- π Hi, Iβm Brenda
- π Passionate about solving real-world problems using data-driven approaches. Experienced in machine learning, optimization, and data analysis with a strong background in financial, logistics, and transportation applications. Always open to learning new techniques and collaborating on innovative projects.
- π« How to reach me [email protected]
- Canada
-
17:59
(UTC -04:00) - https://www.brendacobena.com/
- @BrendaCobenaT
- in/brenda-cobena
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Solving-Discrete-Optimization-Problems-with-Greedy-GRASP-and-Local-Search
Solving-Discrete-Optimization-Problems-with-Greedy-GRASP-and-Local-Search PublicDiscrete Optimization
C 1
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Advanced-Reinforcement-Learning-for-Financial-Option-Pricing
Advanced-Reinforcement-Learning-for-Financial-Option-Pricing PublicImplementation of Q-Learning, Double Q-Learning, and LSPI for pricing American options under the Black-Scholes model
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From-Scratch-Logistic-Regression-and-Decision-Trees-for-Churn-Prediction
From-Scratch-Logistic-Regression-and-Decision-Trees-for-Churn-Prediction PublicChurn prediction for banking customers using logistic regression and decision trees, implemented from scratch in R.
R 1
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Predicting-Travel-Mode-Choices-with-Logistic-Regression-and-SVM
Predicting-Travel-Mode-Choices-with-Logistic-Regression-and-SVM PublicMachine learning models for travel mode prediction, using logistic regression and SVM on real-world transportation data.
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Uncapacitated-Facility-Location-Problem
Uncapacitated-Facility-Location-Problem PublicSolves the Uncapacitated Facility Location Problem using CPLEX, heuristic methods, and Lagrangian relaxation (C language implementation).
C
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mnist-cnn-digit-classifier
mnist-cnn-digit-classifier PublicA clean PyTorch implementation of a Convolutional Neural Network (CNN) for handwritten digit recognition using the MNIST dataset, with educational-oriented version.
Jupyter Notebook
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