
# Lasso Regression Package

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)

## Overview

This Python package provides a simple implementation of Lasso Regression (L1 regularization) 
using the Python Standard Library and `NumPy`. 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

```bash
pip install lassoreg
```

## Usage

```python
from lassoreg.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.

```bash
cd example
python example.py
```

## Testing

To run the unit tests, use the following command:

```bash
pytest tests
```

## License

This package is licensed under the [MIT License](LICENSE).
