Modeling

We have finally come to the step that appears to be the most exciting one—the ML modeling part. But it is worth noting here that a good ML project is not just about this part. All of the previously mentioned parts contribute equally to the standard of the project. In fact, it matters a lot how the data is being collected for the project, and for this, we are helped by powerful data engineers. For now, let's leave that part aside.

We already have the data in pretty good shape by now. In the process of modeling the data, we feed the training data to ML models for training them, we monitor their training progress and tune different hyperparameters so their performance is optimized, and we evaluate the model on the test set. Model comparison is also a part of this phase. It is indeed an iterative process and involves trial and error to some extent.

The main objective here is to come up with an ML model that best represents the data, that is, it generalizes well. Computation time is another factor we must consider here because we want a model that performs well but within a feasible time frame and thereby optimizing a certain business outcome. 

Following are the parts that constitute the core of modeling:

• Model training
• Model evaluation 
• Model tuning