Leakage Loophole

Emily's model had the best performance in the entire class, but there was a problem: the results were too good to be correct. The professor decided to review her process step by step. Emily stood in front of the class and wrote down her process:

Loaded the entire dataset in memory.
Replaced one column's missing values with the mean of the column and scaled another column using Min-Max Scaling.
Split the dataset into a train and a test set.
And finally, trained the model.

As soon as she finished, the professor knew what the issue was.

Which of the following is the reason for the model's inflated performance?

Emily loaded the entire dataset in memory. She should have loaded it in batches.

Emily used the mean of the column to impute missing values. She should have used the median instead

Emily transformed her data before splitting the dataset. She should have split the data before transforming it.

Emily used Min-Max Scaling to transform one of the columns. She should have applied log transformation instead.