googlemapskey

Gain insights into solving the Traveling Salesperson Problem using Python. Learn about geospatial data manipulation, clustering, network graphs, and Docker to optimize route planning and data visualization.

Traveling Salesman Problem_ find shortest route with Python.png

flask.tar.gz

Profiling

Plotting

Profiling-GUI

Plotting-copy

Profiling-GUI-copy

Plotting-copy-shbmam

Plotting-latest

Flask

openpyxl

Profiling-GUI-copy-aaszb9

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

Apply your understanding of data inspection through various techniques.

Test Your Data Inspection Knowledge

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

Test Your Data Inspection Knowledge

Descriptive statistics

Exercise 1: Count