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SColson82/Detecting_Fraudsters_ML

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Using R to develop a model to detect a niche of fraudsters.

active 2023-11-022023-11-28 (UTC)

Complete coverage26,725 / 26,725 hourly files (100%) · 2 absent upstream2023-08-152026-09-01 (UTC)
Events
90
Pushes
52
Pull requests
28
Issues
0
Stars
0
Forks
0

Activity over time

Daily event counts in the loaded window

Line chart, 27 days from 2023-11-02 to 2023-11-28. Pushes: 52 total, peak 9 in a day. Pull requests: 28 total, peak 7 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.

  • Pushes
  • Pull requests
  • Issues
  • Comments
  • Stars

Top contributors

Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity

ContributorContributionsPushesPRsComments
SColson826739280
coutRun8800
Gavmcd425500

Recent activity

Latest issues, pull requests and releases

  • Pull request#15SColson822023-11-28 14:42
  • Pull request#15SColson822023-11-28 14:42
  • Pull request#14SColson822023-11-27 12:49
  • Pull request#13SColson822023-11-25 20:53
  • Pull request#13SColson822023-11-25 20:53
  • Pull request#12SColson822023-11-25 15:00
  • Pull request#12SColson822023-11-25 15:00
  • Pull request#11SColson822023-11-24 18:19
  • Pull request#11SColson822023-11-24 18:19
  • Pull request#10SColson822023-11-24 18:12
  • Pull request#9SColson822023-11-24 17:57
  • Pull request#9SColson822023-11-24 17:57
  • Pull request#8SColson822023-11-24 17:55
  • Pull request#8SColson822023-11-24 17:55
  • Pull request#7SColson822023-11-23 00:10
  • Pull request#7SColson822023-11-23 00:10
  • Pull request#6SColson822023-11-16 18:43
  • Pull request#6SColson822023-11-16 18:43
  • Pull request#5SColson822023-11-09 17:08
  • Pull request#5SColson822023-11-09 17:08
  • Pull request#4SColson822023-11-09 01:19
  • Pull request#4SColson822023-11-09 01:18
  • Pull request#3SColson822023-11-08 21:41
  • Pull request#3SColson822023-11-08 21:41
  • Pull request#2SColson822023-11-08 21:24

Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.