Pinned Repositories
08-weather-conditions
archive
Archive of documents related to the project governance and management
Bennykillua
docToolchain
a Gradle based AsciiDoc Toolchain for Software Architecture Documentation
engineering-education
A community-generated pool of educational content which is useful for engineers of every level.
Getting-started-in-Technical-Writing
A compilation of useful resources for anyone who wants to get started in technical writing.
Hamoye-Machine-Learning-Regression---Predicting-Energy-Efficiency-of-Buildings
Project
Machine Learning and Data Analytics Projects for various articles
Pyspark-Online-Job-Postings
Dataset of 19,000 online job posts from 2004 to 2015
Twilio_wordOfAffirmationServiceApp
Bennykillua's Repositories
Bennykillua/Hamoye-Machine-Learning-Regression---Predicting-Energy-Efficiency-of-Buildings
Bennykillua/08-weather-conditions
Bennykillua/g01-fraud-detection
This is the Stage G of the Hamoye Data Science Internship
Bennykillua/Hamoye
Bennykillua/Hamoye-Amazon-Kaggle
Every minute, the world loses an area of forest the size of 48 football fields. And deforestation in the Amazon Basin accounts for the largest share, contributing to reduced biodiversity, habitat loss, climate change, and other devastating effects. But better data about the location of deforestation and human encroachment on forests can help governments and local stakeholders respond more quickly and effectively. Planet, designer and builder of the world’s largest constellation of Earth-imaging satellites, will soon be collecting daily imagery of the entire land surface of the earth at 3-5 meter resolution. While considerable research has been devoted to tracking changes in forests, it typically depends on coarse-resolution imagery from Landsat (30 meter pixels) or MODIS (250 meter pixels). This limits its effectiveness in areas where small-scale deforestation or forest degradation dominate. Furthermore, these existing methods generally cannot differentiate between human causes of forest loss and natural causes. Higher resolution imagery has already been shown to be exceptionally good at this, but robust methods have not yet been developed for Planet imagery. In this competition, Planet and its Brazilian partner SCCON are challenging Kagglers to label satellite image chips with atmospheric conditions and various classes of land cover/land use. Resulting algorithms will help the global community better understand where, how, and why deforestation happens all over the world - and ultimately how to respond.
Bennykillua/stage-f-02-school-progress
This is an open source project for the stage E of the Hamoye Data Science Internship program, cohort 2020, with real life applications in the health, engineering, demography, education and technology.