Education is used to reach new heights in the world. Educational Data Mining is used to extract useful information from previously acquired knowledge. Educational data mining is the process of analysing and visualising data from an educational institution using various data mining tools and techniques in order to discover a unique pattern of students' academic performance and behaviour. The purpose of this paper is to improve students' academic performance by utilising data mining techniques. The Naive Bayesian algorithm can be used to predict the academic performance and behaviour of students. The training and testing phases are involved in classifying students into two groups, pass and fail. The Naive Bayes classifier is built during the training phase, and it is used to make predictions during the testing phase. The WEKA tool is used to calculate the classifier's accuracy. The obtained classifier accuracy is 87%, which can be improved further by selecting appropriate attributes. Developing Classification algorithms in this manner aids in the development of a more efficient student performance predictor tool using other data mining algorithms, as well as in the improvement of educational quality in institutions.