aws-solutions / discovering-hot-topics-using-machine-learning

The Discovering Hot Topics Using Machine Learning solution helps brand-conscious customers understand the most popular topics being actively discussed by ingesting digital assets and performing near real-time inferences and analytics

Summary

		Main Code: 7,522 LOC (139 files) = TS (48%) + PY (27%) + JS (24%) Secondary code: Test: 33,862 LOC (155); Generated: 56,350 LOC (17); Build & Deploy: 249 LOC (2); Other: 57,369 LOC (46);
		Duplication: 20%
		File Size: 0% long (>1000 LOC), 84% short (<= 200 LOC)
		Unit Size: 21% long (>100 LOC), 45% short (<= 10 LOC)
		Conditional Complexity: 0% complex (McCabe index > 50), 76% simple (McCabe index <= 5)
		Logical Component Decomposition: primary (35 components)
		1 year, 5 months old 72% of code older than 365 days 1% 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 4 files

Commits Trend

Latest commit date: 2022-01-19

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

Commits 2 28 20 Contributors 2 6 3 2022 2021 2020

show commits trend per language

Commits	2022 2021 2020 8 4 py 2 4 4 js 5 4 ts
Contributors	2022 2021 2020 2 3 1 js 3 1 py 3 1 ts