Customer Churns Prediction Model Based on Machine Learning Techniques: A Systematic Review
- 10.2991/ahis.k.210913.021How to use a DOI?
- Customer Churn Prediction, Firefly Algorithms, Machine Learning Techniques, Particle Swarm Optimization (PSO), Random Forest
The customer churn prediction model is required by many companies to predict the risk of customer churn and take necessary actions to prevent churn. Recently, machine-learning techniques are highly applied in customer churn prediction. In this paper, machine learning-based models are applied to the customer churn prediction, which is reviewed with their advantages and limitations. Random Forest methods were highly used in the existing customer churn prediction models due to their advantages of effectively analyzing the features in the data. Feature selection methods such as Particle Swarm Optimization (PSO) and Firefly algorithms were applied to improve the prediction process. The Ensemble classifiers of bagging and boosting of random forest are applied to the prediction of customer churns which achieves higher performance. Deep learning models such as Long Short Term Memory (LSTM) and Convolution Neural Network (CNN) were applied for prediction and achieves higher performance. Random forest model, LSTM, and CNN models have the limitations of overfitting problem of customer churn prediction. Feature selection techniques of PSO and firefly methods have the limitations of poor convergence and lower efficiency in handling the imbalanced dataset.
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Venkata Pullareddy Malikireddy AU - Madhavi Kasa PY - 2021 DA - 2021/09/13 TI - Customer Churns Prediction Model Based on Machine Learning Techniques: A Systematic Review BT - Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021) PB - Atlantis Press SP - 167 EP - 174 SN - 2589-4900 UR - https://doi.org/10.2991/ahis.k.210913.021 DO - 10.2991/ahis.k.210913.021 ID - Malikireddy2021 ER -