A Novel Stochastic Gradient Descent Based Logistic Regression (SGD-LR) Framework for Customer Churn Prediction

Customer churn is a crucial issue in any company or organization, and it describes the loss of customers as a result of them switching to competitors. When there is an opportunity to discover customer churn sooner, the organization may take steps such as providing important knowledge for keeping and boosting the client count. Deep Learning (DL) models have recently gained popularity because to their remarkable performance boost in a variety of fields. In this paper, a DL-based Customer Churn Prediction (CCP) is introduced using Stochastic Gradient Descent Based Logistic Regression (SGD-LR) with an LR classifier model. Effective categorization may be achieved by combining SGD with LR. The provided SGD-LR model is evaluated against a benchmark dataset, with the outcomes examined over a range of epochs. Furthermore, a comparison study with the outcomes of existing approaches is conducted. The implementation results demonstrated that the provided SGD-LR model outperformed the current CCP models on the same dataset.

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