https://github.com/ojas-arora/datavision-mastermind

https://github.com/ojas-arora/datavision-mastermind

Last synced: 4 months ago
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• Host: GitHub
• URL: https://github.com/ojas-arora/datavision-mastermind
• Owner: Ojas-Arora
• Created: 2024-07-04T07:21:58.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-10-01T11:05:14.000Z (9 months ago)
• Last Synced: 2025-02-20T02:15:16.490Z (4 months ago)
• Language: Python
• Homepage: https://mainpy-zqpkjoecaeoxnbaqv68olv.streamlit.app/
• Size: 29.3 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
                                              DataVision Mastermind

![image](https://github.com/user-attachments/assets/46474ad8-bc04-4a06-979d-1813de2eebde)

![image](https://github.com/user-attachments/assets/04f388a8-dbaf-4c8b-9cfa-43a7538cd160)

![image](https://github.com/user-attachments/assets/2620045d-d549-4325-8dc2-c0128611bb73)

MLDatasetComparison is an interactive tool built with Streamlit for comparing machine learning classifiers across different datasets. It allows users to select from various datasets (IRIS, Breast Cancer, Wine) and classifiers (KNN, SVM, Random Forest), tune hyperparameters, and visualize results using PCA scatter plots.

I. Features

1. Interactive Widgets: Select datasets and classifiers from the sidebar for dynamic updates.

2. Performance Metrics: View accuracy scores to evaluate model performance.

3. Visualization: PCA scatter plot visualization of dataset classes.

4. Custom Styling: Enhanced with custom CSS for a better user interface.

II. Getting Started

1. Installation:

Clone the repository: git clone 

Install dependencies: pip install -r requirements.txt

2. Running the App:

Navigate to the project directory.

Run Streamlit app: streamlit run main.py

3. Usage:

Open the app in your browser.

Select a dataset and classifier.

Adjust hyperparameters using the sidebar sliders.

View accuracy scores and PCA plots dynamically.

III. Technologies Used

1. Python

2. Streamlit

3. Scikit-learn

4. Matplotlib

IV. Contributing

Contributions are welcome! Please fork the repository and submit pull requests.

1. Fork the project (https://github.com/Ojas-Arora/DataVision-Mastermind/fork)

2. Create your feature branch (git checkout -b feature/AmazingFeature)

3. Commit your changes (git commit -am 'Add some AmazingFeature')

4. Push to the branch (git push origin feature/AmazingFeature)

5. Open a pull request

V. Project Link: 

https://mainpy-zqpkjoecaeoxnbaqv68olv.streamlit.app/