Algorithms for Predicting using Machine Learning

Article Sidebar

PDF HTML
Published: Mar 2, 2020
Keywords:
Machine learning, Predictive analytics, K nearest neighbor, SVM, Naive Bayes

Main Article Content

Authors

Jyotsna Tiwari

Research Scholar, Shri Krishna University, Chhatarpur, M.P.

Dr. Monika Tripathi

Professor, Shri Krishna University, Chhatarpur, M.P.

Abstract

Given the vast amount of real-world statistics that are easily accessible and the growing popularity of analytics, selecting the best prediction algorithm is crucial. Even though there are a number of forecasting models that are regularly used for predictive analytics, it may be challenging to decide which algorithm is optimal for a certain real-world dataset & research topic. The three most well-known machine learning and predictive analytics algorithms are discussed in this article in addition to the implementation outcomes on real datasets. These algorithms were evaluated and compared using performance comparison metrics such time training, accuracy, sensitivity, specificity, accuracy, the area under the curve and error.

Downloads

Download data is not yet available.

Article Details

Issue

Vol. 17 No. 1 (2020): Journal of Advances in Science and Technology (JAST)

Section

Articles

References

  1. Arroyo, J., & Maté, C. (2009). Forecasting histogram time series with k-nearest neighbours methods. International Journal of Forecasting, 25(1), 192-207.
  2. Barga, R., Fontama, V., Tok, W. H., & Cabrera-Cordon, L. (2015). Predictive analytics with Microsoft Azure machine learning. Berkely, CA: Apress. [
  3. Deepak, E., Pooja, G. S., Jyothi, R. N., Kumar, S. P., & Kishore, K. V. (2016, August). SVM kernel based predictive analytics on faculty performance evaluation. In 2016 International Conference on Inventive Computation Technologies (ICICT) (Vol. 3, pp. 1-4). IEEE.
  4. Fernando, Z. T., Trivedi, P., & Patni, A. (2013, August). DOCAID: Predictive healthcare analytics using naive bayes classification. In Second Student Research Symposium (SRS), International Conference on Advances in Computing, Communications and Informatics (ICACCI’13) (pp. 1-5).
  5. Kelleher, J. D., Mac Namee, B., & D'arcy, A. (2015). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT press.
  6. Kendale, S., Kulkarni, P., Rosenberg, A. D., & Wang, J. (2018). Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension. Anesthesiology, 129(4), 675-688.
  7. Lin, J., & Kolcz, A. (2012, May). Large-scale machine learning at twitter. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (pp. 793-804).
  8. Mishra, N., & Silakari, S. (2012). Predictive analytics: A survey, trends, applications, oppurtunities & challenges. International Journal of Computer Science and Information
  9. Nirbhay Bhuyar , Samadrita Acharya , Dipti Theng, 2020, Crop Classification with Multi-Temporal Satellite Image Data, International Journal of Engineering Research & Technology (IJERT) Volume 09, Issue 06 (June 2020).
  10. Nithya, B., & Ilango, V. (2017, June). Predictive analytics in health care using machine learning tools and techniques. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 492-499). IEEE.
  11. Rajeshkanna, A., Preetha, V., & Arunesh, K. (2019, March). Experimental Analysis of Machine Learning Algorithms in Classification Task of Mobile Network Providers in Virudhunagar District. In International Conference on E-Business and Telecommunications (pp. 335-343). Springer, Cham.
  12. Ratner, B. (2017). Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data. CRC Press.
  13. Satpute, P. C., & Theng, D. P. (2013, April). Intellectual climate system for monitoring Industrial environment. In 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT) (pp. 36-39). IEEE.
  14. Shin, S. J., Woo, J., & Rachuri, S. (2014). Predictive analytics model for power consumption in manufacturing. Procedia Cirp, 15, 153-158.