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.

- 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

- 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

- 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

- Performance Metrics:

- Regression: R², MAE, MSE
- Classification: Accuracy, F1-Score

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

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

- Python 3.8 or higher
- pip or uv package manager

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

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

Inutils/read_csv_file.py, update thepathvariable to match your own project directory on your computer:

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

In Claude Desktop, add the following block to yourclaude_desktop_config.jsonfile 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.

- 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)

from server import mcp # Run the server mcp.run()

The platform provides the following MCP 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_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.)

- 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

# 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

- Set your preferred random seed for reproducible results
- Configure MCP server settings inserver.py

- Add new ML algorithms inutils/model_selection.py
- Extend preprocessing steps inutils/preprocessing.py
- Create custom visualization functions inutils/visualize_data.py

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.
- 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

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

- Model Context Protocolfor the MCP framework
- scikit-learnfor machine learning algorithms
- XGBoostfor gradient boosting
- CatBoostfor categorical boosting
- pandasfor data manipulation

If you encounter any issues or have questions:
- Check theIssuespage
- Create a new issue with detailed information
- Contact the maintainers

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