This repository contains a simple implementation of linear regression using the scikit-learn library in Python.

Overview

Linear regression is a fundamental technique in statistics and machine learning for modeling the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the input variables (features) and the output variable (target).

In this repository, we demonstrate how to perform linear regression using the scikit-learn library, which is a powerful tool for machine learning in Python. We provide a simple example along with explanations to help you understand how to apply linear regression to your own datasets.

Contents

• Linear Regression.ipynb: Jupyter Notebook containing the implementation of polynomial regression using Python.
• bmi_and_life_expectancy.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/Linear-Regression-in-scikit-learn.git

2. Navigate to the repository directory:

cd Linear-Regression-in-scikit-learn

3. Open and run the Jupyter Notebook Linear-Regression-in-scikit-learn.ipynb using Jupyter Notebook or JupyterLab.

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