input: Write a function that constructs a neural network in Python to perform basic regression on a given dataset. The function should load the data, preprocess it, create and compile the model, train it, and finally evaluate its performance. The function should return the test score.
  
  
  assert regression_neural_network('data.csv') is not None
  assert isinstance(regression_neural_network('data.csv'), float)
  
output: def regression_neural_network(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Load data
      data = pd.read_csv(file_path)
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
  
      # Preprocess data
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Create model
      model = Sequential()
      model.add(Dense(10, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(1))
  
      # Compile model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train model
      model.fit(X_train, y_train, epochs=100, batch_size=32, verbose=0)
  
      # Evaluate model
      test_score = model.evaluate(X_test, y_test, verbose=0)
      return test_score