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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      07 April 2023, Volume 38 Issue 2   
    Article

    PREDICTIVE ANALYTICS WITH MACHINE LEARNING FOR FRAUD DETECTION OF ONLINE MARKETING TRANSACTIONS
    Tatarao Vana1*, S Mallikarjun2, P Pavan Kumar3, V Rahul4
    Journal of Data Acquisition and Processing, 2023, 38 (2): 3566-3572 . 

    Abstract

    Predictive analytics with machine learning has become an essential tool for detecting fraudulent transactions in online marketing. Fraudulent activities in online marketing can cause significant financial losses to businesses and harm their reputation. Therefore, detecting fraudulent transactions has become a critical challenge for online marketing platforms. This paper presents an approach to fraud detection using machine learning techniques. The proposed approach uses supervised learning algorithms ie: Random Forest, KNN, and XGBoost to classify whether the transaction is fraudulent or not. The datasets used here is obtained from an online marketing platform and consists of various features such as the IP address of the user, time of the transaction, and device type used for the transaction. The study also shows that the features that have the most significant impact on the detection of fraudulent transactions are the time of the transaction and the device type used. The time of the transaction is more crucial in detecting fraudulent transactions than the device type used. The study suggests that combining these features with other relevant features could further improve the performance.

    Keyword

    Machine learning, Online Marketing System, Deep Learning, Random Forest, supervised learning.


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ISSN 1004-9037

         

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