Use our Best Buy products to collect ratings, prices, and descriptions about products from an e-commerce online web. You can purchase either the entire dataset or a customized subset, depending on your requirements. The Best Buy Products Dataset stands as a comprehensive resource for businesses, researchers, and analysts aiming to navigate the vast array of products offered by Best Buy, a leading retailer in consumer electronics and technology. Tailored to provide a deep understanding of Best Buy's e-commerce ecosystem, this dataset facilitates market analysis, pricing optimization, customer behavior comprehension, and competitor assessment. At its core, the dataset encompasses essential attributes such as product ID, title, descriptions, ratings, reviews, pricing details, and seller information. These fundamental data elements empower users to glean insights into product performance, customer sentiment, and seller credibility, thereby facilitating informed decision-making processes. Whether you're a retailer looking to enhance your product portfolio, a researcher investigating trends in consumer electronics, or an analyst seeking to refine e-commerce strategies, the Best Buy Products Dataset offers a valuable resource for uncovering opportunities and driving success in the ever-evolving landscape of retail.

matchbench/Abt-Buy dataset hosted on Hugging Face and contributed by the HF Datasets community

This is a dataset inspired from a beginner friendly problem assignment given from Green University of Bangladesh.

The dataset has few features but as you can see we have conditions to fit within two category P(YES) and P(NO). You will see the solution how we do that classification if a student under age 30, income medium, credit is fair will buy computer or not. We will solve this dataset problem using Naive Bayes classification.

Thank you! Al Maruf Bin Alam 221902284 - CSE 221

Use our TikTok Shop dataset to extract detailed e-commerce insights, including product names, prices, discounts, seller details, product descriptions, categories, customer ratings, and reviews. You may purchase the entire dataset or a customized subset tailored to your needs. Popular use cases include trend analysis, pricing optimization, customer behavior studies, and marketing strategy refinement. The TikTok Shop dataset includes key data points: product performance metrics, user engagement, customer reviews, and more. Unlock the potential of TikTok's shopping platform today with our comprehensive dataset!

This dataset contains the predicted prices of the asset BUY over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Context

You get many visitors to your website every day, but you know only a small percentage of them are likely to buy from you, while most will perhaps not even return. Right now you may be spending money to re-market to everyone, but perhaps we could use machine learning to identify the most valuable prospects?

Content

This data set represents a day's worth of visit to a fictional website. Each row represents a unique customer, identified by their unique UserID. The columns represent feature of the users visit (such as the device they were using) and things the user did on the website in that day. These features will be different for every website, but in this data a few of the features we consider are: - basket_add_detail: Did the customer add a product to their shopping basket from the product detail page? - sign_in: Did the customer sign in to the website? - saw_homepage: Did the customer visit the website's homepage? - returning_user: Is this visitor new, or returning?

In this data set we also have a feature showing whether the customer placed an order (ordered), which is what we predict on.

This is the Parent folder for GSA e-Buy Fiscal Year information, e-Buy is an electronic Request for Quote (RFQ) / Request for Proposal (RFP) system designed to allow government buyers to request information, find sources, and prepare RFQs/RFPs, online, for millions of services and products offered through GSA's Multiple Award Schedule (MAS) and GSA Technology Contracts.

The United States Buy Now Pay Later Services Market is Segmented by Channel (Online and POS), End User Type (Consumer Electronics, Fashion & Apparel, Healthcare and Wellness, Home Improvement, and More), Age Group (Generation Z, Millennials, Generation X, and More), and Provider (Fintechs, Banks, Others). The Market Forecasts are Provided in Terms of Value (USD).

Public Contracts Code section 12211(a) mandates that each State agency shall report annually to the California Department of Resources Recycling and Recovery (CalRecycle) their progress in meeting recycled-content product purchasing requirements using the report format provided by the CalRecycle. This data states the report submission status for each agency and any important comments regarding the annual report.

