Pinned Repositories
Curso-Astroparticulas
Curso de Astropartículas y Rayos Cósmicos
Curso-Introduccion-Machine-Leaning
Introducción a Machine Learning con Python
data_science_course_UTB
Material para el curso de Ciencia de Datos y Análisis de Datos
DESI-SV1-redshift-success-rate
DESI_depth_exposure_time
Galaxy-clustering
This repository have a simple code for measure density distribution for clustering data
InteligenciaAnaliticaDeDatos_MinTIC
Repositorio de consulta para el Diplomado en Inteligencia Analitica De Datos con Python como parte de los cursos de oferta de formación de MinTIC
introductory-journey-data-science
PIP_WEIGHT_TWOPCF
Files needed for the calculation of the two-point correlation function using the PIP weight scheme
sierraporta.github.io
sierraporta's Repositories
sierraporta/Curso-Introduccion-Machine-Leaning
Introducción a Machine Learning con Python
sierraporta/Curso-Astroparticulas
Curso de Astropartículas y Rayos Cósmicos
sierraporta/data_science_course_UTB
Material para el curso de Ciencia de Datos y Análisis de Datos
sierraporta/InteligenciaAnaliticaDeDatos_MinTIC
Repositorio de consulta para el Diplomado en Inteligencia Analitica De Datos con Python como parte de los cursos de oferta de formación de MinTIC
sierraporta/introductory-journey-data-science
sierraporta/VisibilityGraphAnalysisCosmicRays
sierraporta/ModeladoMatematico
Notas, Notebooks y Material para un curso de Modelado Matemático
sierraporta/sensor-calibration-low-cost
sierraporta/PIP_WEIGHT_TWOPCF
Files needed for the calculation of the two-point correlation function using the PIP weight scheme
sierraporta/sierraporta.github.io
sierraporta/CursoUTB_ENO_CANCOA_2024
Material para el curso...
sierraporta/Data_Mining_Excersices
Algunos ejemplos y prácticas en Minería de datos.
sierraporta/Data_Science_Introduction
An compressive introduction to Data Science. Exploration of basis
sierraporta/Defensa_UTB
Código y presentación para defensa de microclase en la UTB
sierraporta/Ejercicio_HorizontalVisibilityGraph
Ejercicio_sencillo para mostrar como funciona Horizontal Visibility Graph
sierraporta/gma
Página del Grupo de Investigación en Gravitación y Matemáticas Aplicadas de la Facultad de Ciencias Básicas de la Universidad Tecnológica de Bolivar (UTB), Cartagena de Indias Colombia
sierraporta/HDI_ODS_DataMining_UMAP
sierraporta/Herramientas-Computacionales-Basicas
Curso de Herramientas Computacionales
sierraporta/HerramientasAnalisisIMMAP
Ejercicios para IMMAP
sierraporta/LibroSeriesDeTiempo
Material complementario para el libro de Series de Tiempo
sierraporta/Magnetic-Field-Colombia
Calculating grid magnetic field in Colombia
sierraporta/MasterClassUTB_Bioinformatica
Material para masterclass UTB Maestría en Bioinformática
sierraporta/nbodykit
Analysis kit for large-scale structure datasets, the massively parallel way
sierraporta/Plaid_Simulated_Data
A little excersice wit Plaid transactions
sierraporta/Resursos_clases
Códigos varios para apoyar mis clases
sierraporta/sierraporta
sierraporta/SunspotCalc
Working with sunsport to get the Sun's rotation
sierraporta/UNICEF-LACRO_datos_DEEP
Análisis de los datos DEEP para UNICEF-LACRO
sierraporta/Unsupervised_Learning_Productivity_Garment_Employees
The apparel industry has become one of the most profitable economic activities in the world. In its beginnings and until the end of the 1970s, European and North American companies engaged in the apparel business produced clothing in these countries. In these production units, all the steps for the manufacture of their clothes are carried out, from the cutting of the fabric to the finishing. In order to carry out the production, many workers were hired, and the company itself was responsible for their wages, social security and working conditions. Regardless of the globalization of the market and the international demand that the industry has created, today the industry relies on human capital, on employees, to ensure effectiveness and efficiency of the products and to achieve the goals of the industry. Due to the dependence of labor on manpower, the production of a garment company depends depends on the productivity of employees working in different departments of the company. When employees do not meet the goals set by the company's management, some of the links in the production chain fail, negatively impacting the quality and efficiency of the company. Analyzing data from a major garment company in Bangladesh, we created a predictive model for employee productivity in terms of various variables involved in the employee labor process. Data mining has been used for data manipulation and cleaning, while Random Forest, Gradient Boosting and Extreme Gradient Boosting has been used for prediction as predictability estimates. The Extreme Gradient Boosting model proves to be the most efficient in predicting employee productivity with a mean absolute error (MAE) of 0.0200, a mean square error (MSE) of 0.0038, a mean absolute percentage error (MAPE) of 3.0764 and a correlation coefficient of 0.94 between original and predicted data. These estimators are much lower than other models previously built in the literature in the field. The model constructed remains an important tool for decision makers to evaluate the actions to be taken by the company in terms of profit maximization when certain variables are known to have a certain performance.
sierraporta/YouTubeVideoCode
Code related to my YouTube vids!