Visualization of Data Models

📚 Overview

This repository provides a collection of Jupyter notebooks dedicated to the visualization of different data models, including:

• Machine Learning visualizations
• Echelon visualizations
• Custom data structure visualizations

These notebooks are designed to help data scientists, developers, and researchers explore, analyze, and interpret the underlying structure and behavior of models, algorithms, and datasets. Each notebook offers a mix of intuitive visualizations and practical insights into the data flow, structure, and results.

✨ Key Features

• Interactive Jupyter Notebooks: Hands-on notebooks that let you explore visualizations of various models in real-time.
• Machine Learning Visualizations: Understand how your ML models behave, their decision boundaries, feature importances, and performance.
• Echelon Visualizations: Dive into echelon graphs that help represent hierarchical data structures and their interconnections.
• Data Structure Representations: Visualize relationships between different entities in a dataset, such as database schemas or graph models.

🔍 Notebooks Included

1. Machine Learning Model Visualizations

• Visualize decision boundaries of classification models (e.g., SVMs, decision trees).
• Interpret feature importance and relationships in regression and classification tasks.
• Explore clustering algorithms visually (K-means, DBSCAN, etc.).

2. Echelon Visualizations

• Visualize hierarchical structures and nested relationships.
• Understand multi-level data relationships, useful in representing nested data or taxonomies.

3. Data Model Diagrams

• Visualize data schemas, graph models, and relational models.
• Create visual representations of custom data structures to enhance understanding and debugging.

🛠️ Getting Started

Prerequisites

Ensure that you have the following installed:

• Python 3.x
• Jupyter Notebook or Jupyter Lab
• Required Python libraries (listed below)

Installation

1. Clone the repository:

   git clone https://github.com/saineshnakra/visualization-of-data-models.git
   cd visualization-of-data-models

2. Launch Jupyter notebooks:

   jupyter notebook

   or

   jupyter lab

3. Open and run the notebooks from the browser interface.

📊 How to Use the Notebooks

Each notebook in the repository is designed for a specific type of data visualization. You can open any notebook to:

• Interact with data: Load sample datasets or use your own.
• Visualize: Run the code cells to generate the visualizations.
• Modify and experiment: Play around with parameters to see how the visual outputs change in real-time.

For example, in the Machine Learning Visualization notebook, you can experiment with different algorithms and observe how their decision boundaries evolve.

🧩 Example Visualizations

Machine Learning Models

• Decision Boundary Visualization

• Clustering Visualization

Echelon Visualization

Matrix Visualization

Gaussian Visualization for d dimensions with videos!

🛡️ License

This project is licensed under the MIT License.

🙌 Contributing

We welcome contributions! If you'd like to add new features or improve existing visualizations, feel free to open an issue or submit a pull request.

Steps to contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes and commit:

   git commit -m "Add some awesome feature"

4. Push the branch:

   git push origin feature-branch

5. Open a pull request.

⭐️ Show Your Support

If this project helps you or you find it interesting, consider giving it a ⭐️ on GitHub!

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