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Jasmeet0810/Depression-Detection-Using-Time-Series-Model

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This project focuses on detecting depression using time series modeling techniques applied to EEG (electroencephalogram) data. By analyzing brainwave patterns over time, we aim to build accurate and robust models to classify depressive and non-depressive states.

active 2025-04-16 → 2025-06-29 (UTC)

Complete coverage27,454 / 27,454 hourly files (100%) · 2 absent upstream2023-08-15 → 2026-10-01 (UTC)
Events
7
Pushes
5
Pull requests
0
Issues
0
Stars
0
Forks
0

Activity over time

Daily event counts in the loaded window

Line chart, 75 days from 2025-04-16 to 2025-06-29. Pushes: 5 total, peak 4 in a day. Pull requests: 0 total, peak 0 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

Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.

Top contributors

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

ContributorContributionsPushesPRsComments
Jasmeet08105500

Recent activity

Latest issues, pull requests and releases

No issue or PR events — this repo's activity is pushes only.

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.