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Yu-ChangCheng/manufacturing-forecasting-framework

Data & analyticsPython
Total stars
73
Today · UTC
—No data
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Reproducible monthly demand forecasting with Random Forest, chronological backtests, transparent baselines, and a guided local app.

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About the project

A reproducible framework for monthly item-level demand forecasting with Random Forest, chronological backtests, simple baselines, and a local Streamlit app for uploading CSV/Excel files.

Useful for
  • Upload your own CSV or Excel demand data and get a forecast with a report via the local app
  • Compare Random Forest against last-month and same-month-last-year baselines on a rolling-origin backtest
  • Configure lag/rolling/calendar features and train a model in the Jupyter notebook on your data

README summarized by DeepSeek V4.1 Flash. Details may be inaccurate.

5.6 Early signal Magnitude out of 10 — how fast interest is growing, not a quality score.

Star growth looks organic. Data as of October 9, 2026, 00:17 UTC.

The star-growth assessment does not verify whether the project is safe to run.

Stars per day

04080October 4, 2026October 9, 2026

Bars show daily stars; the line is a moving average of the available history. Short histories do not yet establish a reliable usual pace. Red marks spike days.

Why it’s trending

  • There are not enough fresh counter measurements for today's UTC day. The gain is unknown.
  • The repository is 2 days old and already has 73 stars. With less than two weeks of history, there's no usual pace to compare the spike against yet.
  • Top new repositories this week: #194.
  • About 240 forks a day — people are taking the code.

Three GitHub discoveries in each edition

What they do, why they are gaining interest, and what to check before using them.

Editions at 10:00, 16:00, 22:00 MSK.

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Numbers

Total stars
73
Today
Not enough measurements today
Forks
13
Issues and pull requests
0
Watchers
0
Language
Python
License
MIT
Created
October 7, 2026
Last push
October 7, 2026

Star trust

Growth looks organic: forks and discussion are in line with active projects, and stars arrive unevenly, the way people give them.

These are heuristics, not a verdict: we judge by the repository’s behavior, not by a list of stargazers.

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GitNova: 5.6
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