Chapter 2. Putting Data in Place – Supervised Learning for Predictive Analytics
In this lesson, we will discuss supervised learning from the theoretical and practical perspective. In particular, we will revisit the linear regression model for regression analysis discussed in Lesson 1, From Data to Decisions – Getting Started with TensorFlow, using a real dataset. Then we will see how to develop Titanic survival predictive models using Logistic Regression (LR), Random Forests, and Support Vector Machines (SVMs).
In a nutshell, the following topics will be covered in this lesson:
- Supervised learning for predictive analytics
- Linear regression for predictive analytics: revisited
- Logistic regression for predictive analytics
- Random forests for predictive analytics
- SVMs for predictive analytics
- A comparative analysis
