Let's shift our focus to building a predictive model that will now take a different step. We started by solving the prediction problem that can predict a continuous outcome, but we didn't achieve great results. John's team requires a solution that they can leverage to predict the end quality of the detergent being manufactured. It could be achieved in multiple ways; the first one was to predict the most critical output quality parameter and the second was to predict the actual end outcome, Good or Bad. Both the methods have their own advantages and disadvantages. Predicting the continuous outcome, Output Quality Parameter 2, actually gives us a sneak peek to understand the actual quantified deviation from the benchmark, say below or above 60%. Such crisp information aids the technician in taking more accurate corrective countermeasures.

On the other hand, predicting the categorical outcome, Good/Bad Quality, has its interpretational advantage...