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    政大機構典藏 > 理學院 > 應用數學系 > 學位論文 >  Item 140.119/149594
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/149594

    Title: 機器學習下信用卡詐欺之預測分析: 以美國市場為例
    Predictive Analysis of Credit Card Fraud via Machine Learning : Evidence from the United State
    Authors: 陳彥霖
    Chen, Yen-Lin
    Contributors: 洪芷漪

    Hong, Jyy-I
    Lin, Shih-Kuei

    Chen, Yen-Lin
    Keywords: 信用卡詐欺模型
    Credit Card Fraud Model
    Machine Learning
    Nonlinear Problem
    Date: 2024
    Issue Date: 2024-02-01 11:25:27 (UTC+8)
    Abstract: 本研究採用包含 180 萬筆美國信用卡詐欺資料集,旨在深入探討消費詐
    斯迴歸模型的表現,結果顯示在這類非線性問題中,隨機森林與 XGBoost
    This study employs a dataset containing 1.8 million instances of credit card fraud in the United States to delve into fraudulent transaction behaviors. By modeling two major categories of variables—customer transaction behaviors and personal information—we aim to explore the influence of various factors on fraudulent transactions. Comparative analysis between tree-based models and logistic regression in machine learning reveals that in such non-linear scenarios, Random Forest and XGBoost demonstrate superior predictive performance. Additionally, we identified four significant variables—transaction amount, merchant type, and the day of the week of the transaction —as influential factors in predicting fraudulent behavior, resulting in the development of a model with higher recall rates.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110751015
    Data Type: thesis
    Appears in Collections:[應用數學系] 學位論文

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