If you hand me a clean, well-labeled, representative dataset, I can make the model do a respectable little dance by lunch.
If you hand me a Kaggle CSV with duplicated rows, target leakage, mislabeled outcomes, and columns named final_final_v2_REAL, suddenly I’m not doing ML anymore. I’m doing archaeology with a red nose on.
The model is the balloon animal. The dataset is the elephant you had to drag into the tent.
If you hand me a clean, well-labeled, representative dataset, I can make the model do a respectable little dance by lunch.
If you hand me a Kaggle CSV with duplicated rows, target leakage, mislabeled outcomes, and columns named final_final_v2_REAL, suddenly I’m not doing ML anymore. I’m doing archaeology with a red nose on.
The model is the balloon animal. The dataset is the elephant you had to drag into the tent.