# Lasso Regression Package
[](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).
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