lassoreg 0.1.0

My wonderful Python package

Lasso Regression Package

Overview

This Python package provides a simple implementation of Lasso Regression (L1 regularization) using the Python Standard Library. Lasso Regression is a linear regression technique that adds a penalty term proportional to the absolute values of the regression coefficients, promoting sparsity in the model.

Installation

pip install lasso-regression-package

Usage

from lasso_regression.lasso_regression import LassoRegression

# Create an instance of Lasso Regression
lasso_model = LassoRegression(alpha=0.01, max_iter=1000, tol=1e-4)

# Fit the model to training data
lasso_model.fit(X_train, y_train)

# Make predictions on new data
predictions = lasso_model.predict(X_test)

Documentation

For detailed information on the parameters and methods, please refer to the docstring in the source code.

Example

An example of generating synthetic data and fitting the Lasso Regression model is provided in the example directory.

cd example
python example.py

Testing

To run the unit tests, use the following command:

python -m unittest discover tests

License

This package is licensed under the MIT License.

Replace placeholders like `[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)` with appropriate badges and links. The example README includes sections for installation, basic usage, documentation, an example, testing, and the license. You can expand and tailor this README based on the specifics of your package.