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  • 👋 Hello, I'm @evertonrubens
  • 👀 I'm interested in building, promoting and collaborating with the community, exposing and publishing different ecosystems in java, javascript, micro-services in API's rest with spring-boot, nodejs, go-server, rust, python, mojo and mulesoft anypoint platform and studio for integration. I am also an architect specializing in Cloud and FaaS (function as a service) and AWS, GCP and Azure Cloud architecture. Additionally, I am studying machine learning, deep learning, GAN’s (Generative Adversarial Networks), Generative AI, R and Python for data science and analysis. With that, I will post here several Python and R projects for Data Analysis, Data Wrangling, Machine Learning, Deep Learning, among others...
  • 🌱 I'm currently delving into Mulesoft tools, webMethods API Gateway, Sensedia API Gateway, CA Gateway.
  • 💞️ I seek to collaborate with the Google, AWS, MuleSoft and Sensedia API Gateway communities with recent paradigm shifts, with the digital acceleration of large companies in the national and international market.
  • 📫 How to find me? Through linkedin.com/in/everton-rubens-martins-366b86b4.

Everton Martins's Projects

extractorinfoarchive icon extractorinfoarchive

Sistema batch criado em python para buscar informações em estruturas de arquivos compactadas, procurando por arquivos com extensões e trechos de códigos pre configurados em um .env. Se encontrado ele irá extrair uma série de informações e irá gerar como saída um relatório com header, body e footer em .csv

mscadendbr icon mscadendbr

Projeto de Laboratório apenas para provar conceito de integração e orquestração no MuleSoft App

nginxnode icon nginxnode

Criação de uma Nginx com Node.JS. A ideia é fazer uma brincadeira, como poc, uma validação de conceito.

pandas-pln-analise-texto-tf-idf icon pandas-pln-analise-texto-tf-idf

Este exemplo prático demonstra a construção de um pipeline de dados completo usando Pandas para manipulação de dados, TfidfVectorizer para processamento de linguagem natural e LogisticRegression para machine learning. Ele destaca a versatilidade e o poder da biblioteca Pandas em aplicações avançadas de Data Science Analytics.

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