answering our initial question finallywe have arrived at model that we think represents the underlying process bestit is now simple task of finding out when our infrastructure will reach , requests per hour we have to calculate when our model function reaches the value , having polynomial of degree we could simply compute the inverse of the function and calculate its value at , of coursewe would like to have an approach that is applicable to any model function easily this can be done by subtracting , from the polynomialwhich results in another polynomialand finding the root of it scipy' optimize module has the fsolve function to achieve this when providing an initial starting position let fbt be the winning polynomial of degree print(fbt + print(fbt - + from scipy optimize import fsolve reached_max fsolve(fbt - )/( * print(" , hits/hour expected at week %freached_max[ ] , hits/hour expected at week our model tells us that given the current user behavior and traction of our startupit will take another month until we have reached our threshold capacity of coursethere is certain uncertainty involved with our prediction to get the real pictureyou can draw in more sophisticated statistics to find out about the variance that we have to expect when looking farther and further into the future and then there are the user and underlying user behavior dynamics that we cannot model accurately howeverat this point we are fine with the current predictions after allwe can prepare all the time-consuming actions now if we then monitor our web traffic closelywe will see in time when we have to allocate new resources