E-Business Churn Prediction Model Using Machine Learning

Authors

  • Ayyapureddi Siva Sai Rupesh  Department of Computer Science and Engineering, Amity University Chhattisgarh, Raipur, Chhattisgarh, India
  • Advin Manhar  Assistant Professor, Department of Computer Science and Engineering, Amity University Chhattisgarh, Raipur, Chhattisgarh, India

DOI:

https://doi.org//10.32628/CSEIT23903141

Keywords:

Machine learning, Logistic Regression, Random Forest, and Customer Churn Customer retention, classification, e-business churn forecast, accuracy, precision, recall, F1-score, Log loss, ROC AUC, calibration loss, cost matrix gain

Abstract

Businesses need to keep their clients in the present competitive environment in order to remain in the market. To achieve this, they must anticipate customer attrition and take proactive steps to keep clients. In this research, we offer a model for predicting customer churn based on machine learning that can forecast the probability of consumers leaving with accuracy. To anticipate customer turnover, we employ a variety of machine learning techniques, including logistic regression, random forest, and support vector machines. To assess the effectiveness of our methodology, we additionally employ a number of assessment measures. Our findings show that the suggested model works better than the current models and can aid companies in keeping consumers. Keywords : Machine learning, Logistic Regression, Random Forest, and Customer Churn Customer retention, classification, e-business churn forecast, accuracy, precision, recall, F1-score, Log loss, ROC AUC, calibration loss, cost matrix gain

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-06-30

Issue

Volume 9, Issue 4, July-August-2023

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ayyapureddi Siva Sai Rupesh, Advin Manhar, " E-Business Churn Prediction Model Using Machine Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 4, pp.15-23, July-August-2023. Available at doi : https://doi.org/10.32628/CSEIT23903141