This project presents an end-to-end data-driven analysis of aviation accidents in the United States, using real-world data from the National Transportation Safety Board (NTSB). The study focuses on exploring accident patterns, severity factors, and the statistical relationships between aircraft, weather, operational conditions, and outcomes. To uncover actionable insights that help understand the causes, frequency, and severity of aviation incidents, while identifying factors that significantly influence accident outcomes.

Various approaches on how to de-blend resolved and unresolved photometry using ProFound routines.

This project explores the factors influencing the risk of heart attacks using real-world health data. The analysis involves comprehensive data cleaning, visualization, and model building to identify key predictors of heart disease. Various machine learning techniques such as Logistic Regression, K-Nearest Neighbors (KNN), and Clustering were applied and compared based on performance metrics and interpretability. The report provides insights into how attributes like age, cholesterol, blood pressure, chest pain type, and exercise patterns contribute to cardiovascular risk. The goal is to support early detection and clinical decision-making through data-driven analysis.

Danna Cecilia Caldas Laura Valentina Manyoma