MCP server for training Linear Regression Model
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[](https://mseep.ai/app/heetvekariya-linear-regression-mcp) # 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). [](https://mseep.ai/app/faff0b8f-a4c5-42f6-88c4-b3cb210f4559) <br> ## Setup and Installation ### 1. Clone the Repository: First, clone the repository to your local machine: ```bash 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](https://docs.astral.sh/uv/#installation). ### 3. Install Dependencies: Once uv is installed, run the following command to install all necessary dependencies: ```bash 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: ```bash code ~/Library/Application\ Support/Claude/claude_desktop_config.json ``` - For Windows: ```bash 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: ```bash { "mcpServers": { "linear-regression": …
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