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🔍 About me:

Bachelor's degree in Medical Physics ⚛️ at UFG (Federal University of Goiás) and currently studying for a master's degree in Electrical and Computer Engineering at UFG, with a project involving prediction of patient no-shows in procedures at the Clínica Radiológica de Anápolis.

I am an active member of the Artificial Intelligence & Medical Image Processing (AIMIP) research group at UFG, whose purpose is to apply Machine Learning 🤖 methods or Data Science 📈 in the context of healthcare. As a member of the group, I was directly or indirectly involved in several scientific works.

I am a Data Science and Machine Learning enthusiast, with a lot of experience in Python, especially with regard to the pandas, scikit-learn, pytorch, numpy, scipy and matplotlib packages. At the moment, I have been trying to improve myself in SQL, Power BI and cloud computing (Azure).

As a data alchemist, my greatest interest is transmuting data into valuable solutions and insights! ⚗️🧪📈📊

💻 Skills:

📊 Stats:

ilustração do status do github

💌 Contact: ⤵️

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Carlos Eduardo Gonçalves de Oliveira's Projects

an-lise-dos-tempos-de-espera-de-pacientes-em-cintilografia-do-mioc-rdio icon an-lise-dos-tempos-de-espera-de-pacientes-em-cintilografia-do-mioc-rdio

Em medicina nuclear, os exames seguem uma logística bastante específica. Primeiro o paciente é encaminhado para a injeção do radiofármaco e, em seguida, são conduzidos para a aquisição das imagens (seja de PET ou de SPECT). No caso deste projeto, a análise é referente aos tempos de espera em cintilografia do miocárdio (SPECT), que é um exame constituído de duas etapas de imageamento: a etapa de estresse e repouso. As duas etapas são necessárias para estimar a perfusão do miocárdio nas condições de repouso e esforço físico.

apartment-prices-goi-nia icon apartment-prices-goi-nia

this project contains the code for collecting data from vivareal (educational purposes, only) and for the deployment of a webapp using streamlit.

deep-learning-aplicado-classifica-o-de-100-categorias-de-esportes-usando-imagens icon deep-learning-aplicado-classifica-o-de-100-categorias-de-esportes-usando-imagens

Este projeto consiste no desenvolvimento do primeiro trabalho avaliativo pela disciplina de Redes Neurais Profundas (UFG). Meu objetivo foi de atender aos critérios do trabalho e também de saciar minha curiosidade quanto ao impacto das técnicas de data augmentation e transfer learning na performance de modelos de deep learning.

dirtycategoriesencoding icon dirtycategoriesencoding

Repository containing two classes (StringAgglomerativeEncoder and StringDistanceEncoder) useful for grouping or visualizing the distance between dirty categorical variables. They are compatible with the scikit-learn API.

pca-and-lr-as-a-diagnostic-tool-for-ad icon pca-and-lr-as-a-diagnostic-tool-for-ad

Repository containing all files and code pertinent to data transparency related to the results of the article "PCA and logistic regression in 2-[18F]FDG PET neuroimaging as an interpretable and diagnostic tool for Alzheimer's disease". DOI: 10.1088/1361-6560/ad0ddd

russianalcoholconsumption icon russianalcoholconsumption

This repository includes a solution to a business problem in which a company wants to discover 10 other regions in Russia that have similar alcohol consumption patterns to Saint Petersburg. Dynamic Time Warping and Principal Component Analysis techniques were used to get to the final list of regions.

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