Let's suppose I have a dataset with numerical attributes of different types.
Let's suppose I want to employ a Neural Network for supervised classification with that dataset. For that, I need to extract feature vectors from that data.
Those feature vectors must be suitable for NNs. (should be normalized/standarized vectors...)
As an example, our dataset consist of data from football games.
DATASET:
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| local_elo| vis_elo| local_pts | vis_pts | loc_goals | vis_goals |
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| 2820 | 3250 | 45 | 54 | 13 | 17 |
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| 4230 | 5125 | 87 | 81 | 67 | 65 |
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The feature vectors this two data points:
x_1 = [2820, 3250, 45, 54, 13, 17]
x_2 = [4230, 5125, 87, 81, 67, 65]
but they are not suitable for feed them into a Neural Network.
How could this dataset be preprocessed in order to extract feature vectors suitables for feeding them into a Neural Network?