This report analyzes the New York City Jobs dataset using Principal Component Analysis (PCA) and K-means clustering. The analysis focuses on numeric features such as the number of positions and salary ranges to identify patterns in job postings. First, the data is cleaned and numeric columns are standardized. PCA is then applied to reduce dimensionality, summarizing the main variation in the dataset into two principal components. The optimal number of clusters is determined using the Elbow and Silhouette methods, followed by K-means clustering on the PCA-reduced data. The resulting clusters are visualized and summarized in a table, providing insights into the distribution of job positions and salary ranges. This workflow allows readers to quickly understand patterns in the NYC Jobs dataset and can serve as a foundation for further analysis, such as investigating salary trends by agency or job type.

Cumple_María_2026

Análisis de consistencia interna mediante Alfa de Cronbach de un instrumento de 10 ítems Likert aplicado a 155 estudiantes de Ingeniería de Sistemas. Incluye correlaciones item-total, matriz de correlaciones y visualización de distribuciones. α = 0.89

This project aims to examine American households and chosen demographics in terms of their financial behaviours and responsibility. The analysis has been conducted using the association rule mining technique (Apriori algorithm).

Este estudio evalúa la eficiencia del sistema soya en Bolivia mediante el Análisis Envolvente de Datos (DEA), utilizando información oficial del Reporte Sectorial N.º 2 – Junio 2024. Los resultados muestran que el crecimiento del sector no siempre implica mejoras en eficiencia técnica. A pesar de la limitada disponibilidad de datos, el análisis evidencia la utilidad del enfoque DEA para identificar brechas y orientar políticas sectoriales.