This chapter will provide you with an overview of regression algorithms and insights into the basics of Core ML, and will introduce you to creating a machine learning program leveraging a regression algorithm and predicting the housing price for a given set of housing-related data using Core ML in iOS.

As we already saw in Chapter 1, Introduction to Machine Learning on Mobile, any machine learning program has four phases. We will see what we are going to cover in the four phases and what tools we are going to use to solve the underlying machine learning problem.

Problem definition: The housing information of a certain area is provided and we want to predict the median value of a home in this area.

We will be covering the following topics in the chapter:

• Understanding what regression is and how to apply it to solve an ML problem
• Understanding regression using a sample dataset and Excel
• Understanding the basics of Core ML
• Solving the problem using regression in Core ML:
  • Technical requirements
  • How to create the model file using scikit-learn 
  • Testing the model
  • Understanding how to import the scikit-learn model into the Core ML project
  • Writing an iOS mobile application and using the scikit-learn model in it and doing the housing price prediction