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

Introducing the business scenario

Imagine being a business analyst who works for the state of Iowa. The state monitors the retail distribution of liquors and spirits by collecting data from every shop in the territory. Controlling liquor sales is particularly important for monitoring citizen health and for checking tax income.

In the following screenshot, you can see a picture of typical shelves of liquors and spirits in a shop:

Figure 8.1 – The shelves in a liquor shop

For our business scenario, we can imagine that the state of Iowa wants to predict the number of liters that will be sold in the first 30 days of 2020 leveraging the historical data collected in the previous years.

Your manager may ask you to predict the number of liters that will be sold by all the shops in the state by leveraging the time series data that was already collected in the database.

As a business analyst, your job is to find the best algorithm to forecast the sales...