Analytics-2

import numpy as np import pandas as pd 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):
    data = pd.DataFrame([marketing_expenditure, units_sold], columns = ['marketing_expenditure','units_sold'])   
    y = data['marketing_expenditure']
    x = data['units_sold']
    lm = linear_model.LinearRegression()
    lm.fit(x, y)
    return lm.predict(desired_unit_sold)

#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))