Data
Sales_DataSet_of_SuperMarket

Sales_DataSet_of_SuperMarket

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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The Story This data set was part of my online course material for Data Analysis using Python over at Udemy. The Contents The dataset is very useful for beginners and novice number crunchers looking to run queries in a relatable and easy-to-understand dataset. It includes the data about shoppers of a supermarket chain having different locations and the total of their purchases. Acknowledgements This dataset was organised with the help of Ashutosh Pawar at Udemy. Inspiration I want this database to be for beginners venturing into Data Science, a dataset so relatable and commonplace. Ultimately, driving home the point that Data Science itself is for solving real life problems.

13 features

Invoice_IDnumeric1000 unique values
0 missing
Datestring89 unique values
0 missing
Timestring506 unique values
0 missing
Genderstring2 unique values
0 missing
Locationstring3 unique values
0 missing
Citystring3 unique values
0 missing
Memberstring2 unique values
0 missing
Categorystring6 unique values
0 missing
Pricenumeric100 unique values
0 missing
Quantitynumeric7 unique values
0 missing
Totalnumeric334 unique values
0 missing
Paymentstring3 unique values
0 missing
Ratingnumeric5 unique values
0 missing

19 properties

1000
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.
5
Number of numeric attributes.
0
Number of nominal attributes.
0.01
Number of attributes divided by the number of instances.
38.46
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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