Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition

Chapter 2. End-to-End Machine Learning Project

In this chapter you will work through an example project end to end, pretending to be a recently hired data scientist at a real estate company. This example is fictitious; the goal is to illustrate the main steps of a machine learning project, not to learn anything about the real estate business. Here are the main steps we will walk through:

1. Look at the big picture.

2. Get the data.

3. Explore and visualize the data to gain insights.

4. Prepare the data for machine learning algorithms.

5. Select a model and train it.

6. Fine-tune your model.

7. Present your solution.

8. Launch, monitor, and maintain your system.

Working with Real Data

When you are learning about machine learning, it is best to experiment with real-world data, not artificial datasets. Fortunately, there are thousands of open datasets to choose from, ranging across all sorts of domains. Here are a few places you can look to get data:

• Popular open data repositories:

  • OpenML.org

  • Kaggle.com

  • PapersWithCode.com

  • UC Irvine Machine Learning Repository

  • Amazon’s AWS datasets

  • TensorFlow datasets

• Meta portals (they list open data repositories):

  • DataPortals.org

  • OpenDataMonitor.eu

• Other pages listing many popular open data repositories:

  • Wikipedia’s list of machine learning datasets

  • Quora.com

  • The datasets subreddit

In this chapter we’ll use the California Housing Prices dataset from the StatLib repository⁠¹ (see Figure 2-1). This dataset is based on ...