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All Journal Bulletin of Electrical Engineering and Informatics
Esam Mohamed Elgohary
Institute of National Planning
Electrical & Electronics Engineering

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Smart evaluation for deep learning model: churn prediction as a product case study Esam Mohamed Elgohary; Mohamed Galal; Ahmed Mosa; Ghada Atef Elshabrawy
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4180

Abstract

Customer churn prediction recently is one of the vital issues that confronts diverse business industries to sustain the customers base and profits. On the other hand, data scientists employ gigantic customer data to automate the data modelling process to offer these models as a generally portable service. This research has two main contributions: deep learning customer churn prediction model and smart evaluation prediction model service. So, this service harnesses any customer data to automate building, evaluation, and deployment of the churn prediction model. The research consists of three main parts. Firstly, it illustrates the dataset labelling which annotates customers data into churn or non-churn. Secondly, the deep learning churn prediction framework using convolutional neural network (CNN) algorithm. Finally, a case study is presented to show how churn prediction service is automatically trained and generated based on real customer data, where CNN parameters are adapted to achieve the most reliable performance in line with customers' behavior. The applied case study achieves accuracy 0.77, area under the curve (AUC) 0.84 and f1 score 0.83.
Co-Authors Ahmed Mosa Ghada Atef Elshabrawy Mohamed Galal