This dataset contains the predicted prices of the asset Buy the DIP over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

According to a survey conducted in May 2024, 45 percent of respondents in the United States were somewhat willing to purchase premium products or services from companies with stronger data protection policies. The same trend can be noticed among different age groups, as more than 40 percent of U.S. respondents in each age group were somewhat willing to buy premium products when it came to data protection. Around half of respondents aged between 25 and 44 were willing to pay extra for better data security, while a comparatively smaller share of 42 percent among respondents aged 18 to 24 years old were willing to do so.

Concept: For the sake of time series organization, exchange rates have been grouped in two segments: I – Administered or free rates, covering the whole period since 1899, and II – Floating rates, which have been in place in the period of January 1989 to January 1999 and coexisted with the first segment. I – Administered or free exchange rates Available since 1899. In this period covered by the time series a great diversity of foreign exchange policies have been adopted. During some times, exchange rates were fixed (i.e. administered) by the monetary authorities, whereas in other times rates were freely agreed by market participants (i.e. they were free) and there were even times when both administered and free rates have existed at the same time. It should also be emphasized that between 1953 and 1961 a system of multiple exchange rates have been in place. For these time series the following kinds of exchange rates have been considered: - From January 1899 to January 1953 – administered rates; - From February 1953 to October 1961 – free rates, coming from the Exchange Portfolio of the Banco do Brasil. In this period administered rates have also been in place, with sell rates fixed on: CR$ 18,72, from Feb/1953 to Jul/1953; CR$ 18,82, from Aug/1953 to Dec/1958; and CR$ 18,92, from Jan/1959 to Feb/1961. In the beginning of the period most transactions were channeled through the administered rates system. As time went by, the number of transactions going through the free rates system grew. - From November 1961 to February 1990 – administered rates; and - From March 1990 onwards, free rates (Resolution 1.690 from 18.3.1990). The corresponding time series are the following ones: - Commercial dollar (sell and buy) – daily rates Available from 28.11.1984 onwards, refers to administered rates up to March 13th 1990 and to free rates from this date on (Resolution 1.690 from 18.3.1990). Administered rates are the ones fixed by the Central Bank. Free rates are the average of the rates of transactions effectively closed in the interbank market, weighted by the volume of sell transactions in the day. Outliers and rates presenting evidence of manipulation or other violations of the generally accepted market practices are excluded from the calculation. From March 1992 on, this rate was named PTAX. The series “American dollar – buy and sell – end of period” and “American dollar – buy and sell – period average” are derived respectively from these buy and sell daily rates. - American dollar – end of period Refers to the dollar administered rates expressed in Mil-réis for the period 1899-1941. The Mil-réis/dollar rates for the period 1899-1921 were computed from the pound/dollar parity. Discontinued in 1941. - American dollar (buy and sell) – end of period Annual rates are available from 1942 on and monthly rates from January 1953 on. End of period values correspond to the daily rate of the reference period´s last day. - American dollar (buy and sell) – period average Annual rates are available from 1942 on and monthly rates from January 1953 on. Buy and sell average rates are computed from the reference period daily rates. Monthly and annual rates were computed based on the running days of the reference up until December 1973. From January 1974 on, rates were weighted by the working days. II – Floating exchange rates Created by the Resolution 1.552 from 22.12.1988, this segment of the exchange market allowed markets participants to freely agree on the price of the foreign currency being negotiated. It initially covered only transactions related to international travel motivated by tourism, business, education and health. Later, other kinds of transactions were incorporated in the segment, such as gold, Brazilian investments abroad, unilateral transfers and some services. On 31.1.1999 this segment was terminated and the free and floating rates were merged. Series related to this segment are the following: - Tourism dollar (sell) Daily rates in the floating rate segment, available for the period between 27.5.1993 to 29.1.1999. The computation of this rate takes into account transactions in the interbank market weighted by the volume of sell transactions. Outliers and rates presenting evidence of manipulation or other violations of the generally accepted market practices are excluded from the computation. The series “American dollar – buy and sell – end of period” and “American dollar – buy and sell – period average” are derived respectively from these buy and sell daily rates. - American dollar (buy and sell) – end of period Rates for the last day of the reference period, computed for both buy and sell transactions. - American dollar (buy and sell) – period average Average of the daily rates of the reference period (month or year), computed for buy and sell transactions, weighted by the number of working days. Source: Central Bank Information System – PTAX800 transaction ee2d2d33-2788-458c-9b1b-506150cfd4d1 10813-exchange-rate---free---united-states-dollar-buy

This dataset contains details of 1000 customers who intend to buy a car, considering their annual salaries.

