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| title: AutoML - MCP Hackathon | |
| emoji: 📈 | |
| colorFrom: yellow | |
| colorTo: pink | |
| sdk: gradio | |
| sdk_version: 5.33.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| tags: | |
| - machine-learning | |
| - mcp | |
| - hackathon | |
| - automl | |
| - lazypredict | |
| - gradio | |
| - mcp-server-track | |
| - agent-demo-track | |
| short_description: Automated ML model comparison with LazyPredict | |
| # 🤖 AutoML - MCP Hackathon Submission | |
| **Automated Machine Learning Platform with LazyPredict and Model Context Protocol Integration** | |
| ## Video Links | |
| - **Gradio Demo Link** : https://youtu.be/TNStp8Xae1o | |
| - **MCP Client Demo Link**: https://youtu.be/mdUpdxX_Rcw | |
| ## 🏆 Hackathon Track | |
| **Agents & MCP Hackathon - Track 1: MCP Tool / Server** | |
| ## 🛠️ How It Works | |
| The AutoML provides a streamlined pipeline for automated machine learning: | |
| ### Agent-Friendly Design | |
| - **Single Entry Point**: The `run_pipeline()` function serves as the primary interface for AI agents | |
| - **Flexible Input Handling**: Automatically determines whether input is a file path or URL | |
| - **Comprehensive Output**: Returns all generated artifacts (models, reports, visualizations) | |
| - **Error Resilience**: Robust error handling with informative feedback | |
| ## 🚀 Quick Start | |
| ### Installation & Running the Application | |
| ```bash | |
| # Clone the repository | |
| git clone [repository-url] | |
| cd AutoML | |
| # Install dependencies | |
| pip install -r requirements.txt | |
| # Run the main application | |
| python app.py | |
| ``` | |
| ## 🏆 Demo Scenarios | |
| ### College Placement Analysis | |
| - Upload `collegePlace.csv` included in the project with url: (https://raw.githubusercontent.com/daniel-was-taken/Placement-Prediction/refs/heads/master/collegePlace.csv) | |
| - Automatic feature analysis and model comparison | |
| - Export trained model for future predictions | |
| ## 📈 Current Features | |
| - **🔄 Dual Input Support**: Upload local CSV files or provide public URLs for data loading | |
| - **🤖 One-Click AutoML**: Complete ML pipeline from data upload to trained model export | |
| - **🎯 Intelligent Task Detection**: Automatic classification vs regression detection based on target variable analysis | |
| - **📊 Multi-Algorithm Comparison**: Simultaneous comparison of 20+ algorithms with LazyPredict | |
| - **📋 Comprehensive EDA**: Detailed dataset profiling with statistical analysis and data quality reports | |
| - **💾 Model Export**: Download best performing model as pickle file for production deployment | |
| - **📈 Performance Visualization**: Clear charts showing algorithm comparison and performance metrics | |
| - **🌐 MCP Server Integration**: Full Model Context Protocol support for seamless AI assistant integration | |
| - **🛡️ Robust Error Handling**: Comprehensive validation with informative user feedback | |
| - **🎨 Modern UI**: Clean, responsive interface optimized for both human and agent interactions | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |