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1st-place-south-african-covid-19-vulnerability-map-hackathon

This Challenge aims to infer important COVID-19 public health risk factors from outdated data in South Africa

2nd-place_solution_for_the__african-covid-19__zindi_hackathon

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Can we infer important COVID-19 public health risk factors from outdated data? In many countries census and other survey data may be incomplete or out of date.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.

airbnb_clone

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alx-higher_level_programming

alx-low_level_programming

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alx-pre_course

I'm now a ALX Student, this is my first repository as a full-stack engineer

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alx-zero_day

I'm now a ALX Student, this is my first repository as a full-stack engineer

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binary_trees

competitions

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decision_tree_grid_search

Implementation of Grid Search to find better hyper-parameters for decision tree to reduce the over fitting.

dsn_kowope

Data Science Nigeria Artificial Intelligence bootcamp Qualification Hackathon 1st place Solution..

ensemble-methods-notebooks

A collection of companion Jupyter notebooks for Ensemble Methods for Machine Learning (Manning, 2020)

first_repository

fix_my_code_challenge

Repo for the 0x00. Fix my code ALX programme

forkify-attempt

hamoye

Hamoyes Graded quiz

kowope

3RD PLACE... Kowope Mart is a Nigerian-based retail company with a vision to provide quality goods, education and automobile services to its customers at affordable price and reduce if not eradicate charges on card payments and increase customer satisfaction with credit rewards that can be used within the Mall. To achieve this, the company has partnered with DSBank on co-branded credit card with additional functionality such that customers can request for loan, pay for goods even with zero-balance and then pay back within an agreed period of time. This innovative strategy has increased sales for the company. However, there has been recent cases of credit defaults and Kowope Mart will like to have a system that profiles customers who are worthy of the card with minimum if not zero risk of defaulting. You have been employed as a Data Scientist to leverage Machine learning to predict customers who are likely to default or not. This is Qualification Competition for the Data Science Nigeria AI Bootcamp.

mini-projects-html-css-and-js-

A repo containing personal challenges / tasks on HTML, CSS and JS

ml_course_9-11-20

monty

For ALX monty project

my-machine-learning-and-deep-learning-works-on-kaggle-and-zindi-respectively

ALL MY MACHINE LEARNING, DEEP LEARNING WORKS ( BOTH ON KAGGLE AND ZINDI)

pycon-pandas-tutorial

PyCon 2015 Pandas tutorial materials

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sn-bootcamp-projects

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the-data-science-workshop

A New, Interactive Approach to Learning Data Science

umoja-hack

This repo contains my top winning solutions from zindi hackathons