Project: End-to-End ML Mini Pipeline

Build a complete ML pipeline from preprocessing to evaluation. Pick a real dataset, define a metric, compare at least two model families, and produce a concise model card.

Project brief

Build a complete ML pipeline from preprocessing to evaluation. Pick a real dataset, define a metric, compare at least two model families, and produce a concise model card.

Data quality checks and feature report.
Baseline + improved model comparison.
Error analysis for top failure cases.
Deployment idea (batch API or dashboard).

Minimal training script structure

Train/evaluate function signatures

def load_data(path: str):
    ...

def build_pipeline():
    ...

def train_and_evaluate(X_train, X_test, y_train, y_test):
    ...

if __name__ == "__main__":
    X_train, X_test, y_train, y_test = load_data("data.csv")
    metrics = train_and_evaluate(X_train, X_test, y_train, y_test)
    print(metrics)