ols-regressor 0.4.1

Ausis Car Price Regressor

OLS_Regressor

About

The OLS Regression Package is a Python library designed to streamline the process of performing Ordinary Least Squares (OLS) regression analysis. Whether you're a data scientist, researcher, or analyst, this package aims to provide a simple and efficient tool for fitting linear models to your data. It will fit a linear model with coefficients w = (w1, w2, ..., wn) to minimize Residual Sum of Squares (RSS) between the observed targets values in the dataset, and the targets predicted by the linear approximation for the examples in the dataset.

Installation

$ pip install ols_regressor

Functions

• fit: Fits the linear model according to the OLS mechanism.
• predict: Predicts target values using the fitted linear model.
• score: Calculates the coefficient of determination R-squared value for the prediction.
• cross_validate: Performs cross-validated Ordinary Least Squares (OLS) regression.

OLS_Regressor use in Python ecosystem

The OLS Regression Package seamlessly integrates into the rich Python ecosystem, offering a specialized solution for Ordinary Least Squares (OLS) regression analysis. While various Python libraries provide general-purpose machine learning and statistical functionalities, our package focuses specifically on the simplicity and efficiency of linear regression. scikit-learn is a widely used machine learning library that encompasses regression among its many capabilities scikit-learn. Our package distinguishes itself by providing a lightweight and user-friendly interface tailored for users seeking a straightforward solution for OLS regression without the overhead of extensive machine learning or statistical functionalities. If you find that your needs align more closely with a broader set of machine learning tools or comprehensive statistical modeling, scikit-learn or statsmodels may be suitable alternatives. As of [2024-01-12], no existing package caters specifically to OLS regression with our package's emphasis on simplicity and ease of use.

Contributors

• Xia Yimeng (@YimengXia)
• Sifan Zhang (@Sifanz)
• Charles Xu (@charlesxch)
• Waleed Mahmood (@WaleedMahmood1)

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

OLS_Regressor is licensed under the terms of the MIT license.

Credits

OLS_Regressor was created with cookiecutter and the py-pkgs-cookiecutter template.