input: from typing import List
  
  def construct_neural_network(data_path: str, epochs: int, batch_size: int) -> float:
      """ Construct a neural network in Python that performs basic regression using the given dataset.
      The function loads data from a CSV file, preprocesses it, splits it into training and testing sets,
      constructs a neural network model using Keras, compiles, trains, and evaluates the model.
      Finally, it returns the test score.
      >>> construct_neural_network('data.csv', 100, 20)
      Test score: 2.34
      """
output:     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_path)
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
  
      # Preprocess data
      scaler = StandardScaler()
      X = scaler.fit_transform(X)
  
      # 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)
  
      # Construct 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 model
      model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mean_squared_error'])
  
      # Train model
      model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=0)
  
      # Evaluate model
      test_score = model.evaluate(X_test, y_test, verbose=0)
  
      return test_score[1]