Welcome! This guide helps you download and run the Comparison_ML_Regression_Models application. This application compares different regression models like Lasso, Random Forest, and Artificial Neural Networks (ANN) to predict how popular music tracks might be.
To run this application, ensure your computer meets the following requirements:
- Operating System: Windows 10 or later, macOS, or Linux
- Processor: Intel or AMD with 2 GHz or higher
- RAM: At least 4 GB
- Storage: Minimum of 200 MB of free space
- Software: Python 3.7 or higher installed on your computer
To start, visit the Releases page to download the application.
- Click on the link above to go to the Releases page.
- Look for the latest version. It will have the highest number (e.g., v1.0).
- Click on the link next to Assets to download the application file.
- Save the file in a location you can easily find, like your Desktop or Downloads folder.
After downloading the application, follow these simple steps:
- Navigate to the folder where you saved the downloaded file.
- Double-click on the application file to run it.
- If a security warning appears, confirm you want to proceed.
- Wait for the application to load completely.
Once the application is open, you are ready to start using it!
You will see a user-friendly interface. Here’s how to proceed:
- Upload Data: Click on the option to upload your music track data. Ensure your data file is in CSV format and includes track attributes like track length, genre, and previous popularity metrics.
- Select Models: Choose which regression models you wish to compare: Lasso, Random Forest, or ANN.
- Run Analysis: Click on the "Run" button to start the analysis. The application will process your data and provide results.
- View Results: After processing, results will appear on the screen. You can explore various metrics and visualizations related to your data.
- Multiple Models: Compare Lasso Regression, Random Forest, and ANN for better insights.
- Data Visualization: Understand model effectiveness through intuitive charts and graphs.
- User-Friendly UI: Designed for ease of use, no programming knowledge required.
- CSV Support: Import your music track data easily in CSV format.
If you encounter issues:
- Make sure you have the correct version of Python installed.
- Ensure all software and dependencies are updated.
- Check for any error messages and note them down for help.
For help, check the Issues section of the repository or reach out to the community.
To learn more about the models used in this application:
- Lasso Regression: A simple way of estimating parameters in linear regression.
- Random Forest: A robust algorithm that uses multiple decision trees for prediction.
- Artificial Neural Networks: Mimics the human brain to learn patterns from data.
You can also find tutorials and articles on the main topics of machine learning and regression analysis online.
This project is licensed under the MIT License. You can read more about it in the LICENSE file included in the repository.
For any inquiries, feel free to reach out to the repository maintainer:
- Username: YahyaBalikci
Thank you for choosing Comparison_ML_Regression_Models. We hope this application helps you analyze music track popularity with ease.