u/SoggyDelivery1898

Concepts make sense in class but the second I open an assignment I have no idea where to begin. What's your approach when starting one?

Doing a capstone on bank churn. I built an RFM segmentation as part of it and my groupmate said

that's not ML it's just sorting customers into buckets. Now I'm second guessing whether to even

include it. Is RFM considered ML in a real banking context or is it a separate thing entirely?

I've hit a wall on a few assignments lately and I'm trying to find the right kind of help not someone to do it for me, but something that helps me actually understand what's going wrong so I can work through it myself.

I've looked at a bunch of tutoring and help sites, but honestly most of them feel like content farms or just hand you solutions without any explanation. That's not what I'm after.

Specifically looking for resources where you can paste code or describe a problem and get a real walkthrough of the logic not just a corrected snippet. Bonus if there are practice problems tied to the concepts I'm weak on.

Free or paid is fine. I'm more interested in what's actually worked for people than a list of names. What did you use when you were at that "I don't even know what I don't know" stage?

Tabular data, classic classification problem. 22 features, clean dataset, no missing values. Built a straightforward neural network in PyTorch two hidden layers, batch norm, dropout, Adam optimizer. Random Forest with default sklearn params kept beating it by 3–4% on F1 consistently.

I tuned learning rate, tried different architectures, added more layers, removed layers. Nothing worked. The RF just kept winning.

The actual problem:

# what I was doing
X_train, X_val, y_train, y_val = train_test_split(
    X, y, test_size=0.2, random_state=42
)

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_val_scaled   = scaler.fit_transform(X_val)   # bug here

I was calling fit_transform on the validation set instead of just transform. So my validation data was being scaled on its own distribution, not the training distribution.

The neural network was far more sensitive to this than Random Forest — trees don't care about feature scale, so RF results were completely unaffected. The NN was training on one scale and evaluating on a slightly different one every single epoch.

Fix was one word:

X_val_scaled = scaler.transform(X_val)   # not fit_transform

After fixing: neural network beat Random Forest by 2.1% F1.

Two days of architecture tuning, hyperparameter search, reading papers solved by removing four letters from one line.

I've seen this exact mistake in three different Kaggle notebooks since then. The frustrating part is it doesn't throw an error and the performance gap is small enough to seem like a model architecture problem, not a preprocessing bug.

TL;DR — Never call fit_transform on validation or test data. Fit only on training data, then transform everything else. Neural networks are far more sensitive to this than tree-based models, which makes the bug almost impossible to spot by comparing model performance alone.

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