Car Sales Dataset Download
Christian Alradwan
We have projects(jobs) in NetSuite along with Sales Orders attached to said jobs, so there should be a clear relationship between the two. I'm having trouble finding both Sales Order # and Project/Job # within the same dataset in SuiteAnalytics. Does anyone know which dataset I could find both in? I assumed within the "Sales(Ordered)" dataset, I could pull the Job attached to the sales order at least, but I cannot find that anywhere within the available selections.
I am creating a full workbook using multiple datasets to help save time for one of our project managers, so if I'm hoping to be able to create everything in the one workbook to avoid having the PM find and export multiple reports in the system.
Fashion-MNIST: This retail dataset is perfect for anyone crafting a recommendation system. It contains SKUs across 60,000 training images along with a set of 10,000 test images that are classified in 10 classes.
E-Commerce Data: Compiled by the UCI Machine Learning Repository, this ecommerce dataset features online retail transactions taken between 2010 and 2011 for a UK-based and registered non-store online retailer.
Retailrocket Recommender System Database: Collected from real-world Ecommerce sites, this retail dataset is built around visitor behavior and contains information surrounding click rates, add-to-carts, and checkout data that eventually led to complete transactions.
Brazilian Ecommerce Public Dataset: Brazilian retail dataset containing over 100,000 orders that were placed on Olist spanning between 2016 and 2018 across several marketplace. Information contained and tracked within pertain s to price, order status, payment and freight performance with reviews also featured.
Economic Census: This retail dataset provides a detailed portrait around business happenings across several industries and businesses once every five years. The information ranges from the national level down to the local level.
Ecommerce Sales by Merchandise Category (1999-2015): Containing census data that focuses on total ecommerce sales, this retail dataset provides intimate knowledge of line items such as merchandise line and compound annual growth rate from 1999-2015.
The retail industry has been shaped and fundamentally transformed by disruptive technologies in the past decade. From AI assisted customer service experiences to advanced robotics in operations, retailers are pursuing new technologies to address margin strains and rising customer expectations. AI use cases like personalized product recommendations, demand forecasting for optimized inventory and supply chain management, optimized pricing strategies based on market dynamics, and sales forecasting are generating value for companies who have adopted AI. By leveraging AI, retailers can maintain or increase their competitiveness in a saturated market.
How can retailers use, grow and optimize their use of data and machine learning? For data scientists tasked with building and training machine learning models for retailers, open and free retail datasets are an important starting point. But these datasets for retailers can be hard to come by, since they include personal customer information and business competitive information, which is why not many retailers share this data. This blog post is here to help. Here are 13 excellent open datasets and data sources for retailer data for machine learning.
A comprehensive dataset with sales data across channels and financial information. Data includes SKUs, design numbers, stock levels, product categories, product sizes, product colors, the amount paid, rate per piece, date of sale, gross amounts and much more.
According to the contributors, this data can be used in a number of ways: analyzing sales trends, comparing and analyzing profitability, comparing prices, looking at customer specific data, using stock details, and much more.
There are more than 71,000 online reviews in this dataset, spanning 1,000 different products. Information includes the review text and title, reviewer metadata, the product name and manufacturer, and more.
A dataset with information from 7,000 online reviews for 50 electronic products. The data was taken from online websites like Best Buy and Amazon. Information includes the date of review, its source, the rating, reviewer metadata, title, and more.
This dataset contains anonymized historical sales data from 45 stores. The information provided includes the type of store, its size, department, regional activity, dates, temperature, fuel cost in the region, CPI, unemployment rate, whether the week was a special holiday, and more. While this data is not fresh, it is from 2010-2012, we added it to the list because of the holiday sales data that can be used and could still be relevant.
A list of sales and movement data per item and department for each month. The dataset has 308,000 rows and contains information about the year, month, supplier name, item code, item description, item type and number of items sold.
This is a fictional dataset created for helping the data analysts to practice exploratory data analysis and data visualization. The dataset has data on orders placed by customers on a grocery delivery application.
I am trying to forecast demand based on a 6 years dataset 1/1/2014==> 1/1/2020.first I tried to regroup demand by month and so I ended up with a dataset of 2 columns ( month and sales) and 72rows ( 12month*6years). P.s: I am working with python.
California Department of Tax and Fee Administration sales and use tax rates by jurisdiction. This data is used by the Find Your Tax Rate application to determine the tax rate of an address. are two layers. Layer 0 is the main tax rate map and layer 1 contains additional Tax Area Code (TAC) field with additional geometry for redevelopment areas.
Explore and download sample datasets hand-picked by Maven instructors. Practice applying your data analysis and visualization skills to real-world data, from flight delays and movie ratings to shark attacks and UFO sightings.
Power BI offers different kinds of samples for different purposes. There are built-in samples and apps in the Power BI service, .pbix files, Excel datasets, and SQL databases. Here's a collection of different samples:
The company obviEnce (www.obvience.com) and Microsoft teamed up to create samples for you to use with Power BI. The samples use anonymized data. The samples represent different industries: finance, HR, sales, and more.
This industry sample explores a software company's sales channel. Sales managers monitor their direct and partner sales channels by tracking opportunities and revenue by region, deal size, and channel.
This industry sample contains a report for a fictitious company named Contoso. The Contoso sales manager created this report to understand their products and regions' key contributors for revenue won or loss.
This industry sample analyzes retail sales data of items sold across multiple stores and districts. The metrics compare this year's performance to last year's in these areas: sales, units, gross margin, variance, and new store analysis.
This industry sample analyzes a manufacturing company, VanArsdel Ltd. It allows the Chief Marketing Officer to watch the industry and the market share for VanArsdel. By exploring the sample, you can find the company's market share, product volume, sales, and sentiment.
Indulge your "taste" for data analysis with this free Microsoft Excel sample dataset. Explore the sales orders of a fictional food production company in this comprehensive data set. It even has cookies! ?
Food Sales - Data Analysis: Click here to get the food sales data file, with pivot tables and pivot charts, analyzing the data. The zipped Excel file is in xlsx format, and does not contain any macros.
The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.
The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).
IEEE DataPort Subscribers may upload their dataset files directly to IEEE DataPort's AWS S3 file storage. Please read the Upload Your Files directly to the IEEE DataPort S3 Bucket help topic for detailed instructions.
This is a video game sales data including game sales of North America, European, Japan, and other area, together they make the global sale. The data also gives information about the critic score, user score, and the counts of critics or users who gave these two kind of scores. This data was downloaded from -game-sales-with-ratings#Video_Games_Sales_as_at_22_Dec_2016.csv.
Summary of these variables tells us that some of the games were published in the same name; PS2 is the most popular platform; Action is the most popular Genre; Electronic Arts has the most high frequency among the publishers; Rating T and E are the two most released ratings; For these sales, though the minimums, several quantiles, and medians are small, the maximums are high, which means there are real good sale games among them; Extreme big maximum User count hints so many users scored some special games.
There are lots of interest points in this data set such as the distribution of global and regional sales, their relationship; the correlation of critic score and user score, and their counts; whether these scores are the main effect for sales, or the effect of other factors matter to sales such as genre, rating, platform, publisher, and so on.
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