The "Movie Recommender System Project using Item Collaborative Filtering" was driven by the goal of offering personalized movie recommendations to users based on their movie preferences and viewing history. Employing Item Collaborative Filtering, the system identified movies similar to those the user had previously rated or watched, ensuring a tailored movie experience. This undertaking required comprehensive data analysis and algorithm refinement to deliver precise recommendations. Key considerations included maintaining user data privacy and safeguarding sensitive information throughout the recommendation process. Overcoming challenges related to data accuracy and scalability involved optimizing the system's architecture. The end result was a robust and privacy-conscious system that successfully provided accurate and relevant up to 10 movie recommendations to users.