In a live notebook, clean a messy tabular dataset with missing values and inconsistent strings, and build a sound baseline binary classifier.

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

def baseline(df, num, cat, y):
    df = df.copy()
    for c in cat:
        df[c] = df[c].str.strip().str.lower()
    nums = make_pipeline(SimpleImputer(strategy="median"),
                         StandardScaler())
    cats = make_pipeline(
        SimpleImputer(strategy="most_frequent"),
        OneHotEncoder(handle_unknown="ignore"))
    prep = ColumnTransformer([("n", nums, num),
                              ("c", cats, cat)])
    clf = LogisticRegression(max_iter=1000)
    model = make_pipeline(prep, clf)
    return cross_val_score(model, df[num + cat], df[y],
                           cv=5, scoring="roc_auc")

Look first: types, missing rates, label balance, and columns that leak the target, like anything recorded after the outcome. Normalise strings, and keep imputation, scaling and encoding inside a pipeline so they're fitted only on training folds. Compare against predicting the majority class, with ROC AUC or PR AUC under stratified cross-validation.