Publication: Modelling of Churn in Telecom Industry Using Machine Learning
| dc.contributor.author | Amir Rushaidi bin Mohd Esa | en_US |
| dc.date.accessioned | 2023-05-03T17:23:32Z | |
| dc.date.available | 2023-05-03T17:23:32Z | |
| dc.date.issued | 2020-02 | |
| dc.description | FYP 2 SEM 2 2019/2020 | en_US |
| dc.description.abstract | Churn analysis in telecom industry has long been a one of the major researches in telecom companies in order for them to analyze their customer’s potential whether to stop their subscription or keep the subscription. A huge and vast amount of information are needed to analyze them to get the prediction as accurate as possible. Hence, machine learning model are one of the effective methods required to help to further the analyzation and achieved the desired results. This paperwork proposes a churn analysis or prediction model that uses several methods and steps to make the prediction based on a set of parameters from customers to know their potential to churn or not. The analyzation is mainly conducted on WEKA Explorer Toolkit, a software developed by the University of Waikato. From the raw dataset obtained, the file format is converted into different file format, which then will be undergoing attribute selection process using a method called Correlation Based Feature Selection. Finally, after the attribute selection, customer classification process is executed to finally classify the class of the customer which is narrowed down to two classes; churn or not churn. This paper also introduces several other machine learning models that are used to compare between other models on which will yield the best results based on several factors. | en_US |
| dc.identifier.uri | https://irepository.uniten.edu.my/handle/123456789/21601 | |
| dc.language.iso | en | en_US |
| dc.subject | Modelling Churn Telecom | en_US |
| dc.title | Modelling of Churn in Telecom Industry Using Machine Learning | en_US |
| dspace.entity.type | Publication |