In this section, we will choose the Machine Learning (ML) algorithm based on our intuition and then perform training using our training dataset. This is the first model for this particular chapter, so the trained model is our baseline model, which we will improve later on. So, let's decide which kind of ML algorithm suits this stock price prediction application.

The stock price prediction application is a time-series analysis problem, where we need to predict the next point in the time series. This prediction activity is similar to linear regression, so we can say that this application is a kind of regression problem and any algorithm from the regression family should work. Let's select the ensemble algorithm, which is RandomForestRegressor, in order to develop our baseline model. So let's train our baseline model, and, based on the result of that model, we will modify our approach.