﻿ Solving the Traveling Salesman Problem with Code Interpreter

# Solving the Traveling Salesman Problem with Code Interpreter

In today's video, we are going to explore the Traveling Salesman Problem (TSP) using Code Interpreter and create some visually appealing solutions. But before we delve into the details, let's take a moment to understand what the TSP is.

The Traveling Salesman Problem, as defined by Wikipedia, involves finding the shortest possible route that visits each city from a given list exactly once and returns to the origin city. In simpler terms, it's about determining the most efficient way for a salesman to visit multiple cities, optimizing both time and distance.

To illustrate this problem, let's consider a visual representation of a solution for a TSP with 12 cities. For clarity, the solution is displayed as a route connecting the cities, as shown below:

• Cairo
• Rome
• Paris
• London
• Oslo
• New York City

As you can see, finding the shortest route among multiple cities can be quite a complex task. To solve this problem, we will utilize the power of Code Interpreter and explore different algorithms to obtain the optimal solution.

To kickstart our exploration, we need to upload the data which includes a list of cities and the distances between each pair of cities. For example, the distance from Cairo to Rome is 2187 kilometers. We have a total of 66 city pairs, forming the basis of our analysis.

Let's head over to the Code Interpreter and examine the system prompt we have set up for this task. The system prompt is designed for a data scientist Python expert, possessing skills in Python proficiency, data mining, predictive modeling, simulation running, critical thinking, systematic approach to data analysis, and coding challenges.

In this context, we will be implementing the Nearest Neighbor algorithm to find the optimal solution for the TSP. This algorithm selects the nearest city at each step to form a tour. We will also try running the pairwise exchange algorithm to compare the results obtained by the two approaches.

Our task is to provide instructions to the system prompt and guide it through the TSP-solving process. Let's break it down into a step-by-step approach to ensure a systematic solution:

1. Upload the data containing the cities and their respective distances.
2. Instruct the system prompt to solve the TSP using the Nearest Neighbor algorithm.
3. Retrieve the output, which will provide the shortest route obtained through the Nearest Neighbor approach.
4. Run the pairwise exchange algorithm to double-check the results obtained.
5. Confirm if the two approaches yield the same result, which would validate the accuracy of our solution.

Upon submitting our instructions, the Code Interpreter will begin executing the Nearest Neighbor algorithm. This approach, being less computationally complex than brute force, is a suitable choice for this scenario.

Once the execution is complete, we obtain the optimal solution, which in our case includes the cities Cairo, Rome, Paris, London, Oslo, and New York City. The total distance for this route is found to be 54693 kilometers, representing the shortest possible route.

To further enhance our understanding and visualization of the solution, we can explore different visualizations using Code Interpreter. Let's create five interesting and visually appealing representations of the TSP solution:

1. Distance Matrix Heatmap: This visualization displays the distances between each city pair as a heatmap, allowing us to identify the relative distances between cities more effectively.
2. Tour Distance Plot: By plotting the distances traveled for each city pair, we can compare the results obtained from the Nearest Neighbor and Pairwise Exchange algorithms for further analysis.
3. City Connection Network: Representing the cities as nodes and the connections between them as edges, this visualization provides a network-like representation of the TSP solution.
4. Distance Histogram: By counting the number of distances contributed by each city, we can gain insights into their impact on the total distance traveled.
5. Distance Bar Plot: This plot visualizes the distances contributed by each city as horizontal bars, enabling us to identify the cities with the most significant contributions.

With these visualizations, we can analyze the TSP solution from multiple perspectives, gaining a deeper understanding of the underlying dynamics.

But why stop here? We can take our visualization a step further by plotting the TSP solution on a map using Python code. By uploading the city coordinates and incorporating animations, we can create an engaging visual representation of the shortest route. This interactive map allows zooming, panning, and even includes play and hover functionalities, showcasing the cities along the route.

Impressively, all of these visualizations and analyses were performed seamlessly within Code Interpreter, demonstrating its versatility as a powerful data analysis tool.

In conclusion, our exploration of the Traveling Salesman Problem using Code Interpreter has provided us with valuable insights into solving complex optimization problems. We successfully solved the TSP using the Nearest Neighbor algorithm, with the Pairwise Exchange algorithm yielding the same result.

Additionally, we leveraged various visualizations to analyze the TSP solution from different angles, including distance metrics, network representations, and interactive maps.

The Code Interpreter proved to be an excellent platform for executing these tasks, offering data scientists and Python experts a wide array of tools and functionalities to tackle complex problems, visualize results, and uncover hidden patterns.

We hope this journey has inspired you and given you a glimpse into the practical applications and possibilities offered by Code Interpreter. Stay tuned for more exciting content and practical use cases in your data science and programming endeavors. Have a great day, and we will see you in the next one!

[Article Length: 1043 words]

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