input: Write a function to construct a neural network in Python that can perform basic regression using Keras. The function should read data from a CSV file, split the data into training and testing sets, create a neural network with three layers, compile the model, train it, and finally evaluate it. The function should return the test score.
  
  assert construct_neural_network('data.csv') > 0
output: def construct_neural_network(file_path):
      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(file_path)
      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)
  
      test_score = model.evaluate(X_test, y_test, verbose=0)
      return test_score