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z-score

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Anomaly detection in synthetic transaction and sales data with Python. Generates realistic data, injects unusual events, and applies Isolation Forest, Local Outlier Factor, and Z-score methods to detect outliers. Produces anomaly reports and visualizations for portfolio-ready demonstration of data science skills.

  • Updated Sep 11, 2025
  • Python

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.

  • Updated Aug 6, 2024
  • Jupyter Notebook

Advanced Python quantitative equity scanner and portfolio audit engine featuring stewardship-centric scoring, hierarchical data resilience, and local FinBERT batch sentiment analysis for professional-grade trade signals.

  • Updated Apr 21, 2026
  • Python

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