Working Progress 2: Vanilla RNN and LSTM for prediction

As discussed earlier, RNN and LSTM can be used to predict future time series values. Here, we split the data into train and test where the data of the year 2016 is the testing data and the rest being the training data. The data is split in such a way that Xij(n_steps) = number of previous values the next values will depend on and Yi being the value to be predicted. Here the number of layers considered was 1 as it is vanilla RNN.
The result was 34.33 RMSE and the given predicted visualization: