Real-world applications of machine learning, segmentation, recommendation engines, and analytics frameworks — with Python, R, and SQL. Not theory. Production patterns from the field.
A complete engineering story — from blank page to production app. Fourteen financial service providers. No public API. Geo-restrictions blocking cloud access. Built a multi-strategy scraping pipeline with NLP-driven rate extraction, automated every 3 hours via GitHub Actions, stored in Google Sheets, and surfaced through a live React Native mobile app and web dashboard. Here's exactly how it was built.
def _make(source, code, buy, sell, mid=None, unit=1) -> dict: """Canonical rate record factory. mid = transfer rate proxy.""" if mid is None: # sell_rate: exchange sells foreign to customer = transfer rate mid = sell if sell is not None else buy return { 'source': source, 'currency_code': code, 'buy_rate': buy / unit if buy else None, 'sell_rate': sell / unit if sell else None, 'mid_rate': mid / unit if mid else None, # canonical transfer rate }
Recommendation Engines, RFM & RFMC, NLP in the Enterprise — coming next.