Clustering K-Means, K-Median, sama DBSCAN

Outline of Proposed Presentation for Rome R User Group & IOM Staff Presentation Title: From Field to Forecast: Building a Reproducible Analytical Pipeline for IOM DTM Data in R This 45-minute presentation is structured to balance high-level humanitarian context with technical R implementation. It is designed to keep both “Domain Experts” and “R Developers” engaged by alternating between Why (the mission) and How (the code).

This paper explores different ways to generate random numbers and estimate integrals using computer simulation in R. We start by sampling from the Cauchy distribution and then use it as a tool to generate normally distributed numbers. Next, we work with a special version of the Gamma distribution that is restricted to values above 4, testing two different sampling strategies to see which one wastes fewer attempts. Finally, we calculate a specific integral using five different simulation approaches and compare how accurate and efficient each one is.

An end-to-end unsupervised learning project using K-Means Clustering and PCA to identify high-risk consumer personas. Features a "Lift Analysis" using the Apriori algorithm to uncover behavioral signatures that lead to service friction and churn.

Accessing The Displacement Tracking Matrix (DTM) API using R

Description of the International Organization of Migrations's Displacement Tracking Matrix

TALLER ESTADISTICA II