Demand-Forecasting AmirhosseinHonardoust · PARTIAL
End-to-end demand forecasting with Python using synthetic time-series sales data. Includes data generation, cleaning, ARIMA/SARIMA model selection by AIC, evaluation with RMSE and MAPE, and 90-day forecasts with confidence intervals. Reproducible scripts and visualizations for portfolio showcase.
github.com/AmirhosseinHonardoust/Demand-Forecasting · ★ 32 · Forks 0 · Size 609 KB
SUMMARY
Technologies 12
Scored 9
Observed 9
Practices 6
Evidence 14
Skips 3
COVERAGE
Analyzed 20 files · 210 commits · 0 API calls
TECHNOLOGIES & DEPTH
TOML LANGUAGE Depth 70
1 files · PRODUCTION
Markdown LANGUAGE Depth 70
2 files · PRODUCTION
YAML LANGUAGE Depth 70
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Python LANGUAGE Depth 70
12 files · PRODUCTION
pip BUILD_TOOL Depth 80
1 files · CONFIGURATION
Poetry BUILD_TOOL Depth 80
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Black QUALITY Depth —
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NumPy LIBRARY Depth 78
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Ruff QUALITY Depth —
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mypy QUALITY Depth —
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pandas LIBRARY Depth 78
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pytest TESTING Depth 41
2 files · TEST
PRACTICES
documentation · observedautomated_tests · observedcontinuous_integration · observedcontainerization · absentlinting · observedformatting · absent
ACTIVITY & OWNERSHIP
First commit 2025-09-09
Last commit 2026-08-10
Active months 3
Commits 210