How does the skill evaluate the quality of a model?

It provides standard performance metrics including R-squared values and Root Mean Square Error (RMSE) to help you understand how accurately the model represents your data.

What types of regression does this skill support?

The tool supports various modeling techniques, most commonly linear and polynomial regression, allowing it to adapt to both simple and complex data patterns.

Can I use this for business forecasting like sales or real estate prices?

Yes, it is specifically designed for use cases like predicting house prices based on features or identifying long-term sales trends from annual data.

Does it require specific data formats?

The skill analyzes the provided data structure automatically, but for best results, your data should be clean and preprocessed before initiating the regression analysis.

Regression Analysis & Modeling

Name: Regression Analysis & Modeling
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Automates regression modeling and data analysis to identify relationships between variables and predict future trends.

About

This skill transforms Claude into a data scientist by enabling automated regression analysis through the regression-analysis-tool plugin. It handles the entire modeling workflow—from analyzing raw datasets and selecting appropriate models like linear or polynomial regression to validating results and reporting key performance metrics. It is an essential tool for developers and analysts who need to uncover hidden trends, validate scientific hypotheses, or build predictive models directly within their AI-powered development environment.

Key Features

Model validation and accuracy assessment using R-squared and RMSE
Automated generation of linear and polynomial regression models
Comprehensive dataset structure analysis and variable mapping
Predictive modeling for future value estimation based on historical data
3 GitHub stars
Detailed reporting of mathematical equations and trend visualizations

Use Cases

Identifying and quantifying the relationship between independent and dependent variables
Validating the statistical significance and reliability of data-driven predictions
Predicting financial or business outcomes based on multi-variable historical data

About

Key Features

Model validation and accuracy assessment using R-squared and RMSE
Automated generation of linear and polynomial regression models
Comprehensive dataset structure analysis and variable mapping
Predictive modeling for future value estimation based on historical data
3 GitHub stars
Detailed reporting of mathematical equations and trend visualizations

Use Cases

Identifying and quantifying the relationship between independent and dependent variables
Validating the statistical significance and reliability of data-driven predictions
Predicting financial or business outcomes based on multi-variable historical data