The purpose of this project is to predict whether a patient has heart disease or not, based on clinical and demographic variables. We used the Cleveland Heart Disease dataset and compared three models: a logistic regression, a cost-sensitive decision tree and a random forest. Our goal is to evaluate model performance and see which approach is most reliable for this classification task.

Analysis with data from 2 subjects recruited from Prolific. Note that correlation tables could not be produced because psych::corr.test requires at least 3 subjects to compute confidence intervals

Ejercicio de modelos predictivos con regresión lineal simple.

Presento un análisis de la situación de los últimos años en estos puertos

Este documento presenta un análisis de clustering utilizando UMAP para reducción de dimensionalidad y técnicas no supervisadas, aplicado al dataset ChocolateSales de Kaggle.

This report analyzes the Gapminder dataset to explore life expectancy trends across continents, with a focused regression study on China, India, and Japan.