text: # Machine Learning by Progyan1997
  # MIT License
  # Program: Regression - Estimation of relation between variables
  
  import math
  import pandas
  import quandl
  
  DataFrame = quandl.get('WIKI/GOOGL')		# Getting the actual dataset from Quandl
  DataFrame = DataFrame[['Adj. Open', 'Adj. High', 'Adj. Low', 'Adj. Close', 'Adj. Volume']]
  DataFrame['HL_PCT'] = ( DataFrame['Adj. High'] - DataFrame['Adj. Close'] ) / DataFrame['Adj. Close'] * 100.0					# Calculating % rise of High value
  DataFrame['CNG_PCT'] = ( DataFrame['Adj. Close'] - DataFrame['Adj. Open'] ) / DataFrame['Adj. Open'] * 100.0					# Calculating % change in a Day
  DataFrame = DataFrame[['Adj. Close', 'HL_PCT', 'CNG_PCT', 'Adj. Volume']]
  						# Features for our learning
  
  ForeCast_col = 'Adj. Close'				# Selecting it as our prediction
  DataFrame.fillna(-99999, inplace=True)			# Filling up NaN columns
  ForeCast_out = int(math.ceil(0.0001 * len(DataFrame)))	# 0.01% of the number of Records
  
  DataFrame['Label'] = DataFrame[ForeCast_col].shift(-ForeCast_out)	# Shifting our columns
  DataFrame.dropna(inplace=True)						# Remove NaN
  
  print(DataFrame.head())
  print(DataFrame.tail())
  