<｜begin▁of▁sentence｜>### Instruction:
In the context of improving the efficiency of a machine learning algorithm, consider a data structure optimization where the relationships between data points are graphically represented. You're working with a graph defined by the following connections: (0, 1), (1, 2), (2, 3), (3, 0), (0, 2). To enhance computation and prevent complex overlaps that could hinder performance, it's essential to ascertain if this graph is planar. Utilize NetworkX to perform this check. Should the planarity test fail, it would be beneficial to extract the Kuratowski subgraph. This proof-of-non-planarity would provide insights into the structural complexity of your graph, which might affect the learning algorithm's efficiency as it processes relational data. Could you integrate this evaluation process into your existing machine learning pipeline to ensure the data structure's integrity?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 0), (0, 2)])

# Check if the graph is planar and get the counterexample or embedding
is_planar, certificate = nx.check_planarity(G, counterexample=True)

# Print if the graph is planar
print('Is the graph planar?', is_planar)
# Print the certificate based on the planarity of the graph
if is_planar:
    print('The graph is planar, here is the embedding:')
    print(certificate.get_data())
else:
    print('The graph is not planar, here is the Kuratowski subgraph:')
    print(certificate.edges())
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