dc.contributor.author |
Sarkar, Baiyezid |
|
dc.contributor.author |
Islam, Rashadul |
|
dc.contributor.author |
Hossain, Md. Sujon |
|
dc.date.accessioned |
2021-09-15T06:23:54Z |
|
dc.date.available |
2021-09-15T06:23:54Z |
|
dc.date.issued |
2021-01 |
|
dc.identifier.uri |
http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6149 |
|
dc.description.abstract |
Client turnover is a significant issue and one of the most pressing issues for large businesses. Companies are working to develop methods to predict potential customer churn because it has such a direct impact on their revenues, especially in the telecom industry. As a result, identifying factors that contribute to consumer churn is critical in order to take the appropriate steps to minimize churn. Our work's key contribution is the creation of a Churn Prediction model that helps Telecom operators predict which customers are more likely to churn. The model created in this paper employs machine learning techniques on a big data framework to create a novel approach to feature engineering and selection. The Area under ROC Curve standard measure is used to assess the model's efficiency, and the ROC curve value obtained is 98 percent. |
en_US |
dc.language.iso |
en_US |
en_US |
dc.publisher |
Daffodil International University |
en_US |
dc.subject |
Consumer satisfaction |
en_US |
dc.subject |
Daffodil International University |
en_US |
dc.subject |
Customer |
en_US |
dc.title |
Customer Churn Prediction for Telecommunication Operator |
en_US |
dc.type |
Other |
en_US |