The process to prepare data for machine learning model training looks somewhat similar to the process of preparing food ingredients to cook dinner. You know that in both cases it takes time, but then you are rewarded with a tasty dinner, or in this case, a great ML model.
I will not be diving into data science or discussing how to structure and transform data. It all depends on the use case, and there are so many ways to reformat data to get the most out of it. I would rather focus on a simple but practical example — how to split data into training and test datasets with Python.