Work2023

A Machine Learning Model for Customer Churn Prediction using CatBoost Classifier

Jeyaraj Jane Rubel Angelina · Sottallu Janakiram Subhashini · P. Dheeraj Kumar Reddy · P. V. Sudheer Kumar Reddy · Kabir Khan

6 citations0/0 files storedInternational Conference on Intelligent Computing and Control Systems (ICICCS)

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Quality of Service (QoS) is now attracting an increasing number of clients towards a company. Meanwhile, the ability to provide clients with technologically better QoS is highly demanding in recent times. ON the other hand, effective customer interactions may assist the organization to gain new clients, retain existing ones, and improve customer retention by bringing in more income. This research study has used Cat Boost Classifier to predict the customer behavior, retain the customers and increase the company’s benefit. The proposed model provides a prediction accuracy of 95%, when compared with the existing methods.

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References

2019Customer churn prediction in telecom using machine learning in big data platform 2019A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector 2018Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies 2020Churn Prediction in Telecommunication using Logistic Regression and Logit Boost 2019Incorporating textual information in customer churn prediction models based on a convolutional neural network 2021Integrated Churn Prediction and Customer Segmentation Framework for Telco Business 2020A Survey on Churn Analysis in Various Business Domains 2019Churn Prediction using Neural Network based Individual and Ensemble Models

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