Bhisham Sharma

Work place: Department of Computer Science and Engineering, Chitkara University, Himachal Pradesh, India



Research Interests: Computational Engineering, Engineering


Bhisham Sharma received a Ph.D. in Computer Science & Engineering from the PEC University of Technology (Formerly Punjab Engineering College), Chandigarh, India. He is currently working as an Associate Professor in the Department of Computer Science and Engineering, Chitkara University, Himachal Pradesh, India. He is having 12 years of teaching and research experience at various reputed Universities in India. He has received the Excellence Award for publishing research papers with the highest H-index is given by Chitkara University in 2020, 2021. He is currently serving as an associate editor for the Computers & Electrical Engineering (Elsevier), International Journal of Communication Systems (Wiley) and many more journal of repute.

Author Articles
Building Predictive Model by Using Data Mining and Feature Selection Techniques on Academic Dataset

By Mukesh Kumar Nidhi Bhisham Sharma Disha Handa

DOI:, Pub. Date: 8 Aug. 2022

In the field of education, every institution stores a significant amount of data in digital form on the academic performance of students. If this data is correctly analysed to discover any pattern related to student learning, it can assist the institution in achieving a favorable outcome in the future. Because of this, the use of data mining techniques makes it much simpler to unearth previously concealed information or detect patterns in student data. We use a variety of data mining methods, such as Naive Bayes, Random Forest, Decision Tree, Multilayer Perceptron, and Decision Table, to predict the academic performance of individual students. In the real world, a dataset may contain many features, yet the mining process may only place significance on some of those aspects. The correlation attribute evaluator, the information gain attribute evaluator, and the gain ratio attribute evaluator are some of the feature selection methods that are used in data mining to remove features that are not important for the mining process. Other feature selection methods include the gain ratio attribute evaluator and the gain ratio attribute evaluator. In conclusion, each classification algorithm that is designed using some feature selection methods enhances the overall predictive performance of the algorithms, which in turn improves the performance of the algorithms overall.

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