Popularity-Based Recommendation Systems in RapidMiner

This tutorial uses a movie rating dataset to explain a popularity-based recommendation system. To prevent bias from entering the model, movies and users with few ratings are eliminated from the dataset. All movies are ranked according to their average rating. All users can receive recommendations for the most popular movies, which are those with the highest average rating. The dataset can be found here: https://www.kaggle.com/datasets/ayushimishra2809/movielens-dataset?resource=download&select=ratings.csv MORE VIDEOS: Load data in RapidMiner https://youtu.be/M98QACQug_M Missing values https://youtu.be/k2FPcWFCrdI Decision tree https://youtu.be/sAH2ltmPZsQ Random Forest Classifier https://youtu.be/D9nsTLYAy1c Linear Regression https://youtu.be/3z-Zgxy7zyQ Neural Network Classifier https://youtu.be/xsfSVIA1DvA extension installation https://youtu.be/cUkI5p9CvEU