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SageML: Democratizing Machine Learning

License Release

SageML is an automated machine learning pipeline that democratizes ML by providing a unified, end-to-end solution for data processing, analysis, and model building. It eliminates the complexity of traditional ML workflows, making machine learning accessible to users without advanced technical expertise.

🚀 Features

  • Multi-format Data Support: Process PDF, DOCX, XLSX, HTML, and Images
  • Automated Data Pre-processing: Intelligent handling of missing values, outliers, and feature engineering
  • Advanced NLP Capabilities: Built-in text processing with tokenization, lemmatization, and specialized analysis
  • Automated Model Selection: Smart algorithm selection and hyperparameter optimization
  • User-friendly Interface: Simple PyQt-based GUI for seamless interaction

📋 Requirements

  • Windows 10/11 (64-bit)
  • Minimum 8GB RAM
  • 2GB free disk space
  • Screen resolution: 1280x720 or higher

🔧 Installation

  1. Download the latest release from the Releases page
  2. Extract the ZIP file to your desired location
  3. Run SageML.exe from the extracted folder

📖 Usage Guide

  1. Launch SageML by double-clicking SageML.exe
  2. Click "Select Files" to import your dataset
  3. Select the Data Type of the dataset (Structured/Unstructured)
  4. Select the Model Type for the dataset (Regression/Classification/Clustering/NLP)
  5. Click "Start Training" to begin the automated ML pipeline

Output

SAGE saves two files for each analysis:

  • model.joblib: Serialized machine learning model that can be loaded for predictions
  • model_specs.json: Comprehensive model specifications including:
    • Model type and parameters
    • Performance metrics
    • Preprocessing steps
    • Feature importance
    • Creation date and dataset information

🎯 Use Cases

  • Research Analysis: Process research papers and extract meaningful insights
  • Business Intelligence: Analyze business data for pattern recognition and prediction
  • Educational: Learn about ML workflows and model performance
  • Data Science: Rapid prototyping and baseline model creation

📊 Performance Metrics

  • 95% accuracy in text extraction from PDFs and images
  • 40% reduction in data cleaning time
  • 50% faster hyperparameter optimization
  • 30% overall workflow speedup

🤝 Contributing

While the source code is private, we welcome:

  • Feature requests
  • Bug reports
  • Documentation improvements
  • Use case suggestions

Please use the Issues section for any contributions.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

📬 Contact

For support or queries:

  • Create an issue on GitHub
  • Connect with us on LinkedIn