An Effective Classifier for Predicting Churn in Telecommunication

Jour of Adv Research in Dynamical & Control Systems, Vol. 11, 01-Special Issue, 2019

9 Pages Posted: 17 Jun 2019

See all articles by J. Pamina

J. Pamina

Sri Krishna College of Technology

Beschi Raja

Sri Krishna College of Technology

S. SathyaBama

Soundarya S

Sri Krishna College of Technology

M. S. Sruthi

Sri Krishna College of Technology

Kiruthika S

Sri Krishna College of Technology

Aiswaryadevi V J

Sri Krishna College of Technology

Priyanka G

Sri Krishna College of Technology

Date Written: June 6, 2019

Abstract

In recent days, telecom industry plays a major role in our daily life. The proliferation of telecommunication industry becomes very difficult for the service providers to survive in the market. To stabilize in this field, the service providers have to be aware of the features that make the customer to churn. The proposed predictive model identifies the traits that highly influence customer churn, with the help of machine learning techniques like KNN, Random Forest and XG Boost. IBM Watson dataset has been analysed to forecast the churn. At last a comparative study has been made among the machine learning algorithm to identify the better algorithm of higher accuracy. The proposed model shows that Fiber Optic customers with greater monthly charges have higher influence for churn.

Keywords: Churn, Telecommunication, XGBoost, KNN, Random Forest, IBM Watson

Suggested Citation

Pamina, J. and Raja, Beschi and SathyaBama, S. and S, Soundarya and Sruthi, M. S. and S, Kiruthika and V J, Aiswaryadevi and G, Priyanka, An Effective Classifier for Predicting Churn in Telecommunication (June 6, 2019). Jour of Adv Research in Dynamical & Control Systems, Vol. 11, 01-Special Issue, 2019 , Available at SSRN: https://ssrn.com/abstract=3399937

J. Pamina

Sri Krishna College of Technology ( email )

Arivoli Nagar, Perumal Nagar, Kovai Pudur
Coimbatore, Tamil Nadu 641042
India

Beschi Raja (Contact Author)

Sri Krishna College of Technology ( email )

Arivoli Nagar, Perumal Nagar, Kovai Pudur
Coimbatore, Tamil Nadu 641042
India
9944712302 (Phone)

Soundarya S

Sri Krishna College of Technology ( email )

Arivoli Nagar, Perumal Nagar, Kovai Pudur
Coimbatore, Tamil Nadu 641042
India

M. S. Sruthi

Sri Krishna College of Technology ( email )

Arivoli Nagar, Perumal Nagar, Kovai Pudur
Coimbatore, Tamil Nadu 641042
India

Kiruthika S

Sri Krishna College of Technology ( email )

Arivoli Nagar, Perumal Nagar, Kovai Pudur
Coimbatore, Tamil Nadu 641042
India

Aiswaryadevi V J

Sri Krishna College of Technology ( email )

Arivoli Nagar, Perumal Nagar, Kovai Pudur
Coimbatore, Tamil Nadu 641042
India

Priyanka G

Sri Krishna College of Technology ( email )

Arivoli Nagar, Perumal Nagar, Kovai Pudur
Coimbatore, Tamil Nadu 641042
India

No contact information is available for S. SathyaBama

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