title: Mixing categorial and continuous data in Naive Bayes classifier using scikit-learn
body: <p>I'm using scikit-learn in Python to develop a classification algorithm to predict the gender of certain customers. Amongst others, I want to use the Naive Bayes classifier but my problem is that I have a mix of categorical data (ex: "Registered online", "Accepts email notifications" etc) and continuous data (ex: "Age", "Length of membership" etc). I haven't used scikit much before but I suppose that that Gaussian Naive Bayes is suitable for continuous data and that Bernoulli Naive Bayes can be used for categorical data. However, since I want to have <strong>both</strong> categorical and continuous data in my model, I don't really know how to handle this. Any ideas would be much appreciated!</p>