Hands-On Python Deep Learning for the Web

Feature engineering and extraction/selection

Consider a situation where an employee from an analytics firm is given the company's billing data and is asked by their manager to build a machine learning system with it so the company's overall financial budget could be optimized. Now, this data is not in a format that can be given directly to an ML model since ML models expect data in the form of numeric vectors.

Although the data might be in good shape, the employee will still have to do something to convert that data into a favorable form. Given that the data is already wrangled, they still need to decide what features he is they are going to include in the final dataset. Practically, anything measurable can be a feature here. This is where ...