November 2015
Intermediate to advanced
190 pages
4h 11m
English
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The real world
Now that we have this harness built that can make recommendations on the customers that we should advertise to, we need to think about the kind of algorithms that we want to plug in it. For the probability of a customer placing an order, we can use Logistic Regression or Naïve Bayes. To estimate how much money the customer might spend, we can use (depending on our data) Gaussian Naïve Bayes or Linear Regression.
To start off with, let's use Linear Regression and Logistic Regression. The main purpose of doing this is to use more sklearn as, if we do, we won't have to spend time building the algorithms ourselves.
When we begin, it may be helpful to create a test file just to explore sklearn like in the previous chapter. We already have ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.