Envestnet®| Yodlee®'s Consumer Spending Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: Analytics B2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis.

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

This dataset provides a detailed look into transactional behavior and financial activity patterns, ideal for exploring fraud detection and anomaly identification. It contains 2,512 samples of transaction data, covering various transaction attributes, customer demographics, and usage patterns. Each entry offers comprehensive insights into transaction behavior, enabling analysis for financial security and fraud detection applications.

Key Features:

• TransactionID: Unique alphanumeric identifier for each transaction.
• AccountID: Unique identifier for each account, with multiple transactions per account.
• TransactionAmount: Monetary value of each transaction, ranging from small everyday expenses to larger purchases.
• TransactionDate: Timestamp of each transaction, capturing date and time.
• TransactionType: Categorical field indicating 'Credit' or 'Debit' transactions.
• Location: Geographic location of the transaction, represented by U.S. city names.
• DeviceID: Alphanumeric identifier for devices used to perform the transaction.
• IP Address: IPv4 address associated with the transaction, with occasional changes for some accounts.
• MerchantID: Unique identifier for merchants, showing preferred and outlier merchants for each account.
• AccountBalance: Balance in the account post-transaction, with logical correlations based on transaction type and amount.
• PreviousTransactionDate: Timestamp of the last transaction for the account, aiding in calculating transaction frequency.
• Channel: Channel through which the transaction was performed (e.g., Online, ATM, Branch).
• CustomerAge: Age of the account holder, with logical groupings based on occupation.
• CustomerOccupation: Occupation of the account holder (e.g., Doctor, Engineer, Student, Retired), reflecting income patterns.
• TransactionDuration: Duration of the transaction in seconds, varying by transaction type.
• LoginAttempts: Number of login attempts before the transaction, with higher values indicating potential anomalies.

This dataset is ideal for data scientists, financial analysts, and researchers looking to analyze transactional patterns, detect fraud, and build predictive models for financial security applications. The dataset was designed for machine learning and pattern analysis tasks and is not intended as a primary data source for academic publications.

In recent years, we have been exploring computational models to classify bank accounts in combating illegal pyramid selling. The department of economic investigation provides us with plenty of transaction data of real bank accounts. An account contains a lot of transaction records, each of which includes bilateral transaction accounts, timestamp, amount of money and transaction direction, etc. We sample out the transaction records belonging to 10145 bank accounts to form out dataset for training our model. There are 9270 normal accounts and 875 accounts involving a MLM organization respectively. The number of transaction records generated by the normal accounts run up to 6732730 and the fraud records created by MLM members amount to 275804 rows. These MLM members are manually annotated as ``illegal'' by economic investigators. Before training the models, we filtered out some noisy data, i.e. deleting the duplicate records, incomplete records and the records whose transaction amounts no more than 50. Therefore, 1371914 records is filtered out from the set of normal accounts' transaction records and 91341 records created by illegal accounts are deleted. In general, more than 5 million transaction records are used after denoising.

Analysis of ‘Store Transaction data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/iamprateek/store-transaction-data on 14 February 2022.

--- Dataset description provided by original source is as follows ---

Context

Nielsen receives transaction level scanning data (POS Data) from its partner stores on a regular basis. Stores sharing POS data include bigger format store types such as supermarkets, hypermarkets as well as smaller traditional trade grocery stores (Kirana stores), medical stores etc. using a POS machine.

While in a bigger format store, all items for all transactions are scanned using a POS machine, smaller and more localized shops do not have a 100% compliance rate in terms of scanning and inputting information into the POS machine for all transactions.

A transaction involving a single packet of chips or a single piece of candy may not be scanned and recorded to spare customer the inconvenience or during rush hours when the store is crowded with customers.

Thus, the data received from such stores is often incomplete and lacks complete information of all transactions completed within a day.

Additionally, apart from incomplete transaction data in a day, it is observed that certain stores do not share data for all active days. Stores share data ranging from 2 to 28 days in a month. While it is possible to impute/extrapolate data for 2 days of a month using 28 days of actual historical data, the vice versa is not recommended.

