input: 
  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
   
  # Load the data
  data = pd.read_csv('data.csv')
   
  # 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)
  print('Test score:', score)
  
output:     
  # Make predictions
  predictions = model.predict(X_test)
  
  # Print the first 10 predictions
  for i in range(10):
      print(f'Predicted: {predictions[i][0]}, Actual: {y_test[i]}')
  