Linear-Regression-MCP

Linear-Regression-MCP

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Linear Regression MCP is a project aimed at demonstrating a complete machine learning workflow using the Model Context Protocol (MCP) from dataset uploading to model training and evaluation. It provides tools to automate the training lifecycle of a Linear Regression model with an easy setup and contributions are welcomed.

Linear Regression MCP

Welcome to Linear Regression MCP! This project demonstrates an end-to-end machine learning workflow using Claude and the Model Context Protocol (MCP).

  • Features:
    • Train a Linear Regression model by uploading a CSV file.
    • Handles data preprocessing, training, and evaluation (RMSE calculation).
    • Tools available for working with datasets, including column information retrieval, category checking, label encoding, and model training.

Setup and Installation

  • Clone the repository.
  • Install uv, a Python package and project manager.
  • Install necessary dependencies.
  • Configure Claude Desktop for integration.

Open for Contributions

Contributions are welcome! Feel free to fork the repository and submit pull requests or open issues for new features or suggestions.