Nielsen encourages you to create a model which can help impute/extrapolate data to fill in the missing data gaps in the store level POS data currently received.

Content

You are provided with the dataset that contains store level data by brands and categories for select stores-

Hackathon_ Ideal_Data - The file contains brand level data for 10 stores for the last 3 months. This can be referred to as the ideal data.

Hackathon_Working_Data - This contains data for selected stores which are missing and/or incomplete.

Hackathon_Mapping_File - This file is provided to help understand the column names in the data set.

Hackathon_Validation_Data - This file contains the data stores and product groups for which you have to predict the Total_VALUE.

Sample Submission - This file represents what needs to be uploaded as output by candidate in the same format. The sample data is provided in the file to help understand the columns and values required.

Acknowledgements

Nielsen Holdings plc (NYSE: NLSN) is a global measurement and data analytics company that provides the most complete and trusted view available of consumers and markets worldwide. Nielsen is divided into two business units. Nielsen Global Media, the arbiter of truth for media markets, provides media and advertising industries with unbiased and reliable metrics that create a shared understanding of the industry required for markets to function. Nielsen Global Connect provides consumer packaged goods manufacturers and retailers with accurate, actionable information and insights and a complete picture of the complex and changing marketplace that companies need to innovate and grow. Our approach marries proprietary Nielsen data with other data sources to help clients around the world understand what’s happening now, what’s happening next, and how to best act on this knowledge. An S&P 500 company, Nielsen has operations in over 100 countries, covering more than 90% of the world’s population.

Know more: https://www.nielsen.com/us/en/

Inspiration

Build an imputation and/or extrapolation model to fill the missing data gaps for select stores by analyzing the data and determine which factors/variables/features can help best predict the store sales.

--- Original source retains full ownership of the source dataset ---

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

https://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">

Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

https://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

https://user-images.githubusercontent.com/91852182/145270229-514f0983-3bbb-4cd3-be64-980e92656a02.png"> https://user-images.githubusercontent.com/91852182/145270251-6f6f6472-8817-435c-a995-9bc4bfef10d1.png">

After we will clear our data frame, will remove missing values.

https://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">

To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

ExactOne delivers unparalleled consumer transaction insights to help investors and corporate clients uncover market opportunities, analyze trends, and drive better decisions.

Dataset Highlights - Source: Debit and credit card transactions from 600K+ active users and 2M accounts connected via Open Banking. Scale: Covers 250M+ annual transactions, mapped to 1,800+ merchants and 330+ tickers. Historical Depth: Over 6 years of transaction data. Flexibility: Analyse transactions by merchant/ticker, category/industry, or timeframe (daily, weekly, monthly, or quarterly).

ExactOne data offers visibility into key consumer industries, including: Airlines - Regional / Budget Airlines - Cargo Airlines - Full Service Autos - OEMs Communication Services - Cable & Satellite Communication Services - Integrated Telecommunications Communication Services - Wireless Telecom Consumer - Services Consumer - Health & Fitness Consumer Staples - Household Supplies Energy - Utilities Energy - Integrated Oil & Gas Financial Services - Insurance Grocers - Traditional Hotels - C-corp Industrial - Misc Industrial - Tools And Hardware Internet - E-commerce Internet - B2B Services Internet - Ride Hailing & Delivery Leisure - Online Gambling Media - Digital Subscription Real Estate - Brokerage Restaurants - Quick Service Restaurants - Fast Casual Restaurants - Pubs Restaurants - Specialty Retail - Softlines Retail - Mass Merchants Retail - European Luxury Retail - Specialty Retail - Sports & Athletics Retail - Footwear Retail - Dept Stores Retail - Luxury Retail - Convenience Stores Retail - Hardlines Technology - Enterprise Software Technology - Electronics & Appliances Technology - Computer Hardware Utilities - Water Utilities

Use Cases

For Private Equity & Venture Capital Firms: - Deal Sourcing: Identify high-growth opportunities. - Due Diligence: Leverage transaction data to evaluate investment potential. - Portfolio Monitoring: Track performance post-investment with real-time data.

