input: def construct_neural_network_for_regression() -> Sequential:
      """ Constructs a neural network in Python that can perform basic regression.
      The network is designed to be trained on a dataset loaded from 'data.csv',
      with the label column removed as feature input and the label itself as the target.
      The network uses ReLU activation in hidden layers and mean squared error as its loss function.
      Here is an example of its usage:
      >>> model = construct_neural_network_for_regression()
      >>> score = model.evaluate(X_test, y_test, verbose=0)
      >>> print('Test score:', score)
      """
      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('data.csv')
      
      # 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)
      
      return model
output:     # Save the model to a file
      model.save('regression_model.h5')