Algo Vision is a neon-themed, interactive web application for visualizing Data Structures, Algorithms, and Machine Learning models.
It is designed as an educational cockpit combining real-time animations, step-by-step execution, complexity analysis, and conceptual explanations.
This project follows clean UI architecture, deterministic visualization control, and scalable extensibility, making it suitable for learning, teaching, demos, and portfolio-grade presentation.
▶️ Play / Pause / Step / Reset execution- ⏩ Adjustable animation speed
- 🔀 Multiple traversal modes (BFS, DFS, Inorder, Preorder, Postorder)
- 📐 Dynamic time & space complexity overlay
- 📖 Explanation panel with concepts, steps, and use cases
- 🧩 Modular visualization engine
- 📱 Fully responsive desktop and mobile layout
Description: Arrays store elements in contiguous memory locations, providing constant-time O(1) access to any element by index. Efficient for random access and iteration, but insertions and deletions in the middle are O(n) as elements need to be shifted.
Description: A linear data structure where elements (nodes) are connected via pointers or references. Each node contains data and a link to the next node. Provides O(1) insertion/deletion at known positions but O(n) access time as traversal must start from the head.
Description: A Last-In-First-Out (LIFO) data structure where elements are added and removed from the same end (top). Push adds an element, pop removes the top element. Useful for recursion, undo operations, and expression evaluation. All operations are O(1).
Description: A First-In-First-Out (FIFO) data structure where elements are added at the rear and removed from the front. Enqueue adds at the back, dequeue removes from the front. Essential for BFS algorithms, task scheduling, and print spooling. All operations are O(1).
Description: Uses a hash function to map keys to array indices for near-constant-time lookups, insertions, and deletions. Average O(1) complexity, though worst-case O(n) with many collisions. Collisions are handled via chaining or open addressing.
Description: A hierarchical structure maintaining sorted order where left children are smaller than the parent, and right children are larger. Enables efficient searching, insertion, and deletion in O(log n) average time on balanced trees. Supports multiple traversal strategies (inorder, preorder, postorder).
Description: A flexible data structure consisting of vertices (nodes) and edges connecting them. Can be directed or undirected, weighted or unweighted. Used for modeling networks, social connections, and relationships. Traversal via BFS or DFS explores all reachable nodes in O(V+E) time.
Description: A foundational regression algorithm modeling the linear relationship between input features and a continuous target variable. Uses the equation y = wx + b, where w is the weight and b is the bias. Minimizes mean squared error via gradient descent. Interpretable and computationally efficient, ideal for understanding feature impact.
Description: A classification algorithm that models the probability of a binary outcome using the sigmoid activation function. Despite its name, it performs classification by mapping inputs to a probability between 0 and 1. Serves as a foundation for understanding neural networks and deep learning.
Description: A tree-based classifier that makes decisions by recursively splitting data on feature thresholds. Each internal node represents a feature test, branches represent outcomes, and leaf nodes represent class predictions. Highly interpretable but prone to overfitting on complex data.
Description: An ensemble method that trains multiple decision trees on random subsets of data and features, then aggregates their predictions via majority voting (classification) or averaging (regression). Reduces overfitting and improves generalization by combining diverse weak learners.
Description: A sequential ensemble technique where trees are trained one after another, each correcting errors of the previous ones. Each new tree fits the residuals (errors) of the combined ensemble. Highly effective for both classification and regression tasks, achieving state-of-the-art performance on many benchmarks.
Description: A powerful classifier that finds the optimal hyperplane maximizing the margin between classes in high-dimensional space. Uses kernel trick to handle non-linear separability. Effective for binary and multi-class classification, handles high-dimensional data well but computationally expensive.
Description: A simple instance-based (lazy learning) algorithm that classifies new instances based on the K nearest training examples. Predictions are made by majority voting among neighbors. Non-parametric and intuitive, but slow at prediction time and sensitive to feature scaling.
Description: A centroid-based clustering algorithm that partitions data into K clusters by iteratively assigning points to nearest centroids and updating centroid positions. Minimizes within-cluster variance. Fast and scalable but requires specifying K in advance and is sensitive to initialization.
Description: A dimensionality reduction technique that identifies principal components (directions of maximum variance) in the data. Projects high-dimensional data onto a lower-dimensional space while preserving maximum variance. Useful for visualization, noise reduction, and improving model efficiency.
Description: A multi-layer perceptron consisting of interconnected neurons organized in layers (input, hidden, output). Each neuron applies a weighted sum followed by a non-linear activation function. Can learn complex non-linear patterns. Universal function approximator with sufficient capacity.
