Answer:
The program in Python is as follows:
apples = int(input("Apples: "))
people = int(input("People: "))
apples%=people
print("Remaining: ",apples)
Explanation:
This gets the number of apples
apples = int(input("Apples: "))
This gets the number of people to share the apple
people = int(input("People: "))
This calculates the remaining apple after sharing the apple evenly
apples%=people
This prints the calculated remainder
print("Remaining: ",apples)
Answer:
Compare the predictions in terms of the predictors that were used, the magnitude of the difference between the two predictions, and the advantages and disadvantages of the two methods.
Our predictions for the two models were very simmilar. A difference of $32.78 (less than 1% of the total price of the car) is statistically insignificant in this case. Our binned model returned a whole number while the full model returned a more “accurate” price, but ultimately it is a wash. Both models had comparable accuracy, but the full regression seemed to be better trained. If we wanted to use the binned model I would suggest creating smaller bin ranges to prevent underfitting the model. However, when considering the the overall accuracy range and the car sale market both models would be
Explanation:
As specified in RFC5735, this is an address from the "link local" block. It is assigned to a network interface as a temporary address, for instance if no static address is configured and the DHCP server is not found.
If you boot your PC without a network cable, you'll probably end up with a 169.254.*.* address.
