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  1. import numpy as np
  2. from sklearn import linear_model
  3. from sklearn.linear_model import LinearRegression
  4.  
  5. class MarketingCosts:
  6.  
  7.     # param marketing_expenditure list. Expenditure for each previous campaign.
  8.     # param units_sold list. The number of units sold for each previous campaign.
  9.     # param desired_units_sold int. Target number of units to sell in the new campaign.
  10.     # returns float. Required amount of money to be invested.
  11.     @staticmethod
  12.     def desired_marketing_expenditure(marketing_expenditure, units_sold, desired_units_sold):
  13.         X= []
  14.         Y = []
  15.         for k in units_sold:
  16.             X.append([k])
  17.         X = np.array(X)
  18.         Y=np.array(marketing_expenditure)
  19.         reg = LinearRegression().fit(X, Y)
  20.         hasil = reg.predict(np.array(desired_units_sold))
  21.         return hasil
  22.        
  23.  
  24. #For example, with the parameters below the function should return 250000.0.
  25. print(MarketingCosts.desired_marketing_expenditure(
  26.     [300000, 200000, 400000, 300000, 100000],
  27.     [60000, 50000, 90000, 80000, 30000],
  28.     60000))
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