Overview

Perhaps it's not too surprising at this point, but there are classes in sklearn that will help you perform regularization with your linear regression. You'll get practice with implementing that in this exercise. In this assignment's data.csv, you'll find data for a bunch of points including six predictor variables and one outcome variable. Use sklearn's Lasso(opens in a new tab) class to fit a linear regression model to the data, while also using L1 regularization to control for model complexity.

Requirements

To run the code in the Jupyter Notebook, you need to have Python installed on your system along with the following libraries:

• NumPy
• pandas
• scikit-learn
• matplotlib

You can install these libraries using pip:

pip install numpy pandas scikit-learn matplotlib

Usage

1. Clone this repository to your local machine:

git clone https://github.com/BaraSedih11/Regularization.git

2. Navigate to the repository directory:

cd Regularization

3. Open and run the Jupyter Notebook Regularization.ipynb using Jupyter Notebook or JupyterLab.

4. Follow along with the code and comments in the notebook to understand how requalrization in linear regression is implemented using Python.

Acknowledgements

• scikit-learn: The scikit-learn library for machine learning in Python.
• NumPy: The NumPy library for numerical computing in Python.
• pandas: The pandas library for data manipulation and analysis in Python.
• matplotlib: The matplotlib library for data visualization in Python.