AutoML by emircansoftware - MCP Server

Auto ML - Automated Machine Learning Platform

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

📈 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

AutoML/
├── data/                   # Sample datasets
│   ├── Ai.csv
│   ├── Calories.csv
│   ├── Cost.csv
│   ├── Digital.csv
│   ├── Electricity.csv
│   ├── ford.csv
│   ├── Habits.csv
│   ├── heart.csv
│   ├── Lifestyle.csv
│   ├── Mobiles.csv
│   ├── Personality.csv
│   ├── Salaries.csv
│   ├── Shopper.csv
│   ├── Sleep.csv
│   ├── cat.csv
│   ├── test.csv
│   └── train.csv
├── tools/
│   └── all_tools.py       # MCP tool definitions
├── utils/
│   ├── before_model.py        # Feature preparation
│   ├── details.py             # Data information
│   ├── external_test.py       # External data test with XGBoost
│   ├── feature_importance.py  # Feature importance analysis
│   ├── hyperparameter.py      # Hyperparameter tuning
│   ├── model_selection.py     # Model selection and evaluation
│   ├── prediction.py          # Prediction utilities
│   ├── 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

Clone the repository

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

Install dependencies

# Using pip
pip install -r requirements.txt
pip install uv

Using with Claude Desktop

1. Data Path Setting

In utils/read_csv_file.py, update the path variable to match your own project directory on your computer:

# Example:
path = r"C:\\YOUR\\PROJECT\\PATH\\AutoML\\data"

2. Claude Desktop Configuration

In Claude Desktop, add the following block to your claude_desktop_config.json file and adjust the paths to match your own system:

{
  "mcpServers": {
    "AutoML": {
      "command": "uv",
      "args": [
        "--directory",
        "C:\\YOUR\\PROJECT\\PATH\\AutoML",
        "run",
        "main.py"
      ]
    }
  }
}

You can now start your project from Claude Desktop.

📋 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): Give detailed information about the data
reading_csv(file_name): Read the csv file
visualize_correlation_num(file_name): Visualize the correlation matrix for numerical columns
visualize_correlation_cat(file_name): Visualize the correlation matrix for categorical columns
visualize_correlation_final(file_name, target_column): Visualize the correlation matrix after preprocessing
visualize_outliers(file_name): Visualize outliers in the data
visualize_outliers_final(file_name, target_column): Visualize outliers after preprocessing

Preprocessing Tools

preprocessing_data(file_name, target_column): Preprocess the data (remove outliers, fill nulls, etc.)
prepare_data(file_name, target_column, problem_type): Prepare the data for models (encoding, scaling, etc.)

Model Training & Evaluation

models(problem_type, file_name, target_column): Select and evaluate models based on problem type
visualize_accuracy_matrix(file_name, target_column, problem_type): Visualize the confusion matrix for predictions
best_model_hyperparameter(model_name, file_name, target_column, problem_type, n_trials, scoring, random_state): Tune the hyperparameters of the best model
test_external_data(main_file_name, target_column, problem_type, test_file_name): Test external data with the best model and return predictions
predict_value(model_name, file_name, target_column, problem_type, n_trials, scoring, random_state, input): Predict the value of the target column for new input
feature_importance_analysis(file_name, target_column, problem_type): Analyze the feature importance of the data using XGBoost

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 (All CSV datasets are from Kaggle.)

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

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

📝 License

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

🙏 Acknowledgments

Model Context Protocol for the MCP framework
scikit-learn for machine learning algorithms
XGBoost for gradient boosting
CatBoost for categorical boosting
pandas for data manipulation

📞 Support

If you encounter any issues or have questions:

Check the Issues page
Create a new issue with detailed information
Contact the maintainers