Is this skill part of the Nixtla ecosystem?

Yes, it is designed to work as part of the Nixtla-related plugin concepts, focused on efficient data pipelines and predictive modeling.

What types of regression does this tool support?

The tool supports multiple regression types, including linear and polynomial models, and can automatically suggest the best fit for your specific data structure.

Can I use this for real-time price predictions?

Yes, by providing current features like square footage or bedrooms, the skill can use the generated model to predict prices or other numerical values.

Does it provide statistical reliability metrics?

Absolutely. The skill outputs standard performance metrics such as R-squared and RMSE (Root Mean Square Error) to help you understand the model's accuracy.

Do I need to clean my data before using this skill?

While the skill helps analyze data, it is a best practice to ensure your dataset is clean and preprocessed to achieve the most accurate regression results.

Regression Analysis Tool

Name: Regression Analysis Tool
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Performs automated regression modeling and statistical analysis to uncover relationships between variables and predict numerical outcomes.

About

This skill integrates advanced regression modeling capabilities into Claude Code, allowing developers to perform complex data science tasks via natural language. It automates the end-to-end workflow of analyzing datasets, selecting the most appropriate regression models—such as linear or polynomial—validating their accuracy, and reporting critical performance metrics like R-squared and RMSE. Whether you are forecasting sales, predicting real estate values, or identifying trends within historical data, this tool provides the analytical depth needed to turn raw data into actionable predictive insights.

Key Features

0 GitHub stars
Trend visualization and predictive value generation
Automated model selection for linear and polynomial regression
Comprehensive data structure and relationship analysis
Built-in model validation and accuracy testing
Detailed performance reporting including R-squared and RMSE

Use Cases

Evaluating the strength of relationships between independent and dependent variables
Predicting business KPIs based on multi-variable datasets
Analyzing historical sales data to identify long-term growth trends

About

Key Features

0 GitHub stars
Trend visualization and predictive value generation
Automated model selection for linear and polynomial regression
Comprehensive data structure and relationship analysis
Built-in model validation and accuracy testing
Detailed performance reporting including R-squared and RMSE

Use Cases

Evaluating the strength of relationships between independent and dependent variables
Predicting business KPIs based on multi-variable datasets
Analyzing historical sales data to identify long-term growth trends