Build an Imbalanced Classification Pipeline (scikit-learn + imbalanced-learn)

Context

You are given a tabular dataset with a severely imbalanced binary target (e.g., minority class rate < 5%). Build an end-to-end classification pipeline that:

• Applies standard preprocessing to numeric and categorical features.
• Uses an appropriate resampling method to address imbalance.
• Trains a classifier.
• Evaluates precision, recall, and F1-score on a held-out test set.

Assume the input features X are in a pandas DataFrame and the target y is a pandas Series.

Requirements

1. Split the data into train/test using stratification to preserve class ratios.
2. Preprocess features:
   • Numeric: impute missing values and standardize.
   • Categorical: impute missing values and encode safely.
3. Resample only the training data (avoid leakage) using a suitable method:
   • If only numeric features: SMOTE is acceptable.
   • If mixed types: use SMOTENC to correctly handle categorical features.
4. Train a reasonable baseline classifier (e.g., logistic regression or tree-based model).
5. Report precision, recall, and F1-score on the test set (per-class and macro/weighted averages are acceptable).

Deliverables

• Reproducible Python code using scikit-learn and imbalanced-learn that implements the above and prints metrics on the held-out test set.
• Brief comments justifying major choices (resampling method, pipeline order).