A comparative performance analysis of different machine learning techniques

Abstract

Abstract Over the past many decades, Machine Learning (ML) has advanced from the undertaking of few PC fans misusing the likelihood of PCs figuring out how to play diversions, and a piece of Mathematics (Statistics) that only here and there measured computational methodologies, to an autonomous research obedience that has not just given the essential base to measurable computational standards of learning systems, yet in addition has created different calculations that are routinely utilized for content translation, design acknowledgment, and a numerous other business purposes and has prompted a different research enthusiasm for information mining to recognize shrouded regularities or abnormalities in social information that developing by second. This paper centers around clarifying the idea and development of Machine Learning, a portion of the well known Machine Learning calculations and endeavor to think about three most prevalent calculations dependent on some essential thoughts. Sentiment140dataset was utilized and execution of every calculation as far as preparing time, forecast time and precision of expectation have been reported and analyzed.