Skip to Main Content
Python Machine Learning By Example
book

Python Machine Learning By Example

by Yuxi (Hayden) Liu, Ivan Idris
May 2017
Beginner to intermediate content levelBeginner to intermediate
254 pages
6h 24m
English
Packt Publishing
Content preview from Python Machine Learning By Example

Feature engineering

When it comes to a machine learning algorithm, the first question to ask is usually what features are available, or what the predictive variables are. The driving factors used to predict future prices of DJIA, the Close prices herein, obviously include historical and current Open prices and historical performance (High, Low, and Volume). Note that current or same-day performance (High, Low, and Volume) should not be included as we simply cannot foresee the highest and lowest prices at which the stock traded or the total number of shares traded before the market is closed on that day.

Predicting close price with only these four indicators does not seem promising, and might lead to underfitting. So we need to think of ways ...

Become 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,
and much more.
Start your free trial

You might also like

Python Machine Learning by Example - Third Edition

Python Machine Learning by Example - Third Edition

Yuxi (Hayden) Liu
Python: Deeper Insights into Machine Learning

Python: Deeper Insights into Machine Learning

Sebastian Raschka, David Julian, John Hearty
Python: Real World Machine Learning

Python: Real World Machine Learning

Prateek Joshi, John Hearty, Bastiaan Sjardin, Luca Massaron, Alberto Boschetti

Publisher Resources

ISBN: 9781783553112Supplemental Content