Project Description

This project undertakes a Comparative Analysis of Decision Tree, Random Tree, XG Boost, and Gradient Boosting Models to enhance aircraft maintenance prediction. Through an in-depth examination of noise levels generated by various aircraft models, we aim to discern patterns that signal impending maintenance needs. By establishing a threshold for acceptable noise levels, our predictive models determine when aircraft surpass this limit, thus prompting timely maintenance intervention. This approach ensures proactive maintenance, safeguarding against potential safety risks and optimizing operational efficiency.