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))