Objective

To build a recommendation system to recommend products to customers based on the their previous ratings for other products

Dataset

https://drive.google.com/file/d/1ClBptsK3V5KgKXtK2GSRzFNAW7GnTPDW/view?usp=sharing

Data Description

For this model, we are using the Electronics dataset from Amazon Reviews data repository.

Domain

E- Commerce

Context

Online E-commerce websites like Amazon, Flipkart uses different recommendation models to provide different suggestions to different users. Amazon currently uses item-to-item collaborative filtering, which scales to massive data sets and produces high-quality recommendations in real-time.

Attribute Information

userId: Every user identified with a unique id

productId: Every product identified with a unique id

Rating: Rating of the corresponding product by the corresponding user

timestamp: Time of the rating ( ignoring this column for our experiment)

Learning Outcomes

●Exploratory Data Analysis

●Creating a Recommendation system using real data

●Collaborative filtering

Steps Followed

1.Read and explore the given dataset.

2.Take a subset of the dataset to make it less sparse/ denser.

3.Split the data randomly into train and test dataset.

4.Build Popularity Recommender model.

5.Build Collaborative Filtering model.

6.Evaluate both the models.

7.Get top -K ( K = 5) recommendations. Since our goal is to recommend new products foreach user based on his/her habits, we will recommend 5 new products.

8.Summarise your insights.