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The Pandas Workshop

The Pandas Workshop

By : Blaine Bateman, Saikat Basak , Thomas Joseph, William So
4.8 (16)
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The Pandas Workshop

The Pandas Workshop

4.8 (16)
By: Blaine Bateman, Saikat Basak , Thomas Joseph, William So

Overview of this book

The Pandas Workshop will teach you how to be more productive with data and generate real business insights to inform your decision-making. You will be guided through real-world data science problems and shown how to apply key techniques in the context of realistic examples and exercises. Engaging activities will then challenge you to apply your new skills in a way that prepares you for real data science projects. You’ll see how experienced data scientists tackle a wide range of problems using data analysis with pandas. Unlike other Python books, which focus on theory and spend too long on dry, technical explanations, this workshop is designed to quickly get you to write clean code and build your understanding through hands-on practice. As you work through this Python pandas book, you’ll tackle various real-world scenarios, such as using an air quality dataset to understand the pattern of nitrogen dioxide emissions in a city, as well as analyzing transportation data to improve bus transportation services. By the end of this data analytics book, you’ll have the knowledge, skills, and confidence you need to solve your own challenging data science problems with pandas.
Table of Contents (21 chapters)
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1
Part 1 – Introduction to pandas
6
Part 2 – Working with Data
11
Part 3 – Data Modeling
15
Part 4 – Additional Use Cases for pandas

Chapter 11: Data Modeling – Regression Modeling

In this final chapter on data modeling, you will learn details about linear regression using the sklearn library's LinearRegression method, and non-linear regression modeling using the sklearn library's RandomForestRegressor method. As you learn more about these methods, you will also learn details about measuring model performance using measures such as the sum of square error and root mean squared error, as well as powerful visual methods, including constructing histograms of model errors and other plotting methods.

By the end of this chapter, you will have brought together all you have learned about data modeling and be ready to address a wide range of business and technical data challenges.

This chapter covers the following topics:

  • An introduction to regression modeling
  • Exploring regression modeling
  • Model diagnostics
  • Activity 11.01 – Implementing multiple regression
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