Enhancing Predictive Models in System Development Using Machine Learning Algorithms

Abstract

Predictive models play a crucial role in system development, enabling more informed decision making and improving system efficiency. However, traditional predictive models often struggle with scalability and accuracy in complex environments. This paper explores the use of Machine Learning (ML) algorithms to enhance predictive models, offering more accurate and scalable solutions. By leveraging key ML techniques such as decision trees, regression models, and neural networks, the study demonstrates how these algorithms can improve predictive accuracy and system performance. The methodology involves data collection, model training, and performance evaluation using various metrics to assess the effectiveness of ML enhanced predictive models. The results indicate a significant improvement in model accuracy and scalability, making ML a valuable tool in advancing system development processes. By incorporating ML frameworks specifically tailored to the unique demands of system development, this research offers new methodological adaptations designed to optimize scalability and performance. This study diverges from previous research by implementing and tailoring ML techniques uniquely suited for complex system development environments, enhancing both predictive accuracy and scalability.

Keywords

• Predictive Models
• Machine Learning (ML)
• System Development
• Accuracy
• Scalability