A simple example of how to solve Kaggle's "Titanic: Machine Learning from Disaster" challenge using Python and scikit-learn.
This simple example will get you about 78% accuracy. It shows you how to instantiate and use various classifiers in scikit-learn.
Note: This example combines six different classifiers, just as example of how to run and average multiple classifiers. You can actually get a better accuracy by being smarter about how to combine classifiers and which ones to use. This just shows you the scikit-learn syntax.
This example also assumes you've already done a grid search and found the best hyper parameters for your classifiers (especially the SVM). But if you aren't sure how to do that, the scikit-learn docs have a good example that you can copy.
