title: What is 'x' in def predict_prices(dates, prices, x)
body: <pre><code>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('/')[1]))
              flt_price = row[3].replace('$', '')
              prices.append(float(flt_price))
      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('AAPLHistoricalDataEXAMPLE.csv')
  
  predicted_price = predict_prices(dates, prices, [[14]])
  print(predicted_price)
  </code></pre>
  <p>I am using the following sample code from <a href="https://www.youtube.com/watch?v=SSu00IRRraY&amp;list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU&amp;index=4" rel="nofollow noreferrer">this</a> video and I'm not sure what the 'x' parameter in the predict_prices function is.</p>
  <p>Below, where the function is called, the video uses '29' for x. Can anyone explain what this parameter is?</p>