SageML: Democratizing Machine Learning

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