why use a perceptron when it seems multiple IF staments do the same thing If we are to stick with an X and Y axis, the X axis being Time and the Y axis being test scores. Where more time equates to High test scores. You can use a binary classifcation algorithm to predict success. Wouldn't 2 if statements do the same thing

If (time &gt; someValue)
   User will probably pass


Another Scenario
Is I have papayas, and they have two labels, squishness and color.

If the squishness is greater then 7 //on a scale of 1-10
   AND the color is green 
     Then it is a good papaya

Else 
   It isn't ripe.


I dont understand the value of a perceptron in these scenarios.
Machine learning (perceptrons or not) is all about automatically finding generic but correct rules, be it in the form of If-Else-Rules, encoded formulas, closest occurrences, or others.

The ML algorithm is just a way to (automatically) find this knowledge, whatever the representation may be. In another words, you use it to find the someValue of your example, based on your data. You don't need ML if you can represent such knowledge yourself.