code: def default_modeling(df, y):
      """
      default modeling function to try all classifiers, and return the best one.
      :param df pandas dataframe object
      :param y is the target column
      :return compiled model, which represents the one with the highest accuracy_score
      """
      svm_model = svm.SVC().fit(df["train"], y["train"])
      neighbors = KNeighborsClassifier().fit(df["train"], y["train"])
      tree = DecisionTreeClassifier().fit(df["train"], y["train"])
      sgd = SGDClassifier().fit(df["train"], y["train"])
      grad = GradientBoostingClassifier().fit(df["train"], y["train"])
      adaboost = AdaBoostClassifier().fit(df["train"], y["train"])
      forest = RandomForestClassifier().fit(df["train"], y["train"])
      network = MLPClassifier().fit(df["train"], y["train"])
  
      list_models = [
          svm_model,
          neighbors,
          tree,
          sgd,
          grad,
          adaboost,
          forest,
          network,
      ]
      scores = []
  
      for model in list_models:
          scores.append(
              accuracy_score(model.predict(df["test"]), y["test"])
          )
  
      return list_models[scores.index(max(scores))]
  
imports: from sklearn.tree import DecisionTreeClassifier
  from sklearn import svm
  import numpy
  from sklearn.linear_model import SGDClassifier
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.neural_network import MLPClassifier
  from sklearn.metrics import accuracy_score
  from sklearn.ensemble import ExtraTreesClassifier
  from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
  import warnings
label_desc: Use image node_id 1 for calling a global function with example usage: default_modeling(df, y) and returns: list_models