Fare for a Longer Ride

10 pts · 10 coins

Machine Learning in CodeEasy

Fare for a Longer Ride

A taxi company has a few past rides: the distance in kilometres (km) and the fare paid (fares). They want quick quotes for new distances, including some longer than any ride they have seen.

Write quote_fares(km, fares, new_km).

Fit a LinearRegression model with distance as the only feature.
Predict the fare for every distance in new_km, in the same order.
Return the predictions as a list of plain floats, each rounded to 2 decimal places.

Examples

km = [1, 3, 4, 6, 10]

fares = [4.5, 8.1, 10.2, 13.8, 22.3]

new_km = [2, 8, 16]

returns [6.24, 18.12, 33.96]

km = [2, 4, 6]

fares = [6.0, 9.0, 12.0]

new_km = [5, 20]

returns [10.5, 33.0]

Hints

solution.py

1import numpy as np
2from sklearn.linear_model import LinearRegression
3
4
5def quote_fares(km, fares, new_km):
6    pass
7