input: 
  import numpy as np
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from keras.models import Sequential
  from keras.layers import Dense
  
  def create_and_evaluate_neural_network() -> None:
      """
      Constructs and evaluates a neural network for basic regression using Keras.
      The function loads data from a CSV file, splits it into training and testing sets, 
      builds a neural network with two hidden layers, compiles the model, trains it, 
      and finally evaluates the model on the test set. The test score is printed as 
      the output.
      
      >>> # Assuming data.csv exists and contains appropriate features and labels
      >>> create_and_evaluate_neural_network()
      Test score: 0.0034
      """
      # Load the data
      data = pd.read_csv('data.csv')
      
      # Split the data into features and labels
      X = data.drop(['label'], axis=1).values
      y = data['label'].values
      
      # 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)
      
      # Create the neural network
      model = Sequential()
      model.add(Dense(12, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))
      model.add(Dense(12, kernel_initializer='normal', activation='relu'))
      model.add(Dense(1, kernel_initializer='normal'))
      
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
      
      # Train the model
      model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
      
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      print('Test score:', score)
  
output:     # Load the data
      data = pd.read_csv('data.csv')
      
      # Split the data into features and labels
      X = data.drop(['label'], axis=1).values
      y = data['label'].values
      
      # 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)
      
      # Create the neural network
      model = Sequential()
      model.add(Dense(12, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))
      model.add(Dense(12, kernel_initializer='normal', activation='relu'))
      model.add(Dense(1, kernel_initializer='normal'))
      
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
      
      # Train the model
      model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
      
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      print('Test score:', score)