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3rd-place-solution-on-machinehack-video-game-sales-prediction

The gaming industry is certainly one of the thriving industries of the modern age and one of those that are most influenced by the advancement in technology. With the availability of technologies like AR/VR in consumer products like gaming consoles and even smartphones, the gaming sector shows great potential. In this hackathon, you as a data scientist must use your analytical skills to predict the sales of video games depending on given factors. Given are 8 distinguishing factors that can influence the sales of a video game. Your objective as a data scientist is to build a machine learning model that can accurately predict the sales in millions of units for a given game.

3rd-position-solution_for_the__african-covid-19__zindi_hackathon

Can we infer important COVID-19 public health risk factors from outdated data? In many countries census and other survey data may be incomplete or out of date. This challenge is to develop a proof-of-concept for how machine learning can help governments more accurately map COVID-19 risk in 2020 using old data, without requiring a new costly, risky, and time-consuming on-the-ground survey. The 2011 census gives us valuable information for determining who might be most vulnerable to COVID-19 in South Africa. However, the data is nearly 10 years old, and we expect that some key indicators will have changed in that time. Building an up-to-date map showing where the most vulnerable are located will be a key step in responding to the disease. A mapping effort like this requires bringing together many different inputs and tools. For this competition, we’re starting small. Can we infer important risk factors from more readily available data? The task is to predict the percentage of households that fall into a particularly vulnerable bracket - large households who must leave their homes to fetch water - using 2011 South African census data. Solving this challenge will show that with machine learning it is possible to use easy-to-measure stats to identify areas most at risk even in years when census data is not collected.

5th-place-for-umojahack-3-hotspots-beginner-by-umojahack-africa

Each year, thousands of fires blaze across the African continent. Some are natural occurrences, part of a ‘fire cycle’ that can actually benefit some dryland ecosystems. Many are started intentionally, used to clear land or to prepare fields for planting. And some are wildfires, which can rage over large areas and cause huge amounts of damage. Whatever the cause, fires pour vast amounts of CO2 into the atmosphere, along with smoke that degrades air quality for those living downwind. Figuring out the dynamics that influence where and when these fires will occur can help us to better understand their effects. And predicting how these dynamics will play out in the future, under different climatic conditions, could prove extremely useful. For this challenge, the goal is to do exactly that. We’ve aggregated data on burned areas across the whole of the DRC for each month since 1 April 2000. You’ll be given the burn area data up to the end of 2013, along with some additional information (such as rainfall, temperature, population density etc) that extends into the test period. The challenge is to build a model capable of predicting the burned area in different locations over the 2014 to 2016 test period based on only this information.

calzoomcamp2024

comedy-rating

deep-neural-network

my first deep neural network

disease_classifier

A Django powered image classifier for classifying Pnuemonia and Malaria cell infected images.

e-commerce-system-using-python-and-django-as-my-web-framework

Here is a simple e-commerce system built with python and using django as my web framework

economic-well-being-prediction

fault-impact-analysis

flask-pizza-api

This is a REST-API for Pizza Delivery Service

flood-prediction-malawi

On 14 March 2019, tropical Cyclone Idai made landfall at the port of Beira, Mozambique, before moving across the region. Millions of people in Malawi, Mozambique and Zimbabwe have been affected by what is the worst natural disaster to hit southern Africa in at least two decades. In recent decades, countries across Africa have experienced an increase in the frequency and severity of floods. Malawi has been hit with major floods in 2015 and again in 2019. In fact, between 1946 and 2013, floods accounted for 48% of major disasters in Malawi. The Lower Shire Valley in southern Malawi, bordering Mozambique, composed of Chikwawa and Nsanje Districts is the area most prone to flooding. The objective of this challenge is to build a machine learning model that helps predict the location and extent of floods in southern Malawi.

hello-world

machine-learning-projects

This is a repository for my machine learning Notebooks

mern-amazona

mlops

offensivelanguage

text-summarization

todo

A simple todo list app built in HTML, CSS and JavaScript

todofastapi

womxn-in-big-data-south-africa-female-headed-households-in-south-africa

Female household headship has been on the rise in South Africa in recent years. Compared to male-headed households, female-headed households tend to face greater social and economic challenges. Female-headed households, in general, are more vulnerable to lower household incomes and higher rates of poverty. The South African census collects data on female headship and income levels of every household across the country every 10 years. However, it is important for policymakers and other actors to have accurate estimates of these statistics even in between census years. This challenge explores how machine learning can help improve monitoring key indicators at a ward level in between census years.