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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

Enhance route planning efficiency by mastering Google OR-Tools for optimization.

Operations Research Tools

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

Operations Research Tools

The `create_data_model` function

Use Simulated Annealing for the Traveling Salesman Problem

Operations Research Tools

The create_data_model function

The `create_data_model` function