Earticle

초록

영어

The rise of globalization and market liberalization are changing the face of market competitiveness significantly. The appearance of modern technology in business processes has intensified the competition and put forth new challenges for service providing companies. To cope up with changing scenarios, companies are shifting their attention on retaining the existing customers rather hiring new ones. This is more cost effective and requires lesser resource as well. The phenomenon of abandoning the company by a customer is known as churn and in this context, anticipating the customer's intention to churn is called churn prediction. Data Mining and machine learning techniques, as applied to customer behavior and usage information, can assist the churn management processes. This paper used customer usage and related information from a telecom service provider to analyze churn in telecom industry. The decision trees and its ensembles, Random Forest and Gradient Boosted trees are used as underlying statistical machine learning models for building the binary churn classifier. The implementation part has been done using apache spark which is state of the art unified data analysis framework for machine learning and data mining. In order to achieve better and efficient results, the grid based hyper-parameter optimization is applied.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Proposed Methodology
  3.1. Random Forests
  3.2. Gradient Boosted Trees (GBT)
  3.3. Random Forests versus Gradient-Boosted Trees
  3.4. Churn Dataset Description
  3.5. Decision Tree Classifier
  3.6. Random Forest Classification
  3.7. Gradient Boosted Trees
 4. Result and Discussion
  4.1. Primary Results
  4.2. Optimized Results
 5. Conclusion and Future Work
 References

키워드

저자

• Pretam Jayaswal [ Indian Institute of Information Technology Allahabad, India ]
• Bakshi Rohit Prasad [ Indian Institute of Information Technology Allahabad, India ]
• Divya Tomar [ Indian Institute of Information Technology Allahabad, India ]
• Sonali Agarwal [ Indian Institute of Information Technology Allahabad, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605