<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data Viz on Ahmed Azeez | Portfolio</title><link>https://ahmed-azeez.github.io/tags/data-viz/</link><description>Recent content in Data Viz on Ahmed Azeez | Portfolio</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 15 Jan 2024 00:00:00 +0000</lastBuildDate><atom:link href="https://ahmed-azeez.github.io/tags/data-viz/index.xml" rel="self" type="application/rss+xml"/><item><title>Predicting Infectious Disease Outbreak Severity</title><link>https://ahmed-azeez.github.io/2024/01/15/epi-outbreak/</link><pubDate>Mon, 15 Jan 2024 00:00:00 +0000</pubDate><guid>https://ahmed-azeez.github.io/2024/01/15/epi-outbreak/</guid><description>Introduction Data Exploration Feature Engineering Cleaning Data Clustering data Predicting Outbreak Severity Introduction Early detection and classification of infectious disease outbreaks is one of the most consequential challenges in public health. Syndromic surveillance systems collect weekly data on case counts, fatality rates, vaccination coverage, and environmental conditions — yet translating this information into actionable severity classifications remains difficult.
In this project, I apply supervised machine learning to a multi-district epidemiological surveillance dataset to predict outbreak severity level: Mild, Moderate, or Severe.</description></item></channel></rss>