This dataset provides insights into the spending habits of Gen Z (ages 18-27) across various categories such as rent, groceries, entertainment, education, savings, and more. It contains 1700 records and 15 financial attributes, making it a valuable resource for financial trend analysis, budgeting studies, and machine learning applications in personal finance.

Consumer Spending in the United States increased to 16291.80 USD Billion in the first quarter of 2025 from 16273.20 USD Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Consumer Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Personal Spending in the United States decreased 0.10 percent in May of 2025 over the previous month. This dataset provides the latest reported value for - United States Personal Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset provides values for PERSONAL SPENDING reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Context

This dataset was created from the online retail dataset found here https://www.kaggle.com/roshansharma/online-retail. This has had some processing for customer segmentation so it can be used for nice visualisation of the data.

Content

The following variables are used: | Variable | Description | | --- | --- | |**CustomerID**| This is the same CustomerID field as in the online retail dataset found in the link above and can be linked to this dataset.| |**Frequency**|This is how many times a customer purchased.| |**Recency**|This is how many days ago a customer made a purchase. This is adjusted to reference a point in time.| |**Monetary** |This is how much a customer spent in total. Their total Lifetime monetary value.| |**rankF**|This is the Frequency value divided into different ranges from 1 to 5 using the cut function in R. (5 = lots of visits, 1 = very low visits)| |**rankR**|This is the Recency value divided into different ranges from 1 to 5 using the cut function in R and then flipped. (5 = very Recent, 1 = ages ago) | |**rankM**|This is the Monetary value divided into different ranges from 1 to 5 using the cut function in R. (5 = High spender, 1 = low spender) | |**groupRFM**| The group RFM is a value combining the rankR, rankF and rankM. This uses 1 digit per rank (ie 1 rankR, 2 rankF, 5 rankM would be 125 Group)| |**Country**|This is the customer delivery country from the original online retail dataset.| |**Customer_Segment**| A customer segment is added to give a more human description of the customer and therefore can be treated differently. These segments are listed below.|

Customer Segments

The customer segments below detail the description of the customers from their details processed in the RFM analysis. | Customer Segment | Segment Description | | --- | --- | |**Champions** | Bought recently buy often and spend the most | |**Loyal Customers**|Spend good money Responsive to promotions| |**Potential Loyalist**|Recent customers spent good amount, bought more than once| |**Recent High Spender**|Recent customers not frequent but spend some| |**New Customers**|Bought more recently but not often| |**Promising**|Recent shoppers but haven’t spent much| |**Need Attention**|Above average recency frequency & monetary values| |**About To Sleep**|Below average recency frequency & monetary values| |**At Risk**|Spent big money purchased often but long time ago| |**Can’t Lose Them**|Made big purchases and often but long time ago| |**Hibernating**|Low spenders low frequency purchased long time ago| |**Lost**|Lowestrecency frequency & monetary scores|

Acknowledgements

Thank you to the owners of the online retail dataset. https://www.kaggle.com/roshansharma

Inspiration

The online retail dataset is a great set for finding anomalies and doing some interesting reports, however RFM analysis allows you to treat clusters of data in the same way which is suitable for marketing teams etc.

RFM analysis is a straight forward analytical process that can be achieved by clustering but a more manual process is good as you can adjust these figures to get more even groups. I will post my R code for this and link shortly.| | | | | --- | --- | | | | | | | --- | --- | | | |

Government Spending in Brazil decreased to 57326.04 BRL Million in the first quarter of 2025 from 61109.86 BRL Million in the fourth quarter of 2024. This dataset provides - Brazil Government Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

According to a survey conducted in 2023, many college students surveyed said their biggest expense was food. Specifically, ********* of college students said they spend the most on food in a typical month.

Survey of Household Spending (SHS), average household spending, Canada, regions and provinces.

