Skip to content

Latest commit

 

History

History
65 lines (40 loc) · 2.05 KB

README.md

File metadata and controls

65 lines (40 loc) · 2.05 KB
Raw

Project 36

Title: Predict whether a user will buy an item on ecommerce listing based on click events

Github link: https://github.com/abnvanand/smai-proj/

Team: Gravel (16)

  • Abhijeet Panda
  • Abhinav Anand
  • Nishant Goyal
  • Shubham Rawat

Goal

The main aim is to predict whether a user session will lead to a purchase being made.

Problem definition

What is the problem?

Given a collection of sequences of click events; click sessions. For some of the sessions, there are also buying events. The goal is hence to predict whether the user (a session) is going to buy something or not, and if he is buying, what would be the items he is going to buy.

How things will be done ?

This will be done by training models on click data set and purchase data sets.

Results of the project

What will be done?

Given a sequence of click events performed by some user during a typical session in an e-commerce website, the goal is to predict whether the user is going to buy something or not, and if he is buying, what would be the items he is going to buy.

What is the expected final result?

  • A model which predict whether a given user session will lead to a purchase being made(Yes|No)
  • If yes, what are the items that are going to be bought?

Team members and tasks for each member (What will each team member do?)

What are the project milestones and expected timeline ?

19/03-21/03: Understand Dataset

22/03-25/03: Read papers on recommender systems https://recsys.acm.org/blog/

24/03: Prepare for project progress review (Tentative date: 25-30th March)

26/03-15/04: Build models / Predict / Optimize 26/03-01/04: Iteration 1 02/04-06/04: Iteration 2 07/04-15/04: Iteration 3

25/04: Begin Final Report + Final Presentation (Due Date for report and PPT submission: 30th April, 10AM)

Google drive dataset link

https://drive.google.com/open?id=1RwKszkZIOUyv13sQ-SNlhS1yizIJZc7-