Data
Store-20-Retail-Data-Analytics

Store-20-Retail-Data-Analytics

active ARFF Database: Open Database, Contents: Database Contents Visibility: public Uploaded 23-03-2022 by Dustin Carrion
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About This dataset is based on https://www.kaggle.com/manjeetsingh/retaildataset Historical sales data covers the period from 2010-02-05 to 2012-11-01. Sales and Features data was merged, Weekly sales were estimated at total store level, and only data for Store = 20 was selected. Columns Date - the week Temperature - average temperature in the region Fuel_Price - cost of fuel in the region - MarkDown1-5 - anonymized data related to promotional markdowns. MarkDown data is only available after Nov 2011, and is not available for all stores all the time. Any missing value is marked with an NA CPI - the consumer price index Unemployment - the unemployment rate IsHoliday - whether the week is a special holiday week Weekly_Sales - sales in the given date (week) The original dataset was obtained here: https://www.kaggle.com/manjeetsingh/retaildataset

12 features

Datestring149 unique values
0 missing
IsHolidaynominal2 unique values
0 missing
Weekly_Salesnumeric149 unique values
0 missing
Temperaturenumeric149 unique values
0 missing
Fuel_Pricenumeric136 unique values
0 missing
MarkDown1numeric57 unique values
0 missing
MarkDown2numeric51 unique values
0 missing
MarkDown3numeric57 unique values
0 missing
MarkDown4numeric57 unique values
0 missing
MarkDown5numeric57 unique values
0 missing
CPInumeric149 unique values
0 missing
Unemploymentnumeric12 unique values
0 missing

19 properties

149
Number of instances (rows) of the dataset.
12
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.
10
Number of numeric attributes.
1
Number of nominal attributes.
8.33
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
Average class difference between consecutive instances.
83.33
Percentage of numeric attributes.
0.08
Number of attributes divided by the number of instances.
8.33
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
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.
1
Number of binary attributes.

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