Modeling Risk in Python

This code is part 1 of 3 pieces of code I wrote to build my own financial Robo-advisor. In this code, I use the Chilean database of consumer practices (which is public data available from the Chilean Central Bank) to train a Machine Learning algorithm in order to give an accurate prediction of risk tolerance for an average investor. I find that a Random Forest regressor and an Extra Tree regressor work very well in order to predict risk tolerance.

You can download the code here.