Predictive Model on Churn Customers using SMOTE and XG-Boost Additive Model and Machine Learning Techniques in Telecommunication Industries
DOI:
https://doi.org/10.32628/IJSRST218498Keywords:
SMOTE, XG-Boost, Predictive, Machine LearningAbstract
In this research paper the researcher builds a predictive model on churn customers using SMOTE and XG-Boost additive model and machine learning techniques in Telecommunication Industries. Customer’s churning is one of the global research issues in telecommunication industries. In somehow customers are not satisfying from telecommunication customer services, call rate, international plan, data pack, and others which are having a significant impact on customer’s services. The researcher used the SMOTE and XGboost technique to handle the imbalanced dataset and gives the higher-level accuracy for predictive model to identify the category of customer whether they are in churn or not churn. The researcher used the comparative study between logistics regression and random forest algorithms to classify the category of churn customers and non-churn customers in Telecommunication Industries. The predictive model is verifying at 96% accuracy level and can be capable to handle imbalance dataset. As per the data analysis the score of the confusion matrix is such as accuracy 94%, Precision for “ did not leave “ is 0.97 whereas recall is 0.96, and F1score is 0.97 with the support features of 903. For the churn customers precision is 0.80, recall is 0.81, F1-score is 0.80 and support features is 160, the data analysis report shows that the predictive model is having 94% accuracy whereas at 6% does not predict accurately about the customers status. Finally, the researcher concluded that the predictive model is more accurate and can be capable to handle imbalance dataset. The researchers assure that the predictive model would be benefited for the telecommunication industries to categories the churn/ non-churn customers and accordingly the organization can make changes their business plan and policies which would be benefited for the customers.
References
- YayaXieaXiuLiaE.WTNgaibWeiyunYingc (2008). Customer Predicting using Advanced Informal Forests, Expert Systems with Applications, Volume 36, Issue 3, Part 1, April 2009, Pages 5445-5449, Copyright © 2008 Elsevier Ltd. https: //doi.org/10.1016/j.eswa.2008.06.121.
- Shin-Yuan, HungaDavid, noC. YenbHsiu-YuWangc (2006). Applying Data Mining To Telecom Churn Management, Expert Systems with Applications, Volume 31, Issue 3, October 2006, Pages 515-524, Copyright © 2008 Elsevier Ltd. https://doi.org/10.1016/j.eswa.2005.09.080.
- Hyunseok Hangman Taesoo Jung EuihoSuh (2004). An LTV model and customer segmentation based on value: a case study on the wireless telecommunication industry, Expert Systems with Applications, Volume 26, Issue 2, February 2004, Pages 181- 188, Copyright © 2008 Elsevier Ltd, https://doi.org/10.1016/S0957-4174(03)00133-7
- Miguel A.P.M. Lejeune, (2001). Measuring the impact of data mining on churn management, Internet Research, Vol. 11 Issue: 5, p. 375-387, https://doi.org/10.1108/10662240110410183.
- Chih-Ping, Weia-Tang Chiub (2002). Transforming telecommunications data to predict prediction: data mining method, Application Systems System, Volume 23, Issue 2, August 2002, Pages 103-112, Copyright © 2008 Elsevier Ltd, https://doi.org/10.1016/S0957-4174 (02) 00030-1.
- John Haddena, Ashutosh Tiwari Raj Kumar, and Roya Dymitr Rutab (2007). Computer assisted customer churn management: State-of-the-art and future trends, Computers & Operations Research, Volume 34, Issue 10, October 2007, Pages 2902-2917, Copyright © 2008 Elsevier Ltd, https://doi.org/10.1016/j.cor.2005.11.007.
- Scott A. Neslin, Sunil Gupta, Wagner Kamakura, Junxiang Lu, and Charlotte H. Mason (2006). Error Detection: Measuring and Understanding the Predictable Accuracy of Churn Customer Models. Marketing Research Journal: May 2006, Vol. 43, no. 2, pages 204-211, https: //doi.org/10.1509/jmkr.43.2.204.
- Chris Rygielskia, Jyun-Cheng, and WangbDavid C. He (2002). Methods of data mining customer management data, Technology in Society, Volume 24, Issue 4, November 2002, Pages 483-502, Copyright © 2008 Elsevier Ltd, https://doi.org/10.1016/S0160-791X (02) 00038-6.
- Dudyala Anil Kumar, V. Ravi (2002). Predicting credit card customers in data mining banks, International Journal of Data Analysis Techniques and Strategies, Volume 1, Issue 1, 1 Institute for Development and Research in Banking Technology, Castle Hills Road # 1, Masab Tank, Hyderabad 500 057 (AP), India. https://doi.org/10.1504/IJDATS.2008.02002.
- Shu-Hsien, LiaoPei-Hui, and ChuPei-YuanHsiao (2012). Data mining techniques and their use - Tenth Review from 2000 to 2011, Expert Systems with Applications, Volume 39, Issue 12, 15 September 2012, Pages 11303-11311, Copyright © 2008 Elsevier Ltd, https://doi.org/10.1016/j.eswa.2012.02.063.
