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Siaw-Darko Emmanuel's Projects

sktime icon sktime

A scikit-learn compatible Python toolbox for machine learning with time series

spot-the-mask-challenge-by_zindiweekendz icon spot-the-mask-challenge-by_zindiweekendz

Face masks have become a common public sight in the last few months. The Centers for Disease Control (CDC) recently advised the use of simple cloth face coverings to slow the spread of the virus and help people who may have the virus and do not know it from transmitting it to others. Wearing masks is broadly recognised as critical to reducing community transmission and limiting touching of the face. In a time of concerns about slowing the transmission of COVID-19, increased surveillance combined with AI solutions can improve monitoring and reduce the human effort needed to limit the spread of this disease. The objective of this challenge is to create an image classification machine learning model to accurately predict the likelihood that an image contains a person wearing a face mask, or not. The total dataset contains 1,800+ images of people either wearing masks or not. Your machine learning solution will help policymakers, law enforcement, hospitals, and even commercial businesses ensure that masks are being worn appropriately in public. These solutions can help in the battle to reduce community transmission of COVID-19.

tf2 icon tf2

TensorFlow 2.x Jupyter Notebooks

torchctr icon torchctr

Recommended system(2018-2019); CTR prediction;Recommended-System-PyTorch===>> WIP

urban-air-pollution-challenge-by-zindiweekendz icon urban-air-pollution-challenge-by-zindiweekendz

You may have seen recent news articles stating that air quality has improved due to COVID-19. This is true for some locations, but as always the truth is a little more complicated. In parts of many African cities, air quality seems to be getting worse as more people stay at home. For this challenge we’ll be digging deeper into the data, finding ways to track air quality and how it is changing, even in places without ground-based sensors. This information will be especially useful in the face of the current crisis, since poor air quality makes a respiratory disease like COVID-19 more dangerous. We’ve collected weather data and daily observations from the Sentinel 5P satellite tracking various pollutants in the atmosphere. Your goal is to use this information to predict PM2.5 particulate matter concentration (a common measure of air quality that normally requires ground-based sensors to measure) every day for each city. The data covers the last three months, spanning hundreds of cities across the globe.

wiki-detox icon wiki-detox

See https://meta.wikimedia.org/wiki/Research:Modeling_Talk_Page_Abuse

wine icon wine

wine prediction dataset

wqu-weather-app icon wqu-weather-app

A demo web application for WorldQuant University Introduction to Data Science Module

zindi-agricultural icon zindi-agricultural

Solution to 6th place of audio classification competition (https://zindi.africa/competitions/giz-nlp-agricultural-keyword-spotter/leaderboard)

zindi-solutions-1 icon zindi-solutions-1

Zindi is a social enterprise whose mission is to build the data science ecosystem in Africa.

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