IDEAS home Printed from https://ideas.repec.org/a/bjf/ijltem/v15y2026i8a255.html

A Study Report on Comparative Performance Evaluation of Machine Learning Algorithms for Structured Data Classification

Author

Listed:
  • Badhur Ammulya

    (Data Science Sri Venkateswara University, Tirupati)

  • Dr. G. Nagalakshmi

    (Assistant Professor, Computer Science National Sanskrit University Tirupati)

Abstract

Artificial Intelligence (AI) has significantly transformed data analytics by enabling intelligent and data-driven decision-making through advanced computational models. This study presents a comprehensive comparative evaluation of three widely used machine learning algorithms: Logistic Regression, Decision Tree, and Random Forest, for classification tasks. The analysis is performed on a structured dataset incorporating systematic preprocessing, feature engineering, and model optimization techniques. Model performance is evaluated using standard metrics, including accuracy, precision, recall, and F1-score. The experimental results demonstrate that Random Forest achieves superior generalization performance compared to the other models. The findings emphasize the critical role of selecting appropriate models based on dataset characteristics and provide valuable insights for future research in predictive analytics.

Suggested Citation

  • Badhur Ammulya & Dr. G. Nagalakshmi, 2026. "A Study Report on Comparative Performance Evaluation of Machine Learning Algorithms for Structured Data Classification," International Journal of Latest Technology in Engineering, Management & Applied Science, RSIS International, vol. 15(8), pages 872-881, August.
    • Handle: RePEc:bjf:ijltem:v:15:y:2026:i:8:a:255
    DOI: 10.51583/IJLTEMAS.2026.150800061
    as

    Download full text from publisher

    File URL: https://www.ijltemas.in/uploads/vol15-iss8-pg872-881-202609_pdf.pdf
    Download Restriction: no
    File URL: https://www.ijltemas.in/article/a-study-report-on-comparative-performance-evaluation-of-machine-learning-algorithms-for-structured-data-classification
    Download Restriction: no
    File URL: https://libkey.io/10.51583/IJLTEMAS.2026.150800061?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:bjf:ijltem:v:15:y:2026:i:8:a:255. 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: Dr. Pawan Verma (email available below). General contact details of provider: https://www.ijltemas.in/ .

    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.