data_object = data_importing(directory, N, split, grayscale, recognize, features[0], attributes, create_load)
train, test, nfa = data_object.extract_data()
#Print time after data processing has been completed
tlast = print_time('Data Importing', tlast)
if model_type == 'Logistic Regression':
#Load or import PCA per specifications
if load_PCA==True:
data_object = data_loading(N, split, grayscale)
train, test = data_object.load_PCA()
if load_PCA==False:
data_object = PCA(variance_retained, train, test, grayscale, create_load_PCA)
train, test = data_object.analysis()
#Print time after PCA has been completed
tlast = print_time('Principal Component Analysis', tlast)
#Generate scores of ML models on features
scores = generate_scores(train, test, nfa, load_cnn, features[0])
#Print time after model generation and scoring
tlast = print_time('Model generation on all features', tlast)
#Print out the model accuracies
i = 0
print('')
print('Model Accuracies:')
for feature in features:
