Linear-Regression-MCP
If you are the rightful owner of Linear-Regression-MCP and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.
Linear Regression MCP is a project that demonstrates an end-to-end machine learning workflow using Claude and the Model Context Protocol (MCP).
Linear Regression MCP is a comprehensive project that showcases the capabilities of Claude in training a Linear Regression model autonomously. By simply uploading a CSV file, the system can handle the entire machine learning model training lifecycle, including data preprocessing, model training, and evaluation through RMSE calculation. The project is designed to be user-friendly and efficient, leveraging the power of the Model Context Protocol (MCP) to streamline the process. It is open for contributions, encouraging developers to enhance its features or improve its documentation.
Features
- End-to-end ML workflow: Handles data preprocessing, training, and evaluation.
- CSV file upload: Easily upload datasets for processing.
- Automatic model training: Claude trains the Linear Regression model autonomously.
- RMSE calculation: Evaluates model performance using RMSE.
- Open for contributions: Encourages community involvement for improvements.
Tools
upload_file
The absolute way to file
get_columns_info
Get the column of uploaded dataset
check_category_columns
Check the classification in the dataset
label_encode_categorical_columns
Encode the classification column label into numbers
train_linear_regression_model
Target column name
