  This study develops a robust movie recommendation system using various
machine learning techniques, including Non- Negative Matrix Factorization
(NMF), Truncated Singular Value Decomposition (SVD), and K-Means clustering.
The primary objective is to enhance user experience by providing personalized
movie recommendations. The research encompasses data preprocessing, model
training, and evaluation, highlighting the efficacy of the employed methods.
Results indicate that the proposed system achieves high accuracy and relevance
in recommendations, making significant contributions to the field of
recommendations systems.
