Blogs and Projects

Projects

5 works

2026/ 01

BoxUp

Formula 1 Data Analytics Platform

A comprehensive Formula 1 telemetry and analytics platform that provides real-time race insights, performance analysis, and machine learning-powered predictions. Built with modern web technologies and a scalable microservices architecture.

Features

Real-Time Telemetry
Interactive speed traces, brake analysis, and corner-by-corner performance metrics
Race Analysis
Comprehensive session data including qualifying, practice, and race telemetry
Driver Performance
Comparative analysis between drivers with sector breakdowns and performance matrices
Weather Integration
Real-time weather conditions tracked throughout race sessions
ML Predictions
Machine learning models for predicting championship outcomes and race results

Tech Stack

Next.js 16React 19Tailwind CSSRechartsPlotly.jsFlaskPostgreSQLTimescaleDBKafkaRedisMLflowDocker

Future Scope

Live timing via WebSocket, animated race map overlays, advanced ML model training with historical telemetry, and Kubernetes deployment for production scaling.

// Project Structure

. 
├── frontend/              # Next.js web application
│   ├── app/              # Page routes and layouts
│   ├── components/       # Reusable React components
│   ├── lib/              # Utilities and API clients
│   └── types/            # TypeScript type definitions
├── backend/              # Python Flask API
│   ├── src/backend/     # Core application logic
│   │   ├── api/         # API endpoints
│   │   ├── config.py    # Configuration management
│   │   └── extensions.py # Database and service extensions
│   ├── migrations/      # Alembic database migrations
│   └── tests/           # Test suite
├── packages/
│   ├── ingestion/       # F1 data ingestion pipeline
│   ├── ml/              # Machine learning models
│   └── workers/         # Background job processors
└── infra/
    ├── docker-compose.yml
    └── postgres/        # Database initialization

2026/ 02

Fraudies

Fraud Detection Platform

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A modular, full-stack fraud detection system combining deterministic velocity rules with a machine learning scoring engine to assess transaction risk in real time. Built as a portfolio demonstration of enterprise fraud-monitoring architecture patterns.

Features

Real-Time Scoring
Classifies transactions as APPROVED, PENDING, or FLAGGED in a single synchronous request
ML Risk Engine
Random Forest Classifier with 89% precision on synthetic fraud dataset
Velocity Rules
Redis-based sub-millisecond checks for transaction frequency anomalies
Audit Trail
Append-only AuditLog enforced by PostgreSQL triggers for compliance
Analyst Dashboard
Real-time WebSocket alerts with transaction queue and risk metrics

Tech Stack

Next.js 14TypeScriptTailwind CSSNestJSFastAPIPythonScikit-LearnPostgreSQLPrismaRedisJWTWebSocket

Future Scope

Async job queue with BullMQ for decoupled scoring, Prometheus metrics export, OpenTelemetry tracing, PagerDuty alert integration, and read replicas for analytics queries.

// Project Structure

fintech-fraudies/
├── frontend/             # Next.js analyst dashboard
│   ├── app/
│   │   ├── (auth)/      # Login and registration
│   │   ├── dashboard/   # Main analyst view
│   │   ├── transactions/ # Transaction list/detail
│   │   └── alerts/      # Flagged transaction queue
│   └── components/
│       ├── ui/          # Shared primitives
│       ├── TransactionCard/
│       ├── RiskScoreMeter/
│       └── MetricsPanel/
├── backend/
│   ├── api/             # NestJS REST API
│   │   └── src/
│   │       ├── modules/
│   │       │   ├── auth/        # JWT + bcrypt
│   │       │   ├── transactions/ # Ingestion + ML orchestration
│   │       │   ├── webhook/     # Webhook ingestion
│   │       │   └── audit/       # Append-only audit log
│   │       └── common/
│   └── ml-engine/       # FastAPI inference service
│       └── app/
│           ├── model/   # Random Forest classifier
│           └── schemas.py
└── database/
    ├── schema.prisma
    └── migrations/

2026/ 03

F1 Analytics Dashboard

Slipstreams

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A broadcast-style Formula 1 analysis dashboard — lap-by-lap position traces, tyre strategy Gantt charts, and race analytics. Select any season, Grand Prix, and session to load real telemetry data instantly.

