What is Linear Regression Mcp

Linear-Regression-MCP is a project that demonstrates an end-to-end machine learning workflow utilizing Claude and the Model Context Protocol (MCP) to train a Linear Regression model from a CSV dataset.

Use cases

Use cases include predicting sales based on historical data, estimating housing prices from features, and analyzing trends in financial markets.

How to use

To use Linear-Regression-MCP, clone the repository, install the ‘uv’ package, install dependencies using ‘uv sync’, and configure Claude Desktop by modifying the configuration file to integrate the server.

Key features

Key features include automated data preprocessing, model training, and evaluation with RMSE calculation, all managed by Claude without requiring extensive user input.

Where to use

Linear-Regression-MCP can be used in various fields including data science, finance, and any domain that requires predictive modeling based on linear relationships in data.

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).

Claude can train a Linear Regression model entirely by itself, simply by uploading a CSV file containing the dataset. The system goes through the entire ML model training lifecycle, handling data preprocessing, training, and evaluation (RMSE calculation).

Setup and Installation

1. Clone the Repository:

First, clone the repository to your local machine:

git clone https://github.com/HeetVekariya/Linear-Regression-MCP
cd Linear-Regression-MCP

2. Install uv:

uv is an extremely fast Python package and project manager, written in Rust. It is essential for managing the server and dependencies in this project.

• Download and install uv from here.

3. Install Dependencies:

Once uv is installed, run the following command to install all necessary dependencies:

uv sync

4. Configure Claude Desktop:

To integrate the server with Claude Desktop, you will need to modify the Claude configuration file. Follow the instructions for your operating system:

• For macOS or Linux:

code ~/Library/Application\ Support/Claude/claude_desktop_config.json

• For Windows:

code $env:AppData\Claude\claude_desktop_config.json

• In the configuration file, locate the mcpServers section, and replace the placeholder paths with the absolute paths to your uv installation and the Linear Regression project directory. It should look like this:

{
    "mcpServers":
    {
        "linear-regression": 
        {
            "command": "ABSOLUTE/PATH/TO/.local/bin/uv",
            "args":
            [
                "--directory",
                "ABSOLUTE/PATH/TO/YOUR-LINEAR-REGRESSION-REPO", 
                "run",
                "server.py"
            ] 
        }
    }
}

• Once the file is saved, restart Claude Desktop to link with the MCP server.

Available Tools

The following tools are available in this project to help you work with the dataset and train the model:

Open for Contributions

I welcome contributions to this project! Whether it’s fixing bugs, adding new features, or improving the documentation, feel free to fork the repository and submit pull requests.

If you have any suggestions or feature requests, open an issue, and I’ll be happy to discuss them!

👀