Skip to content
View deborabmfreitas's full-sized avatar
:octocat:
Hi!
:octocat:
Hi!

Block or report deborabmfreitas

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don't include any personal information such as legal names or email addresses. Markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
deborabmfreitas/README.md

Hello world! 👋🏻

Welcome to my github! :octocat:

About me

📍 Recife (PE)

🎓 Education

  • Specialization in deep learning (CIn - UFPE) - 2025-2026
  • Technologist in Data Science (UNINTER) - 2023-2025
  • Bachelor's degree in Biological Sciences (CB - UFPE) - 2018-2022

💙 Former researcher in computational biology and currently working with data analysis and data science

💡 My main goal is to excel in the data field and solve problems through data-driven insights

Also me:

anime pixel art wave


Projects

  1. 💳 Credit score prediction (classification)

  2. Coffee shop sales prediction (time series/regression)

  3. 🛍️ Customer Segmentation (clustering)

  4. 🏪 EDA with Python and SQL (EDA)


Studies

Machine learning / deep learning

  • Hands on ML (link)
  • Deep learning CIn (link)

Generative AI


Contact

Feel free to reach out, ask questions, and give suggestions :)

📬 E-mail: [email protected]

🔗 LinkedIn | ✍🏻 Medium | 💼 Portfolio

Pinned Loading

  1. coffee-shop-sales-prediction coffee-shop-sales-prediction Public

    Projeto autoral de previsão de faturamento de uma cafeteria. Trata-se de um modelo de machine learning com séries temporais (modelo final: XGBoost). O RMSE final foi de $ 300.69, com intervalo de c…

    Jupyter Notebook

  2. credit-score-prediction credit-score-prediction Public

    Projeto autoral com o objetivo de treinar um modelo de machine learning para concessão de crédito, que classifica clientes em duas categorias: bons pagadores ou inadimplentes, com base em dados his…

    Jupyter Notebook