Telecom Churn Analysis using Machine Learning in Smart Cities

Sharma, Ashish; Shukla, Prafullit; Gourisaria, Mahendra Kumar; Sharma, Bhisham; Dhaou, Imed Ben

doi:10.1109/icaisc56366.2023.10085183

Cited by 1 publication

(2 citation statements)

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“…The reported experimental results demonstrated that the ensemble learning techniques (i.e., the AdaBoost classifier and XGBoost classifier) provide optimal accuracy with an area-under-curve (AUC) score of 84% in the churn prediction task. The work in [5] manages telecom customer data and proposes a churn prediction model based on machine learning algorithms. Prior data prediction, preparation, and cleaning are performed to prepare quality data for machine learning.…”

Section: Related Workmentioning

confidence: 99%

“…There are a plethora of machine learning classifiers that have been proposed to analyze customer data for predicting customer churn. These include single classifiers, such as support vector machines, naïve Bayes, decision trees, logistic regression, and k-nearest neighbors, and ensemble classifiers, such as AdaBoost, gradient boosting, XGBoost, CatBoost, and random forests [1,[5][6][7][8][9][10][11][12]. It has been asserted that ensemble classifiers perform better than single classifiers [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predictive Churn Modeling for Sustainable Business in the Telecommunication Industry: Optimized Weighted Ensemble Machine Learning

Khoh,

Pang,

Ooi

et al. 2023

Sustainability

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Customers are prominent resources in every business for its sustainability. Therefore, predicting customer churn is significant for reducing churn, particularly in the high-churn-rate telecommunications business. To identify customers at risk of churning, tactical marketing actions can be strategized to raise the likelihood of the churn-probable customers remaining as customers. This might provide a corporation with significant savings. Hence, in this work, a churn prediction system is developed to assist telecommunication operators in detecting potential churn customers. In the proposed framework, the input data quality is improved through the processes of exploratory data analysis and data preprocessing for identifying data errors and comprehending data patterns. Then, feature engineering and data sampling processes are performed to transform the captured data into an appropriate form for classification and imbalanced data handling. An optimized ensemble learning model is proposed for classification in this framework. Unlike other ensemble models, the proposed classification model is an optimized weighted soft voting ensemble with a sequence of weights applied to weigh the prediction of each base learner with the hypothesis that specific base learners in the ensemble have more skill than others. In this optimization, Powell’s optimization algorithm is applied to optimize the ensemble weights of influence according to the base learners’ importance. The efficiency of the proposed optimally weighted ensemble learning model is evaluated in a real-world database. The empirical results show that the proposed customer churn prediction system achieves a promising performance with an accuracy score of 84% and an F1 score of 83.42%. Existing customer churn prediction systems are studied. We achieved a higher prediction accuracy than the other systems, including machine learning and deep learning models.

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Section: Related Workmentioning

confidence: 99%