Open Contribution

evclust: Evidential c-Means Clustering

Documentation Status tests release python os code-size


Get Started \ pip install

Various clustering algorithms that produce a credal partition, i.e., a set of Dempster-Shafer mass functions representing the membership of objects to clusters. The mass functions quantify the cluster-membership uncertainty of the objects. The algorithms are: Evidential c-Means, Relational Evidential c-Means, Constrained Evidential c-Means, Multiples Relational Evidential c-Means.

wax-mnist

Wax-MNIST is an image dataset created for machine learning. It is similar to the well-known MNIST dataset, which contains images of handwritten digits. However, instead of digits, Wax-MNIST images represent colored patterns found on African fabrics, such as wax, kente, or bogolan. Wax-MNIST was created to encourage cultural diversity in AI projects and raise awareness of the richness of African patterns.


Website \ Github

web scraping with R

I wrote a book on web scraping with R. It’s a pretty practical book with hands-on projects. It covers:

  • Scrapers for web 1.0 & web 2.0
  • Practical scraping projects with or without APIs
  • Scraper automation on local and cloud
  • Web mining & crawling
  • Implementation of crawler & scraper robots


Amazon-ebook \ Amazon-paperback \ A portion of the book

afnews

R build status lifecycle version code-size


I am a developer of the afnews R package. The package contains functions Africa news data collection. A package to scraping all news of all countries of Africa. The news is by country and by date range. The data returned are: title, contents, comments, journal name and category.

Website \ Get Started

Installation

You can install the released version of afnews from GITHUB with:

# install.packages("devtools")
devtools::install_github("armelsoubeiga/afnews")

Traductor Lab / BF


Website \ Github

Traductor Lab / BF est un effort de recherche pour la PNL et la traduction automatique pour les langues Burkinabés. Nous sommes une communauté de développeurs, des data scientist, des ingénieurs et des talents de tous corps de métiers qui s’organisent ensemble pour fournir des données consolidées (corpus), des algorithmes, modéles pré-entrainés et proposer des Apps d’intelligences artificiels. Vous pouvez également contribuer au projet