Prediction of Academic Performance of Engineering Students by Using Data Mining Techniques

In the current age, students' academic performance deterioration is a very crucial problem in engineering education. Prediction of low-performing students at an early stage important so that their faculties and administration could provide timely support. The present study attempts to perform this prediction task entry-time with help four single supervised educational data mining algorithms, namely: Decision tree, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine along ensemble method called “Random Forest”. These classifiers have been applied students‟ dataset Indian Engineering College, having categories parameters viz., student‟s background, academic, social, psychological parameters. Different libraries Python programming language such as Pandas, Seaborn, Scikit-learn, Scipy were used for analysis, visualization, classification, statistics computation, respectively. shows among all five Bayes gives highest accuracy 89%, finally improve results, model proposed which three integrated 'Bagging'. achieved was 91%, recall precision identifying low performers.