This project applies window functions to daily cryptocurrency price data for Bitcoin and Ethereum from September 2024 through February 2026. Using structured time-series data, a year-to-date cumulative average and a six-day moving average were calculated for each cryptocurrency. The year-to-date average resets at the beginning of each calendar year, while the six-day moving average uses a rolling window over the current observation and the previous five days. These calculations demonstrate how window functions can be used to compute dynamic summary measures within grouped and ordered data.

Implementation of a Global Baseline Estimate (GBE) recommender system in R using survey-based movie ratings. The model computes global, user, and movie bias terms to generate one recommendation per user.

Evaluating Classification Model Performance

Basic Data Loading and Transformation