text: #SVM WITH ALL THE VARIABLES
  parameters = {'kernel':('linear', 'rbf',"poly","sigmoid"), 'C':range(1, 10)}
  svc = svm.SVC(random_state=22, class_weight="balanced")
  grid_svm= GridSearchCV(svc, parameters,cv=5)
  grid_svm.fit(X_train, Y_survive_train.values.ravel())
  print(grid_svm.best_params_)
  
  Support_class= svm.SVC(C=2,kernel="linear",random_state=22, class_weight="balanced")
  Support_class.fit(X_train, Y_survive_train.values.ravel())
  y_pred1= Support_class.predict(X_train)
  print(classification_report(Y_survive_train,y_pred1))
  y_pred2= Support_class.predict(X_test)
  print(classification_report(Y_survive_test, y_pred2))
  
  #SVM WITH FEATURE SELECTION
  fs = SelectKBest(score_func = mutual_info_classif)
  fs.fit(X_train, Y_survive_train)
  for i in range(len(fs.scores_)):
    print("Feature %d: %f" % (i, fs.scores_[i]))
   
  np.where(fs.scores_>0.010)
  X_train_rid = X_train.iloc[:, [ 0,   2,   3,  23,  32,  33,  44,  45,  56,  72,  81,  84,  89,
           90, 100, 101, 102]]
  X_test_rid = X_test.iloc[:, [ 0,   2,   3,  23,  32,  33,  44,  45,  56,  72,  81,  84,  89,
           90, 100, 101, 102]]
  parameters = {'kernel':('linear', 'rbf',"poly","sigmoid"), 'C':range(1, 10)}
  svc = svm.SVC(random_state=22, class_weight="balanced")
  grid_svm= GridSearchCV(svc, parameters,cv=5)
  grid_svm.fit(X_train_rid, Y_survive_train.values.ravel())
  print(grid_svm.best_params_)
  
  Support_class= svm.SVC(C=1,kernel="sigmoid",random_state=22, class_weight="balanced")
  Support_class.fit(X_train_rid, Y_survive_train.values.ravel())
  
  y_pred1= Support_class.predict(X_train_rid)
  print(classification_report(Y_survive_train,y_pred1))
  y_pred2= Support_class.predict(X_test_rid)
  print(classification_report(Y_survive_test, y_pred2))
  