| Autor: |
M, Veena Mohan, Vanamala, C. K. |
| Předmět: |
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| Zdroj: |
Grenze International Journal of Engineering & Technology (GIJET); Jan2024, Vol. 10 Issue 1, Part 1, p1031-1037, 7p |
| Abstrakt: |
In the Telecommunication and entertainment Industry, customer churn detection is one of the most important research topics that the company must deal with retaining on-hand customers. Churn means the loss of customers due to existing offers from the competitors or maybe due to network issues. In these types of situations, the customer may tend to cancel the subscription to a service. The Churn rate has a substantial impact on the lifetime value of the customer because it affects the future revenue of the company and the length of service. Due to a direct effect on the income of the industry, the companies are looking for a model that can predict customer churn. The model developed in this work uses machine learning techniques. By using machine learning algorithms, we can predict the customers who are likely to cancel the subscription. Using this, we can offer them better services and reduce the churn rate. These models help telecom services to make them profitable. In this model, we used Logistic Regression, Random Forest, and SVM for churn rate prediction. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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