In my previous blog post, we learned a bit about what affects the survival of titanic passengers by conducting exploratory data analysis and visualizing the data. Then, the data was wrangled in order to prepare for modelling. In this blog post, I will use machine learning algorithms available at Python’s Scikit-learn library to predict which passengers in the testing data survived. A Decision Tree Classifier is used as an example and then its hyperparamaters are tuned to see if it improves prediction accuracy. I’ll also try using an ensemble of models to predict the results.