<｜begin▁of▁sentence｜>### Instruction:
Imagine that you're working on a high-level campaign strategy to boost the visibility of our latest product line across several key locations. You have a set of prominent cities mapped out: 'A', 'B', 'C', and 'D', each offering different promotional opportunities with varying levels of investment required. The costs for setting up events and advertisements in these cities are represented through a connection network where each connection has a cost associated with it, such as [('A', 'B', $3000), ('A', 'C', $17000), and so forth.

Given this network, the objective is to determine the most efficient route to launch our promotional tour, starting from city 'D' and eventually returning there after covering all other cities. The efficiency here relates to minimizing the total cost of traveling and setting up our events.

Using NetworkX, we can explore a method that applies a greedy approach to approximate the most cost-effective circuit for our promotional campaign. Could you please craft a plan using this tool to trace out this tour and calculate the total cost we would incur for this circuit, assuming we start in city 'D'? This would be an essential piece of information for our budgeting and itinerary schedules.
### Response:
import networkx as nx
from networkx.algorithms import approximation as approx

# Create a complete weighted undirected graph
G = nx.Graph()
G.add_weighted_edges_from([
    ('A', 'B', 3), ('A', 'C', 17), ('A', 'D', 14),
    ('B', 'A', 3), ('B', 'C', 12), ('B', 'D', 16),
    ('C', 'A', 13), ('C', 'B', 12), ('C', 'D', 4),
    ('D', 'A', 14), ('D', 'B', 15), ('D', 'C', 2)
])

# Use the greedy algorithm to approximate the TSP solution
# Starting at node 'D'
cycle = approx.greedy_tsp(G, source='D')

# Calculate the total cost of the cycle
cost = sum(G[n][nbr]['weight'] for n, nbr in nx.utils.pairwise(cycle))

# Output the cycle and its cost
print('Cycle:', cycle)
print('Cost:', cost)
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