Build a Reproducible End-to-End Binary Classification Pipeline for a CSV Dataset

Context

You are given a single CSV file that fits in memory. The dataset contains:

• Mixed numeric and categorical feature columns
• A binary label/target column
• Optional ID columns to drop

Your task is to implement a modular, reproducible, and command-line runnable training pipeline that prevents data leakage and produces a test-set report. Use Python with common ML tooling.

Requirements

1. Load data from CSV
2. Validate basic schema (label present and binary, dtypes make sense, optional ID columns dropped)
3. Handle missing values appropriately for numeric and categorical features
4. Split into train/validation/test with stratification on the label
5. Encode categorical variables and scale/normalize numeric features
6. Train a reasonable baseline classifier
7. Choose an appropriate loss and optimizer (justify selection)
8. Track metrics (at least AUC and accuracy)
9. Prevent leakage by ensuring preprocessing is fit only on training folds within a pipeline
10. Evaluate on validation set
11. Perform simple hyperparameter tuning (e.g., grid/random search)
12. Retrain best model on train+validation and generate a test-set report
13. Make the pipeline reproducible (random seeds, configuration)
14. Structure the code to be modular and runnable from the command line
15. Explain how you would detect and fix common bugs (e.g., optimizer not stepping) and how to monitor training and overfitting

Assumptions

• The CSV fits in memory
• The label column name is provided via CLI
• If the label is not numeric 0/1, you may encode it to 0/1
• Use Python, pandas, scikit-learn; keep dependencies minimal