MCP server for training Linear Regression Model.
Overview
What is Linear Regression MCP?
Linear Regression MCP is a project that demonstrates an end-to-end machine learning workflow using Claude and the Model Context Protocol (MCP) to train a Linear Regression model.
How to use Linear Regression MCP?
To use this project, clone the repository, install the necessary dependencies, and configure Claude Desktop to link with the MCP server. You can then upload a CSV file containing your dataset to train the model.
Key features of Linear Regression MCP?
- End-to-end machine learning model training lifecycle.
- Automatic data preprocessing, training, and evaluation (RMSE calculation).
- Tools for uploading datasets and checking column information.
Use cases of Linear Regression MCP?
- Training linear regression models for predictive analytics.
- Evaluating model performance using RMSE.
- Preprocessing datasets for machine learning tasks.
FAQ from Linear Regression MCP?
- Can I use any dataset?
Yes, as long as it is in CSV format and contains the necessary columns for training.
- Is there a specific Python version required?
The project is compatible with Python 3.x.
- How can I contribute to this project?
You can fork the repository, make changes, and submit a pull request.