When I first started working with machine learning, I made this classic mistake: I normalized my entire dataset before splitting it. And guess what? My model performed great a little too great. 😅
Turns out, I was leaking information from the test set into training without even realizing it.

Here’s what I do now (and always recommend):

1. First, split your data into train and test (or train/val/test).
2. Fit your scaler only on the training set — not the whole dataset.
   python

   scaler = StandardScaler()
scaler.fit(X_train)

3. Then use that same scaler to transform both the training and test sets.
   python

   X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)


That way, your model learns only from the training data — just like it would in a real-world setting. No sneak peeks at the test set.
Trust me, once you catch this, you’ll never scale data the old way again. It’s a small thing, but it makes a huge difference in keeping your model honest.