Authors : Anitha R; Aameer Khan S; Harini Murugan; Nithisshkrishna KS

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/bdmvnrs4

Scribd : https://tinyurl.com/5frhvn2d

DOI : https://doi.org/10.38124/ijisrt/24apr643

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Abstract : In the dynamic landscape of today's business environment, customer retention is a critical factor for sustainable growth and success. This project focuses on developing and comparing machine learning models for customer attrition and churn prediction using state-of-the-art algorithms such as Affinity, Birch, KMeans, and Agglomerative Clustering. The objective of this study is to evaluate the effectiveness of these clustering algorithms in identifying patterns and predicting customer churn. Using a dataset containing historical customer data, the project aims to create prediction models that can assist firms in proactively addressing possible churn concerns and implementing targeted retention efforts. The study is significant because it can give businesses predictive analytics capabilities to enhance their customer relationship management strategies, by figuring out which customers are likely to leave. In addition, the project intends to execute label selection by evaluating each feature individually according to its impurity score and to perform cluster classification to choose the optimal cluster according to its metrics. The study concentrates on the crucial machine learning methods for calculating client churn. This can include improving customer service, offering loyalty programs, or adjusting pricing strategies.

Keywords : Customer Attrition - Apache Spark - K-Means Clustering - Web Application - Customer Retention- Logistic Regression - Machine Learning Algorithms.

References :

1. Burez J., & Van den Poel, D “Crm at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services”, Expert Systems with Applications 32, 277– 288.
2. Ledro, C., Nosella, A., & Vinelli, A. (2022). Artificial intelligence in customer relationship management: literature review and future research directions. Journal of Business & Industrial Marketing, 37(13), 48-63.
3. Jain, H., Khunteta, A., & Srivastava, S. (2020). Churn prediction in telecommunication using logistic regression and logit boost. Procedia Computer Science, 167, 101-112.
4. Khulood Ebrah, Selma Elnasir “Churn Prediction Using Machine Learning and Recommendations Plans for Telecoms”.Journal of Computer and Communications > Vol.7 No.11, November 2019.
5. Nagaraju Jajam, Nagendra Panini Challa, Kamepalli S.L.Prasanna “Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction” in IEEE vol 11.
6. Soumi De, Prabu.P” A Sampling-Based Stack Framework for Imbalanced Learning in Churn Prediction in IEEE vol 10.
7. Prabadevi.B, Shalini.R, Kavitha.B.R (2023). Customer Churning analysis using machine learning algorithms. In International Journal of Intelligent Networks.
8. M. Alizadeh, D. S. Zadeh, B. Moshiri and A. Montazeri, "Development of a Customer Churn Model for Banking Industry Based on Hard and Soft Data Fusion," in IEEE Access, vol. 11, pp. 29759-29768, 2023, doi: 10.1109/ACCESS.2023.3257352
9. Anand, M., Shaukat, I., Kaler, H., Narula, J., & Rana, P. S. Hybrid Model for the Customer Churn Prediction
10. Zadoo, A., Jagtap, T., Khule, N., Kedari, A., & Khedkar, S. (2022, May). A review on churn prediction and customer segmentation using machine learning. In 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) (Vol. 1, pp. 174-178). IEEE..
11. Mitchell, T.M. (2015) Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression.
12. Han, J., Pei, J. and Kamber, M. (2011) Data Mining: Concepts and Techniques. Elsevier, Amsterdam.
13. PM, U., & Balaji, N. V. (2019). Analyzing Employee attrition using machine learning. Karpagam Journal of Computer Science, 13, 277-282.
14. Abdulsalam Sulaiman Olaniyi , Arowolo Micheal Olaolu , Bilkisu Jimada- Ojuolape , Saheed Yakub Kayode,,” Customer Churn Prediction in Banking Industry Using K-Means and Support Vector Machine Algorithm. In International Journal of Multidisciplinary Sciences and Advanced Technology Vol 1 No 1 (2020) 48–54.
15. Xiancheng Xiahou and Yoshio Harada , "B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM.
16. Seymen, O. F., Dogan, O., & Hiziroglu, A. (2020, December). Customer churn prediction using deep learning. In International Conference on Soft Computing and Pattern Recognition (pp. 520-529). Cham: Springer International Publishing.
17. Fujo, S. W., Subramanian, S., & Khder, M. A. (2022). Customer churn prediction in the telecommunication industry using deep learning. Information Sciences Letters, 11(1), 24.