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Pyame/Flight-Price-Prediction

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Airfare prices can be incredibly dynamic, influenced by many factors. Predicting these prices is not only useful for travelers but also for airlines, travel agencies, and researchers. This repository provides a comprehensive solution to this problem, leveraging machine learning techniques and the Kaggle flight price dataset.

active 2023-10-182025-10-04 (UTC)

Complete coverage26,421 / 26,421 hourly files (100%) · 2 absent upstream2023-08-152026-08-19 (UTC)
Events
176
Pushes
46
Pull requests
34
Issues
26
Stars
0
Forks
0

Activity over time

Daily event counts in the loaded window

Line chart, 718 days from 2023-10-18 to 2025-10-04. Pushes: 46 total, peak 16 in a day. Pull requests: 34 total, peak 8 in a day. Issues: 26 total, peak 10 in a day. Comments: 19 total, peak 6 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
Pyame8927256
wvffle17512
Monikaa190912732
SiwyKot4110721
review-notebook-app[bot]8008
patrycjarecko3030

Recent activity

Latest issues, pull requests and releases

  • Issue#26wvffle2025-10-04 13:15
    Solve conflict issue
  • Issue#31wvffle2023-12-18 19:02
    improve models effectivness
  • Pull request#32Pyame2023-12-18 11:04
  • Issue comment#32review-notebook-app[bot]2023-12-18 11:04
    effectivness improvement with tets
  • Pull request#32Pyame2023-12-18 11:04
  • Issue#31Pyame2023-12-18 11:02
    improve models effectivness
  • Pull request#30Pyame2023-11-27 10:03
  • Issue comment#30review-notebook-app[bot]2023-11-27 10:02
    update
  • Pull request#30Pyame2023-11-27 10:02
  • Pull request#29Pyame2023-11-27 09:56
  • Issue comment#29review-notebook-app[bot]2023-11-27 09:51
    models and test implementation
  • Pull request#29Pyame2023-11-27 09:50
  • Issue#28Pyame2023-11-27 08:19
    add tests to model
  • Issue#27Pyame2023-11-27 08:19
    add ML model
  • Issue#14Pyame2023-11-27 08:16
    Develop new features based on existing data
  • Pull request#25Pyame2023-11-27 08:16
  • Issue#11Pyame2023-11-27 07:50
    Transform or remove outlier data
  • Pull request#23Pyame2023-11-27 07:50
  • Issue comment#23wvffle2023-11-13 18:14
    feat: outlier data
  • Issue#9wvffle2023-11-13 17:52
    Normalize or standardize numeric variables
  • Pull request#21wvffle2023-11-13 17:52
  • Issue comment#26wvffle2023-11-13 07:37
    Solve conflict issue
  • Issue#26Pyame2023-11-12 11:37
    Solve conflict issue
  • Issue comment#23Pyame2023-11-12 11:34
    feat: outlier data
  • Issue comment#23review-notebook-app[bot]2023-11-12 11:33
    feat: outlier data

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.