code: def svm_stroke(df_1, y, json_file):
      """
      group of svms training function
      :param df_1: dataframe
      :param y: is the target column
      :return compiled svm model
      """
      models = []
      scores = []
  
      clf_1 = svm.SVC().fit(df_1, y)
      clf_2 = svm.SVC(kernel="rbf").fit(df_1, y)
      clf_3 = svm.SVC(kernel="poly").fit(df_1, y)
  
      models.append(clf_1)
      models.append(clf_2)
      models.append(clf_3)
  
      for model in models:
          scores.append(accuracy_score(model.predict(df_1), y))
  
      return 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 10 for calling a global function with example usage: svm_stroke(df_1, y, json_file) and returns: models