Publication Type : Conference Paper
Publisher : Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
Source : 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (2019)
Url : https://ieeexplore.ieee.org/document/9032654
Keywords : Data sets, decision tree regression model, Decision trees, educational administrative data processing, educational system, further education, gradient boost regression model, gradient methods, k-neighbor regression model, Learning model, light GBM regression model, Linear regression, linear regression model, Machine learning, Machine learning algorithms, mean square error methods, nearest neighbour methods, Pattern classification, prediction, Prediction accuracy, Prediction algorithms, Predictive models, random tree classifier model, Regression analysis, Regression model, Regression tree analysis, root mean square error, student community, Student placement prediction, student placement prediction problem, Undergraduate students, XGBoost regression model
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2019
Abstract : As Machine Learning (ML) algorithms are becoming popular to solve challenging and interesting real world prediction problems around us, the interest level of student community has been increased in learning the principles of ML and its different algorithms. This includes by implementing the commonly known machine learning algorithms and tests them by solving simple prediction problems around the student community present in educational system. In this line, this paper proposes to solve the student placement prediction problem using linear regression model, K-neighbor regression model, decision tree regression model, XGBoost regression model, gradient boost regression model, light GBM regression model and random tree classifier model. This work is carried out in two phases. The Phase 1 is done on a simple data set and the Phase 2 is done with an extended data set with added additional features about the students. This research work presents the comparative performance analysis of these seven models by implementing them with these two data sets. The performance measurements considered in this study are prediction accuracy and the root mean square error (RMSE).
Cite this Research Publication : T. Aravind, Reddy, B. S., Avinash, S., and Dr. Jeyakumar G., “A Comparative Study on Machine Learning Algorithms for Predicting the Placement Information of Under Graduate Students”, in 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2019.