Problem Definition

Defining the problem is as important as building your model or improving accuracy. This is because, while you may be able to use the most powerful algorithm and use the most advanced methodologies to improve its results, this may prove pointless if you are solving the wrong problem or using the wrong data.

It is crucial to learn how to think deeply to understand what can and cannot be done, and how what can be done can be accomplished. This is especially important considering that when we are learning to apply machine learning or deep learning algorithms, the problems presented in most courses are always clearly defined, and there is no need for further analysis other than training the model and improving its performance. On the other hand, in real life, problems are often confusing, and data is often messy.

In this section, you will learn about some of the best practices for defining your problem based on the needs of your organization and on the data that...