Improving a basic neural network with only 3 input columns
There is no point in looking at the prediction at this point. Your model has not learnt the data yet.
Suggestions -

When copy from a source. Try copying everything first. Run it and then start tweaking for learning Or to map with your own need. You have changed a few things as compared to what Jason has in his post

Use the wisdom of researchers. So, you can keep a few things as default -
     1. Optimizer - Adam
     2. Activation - Relu for Hidden layer
     3. Loss - MSE(Regression), Cross-entropy(Classification). Beware of Cros-entropy variance.
     4. Activation for Output layer - Linear(Regression), Sigmoid/Softmax(Classification)
     3. Have Batch normalization layer when you have a deep network.

Try to know why behind every point that I have mentioned. It's will be an easy search on the Internet.

A good place to start can be - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, by Aurélien Géron
Further reading -
     Practical Recommendations for Gradient-Based Training of Deep Architectures, by Yoshua Bengio
    Andrej Karpathy blog
    Raúl Gómez blog