https://github.com/udacity-machinelearning-internship/feature-scaling

Applying feature scaling with linear regression in python
https://github.com/udacity-machinelearning-internship/feature-scaling

feature-scaling linear-regression machine-learning sckit-learn

Last synced: 3 months ago
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Applying feature scaling with linear regression in python

• Host: GitHub
• URL: https://github.com/udacity-machinelearning-internship/feature-scaling
• Owner: Udacity-MachineLearning-Internship
• Created: 2024-05-15T06:07:41.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-17T03:40:45.000Z (about 1 year ago)
• Last Synced: 2025-01-21T08:24:10.231Z (5 months ago)
• Topics: feature-scaling, linear-regression, machine-learning, sckit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 12.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        


![Feature_Scaling](https://github.com/BaraSedih11/Feature-Scaling/assets/98843912/b4c173e1-b139-4032-a2ca-018ee9b06ba0)

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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:

```bash

pip install numpy pandas scikit-learn matplotlib

```

## Usage

1. Clone this repository to your local machine:

```bash

git clone https://github.com/BaraSedih11/Feature-Scaling.git

```

2. Navigate to the repository directory:

```bash

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](https://scikit-learn.org/): The scikit-learn library for machine learning in Python.

- [NumPy](https://numpy.org/): The NumPy library for numerical computing in Python.

- [pandas](https://pandas.pydata.org/): The pandas library for data manipulation and analysis in Python.

- [matplotlib](https://matplotlib.org/): The matplotlib library for data visualization in Python.