What We Leave Behind < PC WORKING >

To build a deep feature using a tool like Featuretools, follow this workflow:

: A deep feature could aggregate the frequency and variety of digital interactions over time to measure the "weight" of a person's digital remains. What We Leave Behind

: Choose Aggregation primitives (calculating values across many related records, such as MEAN amount of data left behind) or Transform primitives (performing operations on a single table, such as YEAR from a timestamp). To build a deep feature using a tool

: Identify your "base" table (e.g., Users ) and related tables (e.g., Digital Footprint , Physical Artifacts ). : Specify the max_depth

: Specify the max_depth . A depth of 1 might calculate "average session time," while a depth of 2 could calculate the "average of the maximum session times across all devices".

: By applying mathematical functions to time-series data, you can create features that predict how quickly certain "left behind" artifacts lose relevance or visibility.

In machine learning, developing a for a project like "What We Leave Behind" involves using Deep Feature Synthesis (DFS) to automatically generate complex features from relational data. This process moves beyond simple raw data by stacking mathematical "primitives" (like sum, mean, or count) across related tables to reveal hidden patterns. Core Development Steps