This case study explores historical forex data from 2008–2023 to answer a key business question: Which major currency pairs provide the best balance of profitability and consistency for retail traders? Using data cleaning, transformation, and analysis in R, I evaluated daily returns and volatility for EUR/USD, GBP/USD, and USD/JPY. The study includes visualizations of trends, distributions, and volatility comparisons, and concludes with actionable insights for traders, educators, and prop firms. This project demonstrates my ability to:  Define a business problem  Clean and prepare real-world financial data  Perform return and volatility analysis  Communicate insights clearly with visualizations and recommendations

Estadística Descriptiva

This model classifies telecommunications customers at risk of churn to enable more targeted retention interventions.

Dealova 112031128

This report explores the NOAA Storm Database from 1950 to 2011 to identify which types of severe weather events are most harmful to population health and which have the greatest economic consequences. Using R, we process the raw dataset, standardize event types, and convert damage estimates into U.S. dollars. Results show that tornadoes cause the highest number of fatalities and injuries, while floods and hurricanes generate the largest economic losses