input: def construct_neural_network_for_regression():
      """ Constructs a neural network in Python that can perform basic regression.
      The network is built using Keras with TensorFlow backend. It reads data from a CSV file,
      splits it into training and testing sets, builds a simple feedforward neural network,
      compiles and trains it, and then evaluates the model. The output is a test score which
      indicates the mean squared error.
      Example:
      construct_neural_network_for_regression()
      # Output: Test score: <some_numerical_value>
      """
output:     
      import numpy as np
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Load data
      data = pd.read_csv('data.csv')
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
  
      # 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)
  
      # 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(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(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
  
      # Evaluate the model
      test_score = model.evaluate(X_test, y_test, verbose=0)
      print(f"Test score: {test_score}")
  