I want to develop a ML model which will allow my company to highlight employees which are at a risk of leaving the business, based on a variety of parameters such as performance, absence rates, location, age, team manager etc. We have a fairly diverse database of individuals who have already left the business, with values for each of the inputs which can be used to train the model. The output is a simple 1 or 0: based on all of the inputs, either an individual is 'at risk' or 'not at risk' of leaving, with no immediate requirement for any indication of the degree of risk.
I am somewhat of a ML newbie, but having researched the various types of ML models, I cannot find any specific information which relates to training models with datasets where there are combinations of numerical and categorical data. I have looked at examples of similar model requirements which have used k-NN and SVM models, but I cannot find definite clarification on how to approach this task. Worth noting that I code in python and matlab.
Any input would be greatly appreciated.