input: Write a function to construct a neural network in Python that can perform basic regression on a given dataset. The function should load the dataset, split it into training and testing sets, create and compile a neural network model, train the model, and evaluate its performance. If the dataset file does not exist, the function should return a message indicating that the file was not found.
  
  import numpy as np
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from keras.models import Sequential
  from keras.layers import Dense
  
  def basic_regression(filepath):
      try:
          # Load the data
          data = pd.read_csv(filepath)
          
          # Split the data into features and labels
          X = data.drop(['label'], axis=1).values
          y = data['label'].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)
          
          # Create the neural network
          model = Sequential()
          model.add(Dense(12, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))
          model.add(Dense(12, kernel_initializer='normal', activation='relu'))
          model.add(Dense(1, kernel_initializer='normal'))
          
          # 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
          score = model.evaluate(X_test, y_test, verbose=0)
          return score
      except FileNotFoundError:
          return 'File not found'
  
  assert basic_regression('data.csv') is not None
  assert basic_regression('nonexistent.csv') == 'File not found'
output: def basic_regression(filepath):
      try:
          data = pd.read_csv(filepath)
          X = data.drop(['label'], axis=1).values
          y = data['label'].values
          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.shape[1], kernel_initializer='normal', activation='relu'))
          model.add(Dense(12, kernel_initializer='normal', activation='relu'))
          model.add(Dense(1, kernel_initializer='normal'))
          model.compile(loss='mean_squared_error', optimizer='adam')
          model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
          score = model.evaluate(X_test, y_test, verbose=0)
          return score
      except FileNotFoundError:
          return 'File not found'