Call Churn Prediction with PySpark

  • Mark Sheridan Nonghuloo
  • Rangapuram Aravind Reddy
  • Ganji Manideep
  • M. R. SarathVamsi
  • K. LavanyaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1057)


Different markets over the world are ending up progressively more saturated, with an ever-increasing number of customers swapping their enrolled benefits between contending organizations. Consequently, organizations have understood that they should center their promoting endeavors in client maintenance instead of client procurement. It limits client surrender by foreseeing which clients are probably going to cross out a membership to an administration. In spite of the fact that initially utilized inside the telecommunication business, it has turned out to be regular practice crosswise over banks, ISPs, insurance firms and other verticals. In this paper, an end to end churn prediction is done in view of client call information records. We take a gander at what sorts of client information are normally utilized, do some preparatory investigation of the information and create churn prediction models with PySpark. PySpark processes huge datasets at minimal time and when it comes to the synchronization points as well as errors, framework easily handles at the back end. The PySpark API takes advantage of Spark to deliver dramatic improvements in processing speed for large sets of data.


Client procurement Churn PySpark Call information Telecommunication 


  1. 1.
    Lu, J., Lin, H., Lu, N., Zhang, G.: A customer churn prediction model in telecom industry using boosting. IEEE Trans. Indu. Inf. 10(2), 1659–1665 (2014)CrossRefGoogle Scholar
  2. 2.
    Chlang, D.A.: A recommender system to avoid customer churn. Expert Syst. Appl. 36(4), 8071-8075 (2003)Google Scholar
  3. 3.
    Malathi, A., Kamalraj, N.: Applying data mining techniques in telecom churn prediction. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 10 (2013)Google Scholar
  4. 4.
    Baizal, Z.K.A., Effendy, V.: Handling imbalanced data in customer churn prediction using combined sampling and weighted random forest. In: 2014 2nd International Conference (ICoICT), pp. 325–330. IEEE (2014)Google Scholar
  5. 5.
    Siddeshwar, V., Ravi, V., Sundarkumar, G.G.: One-class support vector machine based undersampling: application to churn prediction and insurance fraud detection. In: 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–7. IEEE (2015)Google Scholar
  6. 6.
    Olle, G.D., Cai, S.: A hybrid churn prediction model in mobile telecommunication industry. Int. J. e-Educ. e-Bus. e-Manage. e-Learn. 4(1), 55 (2014)Google Scholar
  7. 7.
    Hung, S.Y., Yen, D.C., Wang, H.Y.: Applying data mining to telecom churn management. Exp. Syst. Appl. 31, 515–524 (2006) CrossRefGoogle Scholar
  8. 8.
    Chiyu, Z., Yihui, Q.: Research of indicator system in customer churn prediction for telecom industry. In: 2016 11th ICCSE, pp. 123–130. IEEE (2016)Google Scholar
  9. 9.
    Ahn, J.-H., Han, S.-P., Lee, Y.-S.: Customer churn analysis: churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry. Telecommun. Policy 30(10), 552–568 (2006) CrossRefGoogle Scholar
  10. 10.
    Neslin, S.A., Gupta, S., Kamakura, W., Junxiang, L., Mason, C.H.: Defection detection: measuring and understanding the predictive accuracy of customer churn models. J. Mark. Res. 43(2), 204–211 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Mark Sheridan Nonghuloo
    • 1
  • Rangapuram Aravind Reddy
    • 1
  • Ganji Manideep
    • 1
  • M. R. SarathVamsi
    • 1
  • K. Lavanya
    • 1
    Email author
  1. 1.School of Computer Science and EngineeringVIT UniversityVelloreIndia

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