This project focuses on predicting vehicle fuel efficiency (mileage) using multiple machine learning algorithms. It was developed as part of my second-year Data Science coursework to understand regression techniques and model evaluation. The dataset used for this project is the Auto MPG dataset, which contains features such as horsepower, weight, acceleration, cylinders, and model year. Data preprocessing steps include handling missing values, feature scaling, and exploratory data analysis (EDA) using visualizations. Various regression algorithms were implemented, including Linear Regression, Decision Tree Regressor, Random Forest Regressor, and other models. The performance of each model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² Score. Among the applied models, Random Forest showed better accuracy and generalization compared to other algorithms. The project demonstrates the complete machine learning pipeline, from data preprocessing to model comparison and evaluation. This repository is well-structured with clean code, visualizations, and detailed explanations, making it useful for learning and reference purposes.