This project demonstrates the use of LIME (Local Interpretable Model-Agnostic Explanations) to explain predictions made by a logistic regression model trained on the Iris dataset. The repository is structured following industry best practices, including thorough documentation, testing, and continuous integration.

• Data Loading: Load the Iris dataset using scikit-learn.
• Model Training: Train a logistic regression classifier.
• LIME Integration: Use LIME to generate explanations for model predictions.
• Testing: Unit tests for model training and explanation generation.
• CI/CD: GitHub Actions for continuous integration.
• Documentation: Detailed installation and usage guides.

Please refer to docs/installation.md for installation instructions.

An example usage script is provided in examples/run_explainer.py. For further details, see docs/usage.md.