Data

Tools

Indexes

Applications

Experiments

Open Source Science

red-wine-quality-ml-model

Red wine quality analysis using machine learning.

https://github.com/chizzy-codes/red-wine-quality-ml-model

Science Score: 44.0%

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

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (1.8%) to scientific vocabulary
Last synced: 7 months ago · JSON representation ·

Repository

Red wine quality analysis using machine learning.

Basic Info
  • Host: GitHub
  • Owner: Chizzy-codes
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 4.49 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 5 years ago · Last pushed about 5 years ago
Metadata Files
Readme Citation

README.md

An analysis on the quality of 1599 red wine samples using 5 different machine learning models.

This was my final project as a student of AI Saturdays Cohort 5

Owner

  • Name: Chizurum Olorondu
  • Login: Chizzy-codes
  • Kind: user
  • Location: Lagos, Nigeria
  • Company: Freelancer

Impactful problem solving is my passion.

Citation (Citation.txt) 

P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. 
  Modeling wine preferences by data mining from physicochemical properties.
  In Decision Support Systems>, Elsevier, 47(4):547-553. ISSN: 0167-9236.

  Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016
                [Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf
                [bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib

1. Title: Wine Quality 

2. Sources
   Created by: Paulo Cortez (Univ. Minho), António Cerdeira, Fernando Almeida, Telmo Matos and José Reis (CVRVV) @ 2009
   
3. Past Usage:

  P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. 
  Modeling wine preferences by data mining from physicochemical properties.
  In Decision Support Systems>, Elsevier, 47(4):547-553. ISSN: 0167-9236.

4. Number of Instances: red wine - 1599; white wine - 4898. 

5. Number of Attributes: 11 + output attribute
  
   Note: several of the attributes may be correlated, thus it makes sense to apply some sort of
   feature selection.

6. Attribute information:

   For more information, read [Cortez et al., 2009].

   Input variables (based on physicochemical tests):
   1 - fixed acidity
   2 - volatile acidity
   3 - citric acid
   4 - residual sugar
   5 - chlorides
   6 - free sulfur dioxide
   7 - total sulfur dioxide
   8 - density
   9 - pH
   10 - sulphates
   11 - alcohol
   Output variable (based on sensory data): 
   12 - quality (score between 0 and 10)

7. Missing Attribute Values: None

GitHub Events

Total
Last Year