correctly solves example case, but linear codependency failed

marketing_costs.py

import numpy as np
from sklearn import linear_model

def desired_marketing_expenditure(marketing_expenditure, units_sold, desired_units_sold):
    """
    :param marketing_expenditure: (list) A list of integers with the expenditure for each previous campaign.
    :param units_sold: (list) A list of integers with the number of units sold for each previous campaign.
    :param desired_units_sold: (integer) Target number of units to sell in the new campaign.
    :returns: (float) Required amount of money to be invested.
    """
    regr = linear_model.TheilSenRegressor()
    units_sold_shaped = np.array(units_sold).reshape(-1,1)
    desired_units_sold_shaped = np.array(desired_units_sold).reshape(-1,1)
    regr.fit(X=units_sold_shaped, y=marketing_expenditure)
    needed_marketing_expenditure = round(regr.predict(desired_units_sold_shaped)[0])
    return needed_marketing_expenditure

#For example, with the parameters below, the function should return 250000.0
print(desired_marketing_expenditure(
    [300000, 200000, 400000, 300000, 100000],
    [60000, 50000, 90000, 80000, 30000],
    60000))