I'm just getting started with ML and am busy with my first Kaggle competition (the titanic one).
I was just wondering what would be the best way to organise the data to avoid redundancy with the following steps:
- Feature Selection
- Account for gaps in data(Imputing)
- One hot encoding to allow non numerical features
- Split data into training and validation sets
- Then proceed to fit and predict with the model.
My main query is whether it is better to split the data before encoding it, or to only do the split after completing the encoding?