Data Science & Machine Learning Solutions
Cutting-edge pricing optimization project in collaboration with MIT, targeting profitability improvements of 5-20% through strategic implementation of machine learning algorithms and web application deployment.
Key Features: Web application deployment, time series analysis, forecasting models, optimization algorithms
Developed cutting-edge pricing models using Feedforward Neural Networks (FNN) and LSTM networks, incorporating Time Series Clustering for demand analysis across national clothing markets.
Key Features: LSTM networks, demand forecasting, unsupervised clustering, short-term forecasting
Spearheaded predictive modeling initiative to guide expansion strategy targeting 542 localities by 2030. Built and validated machine learning models using 117-119 locality-level variables from multiple data sources.
Key Features: Feature engineering, external data integration, ranking algorithms, sales prediction models
Led conceptualization and execution of Computer Vision automation system using transfer learning with MobileNet network, integrated into API for bot automation.
Key Features: Transfer learning, image classification, automated workflows, API development
Developed FER model for in-store customer analysis using transfer learning with Xception and Inception architectures to understand customer reactions and emotions.
Key Features: Transfer learning, emotion classification, retail analytics, real-time processing
Led team of 4 in creating visual search demo leveraging Google Cloud Platform with object detection and semantic segmentation capabilities.
Key Features: Object detection, semantic segmentation, cloud repositories, scalable deployment
Led development of sophisticated NLP model for "National Census of Federal Justice Impartation" to pre-classify federal offenses using natural language processing techniques.
Key Features: Legal text analysis, offense classification, feature engineering, automated processing
Implemented innovative unsupervised clustering algorithm for strategic human resource allocation based on time-series patterns in judicial data.
Key Features: Unsupervised learning, hierarchical clustering, pattern recognition, resource allocation
Enhanced queue management for retail stores nationwide using clustering algorithms and queueing theory to optimize customer flow dynamics and create performance KPIs.
Key Features: Store clustering, KPI development, queueing optimization, national implementation
Developed algorithm delivering personalized recommendations using Pagerank principles with multiprocessing for scalable execution and rapid response times.
Key Features: Graph algorithms, parallel processing, personalization engine, big data handling
Developed graphical interface for teaching generalizations of geometric curves (circle, ellipse, parabola, hyperbola) for the Laboratory of Scientific Computation at UNAM.
Duration: JAN 2007 - DEC 2010
Key Features: Interactive visualization, mathematical education, curve generation, academic research tool