JDS-Marketing-aty

JDS-Marketing-aty.py

import numpy as np
from sklearn import linear_model, metrics

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):
        market_exp = np.array(marketing_expenditure)
        sold = np.array(units_sold)
        LM = linear_model.LinearRegression()
        LM.fit(np.reshape(sold,(len(sold),1)),np.reshape(market_exp,(len(market_exp),1)))
        
        dapred = LM.predict(np.reshape(sold,(len(sold),1)))
        mse = metrics.mean_squared_error(np.reshape(market_exp,(len(market_exp),1)), dapred)
        rmse = np.sqrt(mse) #nilai root mean square error
        mae = metrics.mean_absolute_error(np.reshape(market_exp,(len(market_exp),1)), dapred) #nilai mean absolute error
 
        pred = LM.predict(desired_units_sold)    
        return float(pred) #tanpa pembulatan
        # return np.round(float(pred),-4) :::untuk melakukan pembulatan ke 250000


#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))