IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-1-4842-8670-8_6.html
   My bibliography  Save this book chapter

Predicting Numerical Outcomes

In: Data Science and Analytics for SMEs

Author

Listed:
  • Afolabi Ibukun Tolulope

Abstract

In this chapter, we will explore the popular techniques used for prediction, particularly in the retail business. The approach used in explaining these techniques is to use them in solving a business problem. The business problem to be addressed is the sales prediction problem which is common in the retail business. The chapter first explains the fundamental concept of prediction techniques; next, we look at how such techniques are evaluated. After this, we describe the business problem we intend to solve. We then pick each of the selected techniques one by one and explain the algorithms involved and how they can be used to solve the problem described. The prediction techniques used are the multiple linear regression, the regression trees, and the neural network. To conclude the chapter, we compare the results of the three algorithms and conclude on the problem in question. In this chapter, therefore, the analytics product offered is the sales prediction problem for small retail businesses.

Suggested Citation

  • Afolabi Ibukun Tolulope, 2022. "Predicting Numerical Outcomes," Springer Books, in: Data Science and Analytics for SMEs, chapter 0, pages 113-153, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4842-8670-8_6
    DOI: 10.1007/978-1-4842-8670-8_6
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-1-4842-8670-8_6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.