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EcologicalAI_Experiments

Welcome to the EcologicalAI_Experiments repository! This repository contains Jupyter notebooks for a series of small experiments applying AI techniques to ecological data, with a focus on biodiversity, conservation, and environmental monitoring.

Table of Contents

Project Overview

The purpose of this repository is to explore the application of AI and machine learning to ecological data, with experiments focusing on species identification, bioacoustics, and network analysis, among other areas of ecological research.

Experiments

This repository includes the following Jupyter notebooks:

  1. Missing Link Prediction in Ecological Networks
    A notebook exploring the use of machine learning to predict missing interactions between species in ecological networks.

  2. Bioacoustics Classification
    A notebook that uses AI to classify species based on bioacoustic data, helping to analyze animal vocalizations for biodiversity monitoring.

Setup and Installation

To get started with these notebooks:

  1. Clone the repository:

    git clone https://github.com/your-username/EcologicalAI_Experiments.git
cd EcologicalAI_Experiments
cd "experiment"

  2. Create a virtual environment (optional but recommended):

    python3 -m venv env
source env/bin/activate  # On Windows, use `env\Scripts\activate`

  3. Install the required dependencies:

    pip install -r requirements.txt

  4. Launch Jupyter Notebook:

    jupyter notebook

  5. Open the notebook you'd like to explore from the Jupyter interface.

Usage

For each experiment, navigate to the respective notebook in the repository and execute the cells to reproduce the experiment.

For example, to explore the Missing Link Prediction experiment:

  1. Open the missing_link_prediction.ipynb notebook.
  2. Follow the instructions provided in the notebook to run the analysis.

Contributing

Contributions are welcome! If you'd like to add a new experiment or improve existing notebooks, feel free to fork the repository, create a branch, and submit a pull request.

  1. Fork the repository.
  2. Create a new branch for your changes: 
    git checkout -b feature-name
  3. Make your changes and commit them: 
    git commit -m 'Add some feature'
  4. Push your changes: 
    git push origin feature-name
  5. Submit a pull request.

Contact

For questions or feedback, feel free to reach out to Navodita Mathur via LinkedIn or email: [email protected].