input: Write a function to construct a neural network in Python that can perform basic regression. The function should load data from a CSV file, split the data into features and labels, create the neural network, compile it, train it, and then evaluate it.
  
  def construct_regression_nn(filename):
      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
      
      # Load the data
      data = pd.read_csv(filename)
      
      # 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)
  
  assert construct_regression_nn('data.csv') is None
output: def construct_regression_nn(filename):
      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
      
      data = pd.read_csv(filename)
      X = data.drop(['label'], axis=1).values
      y = data['label'].values
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      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'))
      
      model.compile(loss='mean_squared_error', optimizer='adam')
      model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
      
      score = model.evaluate(X_test, y_test, verbose=0)
      print('Test score:', score)