Basics: What is the correct sequence for preparing simple data for ML?

Question

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:

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?

Stephen Rauch · Accepted Answer · 2018-08-09 14:43:18Z

1

The same encoding needs to happen on both the train and the test set. So, encode before splitting.

Stephen Rauch♦

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answered Aug 9 '18 at 14:09

hssay

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Add a comment |

workingonit · Accepted Answer · 2018-08-09 20:29:36Z

One hot encoding to allow non numerical features on all data.
Split data into training and validation sets. Preferably stratify the sets for the outcome distribution.
Account for gaps in data(Imputing) based on the training set only and apply the same imputation on validation set. Imputation procedures can be seen as parts of a machine learning model.
Feature selection/algorithm selection/hyperparameter tuning in the training set.
Then proceed to fit on the training set and evaluate performance on the validation set.

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