sklearn Pipeline used

StandardScaler in pipeline

train_test_split used

test_size=0.2

random_state=42

Split before preprocessing

EDA present

Feature statistics computed

Data quality check

Pipeline fit on train only

Cross-validation used

Confusion matrix produced

ROC/AUC computed

Feature importance reported

Experiments logged to file

Parameters recorded

Model assumptions documented

Model limitations documented

Multiple metrics reported

Results saved to files

Missing values handled

Categorical encoding applied

Feature selection performed

Ensemble method used

Hyperparameter tuning performed

Model serialized to file

Serialization format correct

Preprocessing in pipeline

Model documentation written

Version or metadata recorded