The awareness among worldwide consumers about companies selling their personal data to third parties has grown in recent years. As of July 2022, three in four consumers in selected countries worldwide said they knew that companies sell personal information. In comparison, in 2020, this share was a little over 60 percent.

We Never Sell Your Personally Identifiable Information Without Your Permission!
Prospect Wallet does “sell” personal information, but only with specific consent, under the CCPA’s broad definition of “sell,” which encompasses even the ordinary flow of data in the digital analytics and advertising ecosystem. Prospect Wallet, like most businesses that run websites and applications, employs online analytics to track how people interact with them

Although a majority of internet users aged between 18 and 75 years in the United Kingdom (UK) are still skeptical when it comes to personal data being collected by companies, a small share (** percent) would be willing to share this data in return for financial compensation. These types of data mainly included purchase history and location data, while a slightly smaller percentage stated they were willing to sell their browsing history and online media consumption to companies.

According to an analysis conducted in 2023 of over *** companies targeting children and families in the United States, only ** percent of the businesses had a privacy-protective mindset and did not sell data. Under the California Privacy Rights Act amendment, companies are supposed to disclose if they sell users' personal data. Around ** percent of companies did not disclose whether they engaged in such practices.

This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method.

Columns:

• Order ID: Unique identifier for each sales order.
• Date:Date of the sales transaction.
• Category:Broad category of the product sold (e.g., Electronics, Home Appliances, Clothing, Books, Beauty Products, Sports).
• Product Name:Specific name or model of the product sold.
• Quantity:Number of units of the product sold in the transaction.
• Unit Price:Price of one unit of the product.
• Total Price: Total revenue generated from the sales transaction (Quantity * Unit Price).
• Region:Geographic region where the transaction occurred (e.g., North America, Europe, Asia).
• Payment Method: Method used for payment (e.g., Credit Card, PayPal, Debit Card).

Insights:

• 1. Analyze sales trends over time to identify seasonal patterns or growth opportunities.
• 2. Explore the popularity of different product categories across regions.
• 3. Investigate the impact of payment methods on sales volume or revenue.
• 4. Identify top-selling products within each category to optimize inventory and marketing strategies.
• 5. Evaluate the performance of specific products or categories in different regions to tailor marketing campaigns accordingly.

Dataset

This dataset was created by AbhishekSatheesh

Contents

This dataset provides information about the number of properties, residents, and average property values for Sell Road cross streets in Banks, OR.

Turnover: MICEX Stock Exchange: MS: Buy & Sell: Depositary Receipts data was reported at 1,443,371,345,587.160 RUB in Jun 2018. This records an increase from the previous number of 1,171,683,615,091.700 RUB for May 2018. Turnover: MICEX Stock Exchange: MS: Buy & Sell: Depositary Receipts data is updated monthly, averaging 724,317,777,374.840 RUB from Aug 2015 (Median) to Jun 2018, with 35 observations. The data reached an all-time high of 1,528,691,553,186.420 RUB in Sep 2017 and a record low of 323,171,503,763.760 RUB in Dec 2016. Turnover: MICEX Stock Exchange: MS: Buy & Sell: Depositary Receipts data remains active status in CEIC and is reported by Moscow Exchange. The data is categorized under Russia Premium Database’s Financial Market – Table RU.ZB004: MICEX Stock Exchange: Turnover: Buy and Sell.

Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Jul 2025 about median and USA.

Goals :

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description).

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

5. Sales Performance Dashboard:

To track the sales performance of the online retail company, a sales performance dashboard can be created. This dashboard can include key metrics such as total sales, sales by product category, sales by customer segment, and sales by geographical location. By visualizing the sales data in an interactive dashboard, it becomes easier to identify trends, patterns, and areas for improvement.

Research Ideas ****:

1. Inventory Management: By analyzing the quantity and frequency of product sales, retailers can effectively manage their stock and predict future demand. This would help ensure that popular items are always available while less popular items aren't overstocked.
2. Customer Segmentation: Data from different countries can be used to understand buying habits across different geographical locations. This will allow the retail company to tailor its marketing strategy for each specific region or country, leading to more effective advertising campaigns.
3. Sales Trend Analysis: With data spanning almost a year, temporal patterns in purchasing behavior can be identified, including seasonality and other trends (like an increase in sales during holidays). Techniques like time-series analysis could provide insights into peak shopping times or days of the week when sales are typically high.
4. Predictive Analysis for Cross-Selling & Upselling: Based on a customer's previous purchase history, predictive algorithms can be utilized to suggest related products that might interest the customer, enhancing upsell and cross-sell opportunities.
5. Detecting Fraud: Analysing sale returns (marked with 'c' in InvoiceNo) across customers or regions could help pinpoint fraudulent activities or operational issues leading to those returns
6. RFM Analysis: By using the RFM (Recency, Frequency, Monetary) segmentation technique, the online retail company can gain insights into customer behavior and tailor their marketing strategies accordingly.

