Rapid software framework for the implementation of machine learning classification models

• Main Authors: Rahman, A.S.A., Masrom, S., Rahman, R.A., Ibrahim, R.
• Format: Article
• Published: IJETAE Publication House 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114115869&doi=10.46338%2fIJETAE0821_02&partnerID=40&md5=2c7d90c223b1de205f4f9eecaa0cdd98; http://eprints.utp.edu.my/29441/

Reseachers have acknowledged that machine learning is useful to be utilized in many different domains of complex real life problem. However, to implement a complete machine learning model involves some technical hurdles such as the steep learning curve, the abundance of the programming skills, the complexities of hyper-parameters, and the lack of user friendly platform to be used for the implementation. This paper provides an insight of a rapid software framework for implementing machine learning. This paper also demonstrates the empirical research results of machine learning classification models from the rapid software framework. Additionally, this paper explains comparisons of results between two platforms of rapid software; the proposed software and Python program. The machine learning model in the two platforms were tested on breast cancer and tax avoidance datasets with Decision Tree algorithm. The results indicated that although the software framework is easier than the programming platform for implementing the machine learning model, the results from the software framework were highly accurate and reliable. © 2021 International Journal of Emerging Technology and Advanced Engineering. All rights reserved.