How does it measure the accuracy of a model?

It provides comprehensive performance metrics, primarily focusing on R-squared (coefficient of determination) and RMSE (Root Mean Square Error) to evaluate model fit.

What types of regression does this skill support?

The skill supports multiple modeling approaches, including linear and polynomial regression, to best fit the specific patterns found in your dataset.

How does this integrate with the Claude Code environment?

It utilizes the regression-analysis-tool plugin to automate calculations and can be combined with other data visualization skills for a full analytical workflow.

Can I use this for predictive forecasting?

Yes, this skill is specifically designed to analyze existing data patterns to predict future outcomes and identify underlying variables that drive those values.

Does this skill require pre-cleaned data?

While the skill performs structural analysis, following best practices for data cleaning and preprocessing before use will result in significantly more accurate models.

Regression Analysis & Modeling

Name: Regression Analysis & Modeling
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Performs advanced regression analysis and predictive modeling to identify variable relationships and forecast data trends using automated statistical tools.

About

This skill empowers Claude to act as a specialized data scientist by leveraging the regression-analysis-tool plugin to build, validate, and interpret complex statistical models. It automates the process of analyzing datasets, selecting appropriate regression types—such as linear or polynomial—and generating rigorous performance metrics like R-squared and RMSE. Whether you are forecasting real estate prices or identifying long-term sales trends, this skill provides the analytical depth needed to understand variable dependencies and make data-driven predictions with high confidence.

Key Features

Detailed performance reporting with R-squared and RMSE metrics
3 GitHub stars
Rigorous model validation to ensure accuracy and reliability
Automated data structure analysis and relationship identification
Trend visualization and predictive forecasting from historical datasets
Generation of multiple model types including linear and polynomial regression

Use Cases

Analyzing historical business data to identify growth trends and seasonal patterns
Quantifying the statistical relationship between independent and dependent variables in research data
Predicting market values or pricing based on multi-variable inputs like square footage and location

About

Key Features

Detailed performance reporting with R-squared and RMSE metrics
3 GitHub stars
Rigorous model validation to ensure accuracy and reliability
Automated data structure analysis and relationship identification
Trend visualization and predictive forecasting from historical datasets
Generation of multiple model types including linear and polynomial regression

Use Cases

Analyzing historical business data to identify growth trends and seasonal patterns
Quantifying the statistical relationship between independent and dependent variables in research data
Predicting market values or pricing based on multi-variable inputs like square footage and location