David Sierra Porta's repositories
Curso-Introduccion-Machine-Leaning
Introducción a Machine Learning con Python
Curso-Astroparticulas
Curso de Astropartículas y Rayos Cósmicos
data_science_course_UTB
Material para el curso de Ciencia de Datos y Análisis de Datos
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
ModeladoMatematico
Notas, Notebooks y Material para un curso de Modelado Matemático
PIP_WEIGHT_TWOPCF
Files needed for the calculation of the two-point correlation function using the PIP weight scheme
Data_Mining_Excersices
Algunos ejemplos y prácticas en Minería de datos.
Data_Science_Introduction
An compressive introduction to Data Science. Exploration of basis
Defensa_UTB
Código y presentación para defensa de microclase en la UTB
Ejercicio_HorizontalVisibilityGraph
Ejercicio_sencillo para mostrar como funciona Horizontal Visibility Graph
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
Herramientas-Computacionales-Basicas
Curso de Herramientas Computacionales
HerramientasAnalisisIMMAP
Ejercicios para IMMAP
LibroSeriesDeTiempo
Material complementario para el libro de Series de Tiempo
Magnetic-Field-Colombia
Calculating grid magnetic field in Colombia
MasterClassUTB_Bioinformatica
Material para masterclass UTB Maestría en Bioinformática
nbodykit
Analysis kit for large-scale structure datasets, the massively parallel way
Plaid_Simulated_Data
A little excersice wit Plaid transactions
Resursos_clases
Códigos varios para apoyar mis clases
SunspotCalc
Working with sunsport to get the Sun's rotation
UNICEF-LACRO_datos_DEEP
Análisis de los datos DEEP para UNICEF-LACRO
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.
YouTubeVideoCode
Code related to my YouTube vids!