What Can AI Learn From James Bond?

“Once is happenstance, twice is coincidence, three times is enemy action.” This is a famous quote from the book “Goldfinger” that highlights how counts can reveal hidden intentions.

When it comes to analyzing data, frequency signals are incredibly useful. They help us count events and understand how often they occur within a given period. By looking at historical data, we can make informed predictions about what will happen in the future.

But why does the count of historical events matter so much? Well, according to credibility theory, the more historical data we have, the more confident we can be in our predictions. This is because a higher count of events means we have more evidence to support our claims.

For example, insurance companies use historical data to determine prices. The more claims a customer has made in the past, the more they are likely to pay in the future. Similarly, businesses use historical data to understand customer behavior. Customers who make frequent purchases are more likely to be loyal and valuable to the brand.

But how do we calculate event counts effectively? It’s not as simple as just counting the events within a given period. To get the most accurate and valuable insights, we need to choose an appropriate time window, filter out irrelevant data, and consider the relationships between different events.

By applying these strategies, we can turn mundane data into world-class insights. For example, we can use domain knowledge to filter out high-impact events and focus on the ones that matter. We can also use data with a one-to-many relationship with an entity, which helps us understand how different events are related to one another.

Counts are a great start to your feature list, but they won’t be enough. We’ve built an open-source feature engineering library that makes it easy to create a dozen signal types! Click here for a free download, with worked examples in Python: https://docs.featurebyte.com/