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#

mc-simulation

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Implements and evaluates TrieHH and SFP heavy-hitter algorithms under local differential privacy, including full data preprocessing, simulation, and F1-score analysis on Wikipedia Clickstream. Designed for reproducible experiments and privacy–utility benchmarking.

  • Updated Dec 11, 2025
  • Python

Our goal is to see how much investment potential these ETFs have based on key risk-management metrics: the daily returns, standard deviations, Sharpe ratios, and betas. We will be working on a Jupyter Notebook and visualizing all of our data using hvplot to create interactive visualizations as well as running an MC Simulation at the end.

  • Updated Feb 24, 2022
  • Jupyter Notebook

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