Data science is the most promising area in computer science today. Data science uses various methods and techniques to deal with large volume of data accumulated day by day. Predictive analytics is the prime concept in data science by processing these large volumes of data to make important predictions. This is being achieved through machine learning family of algorithms. This paper makes a note on the core concept of machine learning and the strategies to adopt suitable machine learning algorithms for the problems in data science. It also reviews different areas of machine learning applications in data science.

Data Science, Machine Learning, Supervised Learning, Reinforcement Learning

[1]. J.D. Kelleher, B.M. Namee, A. D’Arcy, “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies” The MIT Press, 2015.
[2]. H. Chen, R.H.L. Chiang, V.C. Storey, “Business Intelligence and Analytics: From Big Data to Big Impact”, MIS Quarterly, Vol. 36, No. 4, pp. 1165-1188, 2012.
[3]. T.M. Mitchell, “Machine learning and data mining”, Commun. ACM, Vol. 42, No.11, pp.30-36, 1999.
[4]. J. Lin, A. Kolcz, “Large-scale machine learning at twitter”, In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (SIGMOD `12). ACM, New York, NY, USA, pp.793-804, 2012.
[5]. S. Ryu, “Predictive Analytics: The Power to Predict Who Will Click Buy, Lie or Die”, Healthc Inform Res., vol.19, No.1, pp.63-65, 2013.
[6]. L.Wang, et al., “Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis”, Entropy, vol.19, no.6, 222, 2017.
[7]. R.S. Michalski, J.G. Carbonell, T.M. Mitchell, “Machine Learning An Artificial Intelligence Approach”, 1983
[8]. A. Vellido, J.D. Martín-Guerrero, P.J.G. Lisboa, “Making machine learning, models, interpretable”, In Proc. European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning, 2012.
[9]. S.B. Kotsiantis, “Supervised Machine Learning: A Review of Classification Techniques”. In Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, Ilias Maglogiannis, Kostas Karpouzis, Manolis Wallace, and John Soldatos (Eds.). IOS Press, Amsterdam, The Netherlands, pp.3-24, 2007.
[10]. J. Dougherty, R. Kohavi, M. Sahami, “Supervised and Unsupervised Discretization of Continuous Features”, In Proceedings of the Twelfth International Conference on Machine Learning, San Francisco (CA), pp.194-202, 1995.
[11]. T.G. Dietterich,” Ensemble Methods in Machine Learning”, In: Multiple Classifier Systems, MCS 2000, Lecture Notes in Computer Science, vol 1857, Springer, Berlin, Heidelberg. 2000.
[12]. A.J. Smola, & B. Schölkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol.14, No.199, 2004.
[13]. P.K. Chan, M.V. Mahoney, M.H. Arshad, “A machine learning approach to anomaly detection”, (CS-2003-06). Melbourne, FL. Florida Institute of Technology, 2003.
[14]. R. Xu, D. Wunsch, "Survey of clustering algorithms," in IEEE Transactions on Neural Networks, vol. 16, no. 3, pp. 645-678, May 2005.
[15]. M.L. Littman, A.W. Moore, L.P. Kaelbling, “Reinforcement Learning: A Survey”, Journal of Artificial Intelligence Research, Vol.4, pp.237-285,1996.
[16]. M. Nasser, Nasrabadi, "Pattern Recognition and Machine Learning," Journal of Electronic Imaging, vol.16, No.4, 049901, 2007.
[17]. K.C. Fu, “Sequential Methods in Pattern Recognition and Machine Learning”, Vol.52, Academic Press, 1968.
[18]. M.M. Richter, S. Paul, “Signal Processing and Machine Learning with Applications”, Springer International Publishing, 2018.
[19]. J.L. Cabra, D. Mendez, Luis C. Trujillo. “Wide Machine Learning Algorithms Evaluation Applied to ECG Authentication and Gender Recognition”, In Proceedings of the 2018 2nd International Conference on Biometric Engineering and Applications (ICBEA `18). ACM, New York, NY, USA, pp.58-64, 2018.
[20]. R. John Martin, S. Sujatha, S.L. Swapna, “Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection”, International Journal of Computer Applications, Vol.180, No.17. pp.14-20, February 2018.
[21]. R.A. Miller, A. Geissbuhler, “Diagnostic Decision Support Systems”, In: Berner E. (eds) Clinical Decision Support Systems. Health Informatics. Springer, NY, pp. 99-125, 2016.
[22]. E. Chong, C. Han, Frank C. Park, “Deep Learning Networks for Stock Market Analysis and Prediction: Methodology, Data Representations, and Case Studies”, Expert Systems with Applications, Vol;.83, pp.187-205, 2017.
[23]. X. Z. Zhang, "Building Personalized Recommendation System in E-Commerce using Association Rule-Based Mining and Classification," In Proc. International Conference on Machine Learning and Cybernetics, Hong Kong, pp. 4113-4118, 2007.
[24]. D. Freitag, “Machine Learning for Information Extraction in Informal Domains”. Machine Learning, Vol.39, pp.169–202, 2000.
[25]. Rakesh. S.Shirsath, Vaibhav A.Desale, Amol. D.Potgantwar, "Big Data Analytical Architecture for Real-Time Applications", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.4, pp.1-8, 2017.
[26]. V.K. Gujare, P. Malviya, "Big Data Clustering Using Data Mining Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.9-13, 2017
[27]. K.S. Shin, T.S. Lee, H.J. Kim, “An application of support vector machines in bankruptcy prediction model”, Expert Systems with Applications, Vol.28, No.1, pp.127-135,2005.