This project explores the use of logistic regression to predict passenger survival on the Titanic using a dataset of 891 passengers. The analysis begins with an exploratory data analysis (EDA) to identify key factors influencing survival rates, such as passenger class, gender, and age. Significant missing data in columns like Age and Cabin were handled through imputation and column removal, respectively. Categorical variables were transformed into numerical features to prepare the data for model training. A logistic regression model was developed to predict the likelihood of survival based on selected features. The model achieved an accuracy of 83%, with high precision and recall rates for predicting survival. The analysis revealed that female passengers, younger individuals, and those in first class had higher survival rates. While the model provided valuable insights, there is potential for further enhancement by incorporating additional features and exploring more sophisticated machine learning algorithms. The project demonstrates practical applications of data analysis, statistical modeling, and machine learning in deriving actionable insights from historical data.

Diprotic acid curve

Desenvolvi um modelo preditivo utilizando o algoritmo de Floresta Aleatória para prever o resultado de partidas de futebol (vitória ou não). Este projeto fornece insights sobre os fatores que influenciam os resultados das partidas e demonstra a eficácia do uso de Florestas Aleatórias para análises preditivas no futebol.

A. Ñañez, L.Londoño y Y. Franco 2024-11-12