text: import pandas as pd
  
  base = pd.read_csv('credit_data.csv')
  base.loc[base.age < 0, 'age'] = 40.92
                 
  previsores = base.iloc[:, 1:4].values
  classe = base.iloc[:, 4].values
  
  from sklearn.preprocessing import Imputer
  imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
  imputer = imputer.fit(previsores[:, 1:4])
  previsores[:, 1:4] = imputer.transform(previsores[:, 1:4])
  
  from sklearn.preprocessing import StandardScaler
  scaler = StandardScaler()
  previsores = scaler.fit_transform(previsores)
  
  from sklearn.svm import SVC
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.neural_network import MLPClassifier
  
  classificadorSVM = SVC(kernel = 'rbf', C = 2.0)
  classificadorSVM.fit(previsores, classe)
  
  classificadorRandomForest = RandomForestClassifier(n_estimators = 40, criterion = 'entropy')
  classificadorRandomForest.fit(previsores, classe)
  
  classificadorMLP = MLPClassifier(verbose = True, max_iter = 1000,
                                   tol = 0.000010, solver = 'adam',
                                   hidden_layer_sizes=(100), activation = 'relu',
                                   batch_size = 200, learning_rate_init = 0.001)
  classificadorMLP.fit(previsores, classe)
  
  import pickle
  pickle.dump(classificadorSVM, open('svm_finalizado.sav', 'wb'))
  pickle.dump(classificadorRandomForest, open('random_forest_finalizado.sav', 'wb'))
  pickle.dump(classificadorMLP, open('mlp_finalizado.sav', 'wb'))
  