input: def neural_network_regression():
      """Construct a neural network in Python that can perform basic regression.
      The neural network will be constructed using Keras and will be trained on a dataset 
      loaded from a CSV file. The network will be evaluated on a test set after training.
      Example:
      >>> neural_network_regression()
      Test score: 0.0234
      """
output:     import numpy as np
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from keras.models import Sequential
      from keras.layers import Dense
  
      # Load the dataset
      data = pd.read_csv('dataset.csv')
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
  
      # Split the dataset 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 features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Build the neural network model
      model = Sequential()
      model.add(Dense(10, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(1))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=100, batch_size=10, 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:.4f}")