Medicare Spending per Beneficiary

Detailed Hospital Expense Breakdown

By Health [source]

About this dataset

This file allows healthcare executives and analysts to make informed decisions regarding how well continued improvements are being made over time so that they can understand how efficient they are fulfilling treatments while staying within budgetary constraints. Additionally, it’ll also help them map out trends amongst different hospitals and spot anomalies that could indicate areas where decisions should be reassessed as needed

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How to use the dataset

This dataset can provide valuable insights into how Medicare is spending per patient at specific hospitals in the United States. It can be used to gain a better understanding of the types of services covered under Medicare, and to what extent those services are being used. By comparing the average Medicare spending across different hospitals, users can also gain insight into potential disparities in care delivery or availability.

To use this dataset, first identify which hospital you are interested in analyzing. Then locate the row for that hospital in the dataset and review its associated values: value, footnote (optional), and start/end dates (optional). The Value column refers to how much Medicare spends on each particular patient; this is a numerical value represented as a decimal number up to 6 decimal places. The Footnote (optional) provides more information about any special circumstances that may need attention when interpreting the value data points. Finally, if Start Date and End Date fields are present they will specify over what timeframe these values were aggregated over.

Once all relevant data elements have been reviewed successively for all hospitals of interest then comparison analysis among them can be conducted based on Value, Footnote or Start/End dates as necessary to answer specific research questions or formulate conclusions about how Medicare is spending per patient at various hospitals nationwide

Research Ideas

• Developing a cost comparison tool for hospitals that allows patients to compare how much Medicare spends per patient across different hospitals.
• Creating an algorithm to help predict Medicare spending at different facilities over time and build strategies on how best to manage those costs.
• Identifying areas in which a hospital can save money by reducing unnecessary spending in order to reduce overall Medicare expenses

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Medicare_hospital_spending_per_patient_Medicare_Spending_per_Beneficiary_Additional_Decimal_Places.csv | Column name | Description | |:---------------|:--------------------------------------------------------------------------------------| | Value | The amount of Medicare spending per patient for a given hospital or region. (Numeric) | | Footnote | Any additional notes or information related to the value. (Text) | | Start_Date | The start date of the period for which the value applies. (Date) | | End_Date | The end date of the period for which the value applies. (Date) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

The San Francisco Controller's Office maintains a database of budgetary data that appears in summarized form in each Annual Appropriation Ordinance (AAO). This data is presented on the Budget report hosted at http://openbook.sfgov.org, and is also available in this dataset in CSV format. New data is added on an annual basis when the AAO is published for each new fiscal year. Data is available from fiscal year 2010 forward. The City and County of San Francisco's budget is a two-year plan for how the City government will spend money with available resources. In the budget process, a budget is proposed by the Mayor, and then modified and approved by the Board of Supervisors as the Appropriation Ordinance. Each year, the City will update the Budget for the upcoming fiscal year and also set a budget for the subsequent fiscal year, which will be updated and approved in the following year. Enterprise departments do not submit a budget for the second year of the two year budget; rather, estimates of enterprise department budgets in the second year of the budget are incorporated into high-level spending and revenue figures. This dataset and the Appropriation Ordinance departmental views answer the question "How much does each department spend?". To show how much is spent by departments from the General Fund we make the following adjustments to the regular revenues and fund balance & reserves: + Transfers from one department to another (leaving out transfers within the same department) + Recoveries from one department to another (leaving out recoveries within the same department) - GF spent in other funds (this is deducted from GF Sources and added to the other fund's Sources) This is the gross total. By removing the transfers and recoveries that go from one department to the another we see the same net total that is in the Appropriation Ordinance Consolidated Schedule of Sources and Uses. Note that the amount added for transfers into the General Fund that move from one department to another is different than the amount deducted to eliminate the double counting caused by transfers. Transfer Adjustments: To meet accounting needs, money can be moved from one fund or department to another. For example, Public Works provides building maintenance services for the Fire Department for which the Fire Department pays Public Works. To solve this double counting problem, this dataset shows a reduction of $100,000 called Transfer Adjustments (Citywide) to the budgeted spending & revenue for the department providing the service. This lets the dataset display both the gross total of activity for both departments and the net total use of City and County revenues. In the example above, the money is moving both between departments, from Fire to Public Works, and between funds, from General Fund Operating to General Fund Works Orders/Overhead. Transfer Adjustments (Citywide): -Transfer Adjustments (Citywide) are used when money is moved from one department to another. These are deducted from the gross total to create the net total. -Transfer Adjustments are included in the gross total when they are within the same department. A separate sub-object is used to distinguish departmental Transfer Adjustments from Transfer Adjustments (Citywide). -Transfer Adjustments (Citywide) may differ from transfer adjustment lines in other public reports as a result of different approaches used to report transfers; however, the net total will remain the same across this dataset, the Mayor's Budget Book, and the Appropriations Ordinance, with limited exceptions due to error corrections and different methodologies used to present net totals. For more information, contact us. An example of a Transfer Adjustment within a department would be Public Works overhead allocations. Overhead costs cannot easily be isolated to a direct service or unit and so are allocated across those units using accepted accounting methods. Central m

