Interactive Machine Learning Algorithm Visualization Platform
Dashboard here
ML Visualizer is a comprehensive educational platform that provides real-time visualization of machine learning algorithms during training. The application enables users to observe algorithm convergence, parameter optimization, and decision boundary formation through interactive demonstrations of fundamental machine learning concepts. All algorithms are built with vanilla Python and NumPy with minimal use of external libraries.
Linear Regression: Implements gradient descent optimization with real-time visualization of cost function minimization. The system displays the iterative process of finding optimal parameters through least squares estimation, including convergence analysis and loss function tracking.
Support Vector Machine: Demonstrates optimal hyperplane selection through margin maximization. The visualization shows decision boundary formation, support vector identification, and the mathematical optimization process for both linear and non-linear kernels.
Logistic Regression: Provides visualization of sigmoid function optimization and maximum likelihood estimation. The implementation shows probability-based classification learning with real-time parameter updates and convergence monitoring.
K-Means Clustering: Visualizes centroid initialization, iterative cluster assignment, and convergence to optimal cluster centers. The system displays within-cluster sum of squares (WCSS) minimization and demonstrates the effect of different initialization strategies.
Decision Trees: Shows recursive binary partitioning and entropy-based splitting criteria. The visualization demonstrates tree construction, feature selection, and the mathematical foundations of information gain calculations.