Authors - Rupali Ramdas Shevale, Monika Sharad Deshmukh Abstract - For efficient real-time decision-making in a variety of domains, including cybersecurity, finance, and the Internet of Things, accurate and trustworthy event categorization is crucial. By maximizing feature integration, this study explores how incorporating Redpanda, a real-time data streaming platform, into predictive algorithms might improve event categorization. Continuous, high-throughput data processing is made possible by Redpanda's low-latency, fault-tolerant architecture, which enables the real-time extraction of a variety of accurate attributes. Predictive models may use Redpanda's capability to access current, augmented feature sets, which will greatly increase classification accuracy and dependability. The integration process is thoroughly examined in the research, along with its effects on feature variety, model accuracy, and system robustness. The benefits of real-time data streaming in predictive analytics are demonstrated by empirical results, which indicate a significant boost in event categorization performance. By improving feature extraction and enhancing the dependability of predictive systems in dynamic contexts, the results establish Redpanda as a scalable and robust solution.
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