MOVIE RECOMMENDATION SYSTEM USING PYTHON AND MACHINE LEARNING

In the era of digital entertainment, users are overwhelmed with vast amounts of content
available on streaming platforms. Movie recommendation systems play a crucial role in
enhancing user experience by suggesting relevant content based on preferences. This research
paper presents the design and implementation of a movie recommendation system using
Python and machine learning techniques. The system utilizes collaborative filtering and
content-based filtering methods to provide personalized recommendations. Various
algorithms and evaluation techniques are discussed to measure performance and accuracy.
The study highlights the effectiveness of machine learning in improving recommendation
quality and user satisfaction. this system addresses the problem using a hybrid
recommendation approach combining Collaborative Filtering and Content-Based Filtering.
Machine learning techniques such as matrix factorization and cosine similarity are applied on
datasets like MovieLens. The system achieves high accuracy (~85%) and provides
personalized, real-time recommendations through a web interface built using Python and
Flask