This repository contains an implementation of feature scaling in linear regression using Python.

Overview

In this exercise, you'll revisit the same dataset as before and see how scaling the features changes which features are favored in a regularization step. The only thing different for this quiz compared to the previous one is the addition of a new step after loading the data, where you will use sklearn's StandardScaler(opens in a new tab) to standardize the data before you fit a linear regression model to the data with L1 (Lasso) regularization.

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/Feature-Scaling.git

2. Navigate to the repository directory:

cd Feature-Scaling

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

4. Follow along with the code and comments in the notebook to understand how feature scaling 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.