aws-solutions / improving-forecast-accuracy-with-machine-learning

The Improving Forecast Accuracy with Machine Learning solution generates, tests, compares, and iterates on Amazon Forecast forecasts. The solution automatically produces forecasts and generates visualization dashboards for Amazon QuickSight or Amazon SageMaker Jupyter Notebooks???providing a quick, easy, drag-and-drop interface that displays time series input and forecasted output.

Summary

		Main Code: 9,635 LOC (183 files) = PY (95%) + YAML (4%) Secondary code: Test: 4,129 LOC (49); Generated: 0 LOC (0); Build & Deploy: 30 LOC (1); Other: 1,310 LOC (16);
		Duplication: 14%
		File Size: 0% long (>1000 LOC), 63% short (<= 200 LOC)
		Unit Size: 10% long (>100 LOC), 58% short (<= 10 LOC)
		Conditional Complexity: 0% complex (McCabe index > 50), 83% simple (McCabe index <= 5)
		Logical Component Decomposition: primary (24 components)
		1 year, 6 months old 10% of code older than 365 days 3% of code not updated in the past 365 days
		0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits.
		Goals: Keep the system simple and easy to change (4)
	Features of interest: TODOs 9 files

Commits Trend

Latest commit date: 2022-01-03

2 commits (30 days) 2 contributors (30 days)

Commits 2 6 4 Contributors 2 1 1 2022 2021 2020

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Commits	2022 2021 2020 2 5 4 py 2 1 3 yaml
Contributors	2022 2021 2020 2 1 1 py 2 1 1 yaml