Telco Customer Churn Prediction Using ML Models


  • Padmanabh Wanikar Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India.
  • Sudhanshu Maurya Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
  • Muktinath Vishvakarma Visvesvaraya National Institute of Technology Nagpur, India.
  • Karimisetty Sujatha Dadi Institute of Engineering & Technology, Anakapalle, India
  • Nitin Rakesh Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India.
  • Vrince Vimal Graphic Era Hill University, Dehradun; Graphic Era Deemed to be University, Dehradun, India
  • Nilesh Shelke Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India


Artificial Neural Network, Customer, Churn, Machine Learning, Predictive Models, Telecommunications


This study aims to develop a robust customer churn prediction model in the communications industry. Using various machine learning and analysis methods, we aim to improve the accuracy and efficiency of customer churn prediction. Our research explores integration techniques such as bundling and bracing to combine predictions from multiple models and reduce variability. We likewise lead awareness examination to assess the effect of various factors on the presentation model. We calibrate the model through factors like learning rate, number of layers, and group size and decide the best arrangement to assess the misfortune. We likewise broaden the existing examination of client rivalry forecasts in the correspondence business by consolidating the utilization of vast amounts of information. By utilizing the force of extensive information examination, we expect to build the versatility and effectiveness of client-agitate forecast models. Analysis of this study's consequences included using different AI calculations. The fundamental reason for this study is to anticipate client beat in the correspondence business utilizing AI and extensive information. Research has shown that client agitates can be precisely expected using this procedure.


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How to Cite

Wanikar, P. ., Maurya, S. ., Vishvakarma, M. ., Sujatha, K. ., Rakesh, N. ., Vimal, V. ., & Shelke, N. . (2023). Telco Customer Churn Prediction Using ML Models. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 644 –. Retrieved from



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