Consumer Spending in Serbia decreased to 1019908.50 RSD Million in the first quarter of 2025 from 1161254.80 RSD Million in the fourth quarter of 2024. This dataset provides - Serbia Consumer Spending- actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2024, consumers in the United States expected to spend over one thousand U.S. dollars on holiday gifts on average. This is the first time the projected spending estimate reached that one thousand-dollar-mark. Holiday shopping The Christmas, or holiday season, is the single most critical sales period of the year for many retailers: this period includes days, such as Black Friday and Cyber Monday, and an increasing amount of Americans also shop online during this busy time. An incredible shopping hubbub is produced during this period, with a staggering 95 percent of U.S. consumers having said they intended to buy something during the Christmas season in 2024. Gift cards and vouchers Christmas is a public holiday in the United States and is celebrated on December 25th each year. It is known as a big economic stimulus for many people to purchase Christmas gifts for their beloved family and friends. After Christmas and New Year’s Eve, retail sales often peak again in January as many people redeem their received Christmas gift cards and vouchers. In fact, over half of U.S. consumers planned to buy gift cards or gift certificates for others. It is a popular gifting option, with many Americans indicating that it can be very convenient.

This dataset provides detailed information on U.S. federal spending for the cost of zoonotic disease monitoring and surveillance programs. Data are gathered from the USAspending.gov database, an open data source that allows to track how federal money is allocated and spent across various countries. We retrieved information at country-level over the period 2008-2023 for more than 130 countries.

Our aim was to estimate the total yearly spending from US-based organisations on zoonotic disease surveillance worldwide. Hence, we focussed on programs related to zoonotic disease monitoring and surveillance funded by the following agencies: Centers for Disease Control and Prevention (CDC), U.S. Agency for International Development (USAID) and Bureau of Global Health, Security and Diplomacy.

The dataset consists on:

(i) country code;

(ii) country name;

(iii) fiscal year;

(iv) Recipient ($), which is the total amount of funds allocated to a given country in a specific year, referring to the legal business addresses of organizations that are awarded with federal contracts, grants or financial assistance;

(v) Performance ($), which is the total amount of funds associated with the country, specifying the local where most of the work takes place, e.g. for a manufacturing contract this indicates the main place where items are produced;

(vi) Spending ($), which is the sum of recipient and performance funds for a country for each available year.

This dataset can serve as a valuable resource not only for researchers, but also for decision-makers as it supports the analysis of federal spending patterns. Despite the data account for U.S. spending for (excluding extranational spending of other big investors, such as the European Union), they are produced based on a consistent approach providing comparable information among years and countries. The usefulness and applicability of this dataset is indeed manifold: it can be used for examining the trends in funding distribution and outcomes, assessing the effectiveness of spending policies, and analyzing the outcome of investment programs over time. Likewise, analysis of trends in spending can be crucial to improve public services, such as healthcare, by identifying gaps and weaknesses, which may lead to the identification of more targeted actions for reducing the risk of zoonotic disease. Moreover, these data can be exploited to forecast economic trends and identify areas which are underfunded and might require future investment.

