https://github.com/vidhi1290/regression-series
In this repository, we delve into the world of regression analysis. I have provided straightforward examples and in-depth explanations of both simple and multiple regression, helping you gain a solid grasp of these essential statistical techniques.😎✅
https://github.com/vidhi1290/regression-series
datascience linear-regression logistic-regression machine-learning machine-learning-algorithms multiple-linear-regression regression-models simple-linear-regression
Last synced: 6 months ago
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In this repository, we delve into the world of regression analysis. I have provided straightforward examples and in-depth explanations of both simple and multiple regression, helping you gain a solid grasp of these essential statistical techniques.😎✅
- Host: GitHub
- URL: https://github.com/vidhi1290/regression-series
- Owner: Vidhi1290
- Created: 2023-09-16T10:39:45.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-09-16T11:34:21.000Z (about 2 years ago)
- Last Synced: 2025-02-02T18:33:16.958Z (8 months ago)
- Topics: datascience, linear-regression, logistic-regression, machine-learning, machine-learning-algorithms, multiple-linear-regression, regression-models, simple-linear-regression
- Language: Jupyter Notebook
- Homepage:
- Size: 687 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Regression Series 📊
Welcome to the Regression Series repository! Here, we explore the intriguing world of regression analysis, focusing on three fundamental models: **Simple Linear Regression**, **Multiple Linear Regression**, and **Logistic Regression**. Each notebook provides invaluable insights into these models, helping you understand when and how to use them effectively. 🚀
## Table of Contents
- [Simple Linear Regression](simple-linear-regression.ipynb): Dive into the basics of linear regression, understanding how to fit a straight line to your data and make predictions.
- [Multiple Linear Regression](multiple-linear-regression.ipynb): Explore more complex relationships by using multiple predictors in your regression model.
- [Logistic Regression](logistic-regression.ipynb): Transition into classification problems with logistic regression, a vital tool in machine learning.
## Key Differences
- **Simple Linear Regression**: This model is used when you have one independent variable and want to establish a linear relationship with a dependent variable. It's ideal for predicting outcomes when you have a clear, single predictor.
- **Multiple Linear Regression**: When you have multiple independent variables and want to understand how they collectively influence a dependent variable, you turn to multiple linear regression. It's useful for more complex scenarios with multiple predictors.
- **Logistic Regression**: Unlike linear regression, logistic regression is used for classification tasks. It's perfect when you need to predict binary outcomes or probabilities. It's a critical tool in the realm of machine learning for tasks like spam detection and medical diagnosis.
## What You'll Find
- **Code Examples**: Each notebook contains detailed code examples, making it easy to grasp the concepts and implement them in your own projects. 💻
- **Explanation**: We've included in-depth explanations alongside the code, ensuring you understand the "why" behind the "how." 📝
- **Comparison**: Learn the key differences between these three regression models and when to use them. 🧐
We believe learning should be fun and accessible. So, dive in, explore, and level up your data science skills with our Regression Series. Feel free to reach out if you have any questions or suggestions!
Happy coding! 👩💻👨💻
## Connect with Us
Join our community and stay updated on our latest projects:
🌐 [GitHub](https://github.com/Vidhi1290)
🔗 [LinkedIn](https://www.linkedin.com/in/vidhi-waghela-434663198/)
🐦 [Twitter](https://twitter.com/VidhiWaghela)
📝 [Medium](https://medium.com/@datasciencemeetscybersecurity)