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- import numpy as np
- from sklearn import linear_model
- from sklearn.linear_model import LinearRegression
- class MarketingCosts:
- # param marketing_expenditure list. Expenditure for each previous campaign.
- # param units_sold list. The number of units sold for each previous campaign.
- # param desired_units_sold int. Target number of units to sell in the new campaign.
- # returns float. Required amount of money to be invested.
- @staticmethod
- def desired_marketing_expenditure(marketing_expenditure, units_sold, desired_units_sold):
- X= []
- Y = []
- for k in units_sold:
- X.append([k])
- X = np.array(X)
- Y=np.array(marketing_expenditure)
- reg = LinearRegression().fit(X, Y)
- hasil = reg.predict(np.array(desired_units_sold))
- return hasil
- #For example, with the parameters below the function should return 250000.0.
- print(MarketingCosts.desired_marketing_expenditure(
- [300000, 200000, 400000, 300000, 100000],
- [60000, 50000, 90000, 80000, 30000],
- 60000))
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