Data Science

Movie Recommendation System

ML recommender combining collaborative and content-based filtering, built during a data-science internship at YBI Foundation.

Source

Overview

Built during the Data Science and Machine Learning Internship at YBI Foundation. Implements both collaborative filtering and content-based filtering to provide personalised movie recommendations, using Python, Pandas, NumPy, Scikit-learn and NLP techniques for feature extraction from titles and descriptions.

Highlights

Hybrid recommendations
Combines collaborative filtering with TF-IDF content similarity.
Data pipeline
Cleaning, feature engineering and evaluation notebooks included.

Challenges

Cold-start problem for new users and sparse rating matrices.

Learnings

End-to-end recommender workflow — from EDA to serving predictions.

Scriptify — Text to Handwriting

FoundryAI