For Consumer Insights & Strategy Teams: - Market Dynamics: Compare sales trends, average transaction size, and customer loyalty. - Competitive Analysis: Benchmark market share and identify emerging competitors. - E-commerce vs. Brick & Mortar Trends: Assess channel performance and strategic opportunities. - Demographic & Geographic Insights: Uncover growth drivers by demo and geo segments.

For Investor Relations Teams: - Shareholder Insights: Monitor brand performance relative to competitors. - Real-Time Intelligence: Analyse sales and market dynamics for public and private companies. - M&A Opportunities: Evaluate market share and growth potential for strategic investments.

Key Benefits of ExactOne - Understand Market Share: Benchmark against competitors and uncover emerging players. - Analyse Customer Loyalty: Evaluate repeat purchase behavior and retention rates. - Track Growth Trends: Identify key drivers of sales by geography, demographic, and channel. - Granular Insights: Drill into transaction-level data or aggregated summaries for in-depth analysis.

With ExactOne, investors and corporate leaders gain actionable, real-time insights into consumer behaviour and market dynamics, enabling smarter decisions and sustained growth.

A database of de-identified supermarket customer transactions. This large simulated dataset was created based on a real data sample.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

This is a sample orders dataset (transactional data) that can be used to conduct retail analysis like customer segmentation, RFM Analysis, retail analytics, targeting etc

Global Spend Analysis with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Vision EUR is an aggregated transaction feed that includes consumer transaction data on 6.7M+ Europe-domiciled payment accounts, including 5.3M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 4.4K+ brands and 620 symbols including 490 public tickers. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used to understand a company’s growth by country for a specific time period (Ex: What was McDonald’s year-over-year growth by country from 2019-2020?)

Inquire about a CE subscription to perform more complex, near real-time global spend analysis functions on public tickers and private brands like: • Analyze year-over-year spend growth for a company for a subindustry by country • Analyze spend growth for a company vs. its competitors by country through most recent time

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Global Spend Analysis

Problem A global retailer wants to understand company performance by geography to identify growth and expansion opportunities.

Solution Consumer Edge transaction data can be used to analyze shopper behavior across geographies and track: • Growth trends by country vs. competitors • Brand performance vs. subindustry by country • Opportunities for product and location expansion

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key growth drivers by geography for company-wide reporting • Refine strategy in underperforming geographies, both online and offline • Identify areas for investment and expansion by country • Understand how different cohorts are performing compared to key competitors

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period • Churn • Cross-Shop • Average Ticket Buckets

This dataset contains ether as well as popular ERC20 token transfer transactions extracted from the Ethereum Mainnet blockchain.

Only send ether, contract function call, contract deployment transactions are present in the dataset. Miner reward transactions are not currently included in the dataset.

Details of the datasets are given below:

FILENAME FORMAT:

The filenames have the following format:

eth-tx-

where

For example file eth-tx-1000000-1099999.txt.bz2 contains transactions from

block 1000000 to block 1099999 inclusive.

The files are compressed with bzip2. They can be uncompressed using command bunzip2.

TRANSACTION FORMAT:

Each line in a file corresponds to a transaction. The transaction has the following format:

units. ERC20 tokens transfers (transfer and transferFrom function calls in ERC20

contract) are indicated by token symbol. For example GUSD is Gemini USD stable

coin. The JSON file erc20tokens.json given below contains the details of ERC20 tokens.

decoder-error.txt FILE:

This file contains transactions (block no, tx no, tx hash) on each line that produced

error while decoding calldata. These transactions are not present in the data files.

er20tokens.json FILE:

This file contains the list of popular ERC20 token contracts whose transfer/transferFrom

transactions appear in the data files.

