Fraud Detection of Credit Card using Machine Learning Algorithm
Asiya Mariyam A
, Agnus S , Dr. P Kavitha , Raajhalingam T
Credit Card Fraud, Random Forest Algorithm, Logistic Regression, K-Nearest Neighbor algorithm, ROC- Receiver Operating Characteristic Curve
Credit card fraud refers to the loss or theft of the data or the credit card. This frequently occurs when the card is used improperly or when an attacker uses the data for his own benefit. Supervised Learning algorithms are the applied algorithms. For the purpose of detecting fraudulent transactions, the detection of credit card fraud software has been set up. While detecting entirely of the bogus transactions is difficult, the objective here is to cut down on inaccurate fraud categories. The employed algorithms are the algorithm of Random Forest, logistic regression algorithm and K-Nearest Neighbor algorithm. The three algorithms' results are examined using the metrics precision, F1-score, accuracy and recall. The ROC curve is plotted employing the confusion matrix as a foundation. The best algorithm for spotting fraud is one that is the most exact, precise, recallable, and F1-score.
"Fraud Detection of Credit Card using Machine Learning Algorithm", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.675-684, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001107.pdf
Volume 11
Issue 2,
February-2024
Pages : 675-684
Paper Reg. ID: TIJER_151030
Published Paper Id: TIJERB001107
Downloads: 000327
Research Area: Science and Technology
Country: Thiruvallur,Chennai, Tamil Nadu, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
