Polynomial Regression wasn't really needed..

If you copy the data provided and plot it -- the solution is pretty clear.

`

!/bin/python3

import math import os import random import re import sys from sklearn.linear_model import LinearRegression

if name == 'main': timeCharged = float(input().strip()) battery, duration = [], [] with open('trainingdata.txt', 'r') as inf: for line in inf.readlines(): x, y = line.replace('\n', '').split(',') battery.append(float(x)) duration.append(float(y))

lr = LinearRegression()
# Split the data
X, Y = [], []
for x, y in zip(battery, duration):
    if x < 4.0:
        X.append([x])
        Y.append(y)

lr.fit(X, Y)

# predict
print(lr.predict([[timeCharged]])[0] if timeCharged < 4 else 8.0)```