| Title: | GS2-2 Developing Machine Learning and Deep Learning Models for Customer Churn Prediction in Telecommunication Industry* |
|---|---|
| Publication: | ICAROB2022 |
| Volume: | 27 |
| Pages: | 533-539 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2022.GS2-2 |
| Author(s): | Teoh Jay Shen, Abdul Samad Bin Shibghatullah |
| Publication Date: | January 20, 2022 |
| Keywords: | Machine Learning, Deep Learning, Churn Prediction, Telco Industry |
| Abstract: | Customer churn is always a significant problem and one of the biggest concerns of telecommunication companies. The companies are attempting to create and design an approach to predict customer churn. This is why determining factors that causes the customer to churn is significant. The proposed models constructed in this work apply both the machine learning and deep learning algorithms. Those models was constructed and run under the Python environment and it used an open sources dataset that are available to everyone on www.kaggle.com. This dataset contained 7043 rows of customer's data with 21 features, and it was applied in the training and testing process of the models development. These models used four different types of machine learning and deep learning algorithms, which are the Artificial Neural Network, Self-Organizing Map, Decision Tree and a hybrid model with the combination of the Self-Organizing Map and Artificial Neural network algorithms. |
| PDF File: | https://alife-robotics.co.jp/members2022/icarob/data/html/data/GS/GS2/GS2-2.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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