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Predicting Customer Churn Rate in the Telecommunication Industry Using Machine Learning
- 1 Physical Science, University of California, Irvine, California, 92612, United States
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Abstract
The paper mainly focuses on predicting the customer churn rate by using Machine learning models. The customer churn rate becomes a serious problem because of the competitive market. Many companies care about the number of customer rates because the company wants to retain the current customers to let the current customers buy the company’s product and service. In addition, the cost of retaining an existing customer is lower than the cost of acquiring new customers. That means lots of telecommunication companies want to lower the customer attrition rate to make the company more beneficial. To find the attrition rate, companies must find the data and use models to analyze the data. The paper will use several models to analyze the data from Kaggle and find which models are better for analyzing the attrition rate by using Python. It will include several steps to analyze the data: Exploratory Data Analysis, Feature Engineering, Feature Selection, Model Tuning, Stacking model, and Final analysis and results. Lastly, the result showed that these models don’t overfit and it concluded that the logistic regression with the upsampling method is better than other models.
Keywords
Customer Churn, Telecommunication Industry, Logistic Regression
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Cite this article
Zhang,Z. (2025). Predicting Customer Churn Rate in the Telecommunication Industry Using Machine Learning. Advances in Economics, Management and Political Sciences,153,70-76.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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