👋 Hi, I'm Darren¶
I'm a data/software engineer who builds full-stack web applications, cloud infrastructure, and data pipelines. This site showcases the architecture and technical design behind my projects.
Below is a brief overview of each project. For detailed architecture diagrams, technical deep-dives, and implementation details, follow the links to each project's dedicated sub-pages.
Projects¶
🎫 Event Booker — Browser Automation Bot¶
A Playwright-based automation daemon that polls a Cvent registration page, detects available Saturday time slots, fills a 13-field registration form, submits the booking, and sends email notifications — all running 24/7 on AWS EC2 via systemd.
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4 Mermaid diagrams: component architecture, booking pipeline sequence, deployment infrastructure, scheduler state machine
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How it works, technology stack, project structure
Tech stack: Python 3.13 · Playwright · APScheduler · Gmail SMTP · systemd · AWS EC2
📈 Portfolio Tracker (PostgreSQL) — Investment Analytics Platform¶
A Flask web application for tracking investment portfolios across multiple brokers. Features interactive Chart.js/TradingView visualisations, XIRR calculations, multi-currency support, and CSV import from broker platforms — backed by AWS RDS PostgreSQL.
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7 Mermaid diagrams: component architecture, request lifecycle sequence, AWS network infrastructure, data pipeline, ER diagram, deployment sequence, CSV ingestion flow
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How it works, technology stack, API endpoints, visualisation stack
Tech stack: Python 3.13 · Flask · PostgreSQL (AWS RDS) · Chart.js · TradingView Lightweight Charts · Gunicorn · AWS EC2
📝 Portfolio Tracker (SQLite) — Flask Learning Project¶
The original version of the portfolio tracker, built following the official Flask tutorial. A blog/CMS application with user authentication, demonstrating Flask design patterns (application factory, blueprints, request context).
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Application factory pattern, blueprint architecture, database layer, request lifecycle
Tech stack: Python 3.13 · Flask · SQLite · Jinja2 · Werkzeug
🎧 Cats & Dogs Audio Classification — Deep Learning (CNN)¶
A PyTorch CNN for binary audio classification — distinguishing cat meowing from dog barking using MFCC features extracted from raw .wav files. Demonstrates end-to-end ML pipeline design, custom data pipelines, 5-Fold cross-validation, and classical ML baseline comparison.
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ML pipeline, CNN architecture, design decisions, feature engineering methodology
Tech stack: Python 3.11 · PyTorch · torchaudio · librosa · scikit-learn · Google Colab
Screenshots¶
ℹ️ Coming soon — Screenshots of each application will be added here.