Spaces:
Sleeping
Sleeping
File size: 2,891 Bytes
444d31b 993cfb9 444d31b 993cfb9 8d631a4 444d31b 8d631a4 993cfb9 053b42f 2ec2353 053b42f 993cfb9 2712881 993cfb9 2712881 993cfb9 444d31b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | ---
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
|