https://github.com/outlawhayden/beer-model
OLS Linear Model - Regressing User Beer Ratings based on Observations
https://github.com/outlawhayden/beer-model
data-science linear-models modeling statistics
Last synced: about 1 year ago
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OLS Linear Model - Regressing User Beer Ratings based on Observations
- Host: GitHub
- URL: https://github.com/outlawhayden/beer-model
- Owner: outlawhayden
- Created: 2024-05-02T19:57:11.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-02T20:31:28.000Z (about 2 years ago)
- Last Synced: 2025-04-11T21:18:41.547Z (about 1 year ago)
- Topics: data-science, linear-models, modeling, statistics
- Language: Jupyter Notebook
- Homepage:
- Size: 18 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Constructing Linear Models to Predict Beer Ratings on Observed Attributes
*Hayden Outlaw | Tulane MATH 6040 Linear Models | Spring 2024*
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In 2022, New Orleans was one of the most competitive and dense cities for bars in the United States, with 56.3 bars, 1.4 breweries, and 4.3 wine bars per 100,000 residents [(via RealEstateWitch)](https://budgetbranders.com/blog/2020-drinking-statistics-and-habits-trends-you-can-expect-for-your-bar-business/). In the United States, as of 2022 there were 9,709 total registered breweries [(via Statista)](https://www.statista.com/topics/1654/beer-industry-in-the-united-states/#topicOverview) overall - given the large market size and variety of products, is there a way to identify individual product traits that can be used to regress total product quality?
Given a published dataset of consumer reviews from a variety of beers and breweries, I propose a linear model scheme to identify rated attributes that are indicative of overall beer quality with respect to consumer review score.
## Directory
- [final_notebook](final_notebook.ipynb) - Jupyter notebook with data transformations, modeling, and results
- [archive](/archive) - Data and documentation
- [assets](/assets) - Graphical Assets