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Tabular Data - 504 B - 10 Variables, 9 Observations - UNF:6:BLCGIKb7XSiD2AfIt2LsDg==
Jul 13, 2024Universidad de la Costa
Económicas CUC, es una revista científica internacional arbitrada, que difunde artículos generados por investigadores en el área de las ciencias económicas, escritos en el idioma inglés o español.
Jul 9, 2024 - Modelo estadístico computacional para la clasificación del grado de motivación por culminar estudios de formación profesional en estudiantes de pregrado de la Universidad Cooperativa de Colombia sede Villavicencio año 2023
Nova Martínez, Manuel Arturo; Torres Londoño, Carlos Ignacio; Porras García, Yerson Ferney, 2024, "Data for Analysis of University Dropout at UCC Villavicencio", https://doi.org/10.57924/D4WQJT, Papyrus, V1, UNF:6:m5esbmecEZcU6hm+FxNM5w== [fileUNF]
The files included in this dataset contain information related to variables of the university context, academic, sociodemographic, economic, and psychological aspects of students enrolled at the Universidad Cooperativa de Colombia, Villavicencio campus, in the years 2023 and 2024...
Tabular Data - 116.5 KB - 44 Variables, 280 Observations - UNF:6:EtPKTsCCEUUWXnfUj1AuNQ==
This file contains information on the variables considered in the research of the student dropout phenomenon in the 2310 period, Villlavicencio Campus
Tabular Data - 84.6 KB - 44 Variables, 203 Observations - UNF:6:9mbr0Sgso0J2WTz2Oatpmg==
This file contains information on the variables considered in the research of the student dropout phenomenon in the 2320 period, Villlavicencio Campus
Adobe PDF - 224.5 KB - MD5: cacac6ebb809e53a9d62f0d42f332f5d
Plan de gestión de datos del proyecto de investigación "Modelo estadístico computacional para la clasificación del grado de motivación por culminar estudios de formación profesional en estudiantes de pregrado de la universidad cooperativa de colombia sede Villavicencio año 2023"
Jupyter Notebook - 83.8 KB - MD5: 94b08342cb9eb70da765551e9f18bcd1
The code takes categorical data and converts it into numerical values. The ordinal coding technique is used, which assigns a unique integer to each category. This processed data will be useful for training machine learning algorithms.
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