A path forward
So, the inkling of having more than enough data for training a model seems very appealing.
Big data sources would appear to answer this desire, however in practice, a big data source is not often (if ever) analyzed in its entirety. You can pretty much count on performing a sweeping filtering process aimed to reduce the big data into small(er) data (more on this in the next section).
In the following section, we will review various approaches to addressing the various challenges of using big data as a source for your predictive analytics project.
Opportunities
In this section, we offer a few recommendations for handling big data sources in predictive analytic projects using R. Also, we'll offer some practical use case examples.
Bigger ...
Get Mastering Predictive Analytics with R - Second Edition now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
