This project offers a simple way to learn Univariate Linear Regression. It provides a complete guide through the mathematical process. You'll learn about data preparation, the cost function, gradient descent, and how to evaluate results. All of this is done in Python, without using any machine learning libraries.
- Data Preparation: Learn how to clean and prepare your data.
- Cost Function: Understand how to measure the prediction error.
- Gradient Descent: Implement the method to optimize your model.
- Evaluation: Assess the performance of your linear regression.
- Data Analysis
- Univariate Linear Regression
- Gradient Descent
- Python Basics
- Statistics
- Supervised Learning
- Operating System: Windows, macOS, or Linux
- Python Version: Python 3.6 or above
- Memory: At least 4 GB RAM
- Storage: Minimum 100 MB of free space
To get started, follow these steps:
-
Visit the Releases Page: Click the link below to access the releases page.
-
Select a Version: You'll see a list of available versions. Choose the latest version for the best features and updates.
-
Download: Click on the version you want to download. The file will start downloading to your computer.
-
Extract the Files: Once the download is complete, locate the file in your downloads folder and extract its contents.
-
Run the Application: Open a terminal or command prompt and navigate to the extracted folder. Use the command below to run the program:
python main.py -
Follow the Prompts: The application will guide you through the process. Simply follow the on-screen instructions.
If you have questions or need help, feel free to reach out. You can check out the issues section on GitHub for common questions and troubleshooting.
- Python Documentation: Learn the basics of Python here.
- Linear Regression Basics: A great introduction can be found here.
- Gradient Descent Explained: Understand how gradient descent works here.
Contributions are welcome! If you want to improve this project, please fork the repository and submit a pull request.
This project is licensed under the MIT License.
For any inquiries or further information, feel free to contact me through GitHub. Happy analyzing!