code: def create_simple_titanic_data():
      """Create simple Titanic dataset for classification.
  
      return: Tuple of X_train, X_test, y_train, y_test, num_features,
              cat_features.
      rtype: Tuple of pd.DataFrame, pd.DataFrame, numpy.ndarray, numpy.ndarray,
             list, list
      """
      titanic_url = (
          "https://raw.githubusercontent.com/amueller/"
          "scipy-2017-sklearn/091d371/notebooks/"
          "datasets/titanic3.csv"
      )
      data = read_csv(titanic_url)
      # fill missing values
      data = data.fillna(method="ffill")
      data = data.fillna(method="bfill")
      num_features = ["age", "fare"]
      cat_features = ["embarked", "sex", "pclass"]
  
      y = data["survived"].values
      X = data[cat_features + num_features]
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.2, random_state=42
      )
      return (
          X_train,
          X_test,
          y_train,
          y_test,
          num_features,
          cat_features,
      )
  
file_path: responsible-ai-toolbox/rai_test_utils/rai_test_utils/datasets/tabular/classification_data_utils.py
imports: import numpy
  import pandas
  import shap
  from pandas import read_csv
  from sklearn.datasets import load_breast_cancer, load_iris, load_wine, make_classification
  from sklearn.model_selection import train_test_split
  from rai_test_utils.utilities import retrieve_dataset
label_desc: Use image node_id 7 for calling a global function with example usage: create_simple_titanic_data() and returns: X_train,  X_test,  y_train,  y_test,  num_features,  cat_features