Predicting Fuel Efficiency using Tensorflow for Sustainable Transportation

As the automotive industry seeks to enhance environmental sustainability, predicting vehicle fuel efficiency has become crucial. This project aims to develop a deep learning model using TensorFlow to accurately predict fuel efficiency based on various factors such as vehicle specifications, driving conditions, and engine performance. A dataset sourced from the UCI Machine Learning Repository was utilized, encompassing diverse vehicle types and their corresponding fuel efficiency metrics. Through data preprocessing and model training, our approach involves employing a neural network architecture optimized for regression tasks. The results demonstrate the model's ability to provide reliable predictions, contributing to more informed decisions regarding vehicle performance and fuel management. Ultimately, this project serves as a stepping stone towards improving fuel efficiency in vehicles, promoting greener transportation solutions.