Movie-Recommendation-Engine

Repo used to store my movie recommendation engine project.

Directories

• README.md: This file, containing the project overview, directory structure, and credits.
• cosine_similarity.py: Script that computes the cosine similarity between different movies based on their attributes for content-based recommendations.
• movie_dataset.csv: The dataset containing movie information, used by the recommendation engine to make predictions.
• movie_recommender.py: The main script that runs the recommendation engine. This includes both the popularity-based and content-based recommendation logic.
• movie_recommender_starter.py: A starter script providing a simpler, more basic version of the recommendation engine for initial testing and experimentation.

Project Overview

Scripts and files for a movie recommendation engine: Uses machine learning techniques to recommend movies based on user preferences.

1. Popularity-Based Recommendations: Recommend items based on their popularity and current trends.
2. Content-Based Recommendations: Recommend items similar to those the user has liked in the past, based on item attributes.

Credits

This project was inspired by and follows the tutorial from Code Heroku. I followed the tutorial closely while developing this project, and much of the code is based on their guidance.

© Emanuel Alvarez