Columns: User ID Gender Age Annual Salary Purchase Decision (No = 0; Yes = 1)

The global buy now pay later market size was USD 39.65 billion in 2024 & is projected to grow from USD 51.74 billion in 2025 to USD 435.25 billion by 2033.
Report Scope:

This dataset contains the predicted prices of the asset Ape Store Buy Bot over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Global Property Purchase Decision Dataset

This dataset provides a global property purchase decisions with 200,000 records across 20+ countries and major cities.

Dataset Features

Property Details

• Location (countries and major cities)
• Size specifications
• Price information
• Type classifications
• Amenities available

Financial Information

• Salary data
• Loan details
• EMI (Equated Monthly Installments)
• Expenses breakdown

Subjective Ratings

• Satisfaction scores
• Neighborhood ratings
• Connectivity assessments

Target Variable

• Decision → Buy (1) / Not Buy (0)

Use Cases

Classification

Predict buying decision based on property and financial features

Regression

Estimate house price, loan amount, and other continuous variables

Recommendation Systems

Suggest houses based on buyer profiles and preferences

EDA & Visualization

Study global housing trends and market patterns across different regions

Dataset Statistics

• Records: 200,000
• Coverage: 20+ countries and major cities
• Target: Binary classification (Buy/Not Buy)

Listing of all purchase orders and contracts issued to procure goods and/or services within City-Parish. In the City-Parish, a PO/Contract is made up of two components: a header and one or many detail items that comprise the overarching PO/Contract. The header contains information that pertains to the entire PO/Contract. This includes, but is not limited to, the total amount of the PO/Contract, the department requesting the purchase and the vendor providing the goods or services. The detail item(s) contain information that is specific to the individual item ordered or service procured through the PO/Contract. The item/service description, item/service quantity and the cost of the item is located within the PO/Contract details. There may be one or many detail items on an individual PO/Contract. For example, a Purchase Order for a computer equipment may include three items: the computer, the monitor and the base software package. Both header information and detail item information are included in this dataset in order to provide a comprehensive view of the PO/Contract data. The Record Type field indicates whether the record is a header record (H) or detail item record (D). In the computer purchase example from above, the system would display 4 records – one header record and 3 detail item records. It should be noted header information will be duplicated on all detail items. No detail item information will be displayed on the header record. ***In October of 2017, the City-Parish switched to a new system used to track PO/Contracts. This data contains all PO/Contracts entered in or after October 2017. For prior year data, please see the Legacy Purchase Order dataset https://data.brla.gov/Government/Legacy-Purchase-Orders/54bn-2sqf

How high is the brand awareness of Best Buy in the United States?When it comes to consumer electronics online shop users, brand awareness of Best Buy is at ** percent in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Best Buy in the United States?In total, ** percent of U.S. consumer electronics online shop users say they like Best Buy.What is the usage share of Best Buy in the United States?All in all, ** percent of consumer electronics online shop users in the United States use Best Buy. How loyal are the customers of Best Buy?Around ** percent of consumer electronics online shop users in the United States say they are likely to use Best Buy again.What's the buzz around Best Buy in the United States?In 2024, about ** percent of U.S. consumer electronics online shop users had heard about Best Buy in the media, on social media, or in advertising over the past three months.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

The Buy Now Pay Later Platforms Market will grow from USD 42.22 Billion in 2025 to USD 147.27 Billion by 2031 at a 23.15% CAGR.

Supercharge your legal research, risk analysis, or lead generation with verified U.S. court data. Whether you’re a law firm, data aggregator, background checker, or analytics platform, our court data solutions give you direct access to millions of case records—fast, compliant, and 100% reliable. No guesswork. Just powerful legal data when and where you need it.