Home // DBKDA 2018, The Tenth International Conference on Advances in Databases, Knowledge, and Data Applications // View article

Comparative Analysis of Supervised Machine Learning Techniques on K-12 Educational Data

Authors:
Ravi Mattani
Manjeet Rege
Brandan Keaveny

Keywords: Machine Learning, Supervised, Educational, Data

Abstract:
The Anonymous School District (ASD) presented in this paper is implementing a comprehensive plan to intensify the information intelligence capacity to pinpoint educational needs of every student. Their main goal is to create analytical intelligence processes specific to the research needs of the school district and deploy an infrastructure that includes implementation of state of the art data analytics tools. The initial effort towards that goal is to research several machine learning techniques. The focus of this project is to assist the ASD research team in deployment of appropriate standards and procedures for efficiently forecasting and utilizing the large amount of data collected by the district. The goal of the project is to evaluate the most efficient machine learning techniques to forecast future trends. As a result, we developed a framework to transform raw data into minable data and apply several supervised learning techniques. Experiments were conducted to analyze the best technique.

Pages: 1 to 5

Copyright: Copyright (c) IARIA, 2018

Publication date: May 20, 2018

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-61208-637-8

Location: Nice, France

Dates: from May 20, 2018 to May 24, 2018