Fuel at Top Speed

10 pts · 10 coins

Machine Learning in CodeEasy

Fuel at Top Speed

A car's fuel use does not rise in a straight line with speed. It climbs faster and faster. You have a few measurements of speed (speeds) and fuel use (fuel).

Write curve_predict(speeds, fuel, degree, new_speeds).

Build a pipeline with make_pipeline(PolynomialFeatures(degree), LinearRegression()).
Fit it with speed as the only feature.
Predict the fuel use for every speed in new_speeds, in order.
Return the predictions as a list of plain floats, each rounded to 2 decimal places.

Examples

speeds = [30, 50, 70, 90, 110]

fuel = [4.8, 5.0, 6.8, 10.2, 15.2]

degree = 2

new_speeds = [60, 120]

returns [5.7, 18.3]

speeds = [30, 50, 70, 90, 110]

fuel = [4.8, 5.0, 6.8, 10.2, 15.2]

degree = 1

new_speeds = [60, 120]

returns [7.1, 14.9]

Hints

solution.py

1import numpy as np
2from sklearn.linear_model import LinearRegression
3from sklearn.pipeline import make_pipeline
4from sklearn.preprocessing import PolynomialFeatures
5
6
7def curve_predict(speeds, fuel, degree, new_speeds):
8    pass
9