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The Churn Predictor app is designed to analyze customer data and predict churn risk. It helps businesses identify customers who are likely to leave and take proactive measures to retain them.

active 2024-08-172024-09-16 (UTC)

Complete coverage27,182 / 27,184 hourly files (100%) · 2 absent upstream2023-08-152026-09-20 (UTC)
Events
98
Pushes
42
Pull requests
23
Issues
0
Stars
0
Forks
0

Activity over time

Daily event counts in the loaded window

Line chart, 31 days from 2024-08-17 to 2024-09-16. Pushes: 42 total, peak 34 in a day. Pull requests: 23 total, peak 16 in a day. Issues: 0 total, peak 0 in a day. Comments: 1 total, peak 1 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
Nfayem6137171
worlakuma6330
IAmWangari5230

Recent activity

Latest issues, pull requests and releases

  • Pull request#12Nfayem2024-08-31 17:57
  • Pull request#12Nfayem2024-08-31 17:56
  • Pull request#11Nfayem2024-08-31 17:54
  • Pull request#11Nfayem2024-08-31 17:54
  • Pull request#10Nfayem2024-08-31 17:54
  • Pull request#10Nfayem2024-08-31 17:53
  • Pull request#9Nfayem2024-08-31 17:52
  • Pull request#6Nfayem2024-08-31 17:45
  • Pull request#8Nfayem2024-08-31 17:43
  • Pull request#8Nfayem2024-08-31 17:42
  • Pull request#7Nfayem2024-08-31 17:33
  • Pull request#7Nfayem2024-08-31 17:28
  • Pull request#6worlakuma2024-08-31 16:17
  • Pull request#5Nfayem2024-08-31 16:08
  • Pull request#5worlakuma2024-08-31 15:38
  • Pull request#4Nfayem2024-08-31 14:39
  • Pull request#4worlakuma2024-08-26 17:05
  • Pull request#3Nfayem2024-08-18 15:20
  • Pull request#3IAmWangari2024-08-18 15:16
  • Pull request#2Nfayem2024-08-18 14:05
  • Pull request#2IAmWangari2024-08-18 13:54
  • Pull request#1Nfayem2024-08-18 12:51
  • Issue comment#1Nfayem2024-08-18 12:50
  • Pull request#1IAmWangari2024-08-18 12:39

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.