Description: Specialized neural network for image processing using convolution operations. Sliding kernels extract spatial features automatically, pooling reduces dimensions, and multiple layers learn hierarchical representations. Achieves translation invariance and exceptional performance on computer vision tasks.
Description: Networks designed for sequential data with hidden states that carry information across time steps. LSTMs address the vanishing gradient problem via memory cells and gates (input, forget, output). Essential for natural language processing, time series analysis, and speech recognition.
Description: A state-of-the-art architecture based on self-attention mechanisms allowing parallel processing of sequences. Each position attends to all other positions, enabling efficient long-range dependency modeling. Foundation of modern large language models (GPT, BERT) and achieves excellent results on NLP and vision tasks.
Description: A learning paradigm where an agent interacts with an environment, taking actions and receiving rewards. Goal is to learn an optimal policy maximizing cumulative reward. Uses Q-learning, policy gradients, or actor-critic methods. Applications include game playing, robotics, and autonomous systems.
UI Layer (React Components)
↓
State Orchestration Layer
↓
Visualization Engine
↓
Explanation & Complexity Overlays
- UI components are presentational only
- Algorithm logic is isolated per visualization
- Playback control is centralized
- Content is type-driven and reusable
- Layout adapts automatically for desktop and mobile
src/
│
├── components/
│ ├── layout/ # Sidebar, headers, control panels
│ ├── ui/ # shadcn/ui primitives
│ └── visualizations/ # Algorithm & ML visualizations
│
├── pages/
│ ├── Index.tsx # Main orchestration layer
│ └── NotFound.tsx
│
├── hooks/
│ └── use-mobile.tsx # Responsive helpers
│
├── lib/
│ └── utils.ts # Utility helpers
│
├── types/
│ └── visualization.ts # Visualization & traversal enums
│
├── App.tsx # Providers & routing
├── main.tsx # React entry point
└── index.html # HTML shell & metadata
- React root render
- Attaches app to DOM
- Global providers (Router, Query, UI systems)
- Route definitions
- Central application orchestrator
- Manages:
- Active visualization
- Playback state
- Speed & traversal mode
- Complexity overlay
- Desktop/mobile layout switching
- Sidebar & mobile navigation
- Playback control panels
- Explanation and complexity overlays
- Self-contained visualization canvases
- Driven by standardized playback props
- Manage animations and traversal internally
main.tsx
→ App.tsx
→ Providers
→ Index.tsx
User
→ Sidebar / MobileSidebar
→ Index.tsx
→ Mount Visualization
Control Panel
→ Index.tsx (state update)
→ Visualization re-render
- Node.js (LTS recommended)
- npm
npm installnpm run devDefault URL:
http://localhost:5173
To add a new visualization:
- Create a component in
components/visualizations/ - Add its type in
types/visualization.ts - Register it in
Sidebar.tsxandMobileSidebar.tsx - Map its label in
Index.tsx - Add theory and complexity content (recommended)
Contiguous memory structure with constant-time indexed access.
Hierarchical structure maintaining sorted order with multiple traversal strategies.
- BFS explores level-by-level using a queue
- DFS explores depth-first using recursion or stack
Models linear relationships using:
y = wx + b
Rule-based model splitting data on feature thresholds.
Ensemble of decision trees reducing variance via averaging.
Layered structure of neurons with nonlinear activations.
Agent learns optimal actions via reward-based interaction.
- Clean separation of concerns
- Deterministic visualization engine
- Interview-grade UI architecture
- Scales to advanced algorithmic simulations
- Strong open-source and portfolio value
- Pseudocode synchronization
- User-defined inputs
- Algorithm comparison mode
- WebGL acceleration
- Cloud deployment
- Frontend Framework: React 18 + TypeScript
- Build Tool: Vite
- Styling: Tailwind CSS + shadcn/ui
- Icons: Lucide React
- Routing: React Router
- State Management: React Query (TanStack Query)
- Testing: Vitest + Testing Library
- Form Handling: React Hook Form + Zod
npm run dev # Start dev server
npm run build # Build for production
npm run preview # Preview production build
npm run lint # Run ESLint
npm run test # Run tests
npm run test:watch # Watch mode testsWe welcome contributions! To add new visualizations:
- Fork the repository
- Create a feature branch
- Add your visualization component
- Submit a pull request
MIT License — feel free to use this project for learning, teaching, and commercial purposes.
© 2026 All rights reserved to @byadnesh
Built with ❤️ for algorithm enthusiasts and learners by @byadnesh.