Accuracy Testing & Training I have 102 observations. I made standard scale for dataset. I have found the accuracy training and accuracy testing values, but training score is 1.00 and testing score is -217.541. I have run with MLPRegressor score. What does negative accuracy testing score mean? 

FileX_train, FileX_test,FileY_train,FileY_test = train_test_split(FileX,FileY,test_size=0.33, random_state=0)

sc = StandardScaler()
X_train = sc.fit_transform(FileX_train)
X_test = sc.transform(FileX_test)

sc = StandardScaler()
Y_train = sc.fit_transform(FileY_train)
Y_test = sc.transform(FileY_test)

from sklearn.neural_network import MLPRegressor
from sklearn.metrics import accuracy_score
mlp = MLPRegressor(solver='lbfgs',activation='tanh', hidden_layer_sizes=(100,), max_iter=1000000000, learning_rate='constant')
mlp.fit(X_train, Y_train.ravel())
print('Accuracy training : {:.3f}'.format(mlp.score(X_train, Y_train)))
print('Accuracy testing : {:.3f}'.format(mlp.score(X_test, Y_test)))


Results:

Accuracy training : 1.000
Accuracy testing : -217.541
You haven't actually used accuracy_score, and the default scorer for sklearn regressors is R^2:
https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor.score

A negative value for R^2 generally means a very bad fit:
https://stats.stackexchange.com/q/12900/232706