Examinando por Materia "Machine Learning"
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Ítem Análisis de los resultados de la aplicación del instrumento para la evaluación docente de la universidad EAFIT(Universidad EAFIT, 2024) Fernández Carmona, Laura Catalina; Guarín Zapata, Nicolás; Mola Ávila, José AntonioÍtem Análisis predictivo del riesgo de default en microcrédito un enfoque de machine learning en sector financiero(Universidad EAFIT, 2024) Mendoza Trillos, Laura; Suárez Sierra, Biviana MarcelaÍtem Análisis y predicción de ventas de motos haciendo uso de la metodología “Customer Value Map” y técnicas de Machine Learning(Universidad EAFIT, 2024) Díaz Cordero, Sandra Marcela; Martínez Vargas, Juan David; Vallejo Correa, Paola AndreaÍtem Analítica de datos aplicada a la cobranza de cartera(Universidad EAFIT, 2019) Montoya Yepes, Juan David; Parra Giraldo, Diana CarolinaIn order to improve the portfolio collection work performed daily at Cobroactivo LLC, it was decided to use the data stored in this company to optimize its processes. To do this, it was necessary to implement data analytics models, creating a Big Data environment that would allow efficient access to information through a Data Warehouse, as well as some tools to make an exploratory analysis of the existing data and evaluate the weak pointsthat must be improved when managing users. Warehouse, as well as some tools to make an exploratory analysis of the existing data and evaluate the weak points that must be improved when managing users. Likewise, three Machine Learning models were developed in charge of debugging the debtors, predicting the payment probabilities and optimally recommending which advisor should be assigned to each debtor and which is the appropriate contact channel for him / her. Finally, two web applications were developed. The first allows the monitoring of the company's internal processes by automating repetitive processes and decreasing their execution time from weeks o seconds; the second allows the monitoring of collection work by customers and banks, thereby offering added value.Ítem Aprendizaje reforzado profundo para la administración de portafolios de renta fija(Universidad EAFIT, 2023) Mejía Estrada, David; Almonacid Hurtado, Paula MaríaThis paper applies deep reinforced learning techniques to the management of fixed income investment portfolios, specifically sovereign securities issued by the Colombian government. The period of analysis covers seven years, from January 2015 to December 2022. We find that it is possible to generate profitability and achieve efficient risk management because of the trading strategies that deep reinforced learning models foresee more convenient given certain market conditions and of each of the securities, such as their implied risk in metrics like DV01, Duration and Convexity. Finally, this study contributes to the field of machine learning and artificial intelligence applications on investment portfolio management, with a relatively new focus on the fixed income market in general, consolidating itself as one of the first works to apply reinforcement learning techniques to the Colombian public debt market.Ítem Automatic Electrical Meter Forecasting : a Benchmarking Between Quantum Machine Learning and Classical Machine learning(Universidad EAFIT, 2024) Montes Castro, Jonathan Javier; Lalinde Pulido, Juan Guillermo; Sosa-Sierra, DanielThis work benchmarks Quantum Long Short-Term Memory (QLSTM) against classical LSTM networks using electrical meter data (KWh) from EPM, a public utility company, clients. The results show that QLSTM models learn in half the epochs compared to LSTM, as measured by the MSE cost function, while maintaining strong performance with respect to bias (Mean Absolute Percentage Error, MAPE) and variance (R^2) metrics. QLSTM leverages variational quantum circuits (VQC) to replace traditional LSTM cell gates, demonstrating the potential of quantum-hybrid algorithms in forecasting tasks. This study highlights the efficiency and accuracy advantages of quantum machine learning applied to real-world data from EPM’s electrical metering services.Ítem Contrato de prestación de servicios jurídicos por parte de la Inteligencia Artificial en Colombia : viabilidad, caracterización y particularidades(Universidad EAFIT, 2024) Soto Mejía, José Juan; Sánchez Daniels, Catalina del PilarÍtem Deep Learning como alternativa en la predicción del precio de las acciones del mercado de valores colombiano(Universidad EAFIT, 2021) Uribe Ramírez, Sebastián; Almonacid Hurtado, Paula MaríaÍtem Desarrollo de librería para manejo de redes neuronales en Java para tecnofactor(Universidad EAFIT, 2019) Arboleda Echeverry, Juan Camilo; Rincón Bermúdez, Rafael DavidThe aim of the present project is to design and develop a working library that enables the creation and adaptation of Neural Networks, defined in a way that is simple to use by Java developers. The developed library will be used to design and obtain a neural network capable of recognizing handwritten digits, from the MNIST database.Ítem Diseño y evaluación de un score de riesgo crediticio. Evidencia para una empresa comercializadora en Colombia(Universidad EAFIT, 2024) Matiz Ruiz , John Jairo; Cruz Castañeda, Vivian; Álvarez, PilarÍtem Electric Vehicle monitoring system : analysis of driving patterns and their influence on battery degradation(Universidad EAFIT, 2022) Echavarría Correa, Santiago; Mejía Gutiérrez, Ricardo; Montoya, Jose AlejandroÍtem Equidad algorítmica : un estudio aplicado al mercado hipotecario de los Estados Unidos(Universidad EAFIT, 2024) Montoya Mesa, Ana María; Cruz Castañeda, Vivian; Álvarez Franco, PilarÍtem Estrategias de trading en acciones de BVC basadas en Machine Learning. ¿Precisión implica desempeño?