Let's say you are working on a new machine learning project. The journey from getting the raw data to a deployed model consists of various tasks. While every project is unique, there's a typical sequence in which these tasks are usually approached.
What is the correct order of approaching the tasks?
Hint
Think about how we start with the raw data, and then we have to bring the available data to some unified format so that we can take a closer look at the underlying patterns. Observing the underlying patterns in the data can help us to choose the most suitable model.