Development of a Movie Recommendation System - MoviepleX

The content recommendation model, “Development of a Movie Recommendation System - MoviepleX” is aimed at providing accurate movie recommendations to users, on the basis of similarity with the movie they would enter for reference, using machine learning algorithms, functions and metrics. It is built using the tmdb_5000 dataset, taken from Kaggle. The data consists of a number of features like cast, crew, genre, budget, overview, runtime, tagline, popularity, production unit and revenue corresponding to 4803 Hollywood movies that are a part of the tmdb database. Recommendation engines are a subclass of information filtering systems that seek to predict the 'rating' or 'preference' a user would give to an item, a movie in case of a movie recommender. Streaming media services like Netflix & Disney+ Hotstar employ highly efficient content recommendation systems, which can play a huge role as game-changers in a streaming service’s success or failure. These content-based recommenders are what keep our entertainment rhythm going, serving us the best material out there, based on our own personal interests, choices, likes & dislikes. Movie recommendation systems provide a mechanism to assist viewers and subscribers of streaming platforms by classifying movies based on similar interests of users. A movie recommendation is important in our social life due to its strength in providing enhanced entertainment. The model proposed in this paper uses machine learning’s capability to identify patterns and build prediction and recommendation mechanisms using provided data. A machine learning web application was created for the recommendation engine, which was deployed onto Heroku, a container-based cloud Platform as a Service (PaaS), used to deploy, manage, and scale modern apps. The app deployment was made through Streamlit. By having a webpage for the ML - application, it has been made accessible and beneficial to public