You're reading from Machine Learning with BigQuery ML Create, execute, and improve machine learning models in BigQuery using standard SQL queries

Product type Paperback

Published in Jun 2021

Publisher Packt

ISBN-13 9781800560307

Length 344 pages

Edition 1st Edition

Languages

Python

Tools

BigQuery

Concepts

Machine Learning

Author (1):

Alessandro Marrandino

View More author details

Table of Contents (20) Chapters

Preface

1. Section 1: Introduction and Environment Setup

2. Chapter 1: Introduction to Google Cloud and BigQuery FREE CHAPTER

3. Chapter 2: Setting Up Your GCP and BigQuery Environment

4. Chapter 3: Introducing BigQuery Syntax

5. Section 2: Deep Learning Networks

6. Chapter 4: Predicting Numerical Values with Linear Regression

7. Chapter 5: Predicting Boolean Values Using Binary Logistic Regression

8. Chapter 6: Classifying Trees with Multiclass Logistic Regression

9. Section 3: Advanced Models with BigQuery ML

10. Chapter 7: Clustering Using the K-Means Algorithm

11. Chapter 8: Forecasting Using Time Series

12. Chapter 9: Suggesting the Right Product by Using Matrix Factorization

13. Chapter 10: Predicting Boolean Values Using XGBoost

14. Chapter 11: Implementing Deep Neural Networks

15. Section 4: Further Extending Your ML Capabilities with GCP

16. Chapter 12: Using BigQuery ML with AI Notebooks

17. Chapter 13: Running TensorFlow Models with BigQuery ML

18. Chapter 14: BigQuery ML Tips and Best Practices

19. Other Books You May Enjoy

Summary

In this chapter, we've built our time series forecasting machine learning model. After the introduction of the business scenario, we discovered what time series forecasting is, and in particular, the ARIMA algorithm that is used to predict values from historical data points.

Before diving into the development of the BigQuery ML model, we applied some analyses on the data collected by the state of Iowa related to liquor sales in the shops of the territory. For this purpose, we introduced the use of the reporting tool Data Studio, which can be easily accessed by the BigQuery UI and be leveraged to draw a time series chart.

We then created our training table, which includes the time series of historical data, and trained our BigQuery ML model on it. Then, we evaluated the time series forecasting model by leveraging the BigQuery ML SQL syntax.

In the last step, we forecasted the quantity of liquor sold in Iowa with a horizon of 30 days and drew the results in a Data...