation
 Correlation between {advertise,gender,income,brand} and {product view/purchase}
 Stage-III
 Answer question mention in previous slides
6

7. 7