Predict child malnutrition risk in Chad with machine learning to help health workers act early and target care
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Updated
May 11, 2026 - Jupyter Notebook
Predict child malnutrition risk in Chad with machine learning to help health workers act early and target care
ML pipeline predicting child malnutrition risk in Chad using DHS 2014 survey data. Gradient Boosting achieved 92% accuracy and 0.979 AUC on 9,826 children. 52.9% of Chadian children under five are malnourished.
A Python simulation of the Yacouba Loop — combining Zaï pits, biochar, and managed grazing to halt desertification in the Sahel.
This repo contains R code to grab ITF data from NOAA CPC ftp server : https://ftp.cpc.ncep.noaa.gov/fews/itf/
QGIS plugin for Sahel region geospatial analysis with AI chatbot — START Hack 2025
Sahel region geospatial dashboard with AI-powered analysis — START Hack 2025
マリ共和国の直近12ヶ月のテロ・誘拐・襲撃事案を可視化するインタラクティブダッシュボード(地図+分析チャート)
FructoSahel — AI-powered farm management platform for agricultural operations in the Sahel region
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