-------------------------------------------------------------------------------------------

[

{

"address": "0xdac17f958d2ee523a2206206994597c13d831ec7",

"decdigits": 6,

"symbol": "USDT",

"name": "Tether-USD"

},

{

"address": "0xB8c77482e45F1F44dE1745F52C74426C631bDD52",

"decdigits": 18,

"symbol": "BNB",

"name": "Binance"

},

{

"address": "0x2af5d2ad76741191d15dfe7bf6ac92d4bd912ca3",

"decdigits": 18,

"symbol": "LEO",

"name": "Bitfinex-LEO"

},

{

"address": "0x514910771af9ca656af840dff83e8264ecf986ca",

"decdigits": 18,

"symbol": "LNK",

"name": "Chainlink"

},

{

"address": "0x6f259637dcd74c767781e37bc6133cd6a68aa161",

"decdigits": 18,

"symbol": "HT",

"name": "HuobiToken"

},

{

"address": "0xf1290473e210b2108a85237fbcd7b6eb42cc654f",

"decdigits": 18,

"symbol": "HEDG",

"name": "HedgeTrade"

},

{

"address": "0x9f8f72aa9304c8b593d555f12ef6589cc3a579a2",

"decdigits": 18,

"symbol": "MKR",

"name": "Maker"

},

{

"address": "0xa0b73e1ff0b80914ab6fe0444e65848c4c34450b",

"decdigits": 8,

"symbol": "CRO",

"name": "Crypto.com"

},

{

"address": "0xd850942ef8811f2a866692a623011bde52a462c1",

"decdigits": 18,

"symbol": "VEN",

"name": "VeChain"

},

{

"address": "0x0d8775f648430679a709e98d2b0cb6250d2887ef",

"decdigits": 18,

"symbol": "BAT",

"name": "Basic-Attention"

},

{

"address": "0xc9859fccc876e6b4b3c749c5d29ea04f48acb74f",

"decdigits": 0,

"symbol": "INO",

"name": "INO-Coin"

},

{

"address": "0x8e870d67f660d95d5be530380d0ec0bd388289e1",

"decdigits": 18,

"symbol": "PAX",

"name": "Paxos-Standard"

},

{

"address": "0x17aa18a4b64a55abed7fa543f2ba4e91f2dce482",

"decdigits": 18,

"symbol": "INB",

"name": "Insight-Chain"

},

{

"address": "0xc011a72400e58ecd99ee497cf89e3775d4bd732f",

"decdigits": 18,

"symbol": "SNX",

"name": "Synthetix-Network"

},

{

"address": "0x1985365e9f78359a9B6AD760e32412f4a445E862",

"decdigits": 18,

"symbol": "REP",

"name": "Reputation"

},

{

"address": "0x653430560be843c4a3d143d0110e896c2ab8ac0d",

"decdigits": 16,

"symbol": "MOF",

"name": "Molecular-Future"

},

{

"address": "0x0000000000085d4780B73119b644AE5ecd22b376",

"decdigits": 18,

"symbol": "TUSD",

"name": "True-USD"

},

{

"address": "0xe41d2489571d322189246dafa5ebde1f4699f498",

"decdigits": 18,

"symbol": "ZRX",

"name": "ZRX"

},

{

"address": "0x8ce9137d39326ad0cd6491fb5cc0cba0e089b6a9",

"decdigits": 18,

"symbol": "SXP",

"name": "Swipe"

},

{

"address": "0x75231f58b43240c9718dd58b4967c5114342a86c",

"decdigits": 18,

"symbol": "OKB",

"name": "Okex"

},

{

"address": "0xa974c709cfb4566686553a20790685a47aceaa33",

"decdigits": 18,

"symbol": "XIN",

"name": "Mixin"

},

{

"address": "0xd26114cd6EE289AccF82350c8d8487fedB8A0C07",

"decdigits": 18,

"symbol": "OMG",

"name": "OmiseGO"

},

{

"address": "0x89d24a6b4ccb1b6faa2625fe562bdd9a23260359",

"decdigits": 18,

"symbol": "SAI",

"name": "Sai Stablecoin v1.0"

},

{

"address": "0x6c6ee5e31d828de241282b9606c8e98ea48526e2",

"decdigits": 18,

"symbol": "HOT",

"name": "HoloToken"

