Machine Learning Playground is an interactive web-based application designed to provide users with hands-on experience in various machine learning algorithms. The primary goal of this project is to allow users to gain a deeper understanding of how these algorithms work and to observe the impact of different parameters on the results.
The application is built using Dash, a Python framework for building web applications, and Scikit-learn, a popular machine learning library.
Machine Learning Playground allows users to choose from a variety of datasets and machine learning algorithms. Users can then adjust parameters and see how the algorithm performs on the selected dataset. The application provides visualization of the data and the model's decision boundary, as well as performance metrics for the model.
The supported machine learning algorithms include:
Support Vector Classifier
Logistic Regression
Linear Regression
KMeans
Orthogonal Projections
Principle Component Analysis
To run the Machine Learning Playground, follow these steps:
-
Clone the repository to your local machine:
git clone https://github.com/branisk/ml_playground.git -
Change to the project directory:
cd ml_playground -
Create a virtual environment and activate it:
python -m venv venv source venv/bin/activate
-
Install the required packages:
pip install -r requirements.txt -
Run the application:
python index.py -
Open your browser and navigate to http://127.0.0.1:8050/ to access the Machine Learning Playground.
- Add more algorithms (e.g., K-Nearest Neighbors, Decision Trees, Random Forest, etc.)
- Include more datasets and data preprocessing options
- Improve the user interface and user experience
- Implement cross-validation for model evaluation
- Add more performance metrics and visualizations
- Provide explanations and guidance for each algorithm and parameter
Feel free to contribute to the project by submitting pull requests or opening issues on the GitHub repository.