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This report examines whether observable statistical anomalies are present in the certified results of the 2024 Presidential Election in North Carolina.

This project presents a comparative analysis of Decision Tree and Naive Bayes algorithms for maternal health risk classification using the Maternal Health Risk dataset. The study evaluates model performance before and after hyperparameter optimization through tuning techniques. Performance is assessed using Confusion Matrix, Precision, Recall, F1-Score, AUC, and ROC Curve. The results provide insights into the effectiveness of both classification methods in identifying maternal health risk levels and demonstrate the impact of model optimization on predictive performance.

Elaborado por Leonardo Orozco Itzel Evelyn Alcántara

Regresión Logística binomial con datos daños al corazón

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