Performance Analysis of Regression Models on Predicting Movie Ratings

This research explores the application and performance comparison of various machine learning for movie ratings. This study aims to make a comprehensive comparison in performance between various machine learning models such as XGBoost, Random Forest Regressor, K Nearest Neighbor Regression, Support Vector Regressor and Linear Regression. The dataset used is MovieLens 20M which includes a rating dataset and a movie dataset. We employ a content-based recommendation system in conjunction to applying machine learning models to predict movie ratings based on user meta data. Our methodology involves splitting the data into 70:30 train-test data and evaluating them using Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Squared Error (RMSE). The findings of our research conclude that XGBoost is the superior model which is shown from the RMSE value of XGBoost being the lowest (0.8727).