text: import csv
  import numpy as np
  from sklearn.svm import SVR
  import matplotlib.pyplot as plt
  
  dates  = []
  prices = []
  def get_data(filename):
      with open(filename, 'r') as csvfile:
          csvFileReader = csv.reader(csvfile)
          next(csvFileReader)
          for row in csvFileReader:
              dates.append(int(row[0].split('-')[0]))
              prices.append(float(row[1]))
          return
  def predict_prices(dates,prices,x):
      dates = np.reshape(dates,(len(dates), 1))
  
  
      svr_lin = SVR(kernel = 'linear', C = 1e3)
      svr_poly = SVR(kernel= 'poly',C = 1e3,degree = 2)
      svr_rbf =SVR(kernel='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.ylabel('Price')
      plt.title('Support vector Regression')
      plt.legend()
      plt.show()
  
  
      return svr_rbf.predict(x)[0],svr_lin.predict(x)[0],svr_poly.predict(x)[0]
  get_data('HistoricalQuotes.csv')
  
  predicted_price = predict_prices(dates,  prices, 29)
  print(predicted_price)    
  