Evaluación de la oferta inmobiliaria urbana - Jessica María Piedrahita

análisis del mercado para apoyar la toma de decisiones de compra, venta y valoración, identificando: - las relaciones entre las variables - Segmentos - Asociaciones entre variables categóricas

Este informe presenta un diagnóstico robusto del mercado inmobiliario urbano, integrando técnicas de aprendizaje no supervisado para transformar datos masivos en inteligencia de negocio. Mediante el Análisis de Componentes Principales, identificamos los motores de valor de las propiedades; a través de las Distancias Euclidianas y el Clustering, segmentamos la oferta en nichos estratégicos; y con el Análisis de Correspondencia, visualizamos la especialización geográfica de la ciudad. Este enfoque permite a la inmobiliaria optimizar sus inversiones, basando sus decisiones en patrones matemáticos de comportamiento de mercado en lugar de intuiciones.

Basic work with ggplot2, functions, and loops.

A university wants to know if there is a difference in weekly study hours between undergraduate students who work part-time jobs and those who do not work. Students report the number of hours they studied during the previous week. The goal of the study is to determine whether weekly study time differs between working and non-working students. Conduct the appropriate statistical test to evaluate whether the two groups differ in study hours.

El mercado inmobiliario urbano es complejo y depende de múltiples variables como área, precio, ubicación y características estructurales. El objetivo es identificar patrones y segmentaciones estratégicas que permitan optimizar decisiones de compra y venta.

Análisis holístico para identificar patrones, relaciones y segmentaciones que optimicen la toma de decisiones en compra, venta y valoración de viviendas, para una empresa del sector inmobiliario.

For this study, I examined whether student type (domestic or international) is associated with pet ownership (yes or no). Because both variables are categorical, I conducted a Chi-Square Test of Independence to determine whether a relationship exists between them. The null hypothesis stated that there is no association between student type and pet ownership, while the alternative hypothesis stated that an association exists. The results of my analysis determined whether pet ownership differs based on student status. If the results were statistically significant, I rejected the null hypothesis and concluded that a relationship exists; if not, I concluded that the variables are independent. This analysis provides insight into student needs and can help the university better plan for pet-friendly housing and related services.