text: #importing required libraries
  from sklearn.preprocessing import MinMaxScaler
  from keras.models import Sequential
  from keras.layers import Dense, Dropout, LSTM
  import pandas as pd
  
  df=pd.read_csv("stock_price_dada.csv")
  #creating dataframe
  data = df.sort_index(ascending=True, axis=0)
  new_data = pd.DataFrame(index=range(0,len(df)),columns=['Date', 'Close'])
  for i in range(0,len(data)):
      new_data['Date'][i] = data['Date'][i]
      new_data['Close'][i] = data['Close'][i]
  
  #setting index
  new_data.index = new_data.Date
  new_data.drop('Date', axis=1, inplace=True)
  
  #creating train and test sets
  dataset = new_data.values
  
  train = dataset[0:987,:]
  valid = dataset[987:,:]
  
  #converting dataset into x_train and y_train
  scaler = MinMaxScaler(feature_range=(0, 1))
  scaled_data = scaler.fit_transform(dataset)
  
  x_train, y_train = [], []
  for i in range(60,len(train)):
      x_train.append(scaled_data[i-60:i,0])
      y_train.append(scaled_data[i,0])
  x_train, y_train = np.array(x_train), np.array(y_train)
  
  x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))
  
  # create and fit the LSTM network
  model = Sequential()
  model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1],1)))
  model.add(LSTM(units=50))
  model.add(Dense(1))
  
  model.compile(loss='mean_squared_error', optimizer='adam')
  model.fit(x_train, y_train, epochs=1, batch_size=1, verbose=2)
  
  #predicting 246 values, using past 60 from the train data
  inputs = new_data[len(new_data) - len(valid) - 60:].values
  inputs = inputs.reshape(-1,1)
  inputs  = scaler.transform(inputs)
  
  X_test = []
  for i in range(60,inputs.shape[0]):
      X_test.append(inputs[i-60:i,0])
  X_test = np.array(X_test)
  
  X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))
  closing_price = model.predict(X_test)
  closing_price = scaler.inverse_transform(closing_price)
  