A classification problem with more than two classes, such as identifying a pug, bulldog, or trabeation mastiff from a series of dog breed images. Multi-class classification assumes that each sample is assigned to one of several classes; for example, a dog can be a pug or a bulldog, but not both at the same time.
Published in Chapter:
A Machine Learning Approach to Classify the Telecommunication Customers Based on Their Profitability
Selvaratnam Mishoba (Sabaragamuwa University of Sri Lanka, Sri Lanka), Kuhaneswaran Banujan (Sabaragamuwa University of Sri Lanka, Sri Lanka), Senthan Prasanth (Sabaragamuwa University of Sri Lanka, Sri Lanka), and B. T. G. S. Kumara (Sabaragamuwa University of Sri Lanka, Sri Lanka)
Copyright: © 2022
|Pages: 25
DOI: 10.4018/978-1-7998-9553-4.ch001
Abstract
Customer profitability is one of the most critical problems faced by businesses today. Keeping an existing customer is more valuable than gaining a new subscriber in the telecommunication industry. As a result, anticipating customer attrition behavior in advance is challenging. This behavior has prompted most researchers to establish a model for categorizing clients based on their profitability levels in various businesses. This study was carried out with the assistance of a local telecommunication service provider. Approximately 10,000 pre-paid subscriber details with 12 attributes were acquired. Furthermore, the classification technique was used to reduce the dimensionality between features and classify the high profitable customers, low profitable customers, and average profitable customers. The data was then fed into various supervised learning algorithms to choose the optimum algorithm by considering certain evaluation metrics for developing the final prediction model. The proposed approach revealed that the SVM outperformed all other techniques with greater accuracy of 80.00%.