Abstract: Educational Data Mining is an emerging discipline, concerned with studentís performance prediction. In this paper studentís 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 studentís 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 NaÔve Bayes and Neural Networks. This work presents a methodology on how studentís information can provide insights into education at institutional level.

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