---

title: TrainerML - MCP Hackathon emoji: 🤖 colorFrom: blue colorTo: purple sdk: gradio sdk_version: 4.44.0 app_file: enhanced_gradio_app.py pinned: false license: mit tags:

• machine-learning
• mcp
• hackathon
• automl
• model-training
• gradio short_description: Advanced ML trainer with MCP integration for the Agents & MCP Hackathon

---

🤖 TrainerML - MCP Hackathon Submission

Advanced Machine Learning Platform with Model Context Protocol Integration

🏆 Hackathon Track

Agents & MCP Hackathon - Track 1: MCP Tool / Server

🌟 Key Features

Core ML Capabilities

• 📤 Smart CSV Upload: Instant dataset analysis and preprocessing
• 🎯 Auto Problem Detection: Automatically determines regression vs classification
• 🤖 15+ ML Algorithms: From Linear Regression to XGBoost and LightGBM
• 📊 Advanced Metrics: Comprehensive evaluation with interactive visualizations
• 💾 Model Export: Download trained models as pickle files

🚀 Innovative Features

• 🔧 Auto Feature Engineering: Polynomial features and intelligent selection
• 🤝 Ensemble Learning: Combine multiple models for superior performance
• 📈 Interactive Visualizations: Plotly-powered charts and model explanations
• 🔍 SHAP Explanations: Model interpretability and feature importance
• ⚙️ Hyperparameter Tuning: Automated grid search optimization
• 📱 Real-time Analysis: Live dataset profiling and recommendations

🌐 MCP Integration

• Full MCP Server: Complete Model Context Protocol implementation
• 8 Advanced Tools: From dataset analysis to model deployment
• Claude Desktop Ready: Direct integration with AI assistants
• Cursor IDE Support: Seamless developer workflow integration

🛠️ MCP Tools Available

1. analyze_dataset - Comprehensive data analysis with visualizations
2. train_ml_model - Advanced model training with feature engineering
3. compare_models - Side-by-side algorithm comparison
4. generate_model_explanations - SHAP-powered interpretability
5. make_predictions - Real-time predictions with trained models
6. export_model - Model deployment packages
7. get_model_history - Training session management
8. auto_ml_pipeline - Fully automated ML workflow

🚀 Quick Start

Web Interface

Simply upload your CSV file and follow the guided workflow:

1. Upload your dataset
2. Analyze data quality and characteristics
3. Select target column and problem type
4. Configure advanced features (auto feature engineering, ensemble learning)
5. Train your model with one click
6. Download the trained model

MCP Integration

For Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "ml-trainer": {
      "command": "python",
      "args": ["enhanced_mcp_server.py"],
      "env": {}
    }
  }
}

Example MCP Commands

• "Analyze this customer dataset and recommend the best ML approach"
• "Train a Random Forest model to predict house prices with feature engineering"
• "Compare XGBoost vs LightGBM on my classification problem"
• "Generate SHAP explanations for model interpretability"

🎯 Innovation Highlights

1. Intelligent Automation

• Auto Problem Detection: Analyzes target column characteristics
• Smart Preprocessing: Handles missing values and categorical encoding
• Feature Engineering: Creates polynomial features and selects optimal subset

2. Advanced ML Pipeline

• Ensemble Methods: Voting classifiers/regressors for better accuracy
• Hyperparameter Tuning: Grid search optimization
• Cross-Validation: Robust performance estimation

3. Rich Visualizations

• Interactive Plots: Plotly-powered prediction scatter plots
• Feature Importance: Visual ranking of model features
• Correlation Heatmaps: Data relationship analysis
• Performance Metrics: Comprehensive evaluation dashboards

4. Production Ready

• Model Export: Pickle files with preprocessing pipelines
• API Integration: RESTful endpoints for deployment
• MCP Protocol: Seamless AI assistant integration

📊 Supported Algorithms

Regression

• Linear Regression, Ridge, Lasso, ElasticNet
• Decision Tree, Random Forest
• Gradient Boosting, XGBoost, LightGBM
• Support Vector Regression, K-Nearest Neighbors

Classification

• Logistic Regression, Decision Tree
• Random Forest, Gradient Boosting
• XGBoost, LightGBM
• SVM, K-Nearest Neighbors, Naive Bayes

🏆 Demo Scenarios

Business Intelligence

• Customer Churn Prediction: Upload customer data, auto-detect classification problem, train ensemble model
• Sales Forecasting: Regression analysis with feature engineering for revenue prediction
• Fraud Detection: Advanced classification with SHAP explanations

Research & Development

• Automated EDA: Comprehensive dataset analysis with recommendations
• Model Comparison: Benchmark multiple algorithms automatically
• Feature Engineering: Discover optimal feature combinations

MCP Integration Demo

• Claude Desktop: "Train a model to predict customer lifetime value using this dataset"
• Cursor IDE: Integrate ML predictions directly into development workflow
• API Integration: Use trained models in production applications

🚀 Technologies Used

• Frontend: Gradio 4.0+ with custom CSS styling
• Backend: Python with scikit-learn, XGBoost, LightGBM
• Visualizations: Plotly, Matplotlib, Seaborn
• MCP: Custom server implementation with 8 advanced tools
• ML Pipeline: pandas, numpy, SHAP for explainability
• Deployment: Hugging Face Spaces, Docker ready

📈 Performance Features

• Real-time Processing: Optimized for datasets up to 100K rows
• Memory Efficient: Smart sampling for large datasets
• Parallel Processing: Multi-core hyperparameter tuning
• Caching: Model history and feature importance caching

🎯 Hackathon Submission Highlights

1. Complete MCP Implementation: 8 production-ready tools
2. Advanced ML Features: Feature engineering, ensemble learning, SHAP
3. User Experience: Intuitive Gradio interface with guided workflow
4. Innovation: Auto-detection, smart preprocessing, interactive visualizations
5. Production Ready: Exportable models, API integration, deployment ready

📧 Contact & Support

Built with ❤️ for the Agents & MCP Hackathon 2025

This project demonstrates the power of combining advanced machine learning with the Model Context Protocol to create intelligent, automated ML workflows that can be seamlessly integrated into AI assistant conversations and developer tools.

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Ready to revolutionize your ML workflow? Upload your dataset and experience the future of automated machine learning! 🚀