Movies Rating Prediction using Supervised Machine Learning Techniques

Abstract

Social media are the most enormous and fast data technology on the internet. A massive amount of data is generated from the internet day by day. The efficient processing of such massive records is hard, so we require a system that learns from these facts and makes a useful prediction like machine learning. Machine learning strategies make the systems learn it. Applying K-nearest neighbors (KNN), Support Vector Machine (SVM), as well as Random Forest Model (RF), three supervised machine learning approaches, we attempted to develop a model for a movie recommendation system. This research not only compares the three models but also assesses how they compare favorably against one another in the areas of accuracy, accuracy, recalls, and F1 score. First of all, we discuss a summary of machine learning techniques used for recommendations of items in the past. We subsequently detail the most effective machine learning model for making predictions and recommendations about movie ratings. Predictions of movie reviews are made using K-nearest neighbor (KNN) and Support Vector Machine (SVM) models as well as the Random Forest Model (RF). The efficacy of all models was tested with Movie Lens 100k and 1M dataset. The random forest Model performs better as compared to other models. Furthermore, we summarize the current challenges based on state of art approaches. Finally, we present open issues and future directions for further research.