---
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