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Linear Regression MCP

@HeetVekariya

Repository

About Linear Regression MCP

MCP server for training Linear Regression Model.

#mcp#linear-regression#machine-learning

Basic information

Category

Version Control

Runtime

python

Transports

stdio

Publisher

HeetVekariya

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "Linear-Regression-MCP": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Linear Regression MCP?

Linear Regression MCP is a Model Context Protocol server that lets Claude train a linear regression model end-to-end by simply uploading a CSV file. It automates data preprocessing, training, and evaluation (RMSE calculation), making it ideal for users who want to run ML workflows through natural language.

How to use Linear Regression MCP?

Clone the repository, install uv, run uv sync, then configure Claude Desktop by adding the server path to claude_desktop_config.json. Once linked, Claude can invoke tools like upload_file, get_columns_info, and train_linear_regression_model.

Key features of Linear Regression MCP

  • End-to-end ML model training lifecycle with Claude.
  • Upload any CSV dataset for automatic processing.
  • Automatic categorical column label encoding.
  • RMSE-based evaluation after training.
  • Tools to inspect column info and data types.
  • Open-source and welcomes contributions.

Use cases of Linear Regression MCP

  • Train a linear regression model on a local CSV without writing code.
  • Rapidly prototype regression models using natural language instructions.
  • Automate data preprocessing and model evaluation for educational or small-scale projects.

FAQ from Linear Regression MCP

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Comments

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