import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model

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):
        return float (lin_regr.predict(np.array(60_000).reshape(-1, 1)) )


mark_exp = np.array([300000, 200000, 400000, 300000, 100000]).reshape(-1, 1)
units_s = np.array([60000, 50000, 90000, 80000, 30000]).reshape(-1, 1)

lin_regr = linear_model.LinearRegression()
lin_regr.fit(units_s,mark_exp)

lin_regr.score(mark_exp, units_s)
lin_regr.coef_
lin_regr.intercept_
lin_regr.predict(np.array(60_000).reshape(-1, 1))

#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))