input: def build_basic_regression_nn():
      """ Builds a neural network using Keras that can perform basic regression. 
      The network is trained on data loaded from a CSV file with a 'label' column 
      for the target variable. The network has three dense layers, with the first 
      and second using ReLU activation and the last layer for regression without 
      activation. The model is compiled using mean squared error loss and the 
      Adam optimizer, and trained for 100 epochs with a batch size of 20. Finally,
      the model is evaluated on the test data and the test score is printed.
      Example:
      No explicit test case provided as everything is dependent on 'data.csv'.
      """
output:     
      import pandas as pd
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
  
      # Load data
      data = pd.read_csv('data.csv')
      X = data.drop('label', axis=1)
      y = data['label']
  
      # 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)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Build the 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))
  
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
  
      # Train the model
      model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
  
      # Evaluate the model
      test_score = model.evaluate(X_test, y_test, verbose=0)
      print(f'Test score: {test_score}')
  