Analytics for Efficient Use of Crash Prototypes

Project Overview

The project involved the creation of a threshold-based classifier to identify crucial crash tests, leading to a remarkable 73% optimization in the total number of tests. Python-based data mining tools were implemented to process automobile sensor data, extracting over five qualitative features. Additionally, utilizing extracted trend data, 108 future program test scenarios for non-critical tests were synthesized with an impressive 86% accuracy, showcasing the effectiveness of the developed tools in predictive analysis within the context of global automaker crash testing.