PLEASURE to meet you!
Hi! I'm a currently a computer science undergraduate at Federal University of Paraíba (UFPB). I am committed to expanding my horizons and building a solid career in the world of technology. Over the years, I had the opportunity to have experience in different areas, such as web development, mobile development and applications involving the use of artificial intelligence.
In my carrer i have focused most on working with: Python, wich include several libraries and frameworks such as Pandas, Numpy, Selenium, scikit-learn e BS4 - Java, Javascript - mainly using React.js - and also Flutter.
I am currently a member of TAIL (Technology and Artificial Intelligence League), an academic league that develops several applications focused on AI. In this opportunity I was able to delve deeply and professionally into many areas of technology.
As you can probably imagine, I am really passionate about technology, AI and innovation, truly believing in the positive potential that the use of technology can generate in the world creating high value applications, but beyond that, I'm immensely interested in entrepreneurship and the world of start-ups.
In my free time, I spend most of my hours with my hobbies, which include, going to the gym, running, surfing and playing music.
Check out my projects down bellow and also at my GitHub! Feel free to contact me by email or Linkedin
Projects
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FitLeague
A mobile application that aims to improve the interaction of gym members, through the gamification of physical activities. In this project, software engineering concepts were applied, such as prototyping, agile methodology and teamwork.
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Movie Learning
Movie Learning is a project that uses artificial intelligence, capable of generating the story of an Oscar-winning film. The program consists of a synopsis generator, according to the title, genre and beginning of the synopsis entered by the user, the latter being optional. For this, Natural Language Processing (NLP) was used.
Spotify Music Analysis Notebook
The project focuses on analyzing a Spotify songs dataset to explore various variables, understand the distribution of song data, and make predictions. Utilizing a Kaggle CSV file obtained through the Spotify API, the dataset includes additional attributes added by the author. Neural network architectures were employed for predicting the final target.