A Study of Mobile User Satisfaction Based on Feature Extraction
DOI:
https://doi.org/10.54097/hset.v49i.8533Keywords:
XGBoost; Lightgbm Algorithm; China Mobile; Feature Extraction.Abstract
China Mobile is the largest mobile communication operator in China, and with the promotion of its 5G applications, the issue of continuously improving its user satisfaction has become an important goal for sustainable development in the future. In this paper, data pre-processing operations were firstly performed, for data deletion and supplementation, data normalization, null filling and other steps; based on different machine learning algorithms, data feature extraction was performed to construct an effective satisfaction prediction model; entropy value method, XGBoost algorithm and lightgbm algorithm were used to train the model for prediction. The confusion matrix plot of the full variable test set was obtained by the XGBoost method, which shows that the model has some reasonableness and realistic significance.
Downloads
References
Li D.X., Wu C.L., Zou L.F. Study on the factors influencing customer satisfaction of Meituan take-out platform under O2O model Advances in Applied Mathematics 11, 5536, 2022
Xu JW, Yang Y. Integrated learning methods: A review of research. Journal of Yunnan University (Natural Science Edition) 40 (6), 1082-1092, 2018
Dong Yingying, Ge Yang, Li Kunshu, Shen Bin, Huang Shuangshuang. Research on the application of fusion model in mobile network user satisfaction prediction Post & Telecom Design Technology, 08, 2022
Zhang Yuchun. Research on happiness prediction and enhancement path based on stacked fusion method. Pure Mathematics 12, 1679, 202
Bebe Wang. Research on customer churn hierarchical prediction based on stacked integration learning in Zhejiang Mobile Company. Zhejiang University of Industry and Commerce, 2018
Ying Weiyun. Research on random forest method and its application in customer churn prediction. Management Review 24(2), 140-145.2012
Adeola Ogunleye, Qing-Guo Wang.XGBoost model for chronic kidney disease diagnosis. IEEE/ACM transactions on computational biology and bioinformatics 17(6), 2131-2140, 2019
Tetiana Borysova, Grygorii Monastyrskyi, Anetta Zielinska, Mariusz Barczak, Development of innovative activities of urban public transport service providers: a multifactorial economic and mathematical model, Sumy State University, Annual 2019
Jeff W Johnson, A heuristic method for estimating the relative weight of predictor variables in multiple regression, Multivariate behavioral research 35 (1), 1-19, 2000
Tang Q, Xia GN, Zhang Xianquan, Long F. Customer churn prediction model based on XGBoost and MLP.2020 International Conference on Computer Engineering and Applications (ICCEA), 608-612, 2020
Mohammad Javad Shabankareh, Mohammad Ali Shabankareh, Alireza Nazarian, Alireza Ranjbaran, Nader SeyyedamiriJournal of Relationship. A Stacking-Based Data Mining Solution to Customer Churn Prediction. Journal of Relationship Marketing 21 (2), 124-147, 2022
Sivasankar Karuppaiah, NP Gopalan.Enhanced Churn Prediction Using Stacked Heuristic Incorporated Ensemble Model.Journal of Information Technology Research (JITR) 14(2), 174-186, 2021
Anton Borg, Martin Boldt.Using VADER sentiment and SVM for predicting customer response sentiment. Expert Systems with Applications 162, 113746. 2020
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
