A Study on PrediX: Harnessing the power of ML for accurate Predictive Modelling for Movies (MOVIEMAGIC)

Abstract: Machine learning is becoming increasingly important in the technical world, and the entertainment business is growing rapidly. The ways in which people consume content are becoming more complex and changing more quickly than in the past. Recommendation engines powered by machine learning create independent systems that grow and learn from their mistakes without requiring explicit coding. It is a system that enables a user to sift through vast volumes of data and identify useful information for themselves. Every entertainment provider displays relevant information to a user according to his tastes using a sophisticated recommendation algorithm. Both their sales and user base retention are aided by it . Different techniques are used by movie recommendation systems. For example, collaborative filtering (CF) compares people based on how similar they are to each other in terms of content consumption, while content-based filtering makes use of the movie's attributes such actors, genre, and year of release. A hybrid technique combines two or more distinct methods for suggesting movies. In this work, we offer an architecture for a movie recommendation system that addresses the cold-start issue by utilizing ML and the MERN stack.