使用sklearn，numpy和matplotlib在Python中进行多项式回归

Question

I'm trying to make a small program that will plot a graph with best fit line and that will predict the COST value based on inputted SIZE value.

I always get this error, and I do not know what it means:

DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
No handles with labels found to put in legend.

This is the graph that I get (red), and I think that curve should look like green curve that i have draw.

And finally, program makes prediction only when I exit the graph. What am I doing wrong? This is the code:

import numpy as np
from sklearn.svm import SVR
import matplotlib.pyplot as plt

size=[[1],[2],[3],[4],[5],[7],[9],[10],[11],[13]]
cost=[[10],[22],[35],[48],[60],[80],[92],[111],[118],[133]]

def predict(size,cost,x):

    dates=np.reshape(size,(len(size),1))

    svr_poly=SVR(kernel="poly",C=1e3, degree=2)
    svr_poly.fit(size,cost)

    plt.scatter(size,cost, color="blue")
    plt.plot(cost, svr_poly.predict(cost), color="red")

    plt.xlabel("Size")
    plt.ylabel("Cost")
    plt.title("prediction")
    plt.legend()

    plt.show()

predictedcost=predict(size,cost,7)

print(predictedcost)

Answer 1

Here, I found the answer to this problem. So if you are interested, check it

import numpy as np
import matplotlib.pyplot as plt
import math

X = np.array([1,2,3,5,6,7,4,7,8,9,5,10,11,7,6,6,10,11,11,12,13,13,14])
Y=np.array([2,3,5,8,11,14,9,19,15,19,15,16,14,7,13,13,14,13,23,25,26,27,33])

koeficienti_polinom = np.polyfit(X, Y, 2)

a=koeficienti_polinom[0]
b=koeficienti_polinom[1]
c=koeficienti_polinom[2]

xval=np.linspace(np.min(X), np.max(X))   

regression=a * xval**2 + b*xval + c 

predX = float(input("Enter: "))      
predY = a * predX**2 + b*predX + c   

plt.scatter(X,Y, s=20, color="blue" )      
plt.scatter(predX, predY, color="red")    
plt.plot(xval, regression, color="black", linewidth=1)      

print("Kvadratno predvidjanje: ",round(predY,2))