Published Paper Details:

Abstract: Online courses play an significant role in the developing of new skills in students. As a result of open learning platforms and online courses, there is an increasing number of online courses, and certifications available on the internet from various universities. This project is a machine learning-driven course recommendation system that uses the concepts of decision trees in machine learning to provide an effective method for making decisions and generating all the possible outcomes. The K-NN algorithm also helps to store all the available data, classifies a new data point based on the similarity, and produces the desired results. This project suggests appropriate learning courses for students by viewing their study streams and interests. For students to achieve their ideal goals, choosing a learning pathway is essential. However, since there is an abundance of knowledge that is obviously related, it can be difficult for students to select a course that combines their interests and future demands. This proposed framework suggests courses that have previously been taken by similar students, facilitating students to choose a course of study based on their interests. The suggested method makes use of a number of data mining strategies to find correlations between courses. Evaluations based on analysis parts are a key component of the project's methodology. With the help of these methods, we are able to select a suitable course that is given as input, then provide courses that satisfy various requirements. The outcomes meet the requirements for discovering related courses and providing them via cross-platform apps to the students who will use them to enhance their education.