text: import csv
  from sklearn.svm import SVR
  import numpy as np
  import matplotlib.pyplot as plt 
  
  
  
  dates = list(range(251))
  d = []
  price = []
  
  def get_data(filename):
      with open(filename, "r") as file:
          Reader = csv.reader(file)
          next(Reader)
          for row in Reader:
              price.append( float( row[4] ) )
      return
  
  def predict_price(dates, price, x):
      dates = np.reshape( dates, (251,1) )
  
      
      lin = SVR( kernel="linear", C=1)
      poly = SVR( kernel="poly", C=1, degree=2)
      rbf = SVR( kernel="rbf", C=1, gamma=0.1)
  
      lin.fit(dates,price)    
      poly.fit(dates,price)
      rbf.fit(dates,price)
  
      plt.scatter(dates, price, color = "black", label = "Data")
      plt.plot(dates, lin.predict(dates), color = "red", label = "Linear")
      plt.plot(dates, poly.predict(dates), color = "green", label = "Polynomial")
      plt.plot(dates, rbf.predict(dates), color = "blue", label = "RBF")
      plt.xlabel("Dates")
      plt.ylabel("Prices")
      plt.legend()
      plt.show()
  
      return  lin.predict(x)[0], poly.predict(x)[0], rbf.predict(x)[0]
  
  
  get_data("GOOGL.csv")
  
  print( predict_price(dates,price,251) )
  
  