text: import quandl, math, datetime
  import time
  import numpy as np
  import pandas as pd
  from sklearn import preprocessing, cross_validation,model_selection, svm
  from sklearn.linear_model import LinearRegression
  from matplotlib import style
  import matplotlib.pyplot as plt
  import pickle
  
  style.use('ggplot')
  
  df = quandl.get("WIKI/GOOGL")
  df = df[['Adj. Open','Adj. Close','Adj. High','Adj. Low','Adj. Volume']]
  df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low'])/df['Adj. Low'] * 100.0
  df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open'])/df['Adj. Open'] * 100.0
  df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
  #print(df.head())
  
  forecast_col = 'Adj. Close'
  df.fillna(value=-99999, inplace = True)
  forecast_out = int(math.ceil(0.1 * len(df)))
  
  df['label'] = df[forecast_col].shift(-forecast_out)
  
  X = np.array(df.drop(['label'],1))
  X = preprocessing.scale(X)
  X_lately = X[-forecast_out:]
  X = X[:-forecast_out]
  
  df.dropna(inplace=True)
  y = np.array(df['label'])
  
  X_train, X_test, y_train, y_test = model_selection.train_test_split(X,y,test_size=0.2)
  
  clf = LinearRegression(n_jobs=-1)
  clf.fit(X_train, y_train)
  accuracy = clf.score(X_test,y_test)
  
  print(accuracy)
  