This project implements a Machine Learning (ML) model to predict fuel efficiency in vehicles. The model is trained on a dataset containing various features related to vehicles, such as engine size, horsepower, weight, and miles per gallon (MPG) fuel efficiency. By leveraging this data, the model can make predictions about the fuel efficiency of a vehicle based on its characteristics.
- Predicts fuel efficiency (miles per gallon) of vehicles.
- Built on top of state-of-the-art machine learning algorithms.
Before you begin, ensure you have the following dependencies installed:
- Python 3.x
- Jupyter Notebook (optional, for running the provided notebooks)
- Required Python libraries: NumPy, Pandas, Scikit-Learn, Matplotlib, Seaborn
Clone the repository to your local machine:
git clone https://github.com/OpenGenus/fuel-efficiency.git
pip install numpy pandas scikit-learn matplotlib seaborn
The dataset used for training the model is available as auto-mpg.data. It contains information about various vehicle features and their corresponding fuel efficiency in miles per gallon (MPG).
The model is trained using the fuel_efficiency_predictor.ipynb Jupyter Notebook. This notebook contains detailed explanations of the data preprocessing steps, model selection, training, and evaluation.
The model's performance is evaluated using metrics such as Mean Squared Error (MSE), and R-squared score. These metrics provide insights into how well the model is performing in predicting fuel efficiency.