Unraveling Data Complexity in the Metaverse for Anomaly Detection With Python on NYC Taxi

Abstract

In the dynamic environment of the Metaverse, where virtual interactions and transactions thrive, detecting data anomalies becomes imperative for maintaining integrity and security. This paper explores the application of Pythonbased anomaly detection models, including Inter Quartile Range (IQR), Median Absolute Deviation (MAD), and Local Outlier Factor (LOF), in identifying anomalies within NYC Taxi data. Through comprehensive analysis and experimentation, we investigate the effectiveness and comparative performance of these models in detecting outliers amidst the complex and diverse data landscape of the Metaverse. In the NYC Taxi Data, which contains 10320 data, it was analyzed with the mentioned algorithms and 2 anomalies (0.019%) were found with IQR. In the same data set, 1 anomaly (0.009%) was found with MAD model and 1032 anomalies (10%) were found with LOF