It's been a pause since the last post so I am just going to jump in and introduce you to the fourth question: Does your problem have a pattern?
Why this even matters you may ask. The machine learns from regularities, therefore rare or irregular patterns decrease the chances of a machine learning project to succeed.
For instance, a movie producer may use IBM Watson for a Sentiment Analysis of customer reviews on their most recent movie. Watson can produce results by finding a relationship between higher ratings and positive words such as "Love It!", "Amazing", "Best" versus lower ratings and negative words such as "Terrible", "Worst", or "Disappointing".
The bottom line is that you should look for problems where one can intuitively find a correlation in the data such as the above example and avoid rare instances where the chances of the machine finding any correlation is low (examples could include, a Tsunami or rare diseases).
By thoroughly examining the possible answers to this question you will lead your team towards success and avoid wasting organizational resources by focusing on the wrong problem.
There are only 2 more questions left before you maybe off to your first ML journey. Stay tuned!
#MachineLearning