Keras Deep Learning Cookbook

Data processing

It is crucial that we serve the right data as input to the neural architecture for training and validation. We need to make sure that data is in a useful scale and format, and that meaningful features are included. This will lead to better and more consistent results.

We employ the following workflow for data preprocessing:

1. Load the dataset using pandas
2. Split the dataset into input and output variables for machine learning
3. Apply a preprocessing transform to the input variables
4. Summarize the data to show the change

Let's get started step by step:

1. Let's get started by importing important packages and our dataset. We use the pandas library to load data and review the shape of our dataset—it includes 10 features and 5 million ...