To Study and Analyse the Customer Churn Prediction using Machine Learning Algorithm
DOI:
https://doi.org/10.32628/IJSRSET241143Keywords:
Random Forest, Logistics Regression, Customer Churn, Machine LearningAbstract
The customer churn prediction (CCP) is one of the challenging problems in the E-Commerce industry. With the advancement in the field of machine learning and artificial intelligence, the possibilities to predict customer churn has increased significantly. Our proposed methodology, consists of six phases. In the first two phases, data pre-processing and feature analysis is performed. In the third phase, feature selection is taken into consideration. Next, the data has been split into two parts train and test set in the ratio of 80% and 20% respectively. In the prediction process, most popular predictive models have been applied, namely, logistic regression, random forest classifier etc. on train set are applied to see the effect on accuracy of models. In addition, K-fold cross validation has been used over train set for hyper parameter tuning and to prevent overfitting of models. Finally, the obtained results on test set have been evaluated using confusion matrix and AUC curve.
Downloads
References
Omar Adwan, Hossam Faris, Khalid Jaradat, Osama Harfoushi, Nazeeh Ghatasheh”Predicting customer churn in telecom industry using multilayer preceptron neural networks: modeling and analysis” Life Sci. J., 11 (3) (2014), pp. 75-81
Mohammad Ridwan Ismail, Mohd Khalid Awang, M. Nordin A. Rahman, Mokhairi Makhtar”A multi-layer perceptron approach for customer churn prediction”International Journal of Multimedia and Ubiquitous Engineering, 10 (7) (2015), pp. 213-222 DOI: https://doi.org/10.14257/ijmue.2015.10.7.22
Farquad, H. &Vadlamani, Ravi &Surampudi, Bapi. (2014). Churn Prediction using Comprehensible Support Vector Machine: an Analytical CRM Application. Applied Soft Computing. 19. 10.1016/j.asoc.2014.01.031 DOI: https://doi.org/10.1016/j.asoc.2014.01.031
Kumar, Dudyala& Ravi, Vadlamani. (2008). Predicting credit card customer churn in banks using data mining. International Journal of Data Analysis Techniques and Strategies. 1. 4-28. 10.1504/IJDATS.2008.020020. DOI: https://doi.org/10.1504/IJDATS.2008.020020
D. Sikka, Shivansh, R. D and P. M, “Prediction of Delamination Size in Composite Material Using Machine Learning,” 2022 International Conference on Electronics and Renewable Systems (ICEARS), 2022, pp. 1228-1232, doi: 10.1109/ICEARS53579.2022.975212 DOI: https://doi.org/10.1109/ICEARS53579.2022.9752123
S. De, P. P and J. Paulose, "Effective ML Techniques to Predict Customer Churn," 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 2021, pp. 895-902, doi: 10.1109/ICIRCA51532.2021.9544785. DOI: https://doi.org/10.1109/ICIRCA51532.2021.9544785
Fatih Kayaalp”A review and analysis of churn prediction methods for customer retention in telecom industries” 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE (2017), pp. 1-7
Davoud Gholamiangonabadi, Jamal Shahrabi, Seyed Mohamad Hosseinioun, Sanaz Nakhodchi, Soma Gholamveisy Customer churn prediction using a new criterion and data mining; A case study of Iranian banking industry Proceedings of the International Conference on Industrial Engineering and Operations Management (2019), pp. 5-7
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
