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With minimal user input, SageML automates model selection, hyperparameter optimization, and provides a trained machine learning model ready for deployment.\n\n## Table of Contents\n\n- [SageML](#sageml)\n  - [Table of Contents](#table-of-contents)\n  - [Features](#features)\n  - [Installation](#installation)\n  - [Quick Start](#quick-start)\n  - [Usage](#usage)\n    - [Data Preprocessing](#data-preprocessing)\n    - [Model Selection](#model-selection)\n    - [Hyperparameter Optimization](#hyperparameter-optimization)\n    - [Model Evaluation](#model-evaluation)\n  - [Documentation](#documentation)\n  - [Contributing](#contributing)\n  - [License](#license)\n\n## Features\n\n- **Automatic Model Selection**: Chooses the best algorithm based on data characteristics with pre-trained neural network.\n- **Hyperparameter Optimization**: Utilizes Optuna for efficient hyperparameter tuning.\n- **Data Preprocessing**: Handles missing values, categorical encoding, and feature scaling automatically.\n- **Interactive Interface**: User-friendly terminal interface with tutorials and step-by-step guidance.\n- **Extensibility**: Modular architecture allows for easy customization and extension.\n- **Compatibility**: Supports a wide range of algorithms from scikit-learn, CatBoost, XGBoost, and more.\n\n## Installation\n\nSageML is available on PyPI. You can install it using `pip`:\n\n```bash\npip install sageml\n```\n\n\u003e **Note**: For the latest features and updates, you might want to install from the GitHub repository.\n\n## Quick Start\n\nHere's how you can get started with SageML in just a few lines of code:\n\n```python\nfrom turbo_ml import SageML\nimport pandas as pd\n# Initialize SageML with your dataset\nsageML = SageML(pd.read_csv('classified/data.csv'), target='target')\n\n# Make predictions\npredictions = sageML.predict(pd.read_csv('not/classified/data.csv'))\n```\n\n## Usage\n\n### Data Preprocessing\n\nSageML automatically preprocesses your data to make it suitable for machine learning algorithms.\n\n- Handles missing values with appropriate imputation methods.\n- Encodes categorical variables using techniques like One-Hot Encoding.\n- Scales numerical features for algorithms sensitive to feature scales.\n\n### Model Selection\n\n- Analyzes data characteristics (e.g., number of features, class balance).\n- Selects suitable algorithms from a pool that includes scikit-learn classifiers/regressors, CatBoost, XGBoost, etc.\n- Supports both classification and regression tasks.\n\n### Hyperparameter Optimization\n\n- Utilizes Optuna for efficient hyperparameter optimization.\n- Employs advanced features like pruning to reduce computation time.\n\n### Model Evaluation\n\n- Allows selection of evaluation metrics from scikit-learn or custom weighted sums.\n- Supports cross-validation and hold-out validation strategies.\n\n## Documentation\n\nDetailed documentation should be available soon.\n\n## Contributing\n\nWe welcome contributions from the community!\n\n- **Bug Reports \u0026 Feature Requests**: Use the [GitHub Issues](https://github.com/Tole-k/sageml/issues) to report bugs or suggest features.\n\n## License\n\nSageML is licensed under the [GNU General Public License v3.0](LICENSE).\n\n---\n\n*Disclaimer: This project is under active development. Features and interfaces are subject to change.*\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftole-k%2Fsageml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftole-k%2Fsageml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftole-k%2Fsageml/lists"}