---
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]

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://www.kaggle.com/datasets/vivek468/superstore-dataset-final?resource=download


## Uses

<!-- Address questions around how the dataset is intended to be used. -->
 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

<!-- This section describes suitable use cases for the dataset. -->
 1.Forecasting
  -Time series analysis
 2.Prediction
  -Regression
  

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
Any use other than specified above.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
 
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

<!-- Motivation for the creation of this dataset. -->



### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
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

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

We selected clean and easy-to-understand data.

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->



### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->



#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->



#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
 Dataset contains sensitive information such as names, regions and countries of residence, segmentation analysis and order id.


## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->



### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

**APA:**



## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->



## Dataset Card Authors [optional]

Anupama Jayaraman

## Dataset Card Contact

tsnippetblog@gmail.com