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

# 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