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https://github.com/alxhslm/coffee-rating-prediction

A simple ML app to predict the rating of a given coffee

https://github.com/alxhslm/coffee-rating-prediction

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.8%) to scientific vocabulary

Keywords

aws-lambda coffee machine-learning scikit-learn streamlit
Last synced: 5 months ago · JSON representation

Repository

A simple ML app to predict the rating of a given coffee

Basic Info
  • Host: GitHub
  • Owner: alxhslm
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 701 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
aws-lambda coffee machine-learning scikit-learn streamlit
Created about 2 years ago · Last pushed 8 months ago
Metadata Files
Readme

README.md

:coffee: Coffee rating prediction

Streamlit App

The objective of this project is to be able to predict how highly rated a coffee would be on Coffee Review, based upon some meta-data about the selected coffee.

Downloading the dataset

The dataset can be download from Kaggle. You can either download this: - Directly from the website - Using the Kaggle API as follows: bash !kaggle datasets download -d schmoyote/coffee-reviews-dataset -f simplified_coffee.csv -p data

Training the model

To train a linear regression model, run the following script:

bash training/train.py

Generating predictions using the model

The model is served within a separate Docker container. This is hosted using AWS Lambda.

To build the server locally, run the following command: bash docker build server -t coffee-prediction-server:latest

To start the server for the first time, use the following command: bash docker run -it --network coffee-rating-prediction_devcontainer_default --hostname coffee_server --name coffee-prediction-server coffee-prediction-server:latest

To restart the server, run the following: bash docker start -i coffee-prediction-server

Then run the following to test the server: bash python test_predict.py

Using the interactive dashboard

To investigate the predicted rating of a given coffee, you can use the interactive streamlit dashboard. This is hosted on Streamlit Cloud.

You can also launch the dashboard locally by running the following command:

bash streamlit run dashboard.py

Owner

  • Name: Alex Haslam
  • Login: alxhslm
  • Kind: user
  • Company: @optimal-labs

GitHub Events

Total
  • Delete event: 7
  • Push event: 5
  • Pull request event: 10
  • Create event: 6
Last Year
  • Delete event: 7
  • Push event: 5
  • Pull request event: 10
  • Create event: 6

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 33
  • Total Committers: 2
  • Avg Commits per committer: 16.5
  • Development Distribution Score (DDS): 0.03
Past Year
  • Commits: 33
  • Committers: 2
  • Avg Commits per committer: 16.5
  • Development Distribution Score (DDS): 0.03
Top Committers
Name Email Commits
Alex Haslam a****2@g****m 32
Alex Haslam 4****m 1

Issues and Pull Requests

Last synced: 5 months ago

All Time
  • Total issues: 0
  • Total pull requests: 54
  • Average time to close issues: N/A
  • Average time to close pull requests: 3 days
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.07
  • Merged pull requests: 39
  • Bot issues: 0
  • Bot pull requests: 54
Past Year
  • Issues: 0
  • Pull requests: 15
  • Average time to close issues: N/A
  • Average time to close pull requests: 3 days
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 12
  • Bot issues: 0
  • Bot pull requests: 15
View more stats
Top Authors
Issue Authors
Pull Request Authors
  • dependabot[bot] (54)
Top Labels
Issue Labels
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dependencies (54) python (13)

Dependencies

.devcontainer/Dockerfile docker
  • mcr.microsoft.com/vscode/devcontainers/python 0-3.11 build
.devcontainer/docker-compose.yml docker
server/Dockerfile docker
  • python 3.11-slim build
poetry.lock pypi
  • 152 dependencies
pyproject.toml pypi
  • ipython ^8.15.0
  • jupyter 1.0.0
  • kaggle ^1.5.16
  • mypy ~1.0.0
  • numpy 1.23.5
  • pandas 1.5.2
  • pandas-stubs ~1.5.2
  • plotly 5.15.0
  • pre-commit 2.20.0
  • python ~3.11
  • scikit-learn ^1.3.2
  • scipy 1.10.0
  • streamlit 1.25.0
  • xgboost ^2.0.1
server/Pipfile pypi
  • flask ==3.0.0
  • gunicorn ==21.2.0
  • scikit-learn ==1.3.2
server/Pipfile.lock pypi
  • blinker ==1.7.0
  • click ==8.1.7
  • flask ==3.0.0
  • gunicorn ==21.2.0
  • itsdangerous ==2.1.2
  • jinja2 ==3.1.2
  • joblib ==1.3.2
  • markupsafe ==2.1.3
  • numpy ==1.26.2
  • packaging ==23.2
  • scikit-learn ==1.3.2
  • scipy ==1.11.3
  • threadpoolctl ==3.2.0
  • werkzeug ==3.0.1
server/poetry.lock pypi
  • joblib 1.3.2
  • numpy 1.23.5
  • scikit-learn 1.3.2
  • scipy 1.11.4
  • threadpoolctl 3.2.0
server/pyproject.toml pypi
  • numpy 1.23.5
  • python ~3.11
  • scikit-learn 1.3.2