**************Steps :**************

1. Data manipulation and cleaning from raw data using SQL language Google Big Query
2. Data filtering, grouping, and slicing
3. Data Visualization using Tableau
4. Data visualization analysis and result

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

Data Monetization Market size was valued at USD 3.5 Billion in 2023 and is projected to reach USD 8.5 Billion by 2030, growing at a CAGR of 20.3% during the forecast period 2024-2030.Global Data Monetization Market DriversThe growth and development of the Data Monetization Market are attributed to certain main market drivers. These factors have a big impact on how integrated gas systems are demanded and adopted in different sectors. Several of the major market forces are as follows:Increasing Data Volume: As digital technologies have spread widely, the amount of data produced by organizations, people, and networked devices has increased exponentially. Organizations have the opportunity to monetize their data assets due to the volume of data.Advanced Analytics and Data Technologies: Organisations may now extract meaningful insights from their data thanks to developments in analytics techniques like machine learning and artificial intelligence. These insights can be made profitable in a number of ways, such by providing data-driven goods and services or specialized advertising.A Greater Attention to Data Monetization Strategies: Companies are aggressively looking for ways to monetize their data assets as they become more and more aware of their worth. This entails creating plans for how to market, package, and sell data to third parties or how to create value by streamlining decision-making procedures.Regulatory Environment: Organisations are being prompted to investigate compliant methods of monetizing their data assets by regulatory frameworks like the CCPA and GDPR, which have raised awareness regarding data protection and security. Businesses who are involved in data monetization operations must take compliance with these requirements into account.Data marketplaces are becoming more and more popular, offering venues for the purchase, sale, and exchange of data assets. By facilitating trades between users and data producers, these markets increase accessibility and liquidity within the ecosystem of data monetization.Industry Convergence and Partnerships: In order to take advantage of one another's data assets for mutual gain, industries are working together more and more and establishing partnerships. Collaborations across industries help businesses generate new revenue streams and develop creative data-driven solutions.Demand for Personalised Experiences: Customers are coming to expect more and more from companies in a variety of industries when it comes to personalized experiences. Through data monetization, businesses can use consumer information to create customized goods, services, and advertising campaigns that increase client happiness and loyalty.

This dataset provides information about the number of properties, residents, and average property values for Sell Street cross streets in Hartford, WI.

This child item describes R code used to determine whether public-supply water systems buy water, sell water, both buy and sell water, or are neutral (meaning the system has only local water supplies) using water source information from a proprietary dataset from the U.S. Environmental Protection Agency. This information was needed to better understand public-supply water use and where water buying and selling were likely to occur. Buying or selling of water may result in per capita rates that are not representative of the population within the water service area. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Output from this code was used as an input feature variable in the public supply water use machine learning model. This page includes the following files: ID_WSA_04062022_Buyers_Sellers_DR.R - an R script used to determine whether a public-supply water service area buys water, sells water, or is neutral BuySell_readme.txt - a README text file describing the script

This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

These are replication data for Would You Sell Your Vote? (American Politics Research, v 49, n. 5, 2021). See the READ ME file for more details.

A survey conducted online in the United Kingdom (UK) in 2020 revealed that over ** percent of 18 to 24 year olds would be willing to share their personal data with companies in return for payment. Conversely, only slightly more than ** percent of those over 65 years of age said they would do the same. As a whole, ** percent of UK respondents were against the idea of sharing personal data for financial compensation.

United States CSI: Home Selling Conditions: Bad Time to Sell data was reported at 21.000 % in May 2018. This records a decrease from the previous number of 25.000 % for Apr 2018. United States CSI: Home Selling Conditions: Bad Time to Sell data is updated monthly, averaging 41.000 % from Nov 1992 (Median) to May 2018, with 307 observations. The data reached an all-time high of 96.000 % in Mar 2009 and a record low of 17.000 % in May 1999. United States CSI: Home Selling Conditions: Bad Time to Sell data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to sell a house?

United States CSI: Home Selling Conditions: Good Time to Sell data was reported at 76.000 % in May 2018. This records an increase from the previous number of 72.000 % for Apr 2018. United States CSI: Home Selling Conditions: Good Time to Sell data is updated monthly, averaging 53.000 % from Nov 1992 (Median) to May 2018, with 307 observations. The data reached an all-time high of 77.000 % in Mar 2018 and a record low of 3.000 % in Oct 2010. United States CSI: Home Selling Conditions: Good Time to Sell data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to sell a house?

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Buy, sell or hold: ENT Stock Forecast

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data