Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural  Network Algorithms

Abdulsalam, Sulaiman Olaniyi; Arowolo, Micheal Olaolu; Saheed, Yakub Kayode; Afolayan, Jesutofunmi Onaope

doi:10.52549/ijeei.v10i2.2985

Cited by 8 publications

(2 citation statements)

References 28 publications

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“…Decision Trees (DT) are a common machine learning method that may be used for classification as well as regression [29] [30]. Based on various input features, they build a model that predicts the value of a target variable.…”

Section: Decision Trees (Dt)mentioning

confidence: 99%

Forecasting Carbon Dioxide Emission in Thailand Using Machine Learning Techniques

Chimphlee,

Chimphlee

2023

IJEEI

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Machine Learning (ML) models and the massive quantity of data accessible provide useful tools for analyzing the advancement of climate change trends and identifying major contributors. Random Forest (RF), Gradient Boosting Regression (GBR), XGBoost (XGB), Support Vector Machines (SVC), Decision Trees (DT), K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), ensemble methods, and Genetic Algorithms (GA) are used in this study to predict CO2 emissions in Thailand. A variety of evaluation criteria are used to determine how well these models work, including Rsquared (R2), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and correctness. The results show that the RF and XGB algorithms function exceptionally well, with high Rsquared values and low error rates. KNN, PCA, ensemble methods, and GA, on the other hand, outperform the top-performing models. Their lower Rsquared values and higher error scores indicate that they are unable to accurately anticipate CO2 emissions. This paper contributes to the field of environmental modeling by comparing the effectiveness of various machine learning approaches in forecasting CO2 emissions. The findings can assist Thailand in promoting sustainable development and developing policies that are consistent with worldwide efforts to combat climate change.

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Section: Decision Trees (Dt)mentioning

confidence: 99%

Forecasting Carbon Dioxide Emission in Thailand Using Machine Learning Techniques

Chimphlee,

Chimphlee

2023

IJEEI

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“…In order to generate high revenues, three key policies are followed, such as gaining new customers, promoting the existing customers, and raising the retention time of the customers. Comparing these policies and taking the return on investment (RoI) cost of all into account, it can be inferred that the third policy is the most profitable approach [3], since retaining a present customer costs considerably less than gaining a new one. Further, it is also regarded as a simple task compared to the upselling plan.…”

Section: Introductionmentioning

confidence: 99%

Archimedes Optimization Algorithm-Based Feature Selection with Hybrid Deep-Learning-Based Churn Prediction in Telecom Industries

Mengash,

Alruwais,

Kouki

et al. 2023

Biomimetics

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Customer churn prediction (CCP) implies the deployment of data analytics and machine learning (ML) tools to forecast the churning customers, i.e., probable customers who may remove their subscriptions, thus allowing the companies to apply targeted customer retention approaches and reduce the customer attrition rate. This predictive methodology improves active customer management and provides enriched satisfaction to the customers and also continuous business profits. By recognizing and prioritizing the relevant features, such as usage patterns and customer collaborations, and also by leveraging the capability of deep learning (DL) algorithms, the telecom companies can develop highly robust predictive models that can efficiently anticipate and mitigate customer churn by boosting retention approaches. In this background, the current study presents the Archimedes optimization algorithm-based feature selection with a hybrid deep-learning-based churn prediction (AOAFS-HDLCP) technique for telecom companies. In order to mitigate high-dimensionality problems, the AOAFS-HDLCP technique involves the AOAFS approach to optimally choose a set of features. In addition to this, the convolutional neural network with autoencoder (CNN-AE) model is also involved for the churn prediction process. Finally, the thermal equilibrium optimization (TEO) technique is employed for hyperparameter selection of the CNN-AE algorithm, which, in turn, helps in achieving improved classification performance. A widespread experimental analysis was conducted to illustrate the enhanced performance of the AOAFS-HDLCP algorithm. The experimental outcomes portray the high efficiency of the AOAFS-HDLCP approach over other techniques, with a maximum accuracy of 94.65%.

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New Artificial intelligence approaches for brand switching decisions

Erkayman¹,

Erdem²,

Aydın³

et al. 2023

Alexandria Engineering Journal

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Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms

Cited by 8 publications

References 28 publications

Forecasting Carbon Dioxide Emission in Thailand Using Machine Learning Techniques

Forecasting Carbon Dioxide Emission in Thailand Using Machine Learning Techniques

Archimedes Optimization Algorithm-Based Feature Selection with Hybrid Deep-Learning-Based Churn Prediction in Telecom Industries

New Artificial intelligence approaches for brand switching decisions

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