Ramalan Keciciran Pelanggan Telco Menggunakan Algoritma Pembelajaran Mesin

Xinying Chew

Ramalan Keciciran Pelanggan Telco Menggunakan Algoritma Pembelajaran Mesin

Xinying Chew

Abstract

Kertas kerja ini membentangkan satu kajian mengenai peramalan keciciran pelanggan dalam industri telekomunikasi menggunakan algoritma pembelajaran mesin. Satu koleksi data pelanggan sejarah daripada sebuah firma telekomunikasi digunakan dalam kajian ini, yang merangkumi maklumat demografi, butiran akaun pengguna dan trend penggunaan. Lapan algoritma pembelajaran mesin seperti Regresi Logistik (LR), Hutan Rawak (RF), Pengelas Pokok Keputusan (DTC), dan Mesin Vektor Sokongan (SVM), Naif Bayes (NB), K-Jiran Terdekat (KNN), AdaBoost, XGBoost telah diaplikasikan ke atas set data untuk meramalkan kebarangkalian keciciran pelanggan. Pembersihan data, analisis data penerokaan, pemilihan ciri dan, Pengekodan Integer, Pengekodan Satu Panas juga dilakukan sebelum membina model-model tersebut. Kajian menilai prestasi model berdasarkan pelbagai metrik penilaian seperti ketepatan, kejituan, dapatan semula dan skor F1. Keputusan menunjukkan model XGBoost mengatasi model lain dalam meramalkan keciciran pelanggan. Kajian ini memberikan wawasan berharga mengenai aplikasi algoritma pembelajaran mesin untuk meramalkan keciciran pelanggan dalam industri telekomunikasi, yang boleh membantu syarikat mengambil langkah proaktif untuk mengekalkan pelanggan dan mengurangkan kadar keciciran pelanggan

Keywords

Pembelajaran Mesin; Peramalan Keciciran; K-Jiran Terdekat; Pokok Keputusan; Regresi Logistik; Hutan Rawak; Naif Bayes; AdaBoost; XGBoost

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References

Alpaydin, E. (2014). Introduction to machine learning.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naïve Bayes algorithm. Knowledge-Based Systems, 192, 105361. https://doi.org/10.1016/j.knosys.2019.105361

Dalvi, P. K., Khandge, S. K., Deomore, A., Bankar, A., & Kanade, V. (2016). Analysis of customer churn prediction in telecom industry using decision trees and logistic regression. 2016 Symposium on Colossal Data Analysis and Networking (CDAN), 1–4. IEEE.

Du, P., Samat, A., Waske, B., Liu, S., & Li, Z. (2015). Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 38–53.

Hartatik, Kusrini, K., & Prasetio, A. B. (2020). Prediction of student graduation with Naive Bayes algorithm. 2020 Fifth International Conference on Informatics and Computing (ICIC). https://doi.org/10.1109/ICIC50835.2020.928862

Hemlata, J., Ajay, K., & Sumit, S. (2019). Churn prediction in telecommunication using logistic regression and LogitBoost. International Conference on Computational Intelligence and Data Science, 101–102, 10–12.

Joseph, V. R. (2022). Optimal ratio for data splitting. Statistical Analysis and Data Mining: The ASA Data Science Journal, 15(4), 531–538. https://doi.org/10.1002/sam.11583

Langarizadeh, M., & Moghbeli, F. (2016). Applying naive Bayesian networks to disease prediction: A systematic review. Acta Informatica Medica, 24(5), 364–369. https://doi.org/10.5455/aim.2016.24.364-369

Malik, S., & Runwal, S. (2023). A study on customer churn prediction. International Research Journal of Modernization in Engineering Technology and Science, 5(4), 3600–3604. https://doi.org/10.56726/IRJMETS36156

Nibbering, D., & Hastie, T. J. (2022). Multiclass-penalized logistic regression. Computational Statistics & Data Analysis, 169, 107414.

Osisanwo, F. Y., Akinsola, J. E. T., Awodele, O., Hinmikaiye, J. O., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: Classification and comparison. International Journal of Computer Trends and Technology, 48(3), 120–126.

Rodan, A., Faris, H., Alsakran, J., & Al-Kadi, O. (2014). A support vector machine approach for churn prediction in telecom industry. Information – An International Interdisciplinary Journal, 17(8).

Schapire, R. E., & Singer, Y. (1999). Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3), 297–336. https://doi.org/10.1023/A:1007614523901

Schölkopf, B., & Smola, A. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT Press.

Sjarif, N. N. A., Yusof, M. R. M., Wong, D. H., Ya’akob, S., Ibrahim, R., & Osman, M. Z. (2019). Customer churn prediction using Pearson correlation function and K-nearest neighbor algorithm for telecommunication industry. International Journal of Advance Soft Computing and Applications, 11(2).

Wu, Z., Jing, L., & Wu, B. (2022). A PCA-AdaBoost model for e-commerce customer churn prediction. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04526-5

Xie, Y., Li, X., Ngai, E. W. T., & Ying, W. (2009). Customer churn prediction using improved balanced random forests. Expert Systems with Applications, 36(3), 1–? https://doi.org/10.1016/j.eswa.2008.06.121

Zhang, Z. (2016). Introduction to machine learning: k-nearest neighbors. Annals of Translational Medicine, 4(11).

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