input: def construct_regression_nn():
      """ Constructs a neural network in Python using Keras to perform basic regression.
      The network uses ReLU activation function for hidden layers and mean squared error 
      as the loss function. It is trained on data loaded from 'data.csv', which is split
      into training and testing sets. The training process uses 100 epochs and a batch 
      size of 20. The function returns the test score from evaluating the model on the 
      test data.
      >>> construct_regression_nn()
      'Test score: 0.87'
      """
output:     from keras.models import Sequential
      from keras.layers import Dense
      from sklearn.model_selection import train_test_split
      import pandas as pd
  
      # Load data
      data = pd.read_csv('data.csv')
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split 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)
  
      # Construct the neural network
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # 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
      test_score = model.evaluate(X_test, y_test, verbose=0)
  
      return f'Test score: {test_score:.2f}'