def predict_prices(dates, prices, x):
dates = np.reshape(dates, (len(dates, 1)))
svr_len = SVR(kernal='linear', C=1e3)
svr_poly = SVR(kernal='poly', C=1e3, degree=2)
svr_rbf = SVR(kernal='rbf', C=1e3, gamma=0.1)
svr_lin.fit(dates, prices)
svr_poly.fit(dates, prices)
svr_rbf.fit(dates, prices)
plt.scatter(dates, prices, color="black", label='Data')
plt.plot(dates, svr_rbf.predict(dates), color='red', label='RBF model')
plt.plot(dates,
svr_lin.predict(dates),
color='green',
label='Linear model')
plt.plot(dates,
svr_poly.predict(dates),
color='blue',
label='Polynomial model')
plt.xlabel('Date')
plt.xlabel('Price')
plt.title('Support vector Regression')
plt.legend()
plt.show()
return svr_rbf.predict(x)[0], svr_lin.predict(x)[0], svr_poly.prdict(x)[0]
