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Related Experiment Video

Updated: Jan 17, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

A predictive analytics approach to improve telecom's customer retention.

Asem Omari¹, Omaia Al-Omari², Tariq Al-Omari³

¹Computer Information Systems, Higher Colleges of Technology, Al Ain, United Arab Emirates.

Frontiers in Artificial Intelligence

|September 15, 2025

Summary

This study develops a customer churn prediction model for telecom companies. The Support Vector Machine (SVM) model demonstrated the highest performance in identifying customers likely to leave, enabling proactive retention strategies.

Keywords:

KNN Naive Bayes SVM customer retention logistic regression prediction

Related Experiment Videos

Last Updated: Jan 17, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

Area of Science:

Data Science
Machine Learning
Telecommunications

Background:

Customer retention is a significant challenge for telecommunication companies.
Understanding and predicting customer churn is crucial for business strategy improvement.
Proactive measures are needed to retain customers and reduce churn rates.

Purpose of the Study:

To develop an accurate predictive model for identifying potential customer churn.
To improve decision-making processes for telecom providers.
To gain deeper insights into customer behavior for service enhancement.

Main Methods:

Development and evaluation of diverse predictive models using machine learning algorithms.
Application of advanced data analysis techniques for churn prediction.
Integration of data pre-processing, feature selection, and interpretability into models.

Main Results:

A comparative analysis of various predictive techniques was conducted.
The Support Vector Machine (SVM) model achieved the highest performance in churn prediction.
The study successfully integrated effective data pre-processing, feature selection, and interpretability.

Conclusions:

The developed churn prediction models offer valuable insights for telecom companies.
The Support Vector Machine (SVM) is a highly effective method for customer churn prediction.
The research addresses existing gaps in churn prediction by enhancing model integration and interpretability.