• Vaibhav Jha

This study presents a detailed analysis of different models in Machine Learning, aiming to find the best possible model for the Course Recommendation System. AI Educational System has completely changed the student-teacher bond in the past couple of years and with time it enhances the teaching methodology. AI is acting as a strong pillar between both student and teacher as well as student and his learning. With such an advancement in the growth of AI, one of the popular recommendation systems Course Recommendation Systems is becoming an essential part of today’s learning, especially for students who want the perfect course according to the subject they want to learn. So this study shows the best machine learning model to classify the best course for recommending. For this four most popular machine learning models are been compared - Multinomial Naïve Bayes, Support Vector Machine, Random forest, and Logistic Regression. On comparing, the model with the best accuracy comes to the best-fit model for our recommendation system.

Recommendation, Multinomial Naïve Bayes, Support Vector Machine, Random forest, Logistic Regression

"Course Recommender System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.a56-a62, December-2023, Available :http://www.jetir.org/papers/JETIR2312008.pdf

"Course Recommender System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppa56-a62, December-2023, Available at : http://www.jetir.org/papers/JETIR2312008.pdf