Recommendation systems are used by E-commerce sites to suggest products to their customers. Applications and sites using RS are : Amazon, Google, Flipcart, Netflix, Linkedin etc.

1. Product Bundling and
Recommendation System of E-
commerce
PRESENTED BY :
Er. Ankit Dhiman
( Data Science Trainee )

2. Agenda
 Introduction
 Why RS are used ?
 Goal and Objective
 Methodology / Planning of work
 Columns in Resultant DF
 Product Recommendation for Germany
 Tools and Technologies
 Issues and Challenges
 Benefits of RS

3. Introduction
 Recommendation systems are used by E-commerce sites to suggest
products to their customers.
 The product can be recommended based on an analysis of the past buying
behaviour of the customer as a prediction for future buying behaviour.

4. Why RS are used ?

5. Goal and Objective
 To build a recommender system for e-commerce websites with the help of
machine learning algorithms for customising the recommendations as
much as possible for the customers.
 To help the customers to get personalized recommendations , help
customers to take correct decisions in their online transactions, and to
increase the sales.

6. Methodology / Planning of work
CRISP-DM Framework :

7. Cleaned Dataset

8. Countries with Sufficient Transaction Recorded
The total no. of products bought in each of the countries is represented
graphically :

9. Modeling
Categorization of ML system are as :
Unsupervised ML >> Apriori Algorithm >> Association Rules

10. Columns in Resultant Dataframe
 Columns in the resultant DF are :
Antecedents, Consequents, Antecedent support, consequent support,
support, confidence, lift, conviction and leverage.

11. Product Recommendation for Germany

12. Tools and Technologies

13. Issues and Challenges
 Lack of Data
 Cold start Problem
 Synonymy
 Shilling Attacks
 Changing Data

14. Benefits of Product RS
 Increase Revenues
 Create customer satisfaction
 Personalize individual interest
 Reduce Time and effort
 Boost number of items per order