Comparative Analysis Of Classification Techniques Used In Machine Learning As Applied On A Three Phase Long Transmission Line System For Fault Prediction Using Python

Abstract

The recent developments in the technology made by organizations have led to a quicker, simpler and a very accurate data analysis. The use of machine learning techniques have been exponentially increasing in the analysis of data in different fields ranging from medicine to defense, education, finance and energy applications. The machine learning techniques reduce further meaningful information processed by data mining. These significant and meaningful information help organizations to establish their future policies to get more advantages in terms of time and cost. In this paper the author has tried to present the best classification method by having a comparative analysis on various methods such as Logistic Regression, Support Vector Machine, Naïve Bayes and K-Nearest Neighbors etc. for a particular use case i.e. prediction and classification of transmission line faults. The author has made this analysis by utilizing both Python and MATLAB Simulink. This will surely help the researchers to know details such as accuracy,f1 score, mean square error etc of various classification methods.