- Bong-Horng, ChuacMing-ShianTsaia, and Cheng-SeenHob (2007). Toward a hybrid data mining model for retention customer', Knowledge Based Knowledge, Volume 20, Issue 8, December 2007, Pages 703 -718, Copyright © 2008 Elsevier Ltd, https://doi.org/10.1016/j.knosys.2006.10.003.
- Su-YeonKima, Tae-Soo JungbEui-HoSuhc, and Hyun-SeokHwangd (2006). Customer segregation and strategic development according to the value of customer life: Case studies, Application System, Volume 31, Issue 1, July 2006, Pages 101- 107, Copyright © 2008 Elsevier Ltd, https://doi.org/10.1016/j.eswa.2005.09.004.
- Eser Kandogan (2001). Visualizing multi-dimensional cluster, trends, and outliers using star coordinates, KDD '01 Proceedings of ACM SIGKDD's seventh conference on data acquisition and data mining, Pages 107-116, San Francisco , California - August 26 - 29, 2001, ACM New York, NY, USA © 2001, ISBN: 1-58113-391-X, doi. 10.1145 / 502512.502530.
- Jae-Hyeon, AhnaSang-PilHana, and Yung-SeopLeeb (2006). Customer Churn Analysis: Churn Symptoms and Consequences of Few Discrimination in the Korean Telecommunications Industry, Communication Policy, Volume 30, Issues 10 -11, November - December 2006, Pages 552-568, Copyright © 2008 Elsevier Ltd, https://doi.org/10.1016/j.telpol.2006.09.006.
- Pınar Kisioglu and Y. Ilker Topcu (2011). Applying Bayesian Belief Network approach to customer churn analysis: A case study on the telecom industry of Turkey, Expert Systems with Applications Volume 38, Issue 6, June 2011, Pages 7151 -7157, Copyright © 2008 Elsevier Ltd, https://doi.org/10.1016/j.eswa.2010.12.045.
- Chen-FuChiena and Li-FeiChenab (2008). Data mining to improve staff selection and improve human performance: Study studies in the high technology industry, Systems System with Applications, Volume 34, Issue Pages 280-290, 2008 Elsevier Ltd, https://doi.org/10.1016/j.eswa.2006.09.003.
- Chih-Fong and TsaiaYu-HsinLub (2009). Customer churn prediction by hybrid neural network, Expert Systems with Applications, Volume 36, Issue 10, December 2009, Pages 12547-12553, Copyright © 2008 Elsevier Ltd, https: //doi.org/10.1016/j.eswa.2009.05.032.
- Dirk Van den and Poel Bart Larivière (2004). Customer analysis of financial services using risky forms, European Journal of Operational Research, Volume 157, Issue 1, 16 August 2004, Pages 196-217, Copyright © 2008 I -Elsevier Ltd, https://doi.org/10.1016/S0377-2217 (03) 00069-9.
- Kristof Coussementab, Dries F. BenoitbDirk, and Van den Poelb (2010). Improving marketing decisions in the context of customer speculation using standard add-on models, Expert Systems with Applications, Volume 37, Issue 3, 15 March 2010, Pages 2132 -2143, Copyright © 2008 Elsevier Ltd., https://doi.org/10.1016/j.eswa.2009.07.029.
- Amal M. Almana et al (2014). Research on Data Mining Methods in Churn For Customer Analysis For Industry Telecom, Int. Engineering Research and Applications Journal www.ijera.com, ISSN : 2248-9622, Vol. 4, Issue 5( Version 6), May 2014, pp.165-171.
- Dr. Mamta Madan Dr. Meenu Dave Vani Kapoor Nijhawan (2015). A Review on: Data Mining for Telecom Customer Churn Management, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 9, September 2015.
- Nabgha Hashmi, Naveed Anwer Butt and Dr. Muddesar Iqbal (2013). Customer Churn Prediction in Telecommunication A Decade Review and Classification, IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 5, No 2, September 2013.
- Rahul J. Jadhav, Usharani T. Pawar (2011). Churn Prediction in Telecommunication Using Data Mining Technology, International Journal of Advanced Computer Science and Applications, Vol. 2, No. 2, February 2011.
- Li-Shang Yang, Chaochang Chiu (2006). Subscriber Churn Prediction in Telecommunications, 2006.
- Ionut Brandusoiu, Gavril Toderean (2013). Churn Prediction In The Telecommunications Sector Using Support Vector Machines Issue # 1, May 2013.
- Amjad Hudaib, Reham Dannoun, Osama Harfoushi, Ruba Obiedat, Hossam Faris (2015). Hybrid Data Mining Models for Predicting Customer Churn, J. Communications, Network and System Sciences, May 2015, 8, 91-96.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRST

This work is licensed under a Creative Commons Attribution 4.0 International License.