Data
Superstore-Sales-Dataset

Superstore-Sales-Dataset

active ARFF GPL 2 Visibility: public Uploaded 23-03-2022 by Onur Yildirim
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  • Computer Systems Machine Learning
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Context Retail dataset of a global superstore for 4 years. Perform EDA and Predict the sales of the next 7 days from the last date of the Training dataset! Content Time series analysis deals with time series based data to extract patterns for predictions and other characteristics of the data. It uses a model for forecasting future values in a small time frame based on previous observations. It is widely used for non-stationary data, such as economic data, weather data, stock prices, and retail sales forecasting. Dataset The dataset is easy to understand and is self-explanatory Inspiration Perform EDA and Predict the sales of the next 7 days from the last date of the Training dataset!

18 features

Row_IDnumeric9800 unique values
0 missing
Order_IDstring4922 unique values
0 missing
Order_Datestring1230 unique values
0 missing
Ship_Datestring1326 unique values
0 missing
Ship_Modestring4 unique values
0 missing
Customer_IDstring793 unique values
0 missing
Customer_Namestring793 unique values
0 missing
Segmentstring3 unique values
0 missing
Countrystring1 unique values
0 missing
Citystring529 unique values
0 missing
Statestring49 unique values
0 missing
Postal_Codenumeric626 unique values
11 missing
Regionstring4 unique values
0 missing
Product_IDstring1861 unique values
0 missing
Categorystring3 unique values
0 missing
Sub-Categorystring17 unique values
0 missing
Product_Namestring1849 unique values
0 missing
Salesnumeric5757 unique values
0 missing

19 properties

9800
Number of instances (rows) of the dataset.
18
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
11
Number of missing values in the dataset.
11
Number of instances with at least one value missing.
3
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
16.67
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.
0.11
Percentage of instances having missing values.
Average class difference between consecutive instances.
0.01
Percentage of missing values.

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