https://github.com/coder5omkar/simple-linear-regression
Linear regression is one of the most fundamental algorithms in machine learning and statistics used for predicting a continuous dependent variable (target) based on one or more independent variables (features). The key idea behind linear regression is to find the relationship between variables by fitting a straight line through the data points.
https://github.com/coder5omkar/simple-linear-regression
data-science machine-learning simple-linear-regression sklearn-library
Last synced: about 1 month ago
JSON representation
Linear regression is one of the most fundamental algorithms in machine learning and statistics used for predicting a continuous dependent variable (target) based on one or more independent variables (features). The key idea behind linear regression is to find the relationship between variables by fitting a straight line through the data points.
- Host: GitHub
- URL: https://github.com/coder5omkar/simple-linear-regression
- Owner: coder5omkar
- License: mit
- Created: 2024-12-25T08:06:15.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2024-12-25T15:37:25.000Z (about 1 year ago)
- Last Synced: 2025-02-22T12:14:07.994Z (12 months ago)
- Topics: data-science, machine-learning, simple-linear-regression, sklearn-library
- Language: Jupyter Notebook
- Homepage:
- Size: 164 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Simple Linear Regression 🔥
## 📋 General Information
```
This repository provides an in-depth understanding and practical implementation of Simple Linear Regression (SLR), a foundational machine learning algorithm. SLR is used to model the relationship between two variables: a dependent variable (target) and an independent variable (predictor). It assumes a linear relationship between them and fits a straight line (y = mx + c) to the data.
Key features of this repository include:
Explanation of SLR concepts with mathematical derivations.
Python implementation using numpy, scikit-learn, and statsmodels.
Interactive visualizations for better understanding.
Datasets for testing and experimentation.
Performance evaluation metrics like RMSE and R².
This repo is ideal for beginners and enthusiasts aiming to master linear regression.
```
## 🛠️ Technologies Used
- [Python](https://www.python.org/) version: 3.12.4
- [Numpy](https://numpy.org/) version: 1.26.4
- [Pandas](https://pandas.pydata.org/) version: 2.2.2
- [Seaborn](https://seaborn.pydata.org/) version: 0.13.2
- [Matplotlib](https://matplotlib.org/) version: 3.9.2
- [scikit-learn](https://scikit-learn.org/) version: 1.5.1
- [statsmodels](https://statsmodels.org/) version: 0.14.2
## 🚀 **Getting Started** (In Anaconda PowerShell Prompt)
1. Clone the repository:
```bash
git clone https://github.com/coder5omkar/Simple-Linear-Regression.git
```
2. Navigate to the project directory:
```bash
cd Simple-Linear-Regression
```
3. Open the notebook:
```bash
jupyter notebook SLR.ipynb
```
---
## 🤝 Acknowledgements
- This project was inspired by IIT-B AI-ML program at Upgrad
Developed as part of the ML-1 Module assignment required for Post Graduate Diploma in Machine Learning and AI - IIIT,Bangalore.
This project is open source and available under the [MIT License](https://github.com/coder5omkar/Simple-Linear-Regression/blob/master/licence.txt).
## Contact
Created by [@in/omkaramale](https://github.com/coder5omkar) - feel free to contact me!