This repository contains an implementation of polynomial regression using Python.

Overview

Polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial. It is used when the relationship between the variables is non-linear.

In this repository, we demonstrate how to perform polynomial regression using Python. We utilize libraries such as NumPy, pandas, scikit-learn, and matplotlib to implement and visualize the regression model. Additionally, we provide a simple example along with explanations to help you understand how to apply polynomial regression to your own datasets.

Contents

• polynomial_regression.ipynb: Jupyter Notebook containing the implementation of polynomial regression using Python.
• data.csv: Sample dataset used in the notebook for demonstration purposes.
• README.md: This file providing an overview of the repository.

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/Polynomial-Regression.git

2. Navigate to the repository directory:

cd Polynomial-Regression

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

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