IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-031-16620-4_4.html
   My bibliography  Save this book chapter

Applications of Machine Learning Algorithms in Data Sciences

In: Sustainability

Author

Listed:
  • Adeel Ansari

    (Shaheed Zulfikar Ali Bhutto Institute of Science & Technology)

  • Seema Ansari

    (Institute of Business Management)

  • Fatima Maqbool

    (Shaheed Zulfikar Ali Bhutto Institute of Science & Technology
    Institute of Business Management)

  • Rabia Zaman

    (Institute of Business Management)

  • Kubra Bashir

    (Institute of Business Management)

Abstract

Machine Learning, a branch of artificial intelligence (AI) and computer science, focuses on the usage of data and algorithms to copy the humans learning method, slowly increasing its accurateness. The chapter aims at discussing the applications of the machine learning algorithms, essential for developing predictive modeling and for carrying out classification and prediction in both supervised and unsupervised scenarios. The Machine Learning techniques have been applied to many application domains as a result of a humongous amount of data being created, processed, and mined from the evolution of the World Wide Web, mobile applications, and the rise of social media applications. Some of these applications are virtual personal assistants, predictions, surveillance, social media services, malware filtering, search engine result refining, and online fraud detections. The chapter includes the introduction, State of the Art, Machine Learning Algorithms, Applications of Machine Learning Algorithms in data sciences, followed by conclusion and future recommendations.

Suggested Citation

  • Adeel Ansari & Seema Ansari & Fatima Maqbool & Rabia Zaman & Kubra Bashir, 2023. "Applications of Machine Learning Algorithms in Data Sciences," International Series in Operations Research & Management Science, in: Fausto Pedro García Márquez & Benjamin Lev (ed.), Sustainability, pages 53-66, Springer.
    • Handle: RePEc:spr:isochp:978-3-031-16620-4_4
    DOI: 10.1007/978-3-031-16620-4_4
    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.

    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:isochp:978-3-031-16620-4_4. 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.