Build a Collaborative Filtering Recommender System in Python

Build a Collaborative Filtering Recommender System in Python

Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.


Each project comes with 2-5 hours of micro-videos explaining the solution.

Code & Dataset

Get access to 50+ solved projects with iPython notebooks and datasets.

Project Experience

Add project experience to your Linkedin/Github profiles.

Customer Love

Read All Reviews

Dhiraj Tandon

Solution Architect-Cyber Security at ColorTokens

My Interaction was very short but left a positive impression. I enrolled and asked for a refund since I could not find the time. What happened next: They initiated Refund immediately. Their... Read More

Shailesh Kurdekar

Solutions Architect at Capital One

I have worked for more than 15 years in Java and J2EE and have recently developed an interest in Big Data technologies and Machine learning due to a big need at my workspace. I was referred here by a... Read More

What will you learn

What are Recommender systems
Distance based metric
Cosine similarity
Centered cosine similarity
Loading and exploring data
Exploratory Data Analysis
Encoding the data
Normalising ratings
Creating user-item matrix
Identifying similar users
Recommending products to a user

Project Description

Recommender systems are an integral part of many online systems. From e-commerce to online streaming platforms. Recommender systems employ the past purchase pattern on its user to predict which other products they may be interested in and likely to purchase. Recommending the right products gives a significant advantage to the business. A major portion of the revenue is generated through recommendations.

The Collaborative Filtering algorithm is very popular in online streaming platforms and e-commerce sites where the customer interacts with each product (which can be a movie/ song or consumer products) by either liking/ disliking or giving a rating of sorts. One of the requirements to be able to apply collaborative filtering is that sufficient number of products need ratings associated with not them. User interaction is required.

This machine learning project walks you through the implementation of collaborative filtering using memory based technique of distance proximity using cosine distances and nearest neighbours.

Similar Projects

Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.

The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Forecast the business for the upcoming years by Exploring Hidden Trends, Calculating Machine Productivity , Extrapolation and Assumptions and Summarizing Answers through Visualizations.

Curriculum For This Mini Project

What are Recommender Systems
Distance Metric - Cosine Similarity
Centered Cosine Similarity
Loading and Exploring the Data
Visualizations - Exploratory Data Analysis
Visualizations - Understanding Ratings per Product
Encoding Data - Normalized Ratings
Filtering Process
Creating User Item Matrix
Identify Top k Similar Users
Recommend Top k products
Evaluate the Recommender Model