From Notebook Chaos to Repeatable Data Science Projects

The Notebook Trap

Many data science learners build promising notebooks, but when they need repeatable experiments, everything becomes hard to reproduce. The missing piece is project structure discipline.

Step 1: Split exploration and production paths

project/
  notebooks/
  src/
    data/
    features/
    models/
  tests/
  configs/

Step 2: Move feature logic into importable modules

def add_ratio_features(df):
    out = df.copy()
    out["profit_ratio"] = out["profit"] / out["revenue"].clip(lower=1)
    out["cost_ratio"] = out["cost"] / out["revenue"].clip(lower=1)
    return out

Step 3: Make experiments config-driven

experiment:
  model: random_forest
  train_split: 0.8
  random_seed: 42
  features:
    - profit_ratio
    - cost_ratio

Pitfalls

• Hard-coded file paths in notebooks.
• No seed control for model training.
• Feature engineering mixed with plotting code.

Verification

• A fresh clone can run one experiment command end-to-end.
• Two runs with same config produce consistent metrics.
• Feature functions are unit tested outside notebooks.