Concerts are major demand drivers

We’ve created Jupyter notebooks around how to integrate events, such as concerts, into your models to help your Data Science teams get up and running quickly. Use our Data Engineering notebook to get the data and how to use it. Our Data Exploration notebook explores the data and how you can get value from it and our Feature Engineering notebook provides examples of how to build data models and features.

Because concerts big and small can play in the same venue, you can't use venue capacity as a proxy for attendees. PredictHQ models identify a specific predicted attendance for each concert. Our models accurately gauge the number of people attending a performance, so you don’t need to guess how much each show is going to influence demand for food, drinks, or rides around a venue.

Knowing when a concert is starting is helpful if you want to predict how many diners might come in before a show that’s happening around the corner from your restaurant. But if you’re running a ride share business or own a bar near a concert hall, you need to know when the show is ending too. Our API includes a predicted end time for any event that doesn’t state an official end time, enabling you to be prepared.