🌍 Carbon Footprint Prediction Project

Overview

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.

📋 Requirements

• Python 3.x
• TensorFlow (>=2.0)
• NumPy
• Pandas
• Matplotlib (optional for visualization)
• Jupyter Notebook (optional for interactive development)

🚀 Usage

1. Clone the repository to your local machine:

   git clone https://github.com/yourusername/carbon-footprint-prediction.git
   cd carbon-footprint-prediction

2. Install the required dependencies:

   pip install -r requirements.txt

3. 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

4. Experiment with hyperparameters, model architecture, and feature engineering to improve model performance.

📁 File Structure

• 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.

📊 Model Evaluation

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

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.