jschouhan007

jschouhan007 / Public-Library-Survey-FY-2018-US-Data-Science

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🚀 End-to-end data science project analyzing the US Public Library Survey. Features Exploratory Data Analysis (EDA), Random Forest ML models (Regression & Classification), and Power BI dashboarding.

11
0
69% credibility
Found Apr 17, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

An analysis of the 2018 US Public Library Survey data exploring infrastructure patterns, predicting operations with models, and sharing insights for planners.

How It Works

1
👀 Stumble upon library insights

You find an online page sharing a deep look at public libraries across the US, based on a big survey from 2018.

2
📖 Read the overview

You learn about thousands of library locations, their sizes, hours, and how city versus country areas use them differently.

3
💡 Uncover surprising facts

You discover that branches and main hubs provide almost all library access, and size plus nearby population best predict opening times.

4
🧠 See smart guesses

You explore predictions about whether a library is a main hub or branch, and groupings into different facility levels for planning.

5
📊 Check out the visuals

You picture colorful charts and maps bringing the library stories to life, helping planners make better choices.

🎉 Feel informed and inspired

You walk away with clear, useful knowledge about US library setups, ready to share or use for community ideas.

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Star Growth

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AI-Generated Review

What is Public-Library-Survey-FY-2018-US-Data-Science?

This end-to-end data science project in Jupyter Notebook analyzes the FY 2018 US Public Library Survey dataset covering 17,000 outlets. It runs exploratory data analysis, builds Random Forest models for regressing weekly operating hours and classifying central versus branch libraries, and includes Power BI dashboards for visualizing infrastructure trends like urban-rural divides. Developers get ready-to-run Python scripts with Scikit-learn ML, Seaborn plots, and key insights on factors like square footage and county population driving library ops.

Why is it gaining traction?

It packages a full end-to-end data analytics project on real 2018 public data, blending EDA, statistical tests, clustering, and ML predictions into actionable findings for library planning. The hook is its focus on practical outcomes—like 72% classification accuracy and R2=0.61 regression—making it a quick win for demos or baselines over scattered notebooks. Power BI integration stands out for non-coders needing polished viz without extra setup.

Who should use this?

Data analysts diving into public sector datasets for urban planning reports. ML engineers prototyping Random Forest baselines on demographic features. Students or portfolio builders seeking end-to-end data science examples with hypothesis testing and dashboards.

Verdict

Skip unless you're specifically hunting 2018 library stats—11 stars and 0.7% credibility signal low maturity, with docs limited to a solid README but unclear code depth. Fork for inspiration on end-to-end projects, but verify notebooks run before production use.

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