linregressor 0.0.2

Linear Regressor that automatically does all the tests on the data

LinearRegressor — A From-Scratch Linear Regression Library with Statistical Diagnostics



LinearRegressor is a fully custom-built Python library that implements Ordinary Least Squares (OLS) linear regression from scratch, without relying on scikit-learn.

It is designed not only to fit regression models, but also to validate data, check statistical assumptions, detect multicollinearity, and generate diagnostic reports.



This project was created as an educational yet production-grade implementation of classical econometrics and regression analysis.



🚀 Key Features

✔️ End-to-end data validation



Before training, the library automatically checks:



Type validation for X and y



Missing values (NaN), infinite values, and empty entries



Feature dimensionality and dataset size



Non-numeric values



Duplicate columns



Collinearity and matrix singularity



If the data is not suitable for regression, it raises clear, interpretable errors.



✔️ From-scratch OLS estimation



The model computes parameters using:



𝛽

^

=

(

𝑋

′

𝑋

)

−

1

𝑋

′

𝑦

β

^

	​



=(X

′

X)

−1

X

′

y



with custom error handling for singular matrices and multicollinearity issues.



✔️ Automatic assumption diagnostics



After fitting, the library runs classic econometric tests:



Assumption	Test Used	Detects

Linear specification	Ramsey RESET	Nonlinear relationships

Normality of residuals	Jarque–Bera	Skewness & kurtosis issues

Heteroscedasticity	Breusch–Pagan	Non-constant variance

Multicollinearity	VIF	High correlation between features

Autocorrelation (for time series)	Durbin–Watson	Serial correlation



Warnings are issued automatically with actionable suggestions.



✔️ Prediction



Simple .predict() interface using learned weights and bias.



✔️ Model evaluation



Compute common regression metrics:



MSE



RMSE



MAE



R²



✔️ Built-in visualization (optional)



The library can generate:



Regression plot (for single-feature regressions)



Residual plot



to quickly evaluate model quality.



📦 Example Usage

from linreg_prod import LinearRegressor



model = LinearRegressor()

model.fit(X_train, y_train)



predictions = model.predict(X_test)



🧪 Unit-Tested



The library includes a complete test suite validating:



Data validation logic



Preprocessing



Correlation warnings



Fitting and prediction



OLS estimation behavior



Metrics correctness



Error handling for singular matrices and invalid data



All tests are written in pytest and pass successfully.



Change Log

==========



0.0.2 (20/11/2025)

-------------------

- First Release