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Learn about solving the Traveling Salesperson Problem using Python. Explore geospatial data, clustering, network graphs, and Docker to optimize routes and create dynamic, interactive visualizations.

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Solving complex computational problems is a resource-demanding task. The traveling salesperson problem (TSP) is one such problem, which is an NP-hard problem. In the era of data science, data-centric approaches have evolved to be a good choice to approximate the solutions. 

In this course, you’ll dive into the fascinating realm of geospatial data manipulation, distance calculation, clustering, network graphs, and Docker containerization, all tied together to optimize the challenging TSP. You’ll first grasp the intricacies of manipulating geospatial data and plotting it. Next, you’ll delve into clustering sales data, enhancing your ability to make data-driven decisions and visualize the data mining results in interactive dashboards.

By the end of this course, you’ll be skilled in tackling TSP effectively and creating geospatial data visualizations. You’ll also be well-equipped to scale route optimization, data analysis, and cloud deployment.

Solving the Traveling Salesperson Problem in Python

**Flask** is a lightweight web application framework written in Python. It offers features such as routing, request handling, and templating, making it a versatile and customizable framework. It’s designed to be simple, flexible, and easy to learn, making it a popular choice for building small- to medium-sized web applications.

Learn how to interactively plot the shortest route for the traveling salesperson with Dash and Flask.

What the Traveling Salesperson Problem Is About

Preprocessing of Traveling Salesperson Data

Solving the Traveling Salesperson Problem

Traveling Salesperson Data Mining

Building the Traveling Salesperson Dashboard

Scalability

Conclusion

Appendix

Use Simulated Annealing for the Traveling Salesman Problem

Dashboard: Shortest Route

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