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https://github.com/udacity-machinelearning-internship/polynomial-regression

Implementing polynomial regression using sckit-learn
https://github.com/udacity-machinelearning-internship/polynomial-regression

machine-learning polynomial-regression sckiit-learn

Last synced: 9 months ago
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Implementing polynomial regression using sckit-learn

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README 

          



![Polynomial_Regression](https://github.com/BaraSedih11/Polynomial-Regression/assets/98843912/62061da3-fb5f-4f90-9c35-d1ac2befa7ca)



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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:



```bash

pip install numpy pandas scikit-learn matplotlib

```



## Usage



1. Clone this repository to your local machine:



```bash

git clone https://github.com/BaraSedih11/Polynomial-Regression.git

```



2. Navigate to the repository directory:



```bash

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](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.