Hi @Satanik_Mitra!
Why 10 features are chosen from the dataset, why can’t we choose 14 instead?
I believe we just used 10 to keep things manageable, and it may well work if you increase to 14 features. In fact, the original dataset has 28 features, so you could include all of those as long as you ensure there are 14 features input into the quantum part of the network.
However, bear in mind that the 28 features are a result of a PCA, and (this needs to be double checked) the features are ordered by importance. We take the first 10 features, and each new feature added after that may have a diminished usefulness when training the model.