Train, evaluate and iteratively improve a scikit-learn binary classifier, handling preprocessing, validation and class imbalance.

Put preprocessing inside the pipeline, so cross-validation fits imputers and scalers on training folds only.

from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import (
    StratifiedKFold, cross_val_score)
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import (
    OneHotEncoder, StandardScaler)

prep = ColumnTransformer([
    ("num", make_pipeline(SimpleImputer(strategy="median"),
                          StandardScaler()), num_cols),
    ("cat", OneHotEncoder(handle_unknown="ignore"),
     cat_cols),
])
model = make_pipeline(
    prep, LogisticRegression(class_weight="balanced",
                             max_iter=1000))
cv = StratifiedKFold(5, shuffle=True, random_state=0)
scores = cross_val_score(model, X, y, cv=cv,
                         scoring="average_precision")

From this baseline, try gradient boosting and tune with the same CV. For imbalance, class weights or threshold tuning beat blind oversampling. Report PR-AUC, plus precision and recall at a threshold chosen on validation data, then read the errors.