https://github.com/udacity-machinelearning-internship/linear-regression-in-scikit-learn
Implementing linear regression using sckit-learn
https://github.com/udacity-machinelearning-internship/linear-regression-in-scikit-learn
linear-regression machine-learning sckiit-learn
Last synced: 11 months ago
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Implementing linear regression using sckit-learn
- Host: GitHub
- URL: https://github.com/udacity-machinelearning-internship/linear-regression-in-scikit-learn
- Owner: Udacity-MachineLearning-Internship
- Created: 2024-05-14T14:22:39.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-17T03:44:33.000Z (over 1 year ago)
- Last Synced: 2025-01-21T08:24:09.991Z (about 1 year ago)
- Topics: linear-regression, machine-learning, sckiit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 23.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
  [](https://www.python.org/downloads/release/python-380/)
[](https://pypi.org/project/pip/21.0/)
[](https://github.com/BaraSedih11/Linear-Regression-in-scikit-learn/releases/tag/v1.0.0)
[](https://github.com/BaraSedih11/Linear-Regression-in-scikit-learn/graphs/contributors)
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:
```bash
pip install numpy pandas scikit-learn matplotlib
```
## Usage
1. Clone this repository to your local machine:
```bash
git clone https://github.com/BaraSedih11/Linear-Regression-in-scikit-learn.git
```
2. Navigate to the repository directory:
```bash
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](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.