Customer Stress Prediction in Telecom Industries using Machine Learning
Keywords:
Customer Stress, Support Vector Machine, K-Nearest Neighbor, Linear Discriminant Analysis, One Hot EncodingAbstract
In the competitive world especially in enterprises market maintaining valuable customers is becoming a difficult task. In one situation losing a customer is like decrease in profits for telecom industry growth, in another situation the cost of acquiring new customers is much higher than the cost of retaining the existing customers, for this critical situation the telecom industries should focus on retaining existing customers. This project will analyze the customer data which was collected as open dataset and predict the customer stress by applying supervised machine learning algorithms mainly using Linear Discriminant Analysis, Support Vector Machine, K Nearest Neighbor and Random Forest.
Downloads
References
Tou, J. T., & Gonzalez, R. C. (1974). Pattern recognition principles. Addison-Wesley.
Van den Poel, D., & Larivie‘re, B. (2004). Customer attrition analysis for financial services using proportional hazard models. European Journal of Operational Research, 157, 196– 217.
Wei, C. P., & Chiu, I. T. (2002). Turning telecommunications call details to churn prediction: A data mining approach. Expert Systems with Applications, 23, 103–112.
Wong, B. K., Bodnovich, T. A., & Selvi, Y. (1997). Neural network applications in business: a review and analysis of the literature (1988–1995). Decision Support Systems, 19, 301– 320.
Zhan, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural network: The state of the art. International Journal of Forecasting, 14, 35–62.
Zhang, X., Edwards, J., & Harding, J. (2007). Personalised online sales using web usage data mining. Computers in Industry, 58, 772–782.
Berry, M. J. A., & Linoff, G. S. (2003). Data mining techniques: For marketing, sales, and customer support. John Wiley & Sons.
Buckinx, W., & Van den Poel, D. (2005). Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164(1), 252–268.
Burez, J., & Van den Poel, D. (2007). Crm at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems with Applications, 32, 277–288.
Chiang, D. A., Wang, Y. F., Lee, S. L., & Lin, C. J. (2003). Goal-oriented sequential pattern for network banking churn analysis. Expert Systems with Applications, 25, 293–302.
Coussement, K., & Van den Poel, D. (2008). Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications, 34, 313–327. Cybenco, H. (1998). Approximation by super positions ofsigmoidal.
S. Patibandla, M. Archana, and R. C. Tanguturi, “Object Tracking using Multi Adaptive Feature Extraction Technique,” International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 279–286, Jun. 2022, doi: 10.14445/22315381/ijett-v70i6p229.
G. Sadineni, A. M, and R. C. Tanguturi, “Optimized Detector Generation Procedure for Wireless Sensor Networks based Intrusion Detection System,” International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 63–72, Jun. 2022, doi: 10.14445/22315381/ijett-v70i6p208.
S. Patibandla, Dr. M. Archana, and Dr. R. C. Tanguturi, “DATA AGGREGATION BASED HYBRID DEEP LEARNING TECHNIQUE FOR IDENTIFYING THE UNCERTAINTIES AND ACCURATE OBJECT DETECTION,” Indian Journal of Computer Science and Engineering, vol. 13, no. 3, pp. 697–708, Jun. 2022, doi: 10.21817/indjcse/2022/v13i3/221303049.
Dr. S. R. Anand, Dr. R. C. Tanguturi, and S. D S, “Blockchain Based Packet Delivery Mechanism for WSN,” International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 2, pp. 1112–1117, Jul. 2019, doi: 10.35940/ijrte.b1627.078219.
M. V. Bharathi, R. C. Tanguturi, C. Jayakumar, and K. Selvamani, “Node capture attack in Wireless Sensor Network: A survey,” 2012 IEEE International Conference on Computational Intelligence and Computing Research, Dec. 2012, doi: 10.1109/iccic.2012.6510237.