Survey of Household Spending (SHS), average household spending on detailed food categories.

A. SUMMARY This dataset contains data from financial statements of state committees that (1) contribute to or (2) receive funds from a San Francisco committee which was Primarily Formed for a local election, or (3) filed a Late Reporting Period statement with the SFEC during the 90 days before an election. The search period for financial statements begins two years before an election and runs through the next semi-annual filing deadline. The dataset currently filters by the elections of 2024-03-05 and 2024-11-05. B. HOW THE DATASET IS CREATED During an election period, an automated script runs nightly to examine filings by Primarily Formed San Francisco committees. If a primarily formed committee reports accepting money from or giving money to a second committee, that second committee's ID number is added to a filter list. If a committee electronically files a late reporting period form with the San Francisco Ethics Commission, the committee's ID number is also included in the filter list. The filter list is used in a second step that looks for filings by committees that file with the San Francisco Ethics Commission or the California Secretary of State. This dataset shows the committees that file with the California Secretary of State. The data comes from a nightly export of the Secretary of State's database. A second dataset includes Non-Primarily Formed committees that file with the San Francisco Ethics Commission. C. UPDATE PROCESS This dataset is rewritten nightly based on data derived from campaign filings. The update script runs automatically on a timer during the 90 days before an election. Refer to the "Data Last Updated" date in the section "About This Dataset" on the landing page to see when the script last ran successfully. D. HOW TO USE THIS DATASET Transactions from all FPPC Form 460 schedules are presented together, refer to the Form Type to differentiate. Transactions with a Form Type of D, E, F, G, H, F496, or F497P2 represent expenditures or money spent by the committee. Transactions with Form Type A, B1, C, I, F496P3, and F497P1 represent receipts or money taken in by the committee. Refer to the instructions for Forms 460, 496, and 497 for more details. Transactions on Form 460 Schedules D, F, G, and H are also reported on Schedule E. When doing summary statistics use care not to double count expenditures. Transactions from FPPC Form 496 and Form 497 filings are also in this dataset. Transactions that were reported on these forms are also reported on the Form 460 at the next filing deadline. If a 460 filing deadline has passed and the committee has filed a campaign statement, transactions on filings from the late reporting period should be disregarded. This dataset only shows transactions from the most recent filing version. Committee's amendments overwrite filings which come before in sequence. Campaign Committees are required to file statements according to a schedule set out by the California Fair Political Practices Commission. Depending on timing, transactions which have occurred may not be listed as they might not have been reported yet. E. RELATED DATASETS <a href=

The government is committed to setting new standards for transparency so the public can more easily see how and where taxpayers’ money is being spent and hold politicians, government departments and public bodies to account.

All central government departments must publish details of their spending over £25,000 and publish monthly information.

From January 2016 both administration and programme spend are now collected under the same code so appear as a single spreadsheet. See https://data.gov.uk/dataset/uk-trade-and-investment-spend

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.

IoTeX is a decentralized crypto system, a new generation of blockchain platform for the development of the Internet of things (IoT). The project team is sure that the users do not have such an application that would motivate to implement the technology of the Internet of things in life. And while this will not be created, people will not have the desire to spend money and time on IoT. The developers of IoTeX decided to implement not the application itself, but the platform for creation. It is through the platform that innovative steps in the space of the Internet of things will be encouraged. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? Dowiedz się więcej

The National Archives is the UK government's official archive. We are guardians of some of the most iconic national documents dating back over 1,000 years. We give detailed guidance to government departments and the public sector on information management and advise others about the care of historical archives. Our role is to collect and secure the future of the record, both digital and physical, to preserve it for generations to come, and to make it as accessible and available as possible. Find out how we spend our money, where we receive funding, what income we generate, and how this compares with last year's spending and income with the diagrams and data

Indexes of real expenditure per capita in the United States relative to those in Canada for categories of gross domestic income (GDI), Canada=100, on an International Comparison Project Classification (ICP) basis.