See how visibility graphs work in an interactive way using Pyvisgraph and Pygame

Showcase your work using streamlit and Gradio

An interactive concept map of core **Machine Learning** topics built using **NetworkX** and **PyVis** in Python. This project helps visualize relationships between fundamental ML concepts such as Supervised/Unsupervised Learning, key algorithms (e.g., Regression, SVM, Neural Networks), and optimization techniques like Gradient Descent.

Analyzing Facebook ego networks to identify influential users and communities for optimizing political advertising. Leverages network science techniques (Louvain communities, centrality metrics, threshold models) to simulate influence spread.