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The Supervised Learning Workshop

You're reading from   The Supervised Learning Workshop Predict outcomes from data by building your own powerful predictive models with machine learning in Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781800209046
Length 532 pages
Edition 2nd Edition
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Authors (4):
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Blaine Bateman Blaine Bateman
Author Profile Icon Blaine Bateman
Blaine Bateman
Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
Ishita Mathur Ishita Mathur
Author Profile Icon Ishita Mathur
Ishita Mathur
Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
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Introduction

In Chapter 1, Fundamentals, and Chapter 2, Exploratory Data Analysis and Visualization, we introduced the concept of supervised machine learning in Python and the essential techniques required for loading, cleaning, exploring, and visualizing raw data sources. We discussed the importance of fully understanding the data before moving on to further analysis, as well as how the initial data preparation process can sometimes account for the majority of the time spent on the project as a whole. In particular, we considered correlations among all the variables, finding and addressing missing values, and understanding the shape of data via histograms, bar plots, and density plots. In this chapter, we will delve into the model building process and will construct our first supervised machine learning solution using linear regression.

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