May 2019
Beginner
170 pages
4h 9m
English
Content preview from Geospatial Data Science Quick Start Guide
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Summary
In this chapter, we chose a pertinent problem that had both analytics and geospatial components and tried to apply a very robust ML technique known as random forest to it. Before building the model, we had to handle the date component, the spatial component of data, as well as the categorical and continuous variables. We were able to achieve a good score in our first pass and build a world-class model with a few lines of code and a little bit of spatial data processing.
In the next chapter, we will discuss more accurate real-world distance metrics and perform other spatial computations, such as intersection, to make the model better.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.