Most machine learning problems have some element of location involved, whether it is the zip code of a customer, a place name description, address of a business, or latitude and longitude of a shipment.
However, working with geospatial data correctly is difficult. Typically, practitioners ignore geospatial information, or more advanced data scientists put in the effort to handle location data manually–for example, enriching with other data sources, generating neighbor features, and creating spatial clusters. Is this effort worth it?
Download Leveraging Geospatial Data and Analysis with AI to find out:
- How to integrate the best practices for handling geospatial data in common machine learning problems
- How to develop a location-aware model and interpret the results with emphasis on evaluating training data for quality, completeness, and spatial enrichment
- How DataRobot handles complex geospatial problem
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