Project Title: Netflix Recommendation System using the surprise library

Capstone Project - IIT Madras (PgD in DSAI)

Project Overview:

I recently completed a project that aimed to build a recommendation system for Netflix using the surprise library. The project involved collecting data on movie ratings, preprocessing the data, and training a machine learning model for recommendation. The trained model was then used to make recommendations to Netflix users.

What is Surprise Library?

The "surprise" library is a Python library for recommender systems. It provides a variety of algorithms for building and evaluating recommendation models, including collaborative filtering, matrix factorization, and others. The library is designed to be simple and easy to use, with a focus on providing accurate and efficient implementations of the algorithms. It also includes a number of built-in datasets and tools for evaluating and comparing recommendation models, making it a useful resource for researchers and practitioners in the field of recommendation systems.

Objective:

The objective I set for myself was to build an effective recommendation system for Netflix that utilizes the surprise library. I used the surprise library to train and test a machine learning model for recommendation, and evaluated the results to determine the accuracy of the model.

Methodology:

Here's how I approached this project:

• Data Collection: I collected a dataset of movie ratings from Netflix users.
• Data Preprocessing: I preprocessed the collected data to prepare it for modeling.
• Model Training: I trained a machine learning model for recommendation using the surprise library.
• Model Evaluation: I evaluated the model using accuracy metrics, such as mean squared error and root mean squared error.
• Model Deployment: I used the trained model to make recommendations to Netflix users.

Expected Results:

The expected results I achieved were an effective recommendation system for Netflix that utilizes the surprise library. I evaluated the model's accuracy and deployed it to make recommendations to Netflix users.

Conclusion:

In conclusion, I successfully completed a project that builds a recommendation system for Netflix using the surprise library. The results of this project can be useful for Netflix as it can help them make personalized recommendations to their users and improve the user experience. The model I developed can help Netflix to better understand the movie preferences of their users and suggest movies that are likely to be of interest to them.