Early Detection of Prone to Failure Student Using Machine Learning Techniques

Machine learning techniques works on experience uses historical data and process them. The algorithms help to reveal facts and shows the path to move towards success. This study, uses for early detection of prone to failure using machine learning techniques. Supervised approach of machine learning used to analysis data in python colab environment. The Sample size 300 records used to evaluate data. Outcomes shows 82% accuracy with Naive Bayes classifiers. The study classifies records among three classes good, average and poor students.