This dataset contains a sample* of 1,000 observations representing 160 attributes of US households, grouped by zip code. Each observation corresponds to a unique date for unique zip code and includes attributes of households such as income level and age group.
This data can be used for various machine learning tasks, as it can be joined by three keys: country code, postal code and date. Utilizing this dataset may enhance the precision of tasks such as credit scoring, fraud prevention, demand forecasting and customers behavioural analytics. Just join this data to your list of variables and try to increase the accuracy of your model with new features from this dataset.
🔎 Simple Drag & Drop Streamlit App to find relevant data listings & fields for your ML task: https://upgini.com/upgini-widget
* To access the data from 2000 to the present, use the Upgini data search engine.