license: gpl-2.0
task_categories:
- time-series-forecasting
language:
- en
tags:
- finance
pretty_name: Sales-Superstore
size_categories:
- 1K<n<10K
Dataset Card for Dataset Name
Dataset Details
Dataset Description
This is a superstore giant's POS information dataset. The goal is to predict sales and demographics users lie in for a given product from the dataset.This dataset is to understand which products, regions, categories and customer segments the store should target or avoid.
Acknowledgements: I found this dataset on Kaggle which was found on Tableau. All acknowledgements go to the person maintaining it on Kaggle and the original authors on Tableau.
- Curated by: Vivek Chowdhury @ Kaggle
- Funded by [optional]: N/A
- Shared by [optional]: N/A
- Language(s) (NLP): English
- License: GNU public license v2.0
Dataset Sources [optional]
Uses
The data is meant to be visualised, trends observed and noted and a statistical model applied to predict or forecast the quantity of a particular product that will be sold at a future date. So the process looks like this; after extracting the required features, N-beats will be applied as a statistical method to analyze the extracted data and forecast sales.
Direct Use
1.Forecasting -Time series analysis 2.Prediction -Regression
Out-of-Scope Use
Any use other than specified above.
Dataset Structure
Dataset contains 21 columns or features and 9k rows. Following are the column descriptions: Row ID => Unique ID for each row. Order ID => Unique Order ID for each Customer. Order Date => Order Date of the product. Ship Date => Shipping Date of the Product. Ship Mode=> Shipping Mode specified by the Customer. Customer ID => Unique ID to identify each Customer. Customer Name => Name of the Customer. Segment => The segment where the Customer belongs. Country => Country of residence of the Customer. City => City of residence of of the Customer. State => State of residence of the Customer. Postal Code => Postal Code of every Customer. Region => Region where the Customer belong. Product ID => Unique ID of the Product. Category => Category of the product ordered. Sub-Category => Sub-Category of the product ordered. Product Name => Name of the Product Sales => Sales of the Product. Quantity => Quantity of the Product. Discount => Discount provided. Profit => Profit/Loss incurred.
Dataset Creation
Curation Rationale
Source Data
This dataset was sourced from tableau and consists of real data from a Superstore Giant wanting to know how they can improve their sales strategy.
Data Collection and Processing
We selected clean and easy-to-understand data.
Who are the source data producers?
Annotations [optional]
Annotation process
Who are the annotators?
Personal and Sensitive Information
Dataset contains sensitive information such as names, regions and countries of residence, segmentation analysis and order id.
Bias, Risks, and Limitations
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation [optional]
BibTeX:
APA:
Glossary [optional]
Dataset Card Authors [optional]
Anupama Jayaraman