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iamTomson9/AI-Big-Data-Hackerthon-Botswana-Food-Inflation-prediction-Model-

Domain:

socioeconomic

Record type:

project
Creator:
iam
Host:
# Forecasting Botswana Food-Price Inflation Hackathon workspace for the IndabaX Botswana 2026 challenge, **Forecasting Botswana's Human Capital Under Global Economic Shocks**. ## Live Dashboard **Judges can explore the hosted project dashboard here:** ### Open the Autobots Dev Botswana Food Inflation Dashboard The dashboard provides a read-only view of the official 2024 best-model forecast, chronological model-performance evidence, leaderboard feedback and human-capital analysis. The official submission remains `outputs/submission/best_model_predictions_2024.csv`. The team must forecast Botswana's monthly food-price inflation (`FAO Item Code 23014`) for January-December 2024 using only information available through December 2023. The submission must compare a classical model with a deep-learning model, connect the forecast to at least two human-capital indicators, and translate the evidence into Botswana-specific policy advice. ## Start here 1. Read `docs/REPOSITORY_GUIDE.md` for a plain-language map of the repository. 2. Read `docs/codebase-and-development-handbook.docx` to understand every Python module and the full development workflow. 3. Read `Autobots Dev.md` and the files under `context/` in the listed order. 4. Assign team roles in `context/team-working-agreement.md`. 5. Obtain the organiser datasets and place them as described in `data/raw/README.md`. 6. Run the environment and raw-data audit described in `context/specs/01-project-setup-and-data-audit.md`. 7. Record every material choice in `docs/DECISION_LOG.md` while doing the work. 8. Do not train final models until data audit, cleaning, aggregation, and merge checks pass. ## Official Phase 1 deliverables | Ref | Deliverable | Limit | Points | | --- | --- | ---: | ---: | | 1.1a | Best-model predictions CSV: `year_month,forecast`, exactly 12 rows | 5 MB | 20 | | 1.1b | Feature Engineering Report | 4 pages | 12 | | 1.1c | Model Comparison Report covering both models | 5 pages | 12 | | 1.1d | Repr …

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