Use support vector machines to solve CRM issues

Authors

  • Ashok Kumar Reddy Nadikattu

Abstract

Big data in CRM aims to learn the knowledge that is already accessible from the customer connection using machine learning or statistical methods to guide the strategic behaviour to maximise profit. Support vector machines (SVMs) have recently been touted as a powerful tool for data mining and machine learning. At this study, the SVMs are used to tackle a real-world CRM issue in a business. The end results show that SVMs performed well overall for the CRM challenge.

References

kolla, V. ravi kiran. (2012). Heart Disease Prediction using Python Machine Learning. International Journal of Statistical Computation and Simulation, 4(1). Retrieved from https://journals.threws.com/index.php/IJSCS/article/view/149

Meenigea , N. (2014). Type 2 Diabetes mellitus treatment intensification and deintensification. Transaction on Recent Devlopment in Industrial IoT, 6(6). Retrieved from https://journals.threws.com/index.php/TRDAIoT/article/view/153

kolla, V. ravi kiran. (2011). WEATHER PREDICTION USING MACHINE LEARNING. Transaction on Recent Devlopment in Industrial IoT, 3(3). Retrieved from https://journals.threws.com/index.php/TRDAIoT/article/view/152

kolla, V. ravi kiran. (2010). Fake News Detection using Machine Learning. Transaction on Recent Devlopment in Artificial Intellgence and Machine Learning, 2(2). Retrieved from https://journals.threws.com/index.php/TRDAIML/article/view/161

Whig, P., & Ahmad, S. N. (2011a). On the performance of ISFET-based device for water quality monitoring. Int’l J. of Communications, Network and System Sciences, 4(11), 709.

Whig, P., & Ahmad, S. N. (2012a). A CMOS integrated CC-ISFET device for water quality monitoring. International Journal of Computer Science Issues, 9(4), 1694–1814.

Whig, P., & Ahmad, S. N. (2012f). Performance analysis of various readout circuits for monitoring quality of water using analog integrated circuits. International Journal of Intelligent Systems and Applications, 4(11), 103.

Whig, P., & Ahmad, S. N. (2013a). A novel pseudo-PMOS integrated ISFET device for water quality monitoring. Active and Passive Electronic Components, 2013.

Whig, P., & Ahmad, S. N. (2014a). Development of economical ASIC for PCS for water quality monitoring. Journal of Circuits, Systems and Computers, 23(06), 1450079.

Whig, P., & Ahmad, S. N. (2014c). Simulation of linear dynamic macro model of photo catalytic sensor in SPICE. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering.

Published

2014-08-17

How to Cite

Nadikattu, A. K. R. (2014). Use support vector machines to solve CRM issues. Transactions on Recent Developments in Artificial Intelligence and Machine Learning, 6(6). Retrieved from https://journals.threws.com/index.php/TRDAIML/article/view/173

Issue

Section

Articles