Book Image

Applied Supervised Learning with Python

By : Benjamin Johnston, Ishita Mathur
Book Image

Applied Supervised Learning with Python

By: Benjamin Johnston, Ishita Mathur

Overview of this book

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!
Table of Contents (9 chapters)

Chapter 3. Regression Analysis


Learning Objectives

By the end of this chapter, you will be able to:

  • Describe regression models and explain the difference between regression and classification problems

  • Explain the concept of gradient descent, how it is used in linear regression problems, and how it can be applied to other model architectures

  • Use linear regression to construct a linear model for data in an x-y plane

  • Evaluate the performance of linear models and use the evaluation to choose the best model

  • Use feature engineering to create dummy variables for constructing more complicated linear models

  • Construct time series regression models using autoregression


This chapter covers regression problems and analysis, introducing us to linear regression as well as multiple linear regression, gradient descent, and autoregression.