It's time for the second step training your models and finding the optimal hyper parameter configuration. You use the fit function in the format that you see right here. Go to the models dictionary and then the lasso item, which is your untrained lasso model. then dot fit the fit function, which receives two arguments. First, the input training data, the features in your training data, and then the target training data, your available liquidity for that training data. When you execute this cell, your hyperparameters are going to be tuned and your model will be trained. You're probably going to get a data convergence warning when you execute this cell. Don't worry about that. That's totally normal and okay. It will not affect your model's performance. Make sure that you have the code that you see right here in your Jupiter notebook before continuing.