Telecom Churn Prediction based on Deep Learning Approach

Authors

  • Israa N. Mahmood Department of Computer Science, University of Technology, Baghdad, Iraq https://orcid.org/0000-0002-9157-6800
  • Hasanen S. Abdullah Department of Computer Science, University of Technology, Baghdad, Iraq

DOI:

https://doi.org/10.24996/ijs.2022.63.6.32

Keywords:

churn prediction, deep learning, classification algorithms

Abstract

      The transition of customers from one telecom operator to another has a direct impact on the company's growth and revenue. Traditional classification algorithms fail to predict churn effectively. This research introduces a deep learning model for predicting customers planning to leave to another operator. The model works on a high-dimensional large-scale data set. The performance of the model was measured against other classification algorithms, such as Gaussian NB, Random Forrest, and Decision Tree in predicting churn. The evaluation was performed based on accuracy, precision, recall, F-measure, Area Under Curve (AUC), and Receiver Operating Characteristic (ROC) Curve. The proposed deep learning model performs better than other prediction models and achieves a high accuracy rate of 91%. Furthermore, it was noticed that the deep learning model outperforms a small size Neural Network for the customer churn prediction.

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Published

2022-06-30

Issue

Section

Computer Science

How to Cite

Telecom Churn Prediction based on Deep Learning Approach. (2022). Iraqi Journal of Science, 63(6), 2667-2675. https://doi.org/10.24996/ijs.2022.63.6.32
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