Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- en
+- ha
+- yo
+- fr
+tags:
+- healthcare
+- nigeria
+- malaria
+- environmental-health
+- africa
+- autoscientist
+base_model: meta-llama/Llama-3.3-70B-Instruct
 ---
+
+# Nigeria Malaria Health Model
+### AutoScientist Challenge 2026 | Healthcare Category
+
+**Author:** Hussein Adeiza (mabera)  
+**Role:** Licensed Environmental Health Officer, Abuja Nigeria  
+**Base Model:** Llama 3.3 70B  
+**Fine-tuned with:** AutoScientist by Adaption Labs  
+
+## Model Description
+This is a LoRA adapter fine-tuned on Nigeria DHS malaria health data (2010–2021).
+It predicts and explains malaria prevalence risk from ITN coverage, immunization
+and child mortality indicators across Nigeria.
+
+## Training Data
+- Source: DHS Nigeria national surveys (2010–2021)
+- Dataset: malaria parasitemia, ITN coverage, immunization, child mortality
+- Quality improvement: 163.3% (Grade D → B)
+- Kaggle: https://www.kaggle.com/datasets/yunusahusseinadeiza/nigeria-malaria-risk-model-prevalence-prediction
+
+## Training Metrics
+- Win rate: 65% adapted vs 35% base model
+- Base model: meta-llama/Llama-3.3-70B-Instruct
+- Method: LoRA (r=16, alpha=32, all-linear)
+- Epochs: 1
+
+## Key Finding
+Malaria prevalence rose from 36.2% in 2018 back to 39.6% in 2021 despite
+increased ITN coverage — signaling a sustained coverage gap.
+Pregnant women ITN coverage is the strongest protective factor against malaria.
+
+## Why This Matters
+Nigeria carries the world's largest malaria burden. This model addresses the
+gap in African epidemiological AI — built by a Licensed Environmental Health
+Officer with real field experience in Abuja.
+
+## Credits
+Powered by Adaptive Data — Adaption Labs  
+AutoScientist Challenge 2026