Answer:
#Selling vehicles
import locale
locale.setlocale( locale.LC_ALL, 'en_CA.UTF-8' )
#Declaration of variables
total_pay=0
total_sales=0
#Selling details
for i in range(0,2):
#Ask the type of sell
type=input("Enter the type of the car you sold(used/new)? ")
#Check error
while(type.upper()!="USED" and type.upper()!="NEW"):
print('ERROR!!!Should be used or new!!Please Re-enter')
type=input("Enter the type of the car you sold(used/new)? ")
#Input price of the car
price=float(input("Enter the price of the car: "))
#Calculations
if(type.upper()=="NEW"):
total_pay+=1500
else:
total_pay+=price*.05
total_sales+=price
#Display results
print('Total Pay of the sale person = ',locale.currency(total_pay))
print('Total Sales = ',locale.currency(total_sales))
Answer:
the lift equation states that lift L is equal to the lift coefficient CI times the density r times half of the velocity V squared times the wing area A. For given air conditions,shape and inclination of the object, we have to determine a value of CI to determine the lift.
Not really sure but this is all I know
Answer:
books = []
fp = open("bookTitles.txt")
for line in fp.readlines():
title = line.strip()
if title not in books:
books.append(title)
fp.close()
fout = open("noDuplicates.txt", "w")
for title in books:
print(tile, file=fout)
fout.close()
except FileNotFoundError:
print("Unable to open bookTitles.txt")
The breaking fluent would squirt out
Answer:
The dependent variable is MEDV - Median value of owner-occupied homes in $1000's
Explanation:
The median value of the house has to be predicted, based on its properties and neighborhood properties, this can be done by using a linear regression model.
The dependent variable in Machine Learning is the output variable that we want to predict.
Therefore, according to the question given "MEDV" is the dependent variable.
