A comparative study of various machine learning classification algorithms

Abstract

Machine learning has the ability to learn from data and provide data driven insights, decisions, and predictions. Due to huge amount of data which is generated every day, it is very essential to use machine learning tecnhiques. The basic objective of this thesis is to present a brief introduction of various and most used classification algorithms. At this study the Logistic Regression, Naïve Bayes, k-Nearest Neighbors and Support Vector Machine algorithms are described and implemented. Additionally, a set of evaluation metrics is applied to the classifiers and gives useful insights about the predictive ability of each algorithm.