import matplotlib.pyplot as plt
import numpy
from sklearn.metrics import r2_score

time =  [5,10,15,20,25,30,35, 40, 45, 50, 55, 60]
money = [5,7, 25,42,88,91,103,150,152,190,195,200]

model = numpy.poly1d(numpy.polyfit(time, money, 3)) # 3 is degree of the fitting
time_line = numpy.linspace(0, 180, 180) #line from 0 to 60 (on x-axis), 180 dots

r2 = r2_score(money, model(time))
print("the coefficient of correlation =  "+ str(r2)) 

plt.xlabel("Time")
plt.ylabel("Money")
plt.title("Polynomial")
plt.grid()
plt.scatter( time, money)
plt.plot(time_line, model(time_line))

predicted_time = [65,70,90,120,180]
predicted_money =[]
for t in predicted_time:
    predicted_money.append(model (t))
print (predicted_money)

plt.scatter( predicted_time, predicted_money )
plt.show()
plt.savefig('/var/www/html/polynom.png')