Paper Title
Predictive Model for Maintenance of Equipment

Abstract
Predictive maintenance is a proactive approach that utilizes machine learning techniques to forecast potential equipment failures, thereby reducing downtime and improving operational efficiency. This study presents a predictive maintenance model leveraging regression and neural networks to predict machine failures and production output. By analyzing historical data, key variables such as working hours, downtime, safety incidents, and errors are identified as primary indicators of machine performance. The study demonstrates the superiority of neural network models over traditional regression techniques, achieving an R² value of 0.99 for production prediction. The findings highlight the importance of integrating predictive analytics into maintenance strategies to optimize resource allocation and procurement decisions. Future research should explore the role of deep learning techniques and real-time data integration in enhancing predictive accuracy. Keywords - Predictive Maintenance, Machine Learning, Regression Models, Neural Networks, Equipment Management, Downtime Reduction.