STUDENT PERFORMANCE PREDICTION SYSTEM USING DATA MINING CLASSIFICATION

Computer Engineering

In education system, highest level of quality can be achieved by exploring the knowledge regarding prediction about student’s performance. Data mining techniques play an important role in data analysis. An educational institution needs to have an approximate prior knowledge of enrolled students to predict their performance in future academics. This helps them to identify promising students and also provides them an opportunity to pay attention to and improve those who would probably get lower grades. As a solution, we will develop a system which can predict the performance of students from their previous performances using concepts of data mining techniques under Classification. We have analyzed the data set containing information about students, such as gender, marks and rank in entrance examinations and results in Third year of the previous batch of students. Classification is a data mining technique that maps data into predefined groups or classes. It is a supervised learning method which requires labeled training data to generate rules for classifying test data into predetermined groups or classes. It is a two-phase process. The first phase is the learning phase, where the training data is analyzed and classification rules are generated. The next phase is the classification, where test data is classified into classes according to the generated rules. By applying the ID3 (Iterative Dichotomiser 3), C4.5, Improved weighted modified ID3 classification algorithms on this data, we have predicted the general and individual performance of third year students in future examinations.

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