This project uses statistical analysis to detect fraudulent credit card transactions by examining patterns and anomalies in a dataset of 10,000 transactions, calculating averages, medians, frequencies, and identifying outliers to distinguish between legitimate and fraudulent activities.
python location mode average median fraud-detection standard-deviation outlier z-score abnormal-detection interquartile-range
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Updated
Aug 6, 2024 - Jupyter Notebook