Churn prediction using optimized deep learning classifier on huge telecom data

With the increasing number of telecom providers and services, churn prediction gains tremendous interest in the current decade. The prediction models based on machine learning are the greatest fortune for customer retention campaigns as they pave the way to predict potential churners. Though the prediction is accurate by applying the machine learning classifiers, there are huge concerns imputing complexity in the prediction process. In most cases, the churn prediction becomes complex due to the telecom data’s inconsistency, sparsity, and hugeness. An effective churn prediction model is proposed to tackle such kinds of issues, . The proposed churn prediction is based on the optimized deep learning classifier built in the spark architecture to handle the hugeness of the telecom data. The optimized deep learning classifier is established through the optimal training of the deep convolutional neural network (DCNN) using the proposed Firefly-Spider Optimization (FSO), which is the integration of Spider Monkey Optimization (SMO) and firefly optimization algorithm (FA). The proposed prediction model’s effectiveness is analyzed using the Churn in Telecom’s dataset based on the performance measures. The proposed prediction model acquired the maximal dice coefficient, accuracy, and Jaccard coefficient of 94.61%, 94.76%, and 94.80%.