Predicting Survival Rates on the Titanic
In this project the Titanic survivor data set is used. The goal is to fit a model that accurately predicts which passengers survived by learning the relationships between the individual passenger-characteristics and their survival rate. The data set is first studied to determine what pre-processing procedures or ML methods are necessary. A simple model with the original features and a complex model with engineered features is applied to test for interactions or non-linear relationships between the variables. Data is split into training and testing using cross-validation-resampling. The models are tuned using grid search and performance is measured using accuracy score. An ensemble model combines all the models into one. A prediction with the ensemble model is submitted to Kaggle.com using the provided hold out set.