# 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)**

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

---

*By [Federico Calò](https://federicocalo.dev) — Software Developer & Technical Writer*