This repository contains code and resources for building a regression model to predict carbon footprint based on given features. The project aims to demonstrate the implementation of machine learning techniques in environmental impact assessment.
- Python 3.x
- TensorFlow (>=2.0)
- NumPy
- Pandas
- Matplotlib (optional for visualization)
- Jupyter Notebook (optional for interactive development)
-
Clone the repository to your local machine:
git clone https://github.com/yourusername/carbon-footprint-prediction.git cd carbon-footprint-prediction
-
Install the required dependencies:
pip install -r requirements.txt
-
Run the Jupyter Notebook or Python script to train and evaluate the regression model:
jupyter notebook carbon_footprint_prediction.ipynb
or
python carbon_footprint_prediction.py
-
Experiment with hyperparameters, model architecture, and feature engineering to improve model performance.
carbon_footprint_prediction.ipynb
: Jupyter Notebook containing the code for model development, training, and evaluation.carbon_footprint_prediction.py
: Python script version of the regression model.data/
: Directory containing sample or provided dataset for carbon footprint prediction.README.md
: Project documentation and instructions.requirements.txt
: List of required Python packages.
The model's performance is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) score on a test dataset. Continuous improvement and fine-tuning of the model parameters are encouraged to achieve better predictions.
Contributions to this project are welcome! If you have ideas for enhancements, bug fixes, or new features, please fork the repository and submit a pull request.Let us all contribute to the environment.