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SQL for Data Analytics

You're reading from   SQL for Data Analytics Perform fast and efficient data analysis with the power of SQL

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789807356
Length 386 pages
Edition 1st Edition
Languages
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Authors (3):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Matt Goldwasser Matt Goldwasser
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Matt Goldwasser
Upom Malik Upom Malik
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Upom Malik
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Toc

Table of Contents (11) Chapters Close

Preface 1. Understanding and Describing Data 2. The Basics of SQL for Analytics FREE CHAPTER 3. SQL for Data Preparation 4. Aggregate Functions for Data Analysis 5. Window Functions for Data Analysis 6. Importing and Exporting Data 7. Analytics Using Complex Data Types 8. Performant SQL 9. Using SQL to Uncover the Truth – a Case Study Appendix

3. SQL for Data Preparation

Activity 5: Building a Sales Model Using SQL Techniques

Solution

  1. Open your favorite SQL client and connect to the sqlda database.
  2. Follow the steps mentioned with the scenario and write the query for it. There are many approaches to this query, but one of these approaches could be:
    SELECT 
    c.*,
    p.*,
    COALESCE(s.dealership_id, -1),
    CASE WHEN p.base_msrp - s.sales_amount >500 THEN 1 ELSE 0 END AS high_savings 
    FROM sales s
    INNER JOIN customers c ON c.customer_id=s.customer_id
    INNER JOIN products p ON p.product_id=s.product_id
    LEFT JOIN dealerships d ON s.dealership_id = d.dealership_id;
  3. The following is the output of the preceding code:
Figure 3.21: Building a sales model query

Thus, have the data to build a new model that will help the data science team to predict which customers are the best prospects for remarketing from the output generated.

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