Data analysis and machine learning-based modeling for real-time production

Abstract

This article focuses on data analysis and real-time data modeling using linear regression and decision tree algorithms that might make revolutionary predictions on production data. Factual time data points, including temperature, load, and warning on all the presented axis, are the dependent parameters which be contingent on the changes in the autonomous paraments like load. Monitoring and innovative prediction are very much needed in industry as there are recurrent load changes that would create a data drift and, in terms of maintenance, that could impact the production side, the need for continuous monitoring and control. Machine learning-based approaches would work better on these real-time production datasets