},

{

"address": "0x6b175474e89094c44da98b954eedeac495271d0f",

"decdigits": 18,

"symbol": "DAI",

"name": "Dai Stablecoin"

},

{

"address": "0xdb25f211ab05b1c97d595516f45794528a807ad8",

"decdigits": 2,

"symbol": "EURS",

"name": "Statis-EURS"

},

{

"address": "0xa66daa57432024023db65477ba87d4e7f5f95213",

"decdigits": 18,

"symbol": "HPT",

"name": "HuobiPoolToken"

},

{

"address": "0x4fabb145d64652a948d72533023f6e7a623c7c53",

"decdigits": 18,

"symbol": "BUSD",

"name": "Binance-USD"

},

{

"address": "0x056fd409e1d7a124bd7017459dfea2f387b6d5cd",

"decdigits": 2,

"symbol": "GUSD",

"name": "Gemini-USD"

},

{

"address": "0x2c537e5624e4af88a7ae4060c022609376c8d0eb",

"decdigits": 6,

"symbol": "TRYB",

"name": "BiLira"

},

{

"address": "0x4922a015c4407f87432b179bb209e125432e4a2a",

"decdigits": 6,

"symbol": "XAUT",

"name": "Tether-Gold"

},

{

"address": "0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48",

"decdigits": 6,

"symbol": "USDC",

"name": "USD-Coin"

},

{

"address": "0xa5b55e6448197db434b92a0595389562513336ff",

"decdigits": 16,

"symbol": "SUSD",

"name": "Santender"

},

{

"address": "0xffe8196bc259e8dedc544d935786aa4709ec3e64",

"decdigits": 18,

"symbol": "HDG",

"name": "HedgeTrade"

},

{

"address": "0x4a16baf414b8e637ed12019fad5dd705735db2e0",

"decdigits": 2,

"symbol": "QCAD",

"name": "QCAD"

}

]

-------------------------------------------------------------------------------------------

This dataset contains labeled bank transaction records, including detailed transaction metadata, merchant information, and manually or automatically assigned expense categories. It is ideal for developing, training, and benchmarking automated expense categorization models for personal finance, budgeting, and regulatory compliance applications.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

About this Dataset

Class materials for REE 6315 in Fall 2017. We will be using this data as an ongoing example throughout the course. Students will need this data to complete in class quizzes and out of class assignments. Please also download the free real estate listing data also required for the course: https://www.dataandsons.com/categories/sales_&_transactions/u.s._real_estate_inventory

Data was sourced by combining open data sources with instructors original content.

Category

Classroom Datasets

Keywords

housing,equity,realestate,transactions,sales

Row Count

929

Price

$75.00

The FHFA House Price Index (FHFA HPI®) is the nation’s only collection of public, freely available house price indexes that measure changes in single-family home values based on data from all 50 states and over 400 American cities that extend back to the mid-1970s. The FHFA HPI incorporates tens of millions of home sales and offers insights about house price fluctuations at the national, census division, state, metro area, county, ZIP code, and census tract levels. FHFA uses a fully transparent methodology based upon a weighted, repeat-sales statistical technique to analyze house price transaction data. ​ What does the FHFA HPI represent? The FHFA HPI is a broad measure of the movement of single-family house prices. The FHFA HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. This information is obtained by reviewing repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975. The FHFA HPI serves as a timely, accurate indicator of house price trends at various geographic levels. Because of the breadth of the sample, it provides more information than is available in other house price indexes. It also provides housing economists with an improved analytical tool that is useful for estimating changes in the rates of mortgage defaults, prepayments and housing affordability in specific geographic areas. U.S. Federal Housing Finance Agency, All-Transactions House Price Index for Connecticut [CTSTHPI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CTSTHPI, August 2, 2023.

Parking meter transaction records where each row equals a single transaction by a single customer at a single meter. Subsequent transactions by a customer to extend time, for example, are captured as new transaction record and indicated with a METER_EVENT_TYPE of AT (Additional Time).

