input: Write a function to construct a neural network in Python that can perform basic regression. The function should load the data from a CSV file, split it into features and labels, create and compile a neural network model, train the model, and finally evaluate it.
  assert construct_neural_network('data.csv')==0.05, 'Test score: 0.05'
output: def construct_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
  
      # Split data
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize features
      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=10, verbose=0)
  
      # Evaluate model
      loss = model.evaluate(X_test, y_test, verbose=0)
      return loss