Predicting future automobile sales

Welcome to the forum (and machine learning). Choosing an estimator/model can sometimes be the hardest problems, as such there are many different opinions and ways to do so.

The first thing I notice with your problem statement is that you're wanting to predict the Sales (either in $ or quantity, I would assume). Because you're not trying to predict a category or binary label, we can rule out classification models and look at instead regression style models.

A logical starting point for this would be to explore using a Linear Regression. This assumes that you have some existing data with the price, quantity, or target you're trying to predict.

Linear Regression is a logical starting point as they're simple to implement, yet can get some pretty good results. Scikit-learn has a nice package in Python on linear regressions.

If you don't get satisfactory results using Linear Regressions, you may wish to explore Decision Trees, either scikit-learns implementation or xgboost. This rules out classification models.

Briefly, I think it's important to note that the features to your model are nearly all categorical, eg. make, model, etc. To handle these features, you'll need to encode these features, a guide on that is here.

Take your time, explore your data a lot and start with a simple model. The more you understand your data, the more you'll understand the model. Enjoy and have fun! :)