input: Write a function to construct a neural network in Python that can perform basic regression. The function should load data from 'data.csv', split it into training and testing sets, create a neural network model with specified layers and activation functions, compile, train, and evaluate the model.
  assert __ == regression_model()
  assert __ == regression_model()
  assert __ == regression_model()
  assert __ == regression_model()
  assert __ == regression_model()
output: 
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  
  def regression_model():
      data = pd.read_csv('data.csv')
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      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'))
      
      model.compile(optimizer='adam', loss='mean_squared_error')
      model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
      
      loss = model.evaluate(X_test, y_test, verbose=0)
      return loss
  