What happens when scikit-learn does a Lasso Model? I have started an MLS course. As a beginner and non-mathematician it has been hard.

I am trying to understand the exercise about Lasso Models.
I have done Lasso models on R-cran, but this is my first time with Python.

I have a dataset of cars with seven variables. The exercise consists on doing a Lasso Model to predict the gasoline consumption of the cars, the dependent variable, so $x$ is a table with the rest of variables and $y$ is the consumption.

Then, if I launch Lasso on scikitlearn:

modelLasso = Lasso(alpha=0.1).fit(x, y)



I do not understand the result. Has it generated a prediction on $y$ of every row in the table?
If so, how can I access to the array of predictions of the model and how do I use the model to predict the consumption given new $x$ values?
Lasso() created a Lasso regressor object. The .fit() argument makes the regressor "learn" from your data. The parameters are tuned in order to "fit" the training data.

Once the regressor object is fit, you can use it to .predict() on test data.