17 - AI, Analytics and Machine Learning: Data-Driven Decisions

Play The Event analytics microservice: Python FastAPI with 6 ML models (churn, demand, clustering, anomaly, recommender, participation), dashboard and burndown charts.

What you'll learn

• Separate microservice for ML: Python's ecosystem is unmatched for data science; isolating it from the main backend keeps both systems focused
• Start with simple models: Linear regression and K-Means deliver surprisingly good results before investing in complex neural networks
• Handle cold start gracefully: New users need useful defaults while the system accumulates their data
• Scraping adds unique value: Venue pricing intelligence is difficult to obtain through APIs alone and provides genuine competitive advantage
• Visualization is half the battle: The best predictions are worthless if organizers cannot understand and act on them

This article is part of the Play the Event series on federicocalo.dev.

Read the full article

The complete article (15 min read) with code examples, diagrams, and practical exercises is available here:

➡️ 17 - AI, Analytics and Machine Learning: Data-Driven Decisions

https://federicocalo.dev/en/blog/pte-analytics-ml-artificial-intelligence

By Federico Calò — Software Developer & Technical Writer