Nama : Neo Saffana Farhalik NIM : 4112322008 Prodi : Statistika Terapan dan Komputasi

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.

Este practico corresponde al modulo 4 de la Diplomatura en Bioestadística FCA-UNCA

As an exercise using quarto-closeread

Recordatorio 1 para incorporar gráficos interactivos a proyectos.

Analisis Data SUSENAS Maret 2019 (KOR) -- Soal Nomor 6 UAS Metode Multivariat

The cost of medical care significantly impacts both healthcare providers and patients. This project aims to explore the predictive utility of patient features captured by an insurance firm to estimate the annual cost of medical care. The dataset used is the publicly available Medical Cost Personal dataset from Kaggle, containing information on 1338 beneficiaries and 7 variables, including the target variable: medical costs billed by health insurance in a year.In this study, we aim to build upon previous work by applying advanced techniques to improve the accuracy of predictions and enhance model interpretability.