regression-modeler

# 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 ```bash # 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 ```bash python3 scripts/regression_analyzer.py <data_file> --target <target_column> [options] ``` ### Specifying Regression Type ```bash # 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 ```bash # 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) ```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 ```bash pip install pandas numpy statsmodels scipy ``` ## Activation Triggers Triggered by requests for regression modeling, fitting data, testing significance, checking multicollinearity, or keywords like OLS, logit, coefficient, p-value, or R-squared.