I engineered a data-driven analytical framework to quantify and predict the performance gap between
manufacturer fuel ratings and real-world vehicle fuel consumption, using a dataset of 600 trip-level records
across 25 vehicle models.
I built a machine learning pipeline using Multiple Linear Regression, Gradient Boosting,
and Random Forest models to forecast true fuel efficiency under real-world driving conditions.
You can view more about this and other projects Here.
