Authors: J.V.N. Lakshmi
Addresses: Acharya Institute of Management and Sciences, MCA Department, Bangalore, 560 058, India
Abstract: Machine learning algorithms are used to parallelise the workloads. To achieve paramount performance required parameters are tuned in the algorithms. The jobs are implemented using machine learning techniques using various parameters. The performance is examined by executing various features and verifying time constraints depending on the assignments for a cluster. The attempt is made to obtain minimum execution time using python language for implementing machine learning algorithms. Supervised and unsupervised techniques of machine learning algorithms are used differentiating the performance evaluation and time efficiency. Linear regression, logistic regression and K-means clustering techniques are used to evaluate the data analytic jobs. This implementation reveals the best performance of supervised algorithms over unsupervised for data analysis. This paper is an attempt made to analyse the machine learning techniques and evaluates the timer feature on various methods irrespective of supervised or unsupervised.
Keywords: machine learning; linear regression; logistic regression; K-means; EM.
International Journal of Intelligent Systems Technologies and Applications, 2018 Vol.17 No.1/2, pp.3 - 18
Received: 04 Jan 2017
Accepted: 31 Mar 2017
Published online: 03 May 2018 *