IDENTIFYING SPAM MESSAGES FOR KAZAKH LANGUAGEUSING HYBRID MACHINE LEARNING MODEL

Abstract

This paper describes a spam detection system for Kazakh Language using Hybrid Machine Learning Model. The lack of spam detection systems in the Kazakh language calls for the need of a proposed system that can identify unwanted messages. The system integrates multiple Machine Learning algorithms to accurately classify spam and non-spam messages. The performance of the system is evaluated using metrics such as accuracy, precision, recall, and F1-score. Results show that the proposed solution outperforms existing spam detection techniques in terms of detecting spam with a low false positive rate and high accuracy. The findings of this research
contribute to the development of effective spam detection systems for the
Kazakh language and provide insights for future work in this field.