A novel analysis on movie recommendation using machine learning approach

In today’s culture, technology plays a crucial role for any kind of recommendations. In this digital World, Algorithms are used in movie recommendation systems to offer films to users based on their watching history or ratings. With the advent of digital services and the massive quantities of information they accumulate on user preferences, these systems have grown in popularity in recent years. This system employs machine learning to do sentiment analysis on movie reviews to improve the user experience. This article also compares NB, SVM, and KNN on metrics such as Accuracy, Precision, Recall, and F1 Score. We present an overview of the many algorithms utilized in the creation of movie recommendation systems, encompassing text categorization, knowledge filtering, and hybrid techniques, in this review. We also examine the problems and limits of these algorithms, as well as possible future research objectives in this subject