print('ANN: %f'%score_ann) print('LSTM: %f'%score_lstm) print('NN: %f'%score_nn) print('MLP: %f'%score_mlp) ###########Graphs#################### plt.figure(facecolor='w', edgecolor='k') plt.xlabel('No. of Days') plt.ylabel('USD/%s scaled'%cur) plt.plot(y_test) plt.plot(y_pred_svr) plt.plot(y_pred_test_ann) plt.plot(y_pred_test_lstm) plt.plot(y_pred_nn) plt.plot(y_pred_mlp) plt.legend(['Actual Price','SVR Prediction','ANN Prediction','LSTM Prediction','NN Prediction','MLP Prediction']) plt.grid(True) plt.show() if __name__ == '__main__': df = pd.read_csv('Historical Data.csv',usecols=[0,1,2,3,4,5,6,7,8,9,10,11],parse_dates=['Date']) num_cores = mp.cpu_count() pool = Pool(os.cpu_count()) print("No. of Cores : %d \n"%num_cores) mcp(df) result = pool.map(mcp(df), df) pool.close() pool.join() pool.clear()