Linear Regression

🚀 Linear Regression MCP

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

Claude can independently train a linear regression model by simply uploading a CSV file containing the dataset. The system goes through the entire ML model training lifecycle, handling data preprocessing, training, and evaluation (computing RMSE).

🚀 Quick Start

✨ Features

• Claude can train a linear regression model independently with just a CSV dataset file.
• The system covers the full ML model training lifecycle, including data preprocessing, training, and evaluation.

📦 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 a very fast Python package and project manager written in Rust. It's necessary 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 need to modify Claude's configuration file. Do this according to 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, find the mcpServers section and replace the placeholder paths with the absolute path to .local/bin/uv and the absolute path to 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"
            ] 
        }
    }
}

• After saving the file, restart Claude Desktop to connect to the MCP server.

💻 Usage Examples

Basic Usage

# 简单线性回归示例
import numpy as np
from sklearn.linear_model import LinearRegression

# 生成数据
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 5, 4, 5])

# 训练模型
model = LinearRegression()
model.fit(X, y)

# 预测
predictions = model.predict(X)
print("预测值:", predictions)
print("系数:", model.coef_)
print("截距:", model.intercept_)

📚 Documentation

Available Tools

The following tools are available in this project to help you process datasets and train models:

📄 License

No license information provided in the original document, so this section is skipped.

👥 Contributors

• Author: Heet Vekariya

📞 Contact

If you have questions or feedback about this project, please contact us via:

Contact Us