Abstract: Educational Data Mining is an emerging discipline, concerned with student’s performance prediction. In this paper student’s performance is evaluated by selecting some attributes which generates rules for forming the classification of the instances in the dataset. Data from various sources of college can provide valuable knowledge to predict student’s result at institutional level as well as it can provide insights to each individual performance. The process involves various steps, firstly pre-processing has to be carried out on the set of data secondly apply the classification rules on the data that has been processed in the previous step after which we test the results on the different categorical data input. Emerging institutions are using data mining approach to predict their results and also enhance the level of education in the society. The most commonly used algorithm is machine learning technique to predict performance is called Naïve Bayes and Neural Networks. This work presents a methodology on how student’s information can provide insights into education at institutional level.

Keywords: Education, dataset, machine learning, Naïve Bayes, Neural Network.