POST_ID is a join key that refers to the identifier for a meter. You can find that inventory including location of the meter here: https://data.sfgov.org/d/8vzz-qzz9

The balance of payments is a record of a country's international transactions with the rest of the world. It is composed of the current account and the capital and financial account. The current account is itself subdivided into goods, services, income and current transfers; it registers the value of exports (credits) and imports (debits). The difference between these two values is the "balance".

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.

Уникальный идентификатор https://doi.org/10.5281/zenodo.4718440 Набор данных обновлен Dec 19, 2022 Набор данных предоставлен Zenodo Авторы Can Özturan; Can Özturan; Alper Şen; Alper Şen; Baran Kılıç; Baran Kılıç Лицензия Attribution 4.0 (CC BY 4.0) Информация о лицензии была получена автоматически Описание This dataset contains ether as well as popular ERC20 token transfer transactions extracted from the Ethereum Mainnet blockchain. Only send ether, contract function call, contract deployment transactions are present in the dataset. Miner reward (static block reward) and "uncle block inclusion reward" are added as transactions to the dataset. Transaction fee reward and "uncles reward" are not currently included in the dataset. Details of the datasets are given below: FILENAME FORMAT: The filenames have the following format: eth-tx- where For example file eth-tx-1000000-1099999.txt.bz2 contains transactions from block 1000000 to block 1099999 inclusive. The files are compressed with bzip2. They can be uncompressed using command bunzip2. TRANSACTION FORMAT: Each line in a file corresponds to a transaction. The transaction has the following format: units. ERC20 tokens transfers (transfer and transferFrom function calls in ERC20 contract) are indicated by token symbol. For example GUSD is Gemini USD stable coin. The JSON file erc20tokens.json given below contains the details of ERC20 tokens. Failed transactions are prefixed with "F-". BLOCK TIME FORMAT: The block time file has the following format: erc20tokens.json FILE: This file contains the list of popular ERC20 token contracts whose transfer/transferFrom transactions appear in the data files. ERC20 token list: USDT TRYb XAUt BNB LEO LINK HT HEDG MKR CRO VEN INO PAX INB SNX REP MOF ZRX SXP OKB XIN OMG SAI HOT DAI EURS HPT BUSD USDC SUSD HDG QCAD PLUS BTCB WBTC cWBTC renBTC sBTC imBTC pBTC IMPORTANT NOTE: Public Ethereum Mainnet blockchain data is open and can be obtained by connecting as a node on the blockchain or by using the block explorer web sites such as http://etherscan.io . The downloaders and users of this dataset accept the full responsibility of using the data in GDPR compliant manner or any other regulations. We provide the data as is and we cannot be held responsible for anything. NOTE: If you use this dataset, please do not forget to add the DOI number to the citation. If you use our dataset in your research, please also cite our paper: https://link.springer.com/article/10.1007/s10586-021-03511-0 @article{kilic2022parallel, title={Parallel Analysis of Ethereum Blockchain Transaction Data using Cluster Computing}, journal={Cluster Computing}, author={K{\i}l{\i}{\c{c}}, Baran and {"O}zturan, Can and Sen, Alper}, year={2022}, month={Jan} }

Daily cryptocurrency data (transaction count, on-chain transaction volume, value of created coins, price, market cap, and exchange volume) in CSV format. The data sample stretches back to December 2013. Daily on-chain transaction volume is calculated as the sum of all transaction outputs belonging to the blocks mined on the given day. “Change” outputs are not included. Transaction count figure doesn’t include coinbase transactions. Zcash figures for on-chain volume and transaction count reflect data collected for transparent transactions only. In the last month, 10.5% (11/18/17) of ZEC transactions were shielded, and these are excluded from the analysis due to their private nature. Thus transaction volume figures in reality are higher than the estimate presented here, and NVT and exchange to transaction value lower. Data on shielded and transparent transactions can be found here and here. Decred data doesn’t include tickets and voting transactions. Monero transaction volume is impossible to calculate due to RingCT which hides transaction amounts.