Features

Lap Position Chart
Interactive per-lap trace in official team colours with live standings
Tyre Strategy
Gantt-style stint visualisation with compound-coded colour bars
Race Results
Classified results with grid-vs-finish delta and points scored
Race Selector
Any season, Grand Prix, and session type — Race, Qualifying, Sprint, FP1–3

Tech Stack

FastAPIFastF1PandasUvicornNext.js 14TypeScriptTailwind CSSRecharts

Future Scope

Live timing & telemetry via WebSocket, animated race map, Redis pub/sub layer, and deep analytics with telemetry overlays.

// Project Structure

f1-dashboard/
├── backend/
│   ├── models/
│   │   └── schemas.py
│   ├── routes/
│   │   ├── health.py
│   │   ├── positions.py
│   │   ├── results.py
│   │   ├── seasons.py
│   │   ├── sessions.py
│   │   └── tyres.py
│   ├── services/
│   │   ├── fastf1_service.py
│   │   ├── lap_loader.py
│   │   └── session_service.py
│   ├── utils/
│   │   ├── colors.py
│   │   ├── driver_lookup.py
│   │   └── helpers.py
│   ├── f1_cache/
│   ├── main.py
│   └── requirements.txt
└── frontend/
    ├── app/
    │   ├── api/
    │   ├── sign-in/
    │   ├── sign-up/
    │   ├── layout.tsx
    │   └── page.tsx
    ├── components/
    ├── lib/
    ├── state/
    │   └── raceStore.ts
    ├── types/
    │   └── f1.ts
    ├── utils/
    │   └── keepAlive.ts
    ├── public/
    └── package.json

2026/ 04

Aureus

Imperial Multi-Chain Crypto Treasury

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A Roman Empire–inspired multi-chain cryptocurrency treasury platform engineered for modern digital asset management. Designed with institutional aesthetics and trader-focused functionality, Aureus provides a unified environment for managing cryptocurrencies across blockchain ecosystems.

Features

Unified Treasury
Monitor assets, balances, activity, and portfolio insights from a centralized dashboard
Multi-Chain Management
Manage digital assets across supported blockchain ecosystems through unified experience
Real-Time Intelligence
Track market pricing, asset movement, portfolio valuation, and blockchain activity live
NFT Asset Vault
Securely manage and visualize NFT holdings alongside traditional crypto assets
Cross-Chain Infrastructure
Interact seamlessly across blockchain networks through integrated multi-chain support

Tech Stack

Next.jsReactTypeScriptTailwind CSSWeb3Multi-Chain APIsBlockchain Wallet Integration

Future Scope

Institutional treasury tooling, expanded automation systems, enhanced cross-chain capabilities, and advanced portfolio analytics with historical performance tracking.

// Project Structure

aureus/
├── public/
├── components/          # Reusable UI components
├── pages/              # Next.js page routes
├── hooks/              # Custom React hooks
├── services/           # Blockchain service layer
├── utils/              # Utility functions
├── styles/             # Global styles
└── blockchain/         # Web3 integration layer

2025/ 05

FintechPay

Fraud Detection Platform

2025View details

A real-time credit card fraud detection platform using predictive analytics and pattern analysis to identify suspicious transactions, with instant alerts through an intuitive dashboard.

Features

Fraud Pattern Detection
Advanced algorithms analyse transaction behaviour in real time
Instant Alerts
Suspicious activity surfaced immediately through the dashboard
Scalable Monitoring
Architecture designed for both individual users and institutions
Reporting Dashboard
Full audit trail with filtering, export, and institution-level views

Tech Stack

ReactNode.jsExpressMongoDBPostgreSQLPythonML / Predictive Analytics

// Project Structure

fintechpay/
├── backend/
│   ├── api/              # REST endpoints
│   ├── services/         # Fraud detection engine
│   └── database/         # MongoDB + PostgreSQL
└── frontend/
    ├── components/
    ├── pages/
    └── styles/