Sales Transactions Weekly
Donated on 7/15/2017
Contains weekly purchased quantities of 800 over products over 52 weeks. Normalised values are provided too.
Dataset Characteristics
Multivariate, Time-Series
Subject Area
Business
Associated Tasks
Clustering
Feature Type
Integer, Real
# Instances
811
# Features
106
Dataset Information
Additional Information
52 columns for 52 weeks; normalised values of provided too.
Has Missing Values?
No
Introductory Paper
By Swee Chuan Tan, Jess Pei San Lau. 2013
Published in International Conference on Advanced Data and Information Engineering
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| Product_Code | ID | Categorical | no | ||
| W0 | Feature | Integer | no | ||
| W1 | Feature | Integer | no | ||
| W2 | Feature | Integer | no | ||
| W3 | Feature | Integer | no | ||
| W4 | Feature | Integer | no | ||
| W5 | Feature | Integer | no | ||
| W6 | Feature | Integer | no | ||
| W7 | Feature | Integer | no | ||
| W8 | Feature | Integer | no |
0 to 10 of 107
Additional Variable Information
Product_Code 52 weeks: W0, W1, ..., W51. Normalised vlaues of weekly data: Normalised 0, Normalised 1, ..., Normalised 51
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset sales_transactions_weekly = fetch_ucirepo(id=396) # data (as pandas dataframes) X = sales_transactions_weekly.data.features y = sales_transactions_weekly.data.targets # metadata print(sales_transactions_weekly.metadata) # variable information print(sales_transactions_weekly.variables)
Tan,James. (2017). Sales Transactions Weekly. UCI Machine Learning Repository. https://doi.org/10.24432/C5XS4Q.
@misc{misc_sales_transactions_weekly_396,
author = {Tan,James},
title = {{Sales Transactions Weekly}},
year = {2017},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5XS4Q}
}Keywords
Creators
James Tan
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.