Keras: How to normalize dataframe with continuous and categorical data?
Yes it does, you're doing well!

In most cases, categorical features(columns) should be one-hot encoded. However, continuous features might be a little complicated. 

There are two common ways to preprocess continuous feature:


scaling features to range [0, 1] (as you have done)
removing the mean and scaling to unit variance(make the feature has zero mean and 1 standard variance)


In my practice, I take these two ways depending on my dataset.