Deep Learning for non-continuous dataset
Deep Learning excels in problems where the data is relatively unstructured. Stacked layers help find conceptual features that can be used to infer rules. 

Your dataset seems very structured at first glance. And, as you pose, it doens't look like it needs specialised layers that exploit sequential or spatial relations. 

Neural network wise, this would warrant one or two fully connected layers, connected to an output layer (shaped to your wishes). However, typically, problems like these are tackled with a more biased approaches (eg Decision Tree Learners).