Reading through a data science book or taking a course, it can feel like you have the individual pieces, but don't quite know how to put them together. Taking the next step and solving a complete machine learning problem can be daunting, but preserving and completing a first project will give you the confidence to tackle any data science problem. This series of articles will walk through a complete machine learning solution with a real-world dataset to let you see how all the pieces come together. We'll follow the general machine learning workflow step-by-step: Along the way, we'll see how each step flows into the next and how to specifically implement each part in Python. The complete project is available on GitHub, with the first notebook here.

Assembling all the machine learning pieces needed to solve a problem can be a daunting task. In this series of articles, we are walking through implementing a machine learning workflow using a real-world dataset to see how the individual techniques come together. In the first post, we cleaned and structured the data, performed an exploratory data analysis, developed a set of features to use in our model, and established a baseline against which we can measure performance. In this article, we will look at how to implement and compare several machine learning models in Python, perform hyperparameter tuning to optimize the best model, and evaluate the final model on the test set. The full code for this project is on GitHub and the second notebook corresponding to this article is here.