Movie-Recommendation-System

Description

The Movie-Recommendation-System is a machine learning project designed to provide personalised movie suggestions to users. It utilises a collaborative filtering approach, leveraging the K-Nearest Neighbors (KNN) algorithm to analyze and predict user preferences based on a dataset of user ratings and movie metadata.

The dataset used for this project is from the GitHub Repository ml-latest-small by smanihwr.

The project is implemented in Python using the following libraries:

• Pandas
• Numpy
• Scikit-learn
• Fuzzywuzzy

Context

This project serves as an extension to a presentation I delivered on "Recommender Algorithms, Netflix," which was part of a university course focused on pre-professional training. The attached PDF file contains the slides from that presentation, which are in French.

Screenshots

print(movie_recommender_engine("Jurassic Park", user_item_matrix, cf_model, n_recs))

_{Similar to Jurassic Park}

print(movie_recommender_engine("Terminator", user_item_matrix, cf_model, n_recs))

_{Similar to Terminator}

print(movie_recommender_engine("Forest Gump", user_item_matrix, cf_model, n_recs))

_{Similar to Forest Gump}