The termite-flight-model repository is designed to model and predict termite flight patterns by integrating environmental data. Below is an overview of the repository's structure and its primary components.
The repository comprises several directories and Python scripts, each serving a specific function:
__pycache__: Contains cached bytecode files for optimized performance.db: Stores database files related to the project.evaluation_results: Holds results from model evaluations.models: Contains saved machine learning models.output: Stores output data generated by the models.splitted_data: Includes datasets split for training, validation, and testing purposes.
ensemble.py: Implements ensemble learning techniques to improve model performance.evaluate_models.py: Contains functions to assess and validate the performance of different models.fetch_data.py: Responsible for retrieving and processing environmental data relevant to termite flights.generate_models.py: Scripts to initialize and train machine learning models.main.py: Serves as the primary entry point for executing the project's workflow.read_data.py: Handles the loading and preprocessing of datasets.visualization.py: Contains functions for visualizing data and model outputs.
To set up the project locally, follow these steps:
git clone https://github.com/TermCIC/termite-flight-model.git