Examinando por Materia "Aprendizaje de máquinas"
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Publicación Análisis predictivo de la deserción laboral en BPO : aplicaciones avanzadas de Machine Learning(Universidad EAFIT, 2023) Castelblanco Benítez, Julián; Almonacid Hurtado, Paula MariaPublicación Análisis y predicción de recontactos en un contact center(Universidad EAFIT, 2021) Velásquez Gaviria, Diana Catalina; Quintero Montoya, Olga LucíaThe main purpose of this work is to solve in a methodical and formal way, making use of ma- chine learning models, a real problem of the productive sector that allows in addition to adding value for decision making, to provide a methodology and a compact, simple and reliable model that can be deployed and put into production in the technological platform that supports the call handling of a contact center so that benefits can be generated in the provision of the service for different sectors, generating efficiencies in the use of the channel and maximizing the customer experience in the attention of their requirements. To achieve this purpose, an airline dataset was taken containing the detail of all historical calls made by customers to a contact center during a period of 7 months (February to August 2019) and information associated with the performance of the agents handling those calls in order to predict whether users will generate at least one recontact to the contact center for the attention of their requirements before three days with respect to their initial contact. The methodology used was focused on making an ap- propriate selection of features and choosing a machine learning model that generates optimal results and enables an easy implementation allowing to identify in real time those customers with high probabilities of recontacting so that a strategy can be developed with them to impro- ve their experience. It was found that with 7 variables associated with the historical behavior of customers in the use of the channel such as frequency of calls (amount), average duration, number of agents who have served the customer (agents), time elapsed between first and last call (validity), number of days per month in which calls are made (AverageDaysInMonth), ave- rage number of calls per day (Average-Day) and time elapsed since the customer’s last call to the cut-off date of the analysis (Recency), it is possible to predict with a simple model and with very good results (AUC=88.9 %) whether a customer will call the contact center again.Publicación Aproximación para personalización de interfaces basado en datos históricos en un caso aplicado de la banca(Universidad EAFIT, 2025) Vergara Marín, Natalia Andrea; Arbeláez, Juan CarlosThis project proposes an approach to generate personalization proposals through a user grouping model based on pattern identification in transaction history, with the aim of improving each user's experience by allowing them to view options that suit their particular needs. The project has two stages: the development of the user grouping model using machine learning (ML) algorithms through the CRISP-DM methodology, achieving a result of six user clusters with a silhouette coefficient of 64%, which represents adequate cohesion and distinguishable patterns; and secondly, the development of personalized proposals using the design thinking methodology. This was the result of a case study applied to a digital channel of a financial institution in Colombia, where, based on customer validations, a 90% favorability rating was obtained in terms of the perceived value of the proposal made for the specific cluster of each group, with an average of 30% more compared to the control variants.Publicación GRILLA: Grouping Recall Least Lazy Algorithm. Modelo matemático para cobranza selectiva usando técnicas de aprendizaje automático(Universidad EAFIT, 2021) Moreno Zapata, Juan Sevastian; Quintero Montoya, Olga Lucía