input: Write a function to construct a neural network in Python that can perform basic regression. The function should load the data from 'data.csv', split it into training and testing sets, and then train and evaluate the neural network. The neural network should have two hidden layers with 12 neurons each and use the ReLU activation function. The output layer should have a single neuron without an activation function. The model should be compiled using the mean squared error loss function and the Adam optimizer.
  
  assert basic_regression()==None
output: def basic_regression():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      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(12, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(12, activation='relu'))
      model.add(Dense(1))
  
      model.compile(loss='mean_squared_error', optimizer='adam')
      model.fit(X_train, y_train, epochs=100, batch_size=10, verbose=0)
  
      loss = model.evaluate(X_test, y_test, verbose=0)
      return None