Fellipe Franco Couto's repositories
NLP-Affinity-Propagation-Clustering-for-words
Application of an Affinity Propagation Clustering for word vectors. Besides that, a graph is plotted to represent the clusters and the Silhouette Test is performed. Check README for dataset references
Analysis-of-the-Google-App-Market-on-Google-Play
This Python notebook explores the App Market on Google App store aiming to provide visual aids that will be helpful to provide insights to master the app market.
Coronaquiz-Android-IOS-App
An IOS/Android game quiz app developed with Dart language to raise awareness about COVID-19.
eea-bacterial-resistance
This data comes from the Surveillance Atlas of Infections Diseases (European Centre for Disease Prevenction and Control - ECDC) and covers the 2005-2021 time interval for all countries in the European Economic Area (EEA), meaning all countries in the European Union (EU) plus Iceland, Liechtenstein, and Norway.
flask-kanban-app
A simple productivity tool created with flask
K-means-for-word-vectors
A text classifier tool. Application of K-Means to cluster word vectors. Check README for references
Machine-Learning-Predicting-Credit-Card-Approvals
This is a Python Machine Learning project that creates a model to predict if a credit card application will be approved or not. The Dataset can be found in the Machine Learning Repositorium at http://archive.ics.uci.edu/ml/datasets/credit+approval
multinomial-classification-enem-2019
The Exame Nacional do Ensino Médio (also known as ENEM) is a national Brazilian standardized test that allows students to conquer a spot in universities in the country and abroad (Inep, 2016). With millions of examinees from different social backgrounds, this paper aims to use the socio-economic data gathered in the 2019 exam application to predict which social class (A to E, following the methodology explained by Carneiro (2021), and used by IBGE) a given applicant belongs. The micro-data can be retrieved here: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enem (Inep, 2020). Summarily, 24 questions ask specific information about goods, education, or work (e.g., number of cars a family has, if any; level of education of father; type of job the mother does), and the objective of the algorithm is to use all this data and classify an applicant’s social strat among the five possibilities.
Python-Global-Warming-Guided-Visualization
The dataset is derived from several samples of ice from the Vostok region (Petit et al, 1999)collected from different layers and therefore containing different amounts of CO2. Due to the different arrangements of the firn grains in several different depths (the more profound the layer, the more dense it is), bubbles containing gas from distinct ages are trapped into the ice. The sample containing the gas is further analyzed to compare atmospheres from different eons. To understand the patterns and hidden relationships on the data set, data visualization will be provided.