import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  import seaborn as sns
  from tensorflow import keras
  from keras.models import Sequential
  from keras.layers import Dense
  from keras.layers import LSTM
  df = pd.read_csv('monthly_milk_production.csv'
  index_col='Date',
  parse_dates=True)
  df.index.freq = 'MS'
  df.head()
  # Plotting graph b/w production and date
  df.plot(figsize=(12, 6))
  from statsmodels.tsa.seasonal import seasonal_decompose
  results = seasonal_decompose(df['Production'])
  results.plot()
  train = df.iloc[:156]
  test = df.iloc[156:]
  from sklearn.preprocessing import MinMaxScaler
  scaler = MinMaxScaler()
  scaler.fit(train)
  scaled_train = scaler.transform(train)
  scaled_test = scaler.transform(test)
  from keras.preprocessing.sequence import TimeseriesGenerator
  n_input = 3
  n_features = 1
  generator = TimeseriesGenerator(scaled_train,scaled_train,length=n_input,batch_size=1)
  X, y = generator[0]
  print(f'Given the Array: 
  {X.flatten()}')
  print(f'Predict this y: 
   {y}')
  # We do the same thing, but now instead for 12 months
  n_input = 12
  generator = TimeseriesGenerator(scaled_train, scaled_train,length=n_input,batch_size=1)
  # define model
  model = Sequential()
  model.add(LSTM(100, activation='relu',input_shape=(n_input, n_features)))
  model.add(Dense(1))
  model.compile(optimizer='adam', loss='mse')
  model.summary()
  model.fit(generator, epochs=5)