In this lesson, we have seen how predictive models can be trained in Jupyter Notebooks.
To begin with, we talked about how to plan a machine learning strategy. We thought about how to design a plan that can lead to actionable business insights and stressed the importance of using the data to help set realistic business goals. We also explained machine learning terminology such as supervised learning, unsupervised learning, classification, and regression.
Next, we discussed methods for preprocessing data using scikit-learn and pandas. This included lengthy discussions and examples of a surprisingly time-consuming part of machine learning: dealing with missing data.
In the latter half of the lesson, we trained predictive classification models for our binary problem, comparing how decision boundaries are drawn for various models such as the SVM, k-Nearest Neighbors, and Random Forest. We then showed how validation curves can be used to make good parameter choices and how dimensionality reduction can improve model performance. Finally, at the end of our activity, we explored how the final model can be used in practice to make data-driven decisions.