Abstract
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.
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Nonghuloo, M.S., Aravind Reddy, R., Manideep, G., SarathVamsi, M.R., Lavanya, K. (2020). Call Churn Prediction with PySpark. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore. https://doi.org/10.1007/978-981-15-0184-5_67
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DOI: https://doi.org/10.1007/978-981-15-0184-5_67
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