Dealing with a dataset with a mix of continuous and categorical variables How do the choice of machine learning algorithm and preprocessing change when some of the independent variables are categorical while others are continuous? Can such data be directly applied to the algorithm with categorical data converted using one-hot encoding?

For example, 2 columns are: Age and Race, Age being continuous and Race is categorical.

Update: Ideal buckets for a continuous variable is not known.
To clarify, you mean mixed variables in one column? e.g. ABC123

If yes, you create two additional columns: one with categorical and one with numerical values. Afterward, you can encode them (one hot encoding not always necessary). 

Detailed explanation in Chapter 11 "Feature Engineering Mixed Variables":

https://www.udemy.com/feature-engineering-for-machine-learning/learn/v4/content