I have been practicing Data Science/Machine Learning, and I am confused about when to complete the following tasks when using train-test split in scikit learn:

1. EDA
2. Filling in missing data
3. Removing data that isn't needed
4. Working with categorical data

I know that scaling should be done after train-test split. However, I have seen both approaches (train-test split before and after) when working with the above tasks.

I'm confused about which is the correct way for these tasks.

Can someone provide some clarification?

Thank you!

I have been practicing Data Science/Machine Learning, and I am confused about when to complete the following tasks when using train-test split in scikit learn:

1. EDA
2. Filling in missing data
3. Removing data that isn't needed
4. Working with categorical data
5. Feature Engineering
6. Feature Selection

I know that scaling should be done after train-test split. However, I have seen both approaches (train-test split before and after) when working with the above tasks.

I'm confused about which is the correct way for these tasks.

Can someone provide some clarification?

Thank you!