Predicting Event Attendance
Predicting whether a user actually attended an event they RSVP'd to sounds simple, but you never directly observe attendance: you have to infer real-world behavior from digital signals while dealing with sparse labels and temporal dynamics. I'll work through business and ML objectives, system architecture, data and features, modeling, infrastructure, evaluation, and robustness.
Solution Walkthrough
Business Objective
The objective is to accurately predict event attendance to improve downstream systems that depend on this signal. Accurate attendance prediction enables better event recommendations (we can promote events with high attendance rates), better creator insights (showing organizers reliable attendance estimates), better social features (highlighting which friends actually went), and platform health monitoring (detecting fake events or inflated RSVP counts).
There's an important distinction here between RSVP intent and actual attendance. Many users click "interested" or "going" but don't attend, plans change, they forget, the event doesn't match expectations. We need to predict actual real-world behavior, not just stated interest.
The prediction serves multiple use cases with different tolerance for false positives vs false negatives. For showing "Friends who went" on event pages, false positives are embarrassing (saying someone went when they didn't). For predicting attendance rates to help organizers plan, we care about aggregate accuracy more than individual precision. Our system needs calibrated probabilities that different use cases can threshold appropriately.
ML Objective
From an ML perspective, this is binary classification: did this user attend this event? But the ground truth is challenging. We rarely have explicit attendance confirmation. Instead, we need to infer attendance from proxy signals like check-ins, photos posted with event location tags, posts mentioning the event, and engagement with event-related content post-event.
The temporal aspect is critical. We can only definitively determine attendance after the event occurs. But different use cases need predictions at different times: weeks before (for recommendation systems), hours before (for organizer planning), and after the event (for user profiles and friend feeds). Each time point has different information available.
The prediction needs to handle the full spectrum from strong positive signals (user checked in at event location, posted photos) to ambiguous cases (user engaged with event page day-of but no explicit signals) to strong negatives (user posted from a different city during the event).
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