input: 
  def perform_regression():
      """ Constructs a neural network in Python using Keras that can perform basic regression.
      The function loads a dataset from a CSV file, splits it into features and labels,
      divides the data into training and testing sets, creates a neural network model,
      compiles the model, trains it, and then evaluates the model on the test set.
      >>> perform_regression()
      Test score: 0.078125
      """
  
output:     
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from keras.models import Sequential
      from keras.layers import Dense
  
      # Load the dataset
      data = pd.read_csv('data.csv')
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].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)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Create the 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 the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=100, batch_size=32, verbose=0)
  
      # Evaluate the model on the test set
      test_score = model.evaluate(X_test, y_test, verbose=0)
      print(f'Test score: {test_score}')
  