{"id":1868,"date":"2026-08-10T21:07:37","date_gmt":"2026-08-11T00:07:37","guid":{"rendered":"https:\/\/datamining.dc.uba.ar\/datamining\/?p=1868"},"modified":"2026-08-12T13:55:23","modified_gmt":"2026-08-12T16:55:23","slug":"defensa-de-tesis-vanina-beraudo-12-08-2026-13-00-hs","status":"publish","type":"post","link":"https:\/\/datamining.dc.uba.ar\/datamining\/2026\/08\/10\/defensa-de-tesis-vanina-beraudo-12-08-2026-13-00-hs\/","title":{"rendered":"Defensa de Tesis &#8211; Vanina Beraudo \u2013 12\/08\/2026 \u2013 13.00 hs."},"content":{"rendered":"<p>El Mi\u00e9rcoles 12\/08 se realiz\u00f3 la Defensa de Tesis de Vanina Beraudo titulada \u201cDescubriendo patrones en el portal de compras p\u00fablicas COMPR.AR: aplicaci\u00f3n exitosa de t\u00e9cnicas de miner\u00eda de datos\u201d.\u00a0 Cont\u00f3 con la direcci\u00f3n del Mg. Eduardo Poggi. Los jurados fueron el Dr. Ramiro Galvez, el Mg. Guillermo Henri\u00f3n y el Dr. Esteban Feuerstein. La defensa puede verse a trav\u00e9s del siguiente link: <a href=\"https:\/\/youtu.be\/ezl7PfR-foE?t=2\" target=\"_blank\" rel=\"noopener\">https:\/\/www.youtube.com\/live\/ezl7PfR-foE<\/a><\/p>\n<p>Resumen:<\/p>\n<p><span class=\"ytAttributedStringLinkInheritColor\" dir=\"auto\">Este trabajo estudia el sistema de contrataciones p\u00fablicas de la Rep\u00fablica Argentina (COMPR.AR) durante el per\u00edodo 2016\u20132022. El objetivo central consiste en analizar este dominio desde m\u00faltiples perspectivas anal\u00edticas, utilizando el sistema de compras p\u00fablicas como caso de estudio para la aplicaci\u00f3n y evaluaci\u00f3n de diversas metodolog\u00edas de Miner\u00eda de Datos y aprendizaje autom\u00e1tico adquiridas durante la formaci\u00f3n acad\u00e9mica y la pr\u00e1ctica profesional. A partir de la construcci\u00f3n de un dataset integrado compuesto por 69.686 procesos de compra en estados finales que resultados exitosos (adjudicados) o no exitosos (desiertos y dejados sin efecto), 10.076 proveedores que participaron en estos procesos de compra y 103.893 art\u00edculos, se llev\u00f3 a cabo un proceso exhaustivo de preparaci\u00f3n, limpieza y transformaci\u00f3n de datos mediante t\u00e9cnicas de feature engineering. Sobre esta base se desarrollaron los principales ejes metodol\u00f3gicos del trabajo: <\/span><\/p>\n<ul class=\"ytAttributedStringListGroup\" dir=\"ltr\">\n<li><span class=\"ytAttributedStringLinkInheritColor\" dir=\"auto\">Detecci\u00f3n de anomal\u00edas: se aplicaron t\u00e9cnicas de identificaci\u00f3n de outliers multivariados \u2014entre ellas distancia de Mahalanobis, Local Outlier Factor (LOF), Isolation Forest y uCBLOF\u2014 con el objetivo de detectar comportamientos at\u00edpicos en los procesos de compra. <\/span><\/li>\n<\/ul>\n<ul class=\"ytAttributedStringListGroup\" dir=\"ltr\">\n<li><span class=\"ytAttributedStringLinkInheritColor\" dir=\"auto\">Segmentaci\u00f3n de perfiles: se utilizaron algoritmos de clustering (K-means, clustering jer\u00e1rquico, K-modes y K-prototypes) para caracterizar la heterogeneidad de los proveedores del Estado, permitiendo distinguir distintos perfiles, desde grandes actores consolidados hasta proveedores especializados y peque\u00f1as y medianas empresas. <\/span><\/li>\n<\/ul>\n<ul class=\"ytAttributedStringListGroup\" dir=\"ltr\">\n<li><span class=\"ytAttributedStringLinkInheritColor\" dir=\"auto\">An\u00e1lisis de redes: mediante herramientas de Teor\u00eda de Grafos se modelaron las relaciones estructurales entre organismos, proveedores, procesos y art\u00edculos adquiridos, posibilitando la identificaci\u00f3n de comunidades y la aplicaci\u00f3n de t\u00e9cnicas de link prediction para anticipar potenciales v\u00ednculos comerciales futuros. <\/span><\/li>\n<\/ul>\n<ul class=\"ytAttributedStringListGroup\" dir=\"ltr\">\n<li><span class=\"ytAttributedStringLinkInheritColor\" dir=\"auto\">Modelado predictivo: se evaluaron modelos de clasificaci\u00f3n basados en algoritmos de gradient boosting (CatBoost, XGBoost y LightGBM) para estimar el \u00e9xito de las contrataciones p\u00fablicas, evidenciando que la incorporaci\u00f3n de m\u00e9tricas topol\u00f3gicas derivadas del an\u00e1lisis de redes mejora significativamente el desempe\u00f1o predictivo. <\/span><\/li>\n<\/ul>\n<p><span class=\"ytAttributedStringLinkInheritColor\" dir=\"auto\"> Los resultados obtenidos demuestran que la combinaci\u00f3n de t\u00e9cnicas de miner\u00eda de datos, aprendizaje autom\u00e1tico y an\u00e1lisis de grafos permite revelar patrones de comportamiento no visibles mediante enfoques estad\u00edsticos tradicionales, aportando una perspectiva anal\u00edtica integral para el estudio de sistemas complejos de compras p\u00fablicas.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>El Mi\u00e9rcoles 12\/08 se realiz\u00f3 la Defensa de Tesis de [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":534,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1868","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sin-categoria"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Defensa de Tesis - Vanina Beraudo \u2013 12\/08\/2026 \u2013 13.00 hs. - Maestr\u00eda en Data Mining<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datamining.dc.uba.ar\/datamining\/2026\/08\/10\/defensa-de-tesis-vanina-beraudo-12-08-2026-13-00-hs\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" 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