Regression modeler

Skill zebbern/claude-code-guide/skills/regression-modeler

Claude Code Guide - Setup, Commands, workflows, agents, skills & tips-n-tricks go from beginner to power user!

Install

npx -y skills add zebbern/claude-code-guide --skill regression-modeler

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Run regression analysis (OLS or logistic) on uploaded CSV/Excel data, generating coefficients, R², p-values, VIF, and plain-language interpretation. Triggered by requests for regression modeling, fitting data, testing significance, checking multicollinearity, or keywords like OLS, logit, coefficient, p-value, or R-squared.

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SKILL.md

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regression-modeler

Automated regression modeling tool — performs linear regression (OLS) or logistic regression (Logit) on tabular data, producing comprehensive statistical results with plain-language interpretation.

Capabilities

Feature	Description
Linear Regression	OLS with coefficients, R², adjusted R², F-test, AIC/BIC, Durbin-Watson
Logistic Regression	Logit with coefficients, Odds Ratio, Pseudo R², likelihood ratio test
Multicollinearity Detection	VIF values for each predictor with warning levels
Plain-Language Interpretation	Clear explanations of what each metric and coefficient means
Auto Detection	Automatically switches to logistic regression when the target is binary (0/1)

Quick Start

# Linear regression: predict price using all numeric columns as predictors
python3 scripts/regression_analyzer.py data.csv --target price

# Logistic regression: predict churn (0/1) with specified features
python3 scripts/regression_analyzer.py users.csv --target churn --features "age,income,tenure"

# Save results to JSON
python3 scripts/regression_analyzer.py data.csv --target sales --output result.json

Detailed Usage

Basic Invocation

python3 scripts/regression_analyzer.py <data_file> --target <target_column> [options]

Specifying Regression Type

# Force linear regression
python3 scripts/regression_analyzer.py data.csv -t y --type linear

# Force logistic regression
python3 scripts/regression_analyzer.py data.csv -t label --type logistic

# Auto-detect (default)
python3 scripts/regression_analyzer.py data.csv -t y --type auto

Selecting Feature Columns

# Manually specify (comma-separated)
python3 scripts/regression_analyzer.py data.csv -t price -f "sqft,bedrooms,bathrooms"

# Omit to automatically use all numeric columns
python3 scripts/regression_analyzer.py data.csv -t price

Parameters

Parameter	Short	Required	Default	Description
`input`	—	Yes	—	Input file path (CSV/TSV/Excel/JSON)
`--target`	`-t`	Yes	—	Target variable (dependent variable) column name
`--features`	`-f`	No	All numeric columns	Predictor column names, comma-separated
`--type`	`-T`	No	`auto`	Regression type: `linear` / `logistic` / `auto`
`--output`	`-o`	No	stdout	Output JSON file path
`--no-const`	—	No	`false`	Do not add an intercept term
`--keep-na`	—	No	`false`	Keep rows with missing values (for debugging)

Output Structure (JSON)

{
  "type": "linear",
  "r_squared": 0.8523,
  "r_squared_adj": 0.8471,
  "f_statistic": 162.34,
  "f_p_value": 0.0,
  "coefficients": {
    "sqft": {"coefficient": 135.42, "p_value": 0.0001, ...},
    "bedrooms": {"coefficient": 8021.5, "p_value": 0.032, ...}
  },
  "vif": {"sqft": 2.31, "bedrooms": 1.87},
  "interpretation": {
    "model_summary": ["R² = 0.8523 (good model fit...)"],
    "variable_analysis": ["sqft: coefficient = 135.42... positive effect..."]
  }
}

Dependencies

Python 3.8+
pandas
numpy
statsmodels
scipy

pip install pandas numpy statsmodels scipy