Data
Red--White-wine-Dataset

Red--White-wine-Dataset

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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Business Problem It is often said that great wine is made in the vineyard, not in the winery. However, winemakers have the ability to modify certain aspects of the wines they produce, such as the level of acidity, sweetness or alcohol, as well as the shelf life. But which aspects should winemakers modify to positively impact consumers perceptions and/or sales of their wines? Unicorn Winery has hired Digitas Advanced Analytics to help the organization better understand the relationships between certain physiochemical properties and the perceived quality of wines so that its winemaking team can make more informed decisions during production. A Digitas AA analyst already obtained data from a third-party industry organization and started an exploratory analysis. However, she recently was assigned to another project and will not be able to finish the work for Unicorn Winery herself. Your Objective Your objective is to collaborate with your Digitas supervisor to continue the analysis and to provide Unicorn Winery with insights on how to maximize the appeal of its wines. Its also looking for ideas on other analyses it might conduct in the future to support the business, as well as what data would be required to run them.

13 features

fixed_aciditynumeric106 unique values
0 missing
volatile_aciditynumeric187 unique values
0 missing
citric_acidnumeric89 unique values
0 missing
residual_sugarnumeric316 unique values
0 missing
chloridesnumeric214 unique values
0 missing
free_sulfur_dioxidenumeric135 unique values
0 missing
total_sulfur_dioxidenumeric276 unique values
0 missing
densitynumeric998 unique values
0 missing
pHnumeric108 unique values
0 missing
sulphatesnumeric111 unique values
0 missing
alcoholnumeric111 unique values
0 missing
qualitynumeric7 unique values
0 missing
stylestring2 unique values
0 missing

19 properties

6497
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
12
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
92.31
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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