A Comparative Analysis of Machine Learning Techniques
- September 10, 2020
- Posted by: RSIS Team
- Categories: IJRIAS, Physics
International Journal of Research and Innovation in Social Science (IJRISS) | Volume V, Issue IV, April 2020 | ISSN 2454–6186
A Comparative Analysis of Machine Learning Techniques
A. Ozoh1, A. A. Adigun2, L. O. Omotosho3
1,2,3Department of ICT, Osun State University, Osogbo, Nigeria
Abstract- Machine learning is a branch of artificial intelligence that is used to analyze large set of data. Machine learning approach is a statistical approach on learning more about a raw data set. When considering the existing systems in the world, there is a huge output of data which are not well analyzed. The use of machine learning techniques provide a way of analyzing a huge data set in order to find patterns and relationships among different entities which cannot be observed without advanced analyzing techniques. In this paper, the machine learning techniques that will be considered include; Box-Jenkins method, artificial neural network (ANN) technique, and Kalman technique. Each technique will be implemented using python, and the results obtained using the mentioned methods will be compared. This paper explores the application of effective machine learning to overcome challenges associated with data analysis and demonstrates how machine learning techniques have contributed and are contributing to research in machine learning.
Keywords: machine learning, big data, Box-Jenkins, artificial neural network, kalman technique
