论文信息 - Analyzing Student Performance in Engineering Placement Using Data Mining

Analyzing Student Performance in Engineering Placement Using Data Mining

Data mining is the practice of mining valuable information from huge data sets. Data mining allows the users to have perceptions of the data and make convenient decisions out of the information extracted from databases. The purpose of the engineering colleges is to offer greater chances to its students. Education data mining (EDM) is a process for analyzing the student’s performance based on numerous constraints to predict and evaluate whether a student will be placed or not in the campus placement. The idea of predicting the performance of higher education students can help various institutions in improving the quality of education, identifying the pupil’s risk, upgrading the overall accomplishments, and thereby refining the education resource management for better placement opportunities for students. This research proposes a placement prediction model which predicts the chance of an undergrad student getting a job in the placement drive. This self-analysis will assist in identifying the patterns, where a comparative study between two individual methods has been made in order to predict the student’s success and a database has been generated.

Manjusha Pandey | Chandrima Roy | Krishnanshu Agarwal | Ekansh Maheshwari | Siddharth Swarup Rautray

[1] Shubham Kapoor,et al. PPS — Placement prediction system using logistic regression , 2014, 2014 IEEE International Conference on MOOC, Innovation and Technology in Education (MITE).

[2] J Jayanthi,et al. Student Prediction System for Placement Training Using Fuzzy Inference System , 2017 .

[3] Siddharth Swarup Rautaray,et al. Big Data Optimization Techniques: A Survey , 2018, International Journal of Information Engineering and Electronic Business.

[4] Rohit Jha,et al. Predicting Students' Performance Using ID3 And C4.5 Classification Algorithms , 2013, ArXiv.

[5] K. Sreenivasa Rao,et al. Educational data mining for student placement prediction using machine learning algorithms , 2017 .

[6] V. Ramesh,et al. Predicting Student Performance: A Statistical and Data Mining Approach , 2013 .

[7] Siddharth Swarup Rautaray,et al. A Proposal for Optimization of Horizontal Scaling in Big Data Environment , 2018 .

[8] Siddhi Parekh,et al. Results and Placement Analysis and Prediction using Data Mining and Dashboard , 2016 .

[9] Animesh Giri,et al. A Placement Prediction System using k-nearest neighbors classifier , 2016, 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP).