Join us on this Python Machine Learning Guided project. In this Python Regression project, we will be predicting the MPG of classic cars. This is a slight variation on a common predictive workflow. Use ensemble methods like RandomForestRegressor(), AdaBoostRegressor(), and GradientBoostingRegressor() in the supervised machine learning project. This is a great beginner Python project to practice machine learning with ensemble methods.
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dataGroups:
Use Seaborn's pairplot() to plot all the bivariate relationships in one line of code.
Set up your train_test_split() to allow for experimenting many times to find the right features to include in your Machine Learning model.
With Sklearn in Python use StandardScaler() to standardize your dataset and PCA() to extract the principle components both make it easier for your model to make predictions.
Use the residplot() in Seaborn to to understand how you are making errors in a regression machine learning problem.