input: Write a function to construct a neural network in Python that can perform basic regression on a given dataset. The function should load the data, split it into training and testing sets, create the neural network model, compile and train the model, and finally evaluate the model on the test set. Return the test score as the output.
  assert construct_regression_nn('data.csv')==98.123  # Assuming a test score of 98.123 for example purposes
  assert construct_regression_nn('another_data.csv')==75.678  # Assuming a test score of 75.678 for example purposes
output: def construct_regression_nn(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 the data
      data = pd.read_csv(file_path)
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize the data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Create the neural network model
      model = Sequential([
          Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          Dense(32, activation='relu'),
          Dense(1)
      ])
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
  
      # Evaluate the model on the test set
      test_score = model.evaluate(X_test, y_test, verbose=0)
  
      return test_score