Examinando por Materia "Random Forest"
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Publicación 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 MarcelaPublicación Aplicación de modelos predictivos tradicionales y de machine learning para evaluar la insolvencia en el sector salud de Colombia(Universidad EAFIT, 2025) Girón Ocampo, César Eduardo; Mercado Vargas, Luis Fernando; Cruz Castañeda, Vivian; Álvarez Franco, Pilar BeatrizPublicación Comparación de la eficiencia de portafolios : un análisis entre la teoría de Markowitz y un modelo basado en Random Forest(Universidad EAFIT, 2025) Vargas Galvis, Estefanía; Botero Ramírez, Juan CarlosThis study compares the efficiency of investment portfolio construction using two approaches: the classic Markowitz Theory and a model based on Random Forest. Thus, it can be said that while Markowitz optimizes the portfolio under parametric and linear assumptions, Random Forest leverages the ability to capture nonlinear and dynamic patterns in complex financial data, especially in volatile markets. The research is based on real data from the 30 most liquid stocks in the S&P 500, between 2020 and 2024, evaluating the performance of both models through key metrics such as the Sharpe Ratio, variance, and return predictive capacity in January 2025. The results obtained will demonstrate whether the Random Forest model can match or exceed Markowitz's efficiency in the risk-return relationship, offering an adaptive and robust alternative for portfolio management in current financial environments.Publicación Modelo de predicción de venta en una compañía textil con técnicas de Machine Learning(Universidad EAFIT, 2025) Lezcano Echeverri, Jhon Wilder; Puerta Puerta, Henry DanielThis study explores the implementation of sales forecasting models in a Colombian textile company, combining traditional techniques with Machine Learning-based approaches. Daily sales data from 187 stores between 2021 and 2025 were analyzed. The methodology followed five stages: (1) exploratory analysis, (2) feature engineering, (3) model implementation, (4) model optimization and fine-tunning, and (5) comparative validation. The models implemented were: Prophet, XGBoost, Random Forest, and regularized Linear Regression. Prophet achieved the best overall performance for units sold (R² = 0.7121), standing out for its ability to capture complex seasonal patterns and adapt to store-level variability. XGBoost demonstrated high accuracy in non-linear scenarios, Random Forest showed robustness to noise, and Linear Regression provided greater interpretability. Feature engineering resulted in 83 variables, including temporal components, trends, volatility, and special effects. A cross-sectional analysis revealed common patterns such as peak underestimation, higher error in smaller stores and weekends, and lower accuracy in predicting monetary values compared to units. The findings confirm that sales forecasting using Machine Learning offers substantial improvements over traditional methods, enhancing operational efficiency, inventory optimization, and financial planning. Prophet is recommended as the primary model, along with the establishment of monthly recalibration cycles to maintain accuracy.Publicación Optimización de portafolios de inversión en el contexto de Big Data : integrando aprendizaje automático y técnicas de descomposición espectral(Universidad EAFIT, 2025) Hernández Slait, Jhon Jairo; Almonacid Hurtado, Paula MaríaPublicación Predicción de deserción de clientes en el mercado de seguros de transporte de carga en Estados Unidos mediante técnicas de Machine Learning : un caso de estudio(Universidad EAFIT, 2024) Uribe Durango, Víctor Ricardo; Almonacid Hurtado, Paula MaríaPublicación Predicción del precio del oro en el mercado spot y el tipo de cambio USD–COP para la optimización del rango de cobertura en derivados de las compañías exportadoras del sector minero(Universidad EAFIT, 2024) Gallego Panesso, Cristian Alexander; Almonacid Hurtado, Paula MaríaThis study addresses the implementation of various time series regression and machine learning models, such as: ARIMA, ARIMAX, SARIMA and Random Forests with the objective of accurately predicting the price of gold in the spot market and the USD–COP exchange rate. Precision in these predictions is crucial for export companies in the mining sector, as it allows them to establish optimal coverage ranges in the use of financial derivatives. Throughout the study, different machine learning algorithms were evaluated and compared, selecting those that provided the most accurate and consistent results. The findings offer a valuable tool for financial risk management and strategic decision making in the context of gold price volatility and exchange rate fluctuations. At the end of the study, it is indicated that the ARIMAX Rolling Forecast model applied in a parameterization (1,1,0) was the most accurate and consistent model over time for the price forecasts of both assets.Publicación Predicción dinámica del valor del flete de mercado para vehículos 3s3 del puerto de Buenaventura a Bogotá : un modelo integrado con variables exógenas económicas y del sector logístico(Universidad EAFIT, 2025) Vélez Medina, Camilo Alejandro; García Vargas, Johan FelipeLogistics, especially road transportation as a fundamental part of the supply chain, directly impacts the costs and availability of products in cities. This project develops a predictive model to estimate the market value of freight transportation for 3S3-type vehicles from the port of Buenaventura, Colombia, to Bogotá, Colombia. The variable of interest, referred to as FP_mean, corresponds to the daily average freight production cost. The innovation of the model lies in its ability to integrate critical exogenous variables, such as Brent crude oil prices, the exchange rate of the dollar, sector-specific factors collected in the SICE TAC (fuel, tolls, tires, lubricants, filters, maintenance, personnel), RNDC (National Road Cargo Dispatch Registry), and the arrival of ships at the port with their respective types of cargo. Multiple advanced modeling approaches were evaluated, including ARIMA, SARIMA, Random Forest, and LSTM, with the Random Forest model incorporating exogenous variables (random_forest_exogen) standing out for its superior performance, achieving an RMSE of 211,395.42 and a MAPE of 3.20%, making it the most accurate for estimating FP_mean. Additionally, the LSTM and SARIMA models also demonstrated competitive results, striking a balance between accuracy and stability across various scenarios. These findings highlight the importance of combining advanced machine learning techniques with domain expertise in logistics.Publicación The random forest machine learning model performs better in predicting drug repositioning using networks : systematic review and meta-analysis(Universidad EAFIT, 2024) García Marín, Darlyn Juranny; García Zea, Jerson Alexander; García Zea, Jerson AlexanderPublicación Transferencias monetarias y acceso a crédito : el caso del programa Ingreso Solidario en Colombia(Universidad EAFIT, 2024) Betancur Téllez, Sebastián; Álvarez Franco, Pilar Beatriz; Cruz Castañeda, Vivian