def METHOD_NAME():
    X, y = sk_datasets.make_regression(n_samples=5000, n_features=2)
    X = pd.DataFrame(X)
    X.columns = ["feat_1", "feat_2"]
    X["cat"] = np.random.randint(1, 100, X.shape[0])
    X["cat"] = X["cat"].astype("string")
    y = pd.Series(y)
    group1 = compose.Select("cat") | preprocessing.OneHotEncoder()
    group2 = compose.Select("feat_2") | preprocessing.StandardScaler()
    model = group1 + group1 * group2 * group2 | compat.convert_sklearn_to_river(
        sk_linear_model.SGDRegressor()
    )
    model.predict_many(X)
    model.learn_many(X, y)