input: 
  def construct_neural_network_regression():
      """ Construct a neural network in Python using Keras that can perform basic regression on a given dataset.
      The dataset is loaded from a CSV file named 'data.csv'. The neural network consists of three layers:
      an input layer with 12 neurons, a hidden layer with 12 neurons, and an output layer with 1 neuron.
      The model is compiled using mean squared error as the loss function and Adam as the optimizer.
      The model is trained for 100 epochs with a batch size of 20 and evaluated on the test set.
      
      Example:
      construct_neural_network_regression()
      Output: Test score: 0.05 (This is just a hypothetical value for demonstration purposes)
      """
  
output:     
      import pandas as pd
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Load the dataset
      data = pd.read_csv('data.csv')
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split the dataset into training and testing sets
      from sklearn.model_selection import train_test_split
      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
      model = Sequential()
      model.add(Dense(12, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(12, 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 on the test set
      test_score = model.evaluate(X_test, y_test, verbose=0)
      print(f'Test score: {test_score:.2f}')
  