Internet of Things

In our little universe, since we have the data for everyone hence we have easily created a

rule to predict the next diabetic patient. However, in the real world applications, we do not

store the complete dataset of all the patients, and therefore we can borrow the actual power of

ML to find a viable solution for our problems. ML provides prediction even if our dataset does

not contain all the possible samples. For instance, if in our above example, we delete the last

two records. Now, an ML algorithm would process all the attributes of the incoming record

of a person and try to predict whether or not they can contract diabetes or not. This set ...