Student Performance Analysis at Secondary Level Using Machine Learning

EasyChair Preprint 2016

Abstract

In today’s era of information and technology, Big Data & Machine Learning are emphasising a very great impact on every sector i.e. education, healthcare, entertainment etc. Researches have shown that education is the most important juncture in an individual’s life, with the help of Big Data Analytics it has become very easy to identify the consequences and challenges evolving in the education sector with every passing day. This paper mainly emphasises on two factors, which are, reasons for the variations in marks of the student from secondary school level to higher secondary school level and secondly, solutions to solve the above-mentioned problem using Machine Learning and develop a model which may be capable of predicting the marks easily at secondary level with the least amount of errors involved.

Keyphrases: (R-Squared), Big Data, Big Data Analytics, Education Sector, Hadoop, Higher Secondary school, R programming, Regression coefficient, machine learning, secondary school

@booklet{EasyChair:2016,
  author    = {Prashant Johri and Pallavi Goel and Sandesh Kumar Srivastava and Sashant Suhag and Sanskriti Jadaun},
  title     = {Student Performance Analysis at Secondary Level Using Machine Learning},
  howpublished = {EasyChair Preprint 2016},
  year      = {EasyChair, 2019}}