This document presents a Python-based solution to the Traveling Salesperson Problem (TSP) using the [Dataset Name, e.g., Berlin52] dataset. It explores and compares different algorithms, including [Algorithm 1, e.g., Nearest Neighbor], [Algorithm 2, e.g., Simulated Annealing], and implementations from the [Library Name, e.g., NetworkX] library. The analysis includes visualizations of the generated tours, a comparison of tour lengths, and a visual representation of the convergence of the Simulated Annealing algorithm. This work demonstrates a practical application of [mention key concepts, e.g., heuristic algorithms, combinatorial optimization, graph theory] in Python.