title: How to create a neural network for regression?
body: <p>I am trying to use Keras to make a neural network. The data I am using is <a href="https://archive.ics.uci.edu/ml/datasets/Yacht+Hydrodynamics" rel="noreferrer">https://archive.ics.uci.edu/ml/datasets/Yacht+Hydrodynamics</a>. My code is as follows:</p>
  
  <pre><code>import numpy as np
  from keras.layers import Dense, Activation
  from keras.models import Sequential
  from sklearn.model_selection import train_test_split
  
  data = np.genfromtxt(r"""file location""", delimiter=',')
  
  model = Sequential()
  model.add(Dense(32, activation = 'relu', input_dim = 6))
  model.add(Dense(1,))
  model.compile(optimizer='adam', loss='mean_squared_error', metrics = ['accuracy'])
  
  Y = data[:,-1]
  X = data[:, :-1]
  </code></pre>
  
  <p>From here I have tried using model.fit(X, Y), but the accuracy of the model appears to remain at 0. I am new to Keras so this is probably an easy solution, apologies in advance.</p>
  
  <p>My question is what is the best way to add regression to the model so that the accuracy increases? Thanks in advance.</p>