Customer churn prediction model in cloud environment using DFE-WUNB: ANN deep feature extraction with Weight Updated Tuned Naïve Bayes classification with Block-Jacobi SVD dimensionality reduction

Abstract

With progressing competitive market, different organizations were desperate to hold this churn rate as minimum value, hence to achieve this, building an effective (CCP) customer churn prediction model is essential. In order to address those issues in CCP, the study deliberated the churn prediction model using DFE-WUNB (Deep Feature Extraction with Weight Updated Tuned Naïve Bayes classifier) in a cloud-computing environment. Due to the huge non-linear features of the Telco customer churn dataset, the pre-processed features are deeply learned by the subsequent two models of ANN. In ANN, the input feature gets multiplied by the weight value, and the resultant output feature passes to the next dense layer in ANN. The deep feature extraction in ANN models facilitates precise accuracy in determining relevant churn features. However, the higher matrix dimensions of features exhibit complexity in prediction, hence this Block Jacobi SVD algorithm is applied to decrease the dimensions of features, such as to create a sparse dataset projected to get fit to the training model for efficient classification. The dimension-reduced features pass through enhanced weighing Naïve Bayes algorithm gets updated with ANN weights and tunes the parameters having greater and best weights to NB classifier, improvising the classification accuracy performance. The comparative assessment of the proposed DFE-WUNB churn prediction model delineated the efficiency of highly accurate churn prediction, outperforming other conventional churn prediction models.