In the previous chapter, we worked with a dataset that had a categorical target variable, and we went over the steps for developing a classification model using Keras. In situations where the response variable is numeric, supervised learning problems are categorized as regression problems. In this chapter, we will develop a prediction model for numeric response variables. To illustrate the process of developing the prediction model, we will make use of the Boston Housing dataset, which is available within the mlbench package.
In this chapter, we will cover the following topics:
- Understanding the Boston Housing dataset
- Preparing the data
- Creating and fitting a deep neural network model for regression
- Model evaluation and prediction
- Performance optimization tips and best practices
