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It serves as a quick tool for selecting the optimal clustering algorithm and its hyperparameters, providing visualizations and metrics for comparison.\n\n## Features\n\n- **Clustering Algorithms**: Analyzes six clustering algorithms from `scikit-learn`:\n    - `KMeans`\n    - `DBSCAN`\n    - `MiniBatchKMeans`\n    - `AgglomerativeClustering`\n    - `OPTICS`\n    - `SpectralClustering`\n- **Optimization Methods**: Includes Bayesian optimization and random search for hyperparameter tuning.\n- **Flexible Preprocessing**: Allows users to customize how the data is meant to be preprocessed, adjusting methods such as scaling, normalization, and dimensionality reduction.\n- **Evaluation Metrics**: Supports evaluation with `silhouette`, `calinski_harabasz`, and `davies_bouldin` scores.\n- **Report Generation**: Generates reports in HTML format after optimization.\n\n## Installation\n\nTo install `clustermatic`, use pip:\n\n```bash\npip install clustermatic\n```\n\n\n## Usage\n\nFor a quick start, use the following code snippet:\n\n```python\nfrom clustermatic import AutoClusterizer\n\n# Load data\nfrom sklearn.datasets import make_moons\nX, _ = make_moons(n_samples=200, noise=0.1, random_state=42)\n\n# Initialize AutoClusterizer\nac = AutoClusterizer()\n\n# Fit the data\nac.fit(X)\n\n# Generate report\nac.evaluate()\n```\n\nFor more detailed walkthrough, check out [this example Jupyter Notebook](https://github.com/AKapich/clustermatic/blob/main/examples/example.ipynb)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakapich%2Fclustermatic","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fakapich%2Fclustermatic","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakapich%2Fclustermatic/lists"}