Application of Machine Learning Models for Predictive Analytics on AI4I 2020

One of the critical aspects in modern industrial operations is predictive maintenance. It involves leveraging advanced technologies such as machine learning to anticipate equipment failures before they occur. The research not only contributes to the broader understanding of machine learning applications in industrial contexts but also addresses the imperative need for predictive maintenance in optimizing operational efficiency and minimizing downtime and which machine learning model serves best over the dataset selected, incorporating how it can be made more accurate for better performance. The results of the experiments demonstrate that Random Forest and Support Vector Machines achieved comparable, high performance in case of High-quality and Medium-quality machines where the F<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score reached $85.71 \%$ and $90.32 \%$ respectively.