The movie recommendation system is an information filtering tool, which is mainly based on big data to predict the ratings of users and articles in order to recommend their preferences. The movie recommendation system provides a mechanism to help users rank other users with similar interests. It is a major part of e-commerce websites and applications. The project focuses on the evaluation of different models and algorithms, and its main purpose is to compare different algorithms (such as collaborative filtering) and models such as slope 1 etc. It also compares with existing methods and analysis and interprets the results. The average absolute error (MAE), standard deviation (SD), root mean square error (RMSE) and t value of the movie recommendation system give better results because our method provides lower error values. The film lens experiment data set can help you find the best method to achieve high performance in terms of reliability and efficiency, and provide accurate and personalized film recommendations for current methods

Keywords : Recommendation System, Evaluation of Recommendation Systems, Collaborative Filtering, Content Based Filtering, Slope One, k Fold Validation, Precision and Recall, f1 Score