September 2016
Beginner to intermediate
344 pages
7h 44m
English
Content preview from Spark for Data ScienceBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Data preparation
The data exploration stage helped us identify all the issues that needed to be fixed before proceeding to the modeling stage. Each individual issue requires careful thought and deliberation to choose the best fix. Here are some common issues and the possible fixes. The best fix is dependent on the problem at hand and/or the business context.
Too many levels in a categorical variable
This is one of the most common issues we face. The treatment of this issue is dependent on multiple factors:
- If the column is almost always unique, for example, it is a transaction ID or timestamp, then it does not participate in modeling unless you are deriving new features from it. You may safely drop the column without losing any information content. ...
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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.