• Sangeeta Vaibhav Meena

Machine learning provides computers with the ability to learn and improve them from the past knowledge instead of being explicitly programmed. In real life there are many applications of machine learning such as virtual assistance, self-driving cars, email spam classification, image and speech recognition, cancer tumor cells identification, sentiments analysis and many more. Machine learning can be applied through supervised learning, unsupervised learning and reinforcement learning. Supervised machine learning aims is to build model that make likelihoods grounded on evidences in the presence of uncertainty which takes known set of input and response data. This paper focuses on classification and regression algorithms that play a vital role in supervised machine learning, whose goal is to assign a class to an observation from a finite set of classes. The different algorithms discussed are Support Vector Machines, Naïve Bayes, K- Nearest Neighbor, Linear Regression, Decision Trees, Artificial Neural Networks, Random Forest and Logistic Regression. The aim of this paper is to provide a comparative analysis of different supervised machine learning algorithms and provide in depth knowledge by comparing these algorithms on different performance parameters. This paper provides new dimensions in the field of machine learning by strengthening the basis of classification and regression algorithms.

Artificial Neural Networks, K-Nearest Neighbor, Linear Regression, Machine Learning, Naïve Bayes, Supervised Learning, Support Vector Machines, Random Forest

"SUPERVISED MACHINE LEARNING ALGORITHMS: A COMPARATIVE STUDY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.284-290, February-2019, Available :http://www.jetir.org/papers/JETIR1902937.pdf

"SUPERVISED MACHINE LEARNING ALGORITHMS: A COMPARATIVE STUDY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp284-290, February-2019, Available at : http://www.jetir.org/papers/JETIR1902937.pdf