• TUMUHIMBISE BANNET
• Dr. A.P. Siva Kumar
• Chidananda K

Prediction of the academic performance of a student is a major element in their education. Nowadays, the education of a student in an organization plays a vital role. Which is difficult to predict manually. We thus opt for machine learning techniques to evaluate student performance. Machine learning which is subpart of Artificial Intelligence that which helps the computer to learn on own without any external support. Machine learning techniques are used to predict the outputs for the certain inputs that are given. There are two approaches in machine learning. They are, supervised learning and unsupervised learning. From supervised learning we are using K-means algorithm and from unsupervised learning we are using XgBoost on of the algorithm from supervised learning and Random Forest as another algorithm to predict students’ performance. All these machine learning algorithms are combined for evaluating student performance. And based on the predicted outputs we can provide suggestions to student for better improvements

Predicting student performance, Machine Learning, K-Means, XgBoost, Random Forest

"Machine Learning techniques for student performance prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 10, page no.c616-c620, October-2021, Available :http://www.jetir.org/papers/JETIR2110278.pdf

"Machine Learning techniques for student performance prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 10, page no. ppc616-c620, October-2021, Available at : http://www.jetir.org/papers/JETIR2110278.pdf