(Universidad EAFIT, 2022) Cerro Espinal, Carlos Alberto; Agudelo Rueda, Diego AlonsoÍtem Estudio de la relación entre los valores sociales y la aceptación de sobornos como conducta corrupta : un estudio con modelos SEM y datos de la encuesta mundial de valores(Universidad EAFIT, 2024) Gómez Convers, Giovanny Hernando; Castrillón-Orrego, Sergio A.; Almonacid Hurtado, Paula MaríaIn a global context of rapid social change, investigating the relationship between social values and corruption has become increasingly urgent and significant. Which behaviors are desirable? Which do we manifest in daily life? The World Values Survey (WVS) serves as a crucial data source for understanding social values in various contexts. However, how these values influence the acceptance of bribery, and thus corruption, has not been sufficiently explored. This study examines the underlying patterns in response clusters and systematically analyzes them using the holistic possibilities offered by the institutionalism theoretical framework. The objective is to identify the most significant causalities and influences in the relationship between social values and corruption. Through robust data analysis, imputation techniques, dimensionality reduction, clustering analysis, and SEM modeling, we identify the main factors impacting the acceptance of bribery. The results demonstrate that the three pillars of institutionalism provide a valuable approach to understanding corruption by simultaneously considering key variables and components. When internalized, social values facilitate the acceptance of bribery in certain contexts, highlighting the influence of the cognitive dimension. Although legal frameworks can enhance transparency, cultural environment and customs have a more determining influence on the acceptance of corrupt practices. These findings underscore the need to foster a strong ethical culture and implement educational programs that promote integrity and transparency to effectively mitigate corruption.Ítem Evaluación del efecto de incluir la predicción de rendimientos mediante la técnica de Support Vector Machines en la eficiencia del modelo de media-varianza de Markowitz(Universidad EAFIT, 2024) Aristizábal Nieto, Eliana Jiset; García Agudelo, Estefanía; Botero Ramírez, Juan CarlosPortfolio investment optimization aims to maximize expected returns given certain levels of risk. This process requires dealing with different variables in a nonlinear, noisy system due to market complexity. This is understood as a system that is affected by different external conditions that may be uncontrollable, where volatility influenced by unpredictable factors is present. In this study, an analysis of the results obtained by integrating machine learning techniques, specifically the set of algorithms called Support Vector Machines (SVM), into classical portfolio construction models is conducted. These algorithms allow for the analysis of large amounts of data and the estimation of asset return time series, resulting in a hybrid optimization model. Historical data from the stock markets of the United States and Colombia are used for numerical experiments; one set of data is used for model training (Training Set) and another for testing (Testing Set). Finally, the efficiency of the model is evaluated comparatively with the mean-variance portfolio selection theory proposed by Markowitz.Ítem Flujo de trabajo basado en Procesamiento de Lenguaje Natural (PLN) para la extracción de insights del contenido generado por usuarios (CGU). Caso de estudio aplicado a una fintech(Universidad EAFIT, 2024) Barrera Ravelo, Angie Karina; Montoya Múnera, Edwin NelsonÍtem Hacia un modelo predictivo que apoye el logro de KPI comerciales más asertivos : caso Empresa Comercializadora de Madera(Universidad EAFIT, 2023) Tavera Rodríguez, Jhon Walter; Tabares Betancur, Marta SilviaÍtem Hurto a personas en la ciudad de Medellín : análisis predictivo de la cantidad de casos en diferentes zonas de la ciudad a partir de modelos de machine learning implementando técnicas de MLOps(Universidad EAFIT, 2023) Arboleda Colorado, Jeferson Stiven; Martínez Vargas, Juan DavidRobbery of individuals in Medellín is an issue demanding immediate attention. This prompted the study of the phenomenon within an analytics project, spanning data collection, database construction, modeling, and production deployment. It's worth noting that MLOps methodology was employed utilizing AWS services. Visual tools related to the phenomenon were integrated, facilitating analysis.Ítem Implementación de un modelo de scoring de crédito para Mexichem Colombia SAS(Universidad EAFIT, 2024) Vargas Izquierdo, Daniel Camilo; Rojas Ormaza, Brayan RicardoThis project sought to implement a credit scoring model for Mexichem Colombia SAS using machine learning techniques to predict the probability of default in companies. Four algorithms were compared: decision trees, random forest, gradient boosting and neural networks, each with unique characteristics in terms of accuracy and handling of complex data. The research included the selection and evaluation of relevant variables using the Gini index and recursive elimination techniques to avoid overfitting. The results helped to identify the most effective model to predict credit risks, optimizing financial decision-making.Ítem Machine learning model based on LSTM networks optimized with metaheuristic algorithms to predict cardholder churn(Universidad EAFIT, 2024) Correa Jaramillo, Diana Marcela; Aguilar Castro, José Lisandro
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