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)

Autoregression Models

Autoregression models are part of a more classical statistical modeling technique that is used on time series data (that is, any dataset that changes with time) and extends upon the linear regression techniques covered in this chapter. Autoregression models are commonly used in the economics and finance industry as they are particularly powerful in time series datasets with a sizeable number of measurements. To reflect this, we will change our dataset to the S&P daily closing prices from 1986 to 2018, which is available in the accompanying source code.

Figure 3.59: S&P 500 Daily Closing Price

The main principle behind autoregression models is that, given enough previous observations, a reasonable prediction for the future can be made; that is, we are essentially constructing a model using the dataset as a regression against itself, hence autoregression. This relationship can be modeled mathematically as a linear equation:

Figure 3.60: First-order autoregression...