Abstract
The increase in competition in customer service sector has become paramount to invest time in customer behavior and to accurately predict the customer churn. Customer churn occurs when the customer decides to discontinue their relations with the company. Many traditional algorithms have been used to predict the churn, and thus devise various techniques for customer retention, but with the advent of deep learning paradigms, we have witnessed algorithms that give a new prospect to this very task. Deep learning permits multilayered models to represent data in multiple abstraction levels. It also greatly reduces the work of feature engineering as it automatically comes up with good features. This chapter comprises an experimental comparison of various traditional classification algorithms, namely K-nearest neighbors, naive Bayes, random forest, decision tree, and logistic regression, with artificial neural network to predict the customer churn on IBM’s Telco Customer Churn dataset. We have compared these models based on their accuracy in predicting customer churn. Our ANN model achieves an accuracy score of 82.83% on validation data, better than our performance of 79.86% achieved for the traditional approach of using K-nearest neighbors. The results suggest that the multilayered ANN model with self-learning ability and tokenized data input outperforms traditional classification algorithms.
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Momin, S., Bohra, T., Raut, P. (2020). Prediction of Customer Churn Using Machine Learning. In: Haldorai, A., Ramu, A., Mohanram, S., Onn, C. (eds) EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-19562-5_20
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DOI: https://doi.org/10.1007/978-3-030-19562-5_20
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