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Hi Community,
I need to develop a Deep Learning model on sequential data. My dataset has two features Column-1 and Column-2. Both these columns have sequential data. Data in these columns exist in the form of a list., where values of the list indicate chronological sequence. Please check the reference image below for clarity.
For example, for the first record, in Column-1, [a,b,c,d,e,f] refer to the values at six-time points.
I need to prepare the data for an LSTM model, for which I already have a Python function that receives the Dat6aFrame as input, transforms both Column-1 and Column-2, and returns a 3-dimensional Numpy array of shape (num_samples, time_sequence, num_features).
For the example dataset above, one-hot encoding of Column-1 creates 6 columns, and so the final Numpy array would have shape (3,6,7).
I have the following questions:
Operating system used: Red Hat Enterprise Linux