A Customer Churn Prediction Model using XGBoost for the Telecommunication Industry in Nepal

Shrestha, Sagar; Shakya, Aman

doi:10.1016/j.procs.2022.12.067

Cited by 12 publications

(3 citation statements)

References 5 publications

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“…Most of the use of the XGBoost algorithm to date has been used to develop customer churn prediction models. In the paper [36], [37], these two studies discuss the challenges of unbalanced data sets in the telecommunications industry and the variations in real telecommunications data compared to publicly available data sets. By utilizing the application of XGBoost Algorithm on this dataset, it achieves 97% of accuracy evaluation performance result and 88% of F1 score.…”

Section: F Xgboostmentioning

confidence: 99%

“…[52] The final procedure in the model training session is hyperparameter tuning, in which a model is tuned in greater detail to achieve the best performance results. The process of determining the ideal settings of hyperparameters in a machine learning model in order to improve its performance is known as hyperparameter tuning [36]. Hyperparameters are external configuration variables that are used to regulate a machine learning model's training.…”

Section: A Data Collection and Dataset Preparationmentioning

confidence: 99%

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Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

Yuliana,

Iskandar,

Hendrawan

2024

IEEE Access

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5G technology is a key factor in delivering faster and more reliable wireless connectivity. One crucial aspect in 5G network planning is coverage prediction, which enables network providers to optimize infrastructure deployment and deliver high-quality services to customers. This study conducts a comprehensive analysis of machine learning algorithms for 5G coverage prediction, focusing on dominant feature parameters and accuracy. Notably, the Random Forest algorithm demonstrates superior performance with an RMSE of 1.14 dB, MAE of 0.12, and R2 of 0.97. The CNN model, the standout among deep learning algorithms, achieves an RMSE of 0.289, MAE of 0.289, and R2 of 0.78, showcasing high accuracy in 5G coverage prediction. Random Forest models exhibit near-perfect metrics with 98.4% accuracy, precision, recall, and F1-score. Although CNN outperforms other deep learning models, it slightly trails Random Forest in performance. The research highlights that the final Random Forest and CNN models outperform other models and surpass those developed in previous studies. Notably, 2D Distance Tx Rx emerges as the most dominant feature parameter across all algorithms, significantly influencing 5G coverage prediction. The inclusion of horizontal and vertical distances further improves prediction results, surpassing previous studies. The study underscores the relevance of machine learning and deep learning algorithms in predicting 5G coverage and recommends their use in network development and optimization. In conclusion, while the Random Forest algorithm stands out as the optimal choice for 5G coverage prediction, deep learning algorithms, particularly CNN, offer viable alternatives, especially for spatial data derived from satellite images. These accurate predictions facilitate efficient resource allocation by network providers, ensuring high-quality services in the rapidly evolving landscape of 5G technology. A profound understanding of coverage prediction remains pivotal for successful network planning and reliable service provision in the 5G era.

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Section: F Xgboostmentioning

confidence: 99%

Section: A Data Collection and Dataset Preparationmentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

Yuliana,

Iskandar,

Hendrawan

2024

IEEE Access

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“…Nguyen et al [12] in the same year, used the kernel SVM to predict customer's churn for telecom companies and they achieved a classification accuracy rate of 98.9%. Shrestha and Shakya [13] in the same year also used XGBoost to address the same problem of telecom customer's churn prediction and they achieved an accuracy rate of 96%. Pebrianti et al [14] used and compared eight different machine learning models including SVM, RF, LR, and XGBoost, and they confirmed that XGBoost obtained the best accuracy rate that reached 94%.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel artificial intelligent-based approach for real time prediction of telecom customer’s coming interaction

Hussien,

Mahgoub,

Youssef

et al. 2024

IJEECS

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<p><span>Predicting customer’s behavior is one of the great challenges and obstacles for business nowadays. Companies take advantage of identifying these future behaviors to optimize business outcomes and create more powerful marketing strategies. This work presents a novel real-time framework that can predict the customer’s next interaction and the time of that interaction (when that interaction takes place). Furthermore, an extensive data exploratory analysis is performed to gain more insights from the data to identify the important features. Transactional data and static profile data are integrated to feed a deep learning model which is implemented using two methodologies: time-series approach and statistical approach. It is found that the time-series approach gives the best performance and fulfills all the requirements. The experiments show that the proposed framework introduces a good overall performance in comparison to existing approaches based on standard metrics like accuracy and mean absolute error (MAE) values. What makes the proposed work novel and special is that it is the first approach that addresses the telecom customer’s next future interaction not just churn prediction like the other approaches in literature.</span></p>

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Rough Set Decision Rules for Usage-Based Churn Modeling in Mobile Telecommunications

Przybyła-Kasperek,

Sulikowski

2024

Communications in Computer and Information Science

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A Customer Churn Prediction Model using XGBoost for the Telecommunication Industry in Nepal

Cited by 12 publications

References 5 publications

Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

A novel artificial intelligent-based approach for real time prediction of telecom customer’s coming interaction

Rough Set Decision Rules for Usage-Based Churn Modeling in Mobile Telecommunications

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