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Auto ML - Automated Machine Learning Platform

PythonLicenseMCP

An intelligent automated machine learning platform that provides comprehensive data analysis, preprocessing, model selection, and hyperparameter tuning capabilities through Model Context Protocol (MCP) tools.

🚀 Features

📊 Data Analysis & Exploration

  • Data Information: Get comprehensive dataset statistics including shape, memory usage, data types, and missing values
  • CSV Reading: Efficient CSV file reading with pandas and pyarrow support
  • Correlation Analysis: Visualize correlation matrices for numerical and categorical variables
  • Outlier Detection: Identify and visualize outliers in your datasets

🔧 Data Preprocessing

  • Automated Preprocessing: Handle missing values, encode categorical variables, and scale numerical features
  • Feature Engineering: Prepare features for both regression and classification problems
  • Data Validation: Check for duplicates and data quality issues

🤖 Machine Learning Models

  • Multiple Algorithms: Support for various ML algorithms including:
    • Regression: Linear Regression, Ridge, Lasso, ElasticNet, Random Forest, XGBoost, SVR, KNN, CatBoost
    • Classification: Logistic Regression, Ridge Classifier, Random Forest, XGBoost, SVM, KNN, Decision Tree, Naive Bayes, CatBoost
    • Ensemble Methods: Voting classifiers and regressors for improved performance

📈 Model Evaluation & Visualization

  • Performance Metrics:
    • Regression: R², MAE, MSE
    • Classification: Accuracy, F1-Score
  • Confusion Matrix Visualization: For classification problems
  • Model Comparison: Compare multiple models side-by-side

⚙️ Hyperparameter Tuning

  • Automated Tuning: Optimize model hyperparameters using advanced search algorithms
  • Customizable Scoring: Choose from various evaluation metrics
  • Trial Management: Control the number of optimization trials

📁 Project Structure

Auto/
├── data/                   # Sample datasets
│   ├── Ai.csv
│   ├── Calories.csv
│   ├── Cost.csv
│   ├── Digital.csv
│   ├── ford.csv
│   ├── Habits.csv
│   ├── heart.csv
│   ├── Lifestyle.csv
│   ├── Mobiles.csv
│   ├── Personality.csv
│   ├── Salaries.csv
│   └── Sleep.csv
├── tools/
│   └── all_tools.py       # MCP tool definitions
├── utils/
│   ├── before_model.py    # Feature preparation
│   ├── details.py         # Data information
│   ├── hyperparameter.py  # Hyperparameter tuning
│   ├── model_selection.py # Model selection and evaluation
│   ├── preprocessing.py   # Data preprocessing
│   ├── read_csv_file.py   # CSV reading utilities
│   └── visualize_data.py  # Visualization functions
├── main.py                # Application entry point
├── server.py              # MCP server configuration
├── requirements.txt       # Python dependencies
└── README.md             # This file

🛠️ Installation

Prerequisites

  • Python 3.8 or higher
  • pip or uv package manager

Setup

  1. Clone the repository

    git clone https://github.com/emircansoftware/MCP_Server_DataScience.git
cd MCP_Server_DataScience

  2. Install dependencies

    # Using pip
pip install -r requirements.txt

# Or using uv (recommended)
uv sync

  3. Run the application

    python main.py

📋 Dependencies

  • MCP Framework: mcp[cli]>=1.9.4 - Model Context Protocol for tool integration
  • Data Processing: pandas>=2.3.0, pyarrow>=20.0.0, numpy>=2.3.1
  • Machine Learning: scikit-learn>=1.3.0, xgboost>=2.0.0, lightgbm>=4.3.0
  • Additional ML: catboost (for CatBoost models)

🎯 Usage

Starting the MCP Server

from server import mcp

# Run the server
mcp.run()

Available Tools

The platform provides the following MCP tools:

Data Analysis Tools
  • information_about_data(file_name): Get comprehensive dataset information
  • reading_csv(file_name): Read CSV files efficiently
  • visualize_correlation_num(file_name): Visualize numerical correlations
  • visualize_correlation_cat(file_name): Visualize categorical correlations
  • visualize_correlation_final(file_name, target_column): Final correlation analysis
  • visualize_outliers(file_name): Detect and visualize outliers
  • visualize_outliers_final(file_name, target_column): Final outlier analysis
Preprocessing Tools
  • preprocessing_data(file_name, target_column): Automated data preprocessing
  • prepare_data(file_name, target_column, problem_type): Feature preparation
Model Training & Evaluation
  • models(problem_type, file_name, target_column): Train and evaluate multiple models
  • visualize_accuracy_matrix(file_name, target_column, problem_type): Confusion matrix visualization
  • best_model_hyperparameter(model_name, file_name, target_column, problem_type, n_trials, scoring, random_state): Hyperparameter tuning

Example Workflow

# 1. Analyze your data
info = information_about_data("data/heart.csv")

# 2. Preprocess the data
preprocessed = preprocessing_data("data/heart.csv", "target")

# 3. Prepare features for classification
features = prepare_data("data/heart.csv", "target", "classification")

# 4. Train and evaluate models
results = models("classification", "data/heart.csv", "target")

# 5. Visualize results
confusion_matrix = visualize_accuracy_matrix("data/heart.csv", "target", "classification")

# 6. Optimize best model
best_model = best_model_hyperparameter("RandomForestClassifier", "data/heart.csv", "target", "classification", 100, "accuracy", 42)

📊 Sample Datasets

The project includes various sample datasets for testing:

  • heart.csv: Heart disease prediction dataset
  • Salaries.csv: Salary prediction dataset
  • Calories.csv: Calorie prediction dataset
  • Personality.csv: Personality analysis dataset
  • Digital.csv: Digital behavior dataset
  • Lifestyle.csv: Lifestyle analysis dataset
  • Mobiles.csv: Mobile phone dataset
  • Habits.csv: Habit analysis dataset
  • Sleep.csv: Sleep pattern dataset
  • Cost.csv: Cost analysis dataset
  • ford.csv: Ford car dataset
  • Ai.csv: AI-related dataset
  • cat.csv: Cat-related dataset

🔧 Configuration

Environment Variables

  • Set your preferred random seed for reproducible results
  • Configure MCP server settings in server.py

Customization

  • Add new ML algorithms in utils/model_selection.py
  • Extend preprocessing steps in utils/preprocessing.py
  • Create custom visualization functions in utils/visualize_data.py

🤝 Contributing

We welcome contributions! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Contributing Guidelines

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

📞 Support

If you encounter any issues or have questions:

  1. Check the Issues page
  2. Create a new issue with detailed information
  3. Contact the maintainers

Made with ❤️ for the ML community

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