text: from pandas import read_csv
  from datetime import datetime
  from math import sqrt
  from numpy import concatenate
  from matplotlib import pyplot
  from pandas import read_csv
  from pandas import DataFrame
  from pandas import concat
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import LabelEncoder
  from sklearn.metrics import mean_squared_error
  from keras.models import Sequential
  from keras.layers import Dense
  from keras.layers import LSTM
  
  
  # load and process data
  def parse(x):
  	return datetime.strptime(x, '%Y %m %d %H')
  dataset = read_csv('data.csv',  parse_dates = [['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)
  dataset.drop('No', axis=1, inplace=True)
  dataset.columns = ['pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain']
  dataset.index.name = 'date'
  dataset['pollution'].fillna(0, inplace=True)
  dataset = dataset[24:]
  print("||"*40)
  print("** DATA PROCESSING COMPLETED **")
  print(dataset.head(5))
  print("||"*40)
  dataset.to_csv('pollution.csv')
  
  # generating dataset plot
  from pandas import read_csv
  from matplotlib import pyplot
  dataset = read_csv('pollution.csv', header=0, index_col=0)
  values = dataset.values
  groups = [0, 1, 2, 3, 5, 6, 7]
  i = 1
  pyplot.figure()
  for group in groups:
  	pyplot.subplot(len(groups), 1, i)
  	pyplot.plot(values[:, group],'k')
  	pyplot.title(dataset.columns[group], y=0.5, loc='right')
  	i += 1
  pyplot.show()
  
  # Lets normalize all features, and remove the weather variables for the hour to be predicted.
  import pandas as pd 
  from sklearn import preprocessing
  from sklearn.preprocessing import MinMaxScaler
  
  def s_to_super(data, n_in=1, n_out=1, dropnan=True):
  	n_vars = 1 if type(data) is list else data.shape[1]
  	df = pd.DataFrame(data)
  	cols, names = list(), list()
  	for i in range(n_in, 0, -1):
  		cols.append(df.shift(i))
  		names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
  	for i in range(0, n_out):
  		cols.append(df.shift(-i))
  		if i == 0:
  			names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
  		else:
  			names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
  	agg = pd.concat(cols, axis=1)
  	agg.columns = names
  	if dropnan:
  		agg.dropna(inplace=True)
  	return agg
   
  # load dataset
  dataset = read_csv('pollution.csv', header=0, index_col=0)
  values = dataset.values
  encoder = preprocessing.LabelEncoder()
  values[:,4] = encoder.fit_transform(values[:,4])
  values = values.astype('float32')
  scaler = MinMaxScaler(feature_range=(0, 1))
  scaled = scaler.fit_transform(values)
  reframed = s_to_super(scaled, 1, 1)
  # drop columns we don't want to predict
  reframed.drop(reframed.columns[[9,10,11,12,13,14,15]], axis=1, inplace=True)
  print("** NOT REQUIRED DATA COLUMNS DROPPED **")
  print("||"*40)
  # split data into training and testing, futher splitting the train and test sets into i/p and o/p variables
  # reshaped data further into 3D formate expected by LSTMs
  values = reframed.values
  n_train_hours = 365 * 24
  train = values[:n_train_hours, :]
  test = values[n_train_hours:, :]
  # split into input and outputs
  train_X, train_y = train[:, :-1], train[:, -1]
  test_X, test_y = test[:, :-1], test[:, -1]
  train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
  test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
  print("** DATA SPLITTING COMPLETED **")
  print(" Training data shape X, y => ",train_X.shape, train_y.shape," Testing data shape X, y => ", test_X.shape, test_y.shape)
  print("||"*40)
  # defining LSTM with 50 neurons in first hidden layer and 1 neuron in the o/p layer
  # using the MAE loss function and Adma version of stochastic gradient descent
  from keras.models import Sequential
  from keras.layers import LSTM
  from keras.layers import Dense, Dropout
  model = Sequential()
  # 50 neurons in first hidden layer
  model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
  model.add(Dropout(0.3))
  model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
  model.compile(loss='mae', optimizer='adam')
  history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)
  # tracking history for plots
  pyplot.plot(history.history['loss'], 'b', label='training history')
  pyplot.plot(history.history['val_loss'],  'r',label='testing history')
  pyplot.title("Train and Test Loss for the LSTM")
  pyplot.legend()
  pyplot.show()
  # evaluating model
  # make a prediction
  from math import sqrt
  from numpy import concatenate
  yhat = model.predict(test_X)
  test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))
  inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
  inv_yhat = scaler.inverse_transform(inv_yhat)
  inv_yhat = inv_yhat[:,0]
  inv_y = scaler.inverse_transform(test_X)
  inv_y = inv_y[:,0]
  # calculate RMSE
  rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
  print('Test RMSE: %.3f' % rmse)
  
  
  