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Smritigit1202/Pok-Predict-Machine-Learning-Deep-Learning-Analysis

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Developed a data-driven(kaggle) Pokémon analysis using Python to evaluate multiple models achieving an accuracy of 80%. Implemented preprocessing, training, and performance evaluation on structured dataset (~800 Pokémon records, 12+ features) with modular scripts for reproducibility and insights.

active 2025-08-132025-08-15 (UTC)

Partial coverage20,752 / 26,305 hourly files (79%) · 2 absent upstream · 5,552 failed, retryable2023-08-152026-08-15 (UTC)— sampled evenly across the window, so rankings and trends hold; absolute counts scale up.
Events
2
Pushes
1
Pull requests
0
Issues
0
Stars
0
Forks
0

Activity over time

Daily event counts in the loaded window

Line chart, 3 days from 2025-08-13 to 2025-08-15. Pushes: 1 total, peak 1 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
Smritigit12021100

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.