Analyzing Student Performance Prediction: Meta Stacking Classification

This book is a conglomerate framework to investigate, analyze and interpret academic attributes influencing students' performance pursuing Technical education. The vital goal of this research is selection of most optimal features influencing the Cumulative GPA as analogy of academic excellence. Various statistical tools and classifiers are of extracurricular activities on university management performance, in order to bring real contribution in terms of increased quality of university management by diversifying the extracurricular activities' offer within universities, with the effect on performance management growth. The research includes a detailed radiography of specialized studies from the field, in order to determine the current state of scientific knowledge in conceptual terms, and highlights the functionality of the university system. Quantitatively, Meta Stacked Regression model is compared with traditional linear regression and neural networks for feature importance in metrics of mean square error.

Autorentext
Pallavi, Smita Smita Pallavi has done B.E.(CSE), MBA(IT) and PhD (IT) at Birla Institute of Technology, Mesra-Patna Campus, India. With teaching experience of 13 years, she has handled major consultancy projects under Government of Bihar. Her publications include SCI, Scopus and International journals in Soft Computing, Data Mining and Optimization Algorithms.