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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.
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Data is the new oil, and this dataset is a wellspring of knowledge waiting to be tappedđˇ!
Don't forget to upvote and share your insights with the community. Happy data exploration!đĽ°
** For more related datasets: ** https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report/data
Description: Welcome to the world of credit card transactions! This dataset provides a treasure trove of insights into customers' spending habits, transactions, and more. Whether you're a data scientist, analyst, or just someone curious about how money moves, this dataset is for you.
Features: - Customer ID: Unique identifiers for every customer. - Name: First name of the customer. - Surname: Last name of the customer. - Gender: The gender of the customer. - Birthdate: Date of birth for each customer. - Transaction Amount: The dollar amount for each transaction. - Date: Date when the transaction occurred. - Merchant Name: The name of the merchant where the transaction took place. - Category: Categorization of the transaction.
Why this dataset matters: Understanding consumer spending patterns is crucial for businesses and financial institutions. This dataset is a goldmine for exploring trends, patterns, and anomalies in financial behavior. It can be used for fraud detection, marketing strategies, and much more.
Acknowledgments: We'd like to express our gratitude to the contributors and data scientists who helped curate this dataset. It's a collaborative effort to promote data-driven decision-making.
Let's Dive In: Explore, analyze, and visualize this data to uncover the hidden stories in the world of credit card transactions. We look forward to seeing your innovative analyses, visualizations, and applications using this dataset.
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License information was derived automatically
Disposable Personal Income in the United States decreased to 22454.56 USD Billion in May from 22579.58 USD Billion in April of 2025. This dataset provides - United States Disposable Personal Income - 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 Yearâs 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 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.
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.|
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 havenât 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| |**Canât 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|
Thank you to the owners of the online retail dataset. https://www.kaggle.com/roshansharma
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.| | | | | --- | --- | | | | | | | --- | --- | | | |
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View data of PCE, an index that measures monthly changes in the price of consumer goods and services as a means of analyzing inflation.
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.
Name: Characterization of investments profiles on the energy transition for european citizens Summary: The dataset contains: (1) surveyee consent form for the study, (2) different scenarios about the energy transition, (3) determinant factors about those scenarios, (4) socioeconomic description of the surveyee, (5) investment decisions, (6) and household characterization/description. License: cc-BY-SA Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No 891943. Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein. Collection Date: 22/07/2022 Publication Date: 15/10/2023 DOI: 10.5281/zenodo.4455198 Other repositories: Author: University of Deusto Objective of collection: This data was originally collected to analyze quantitatively the decisions of everyday people in relation to their energy consumption and their reactions to specific political interventions. Description: The dataset contains a CSV file file containing data collected from a survey about energy consumption investments. The fields that can be found for each entry are (1) Different scenarios about the energy transition and reactions to those scenarios, (money spent on energy investments, decisions about scenarios, actions taken under a blackout, etc.) (2) Determinant factors about the chosen scenarios in the previous question, which include different choices that could affect your decision about a scenario (3) socioeconomic information about the user (age, country of residence, studies), (4) estimation of the prices of various technologies related to the energy transition and (5) descriptive statistics about the household living situation (gender of user, people living in household, yearly rent, average savings per month, type of house, size of house) and also includes questions about climate change expertise. Next you can found a description of each field in the dataset Section 1 - Scenarios for energy transition. ID90. Rank in order of priority, from top to bottom, in which scenario you will be willing to live or to contribute/invest to make it possible. ID36, ID38, ID43, ID44, ID72. Percentage of money people are willing to spend/save out of their income per scenario ID191, ID192.. Amount of money people would spend based on an assumed case. ID191, ID192. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority for you and 10 stars means it is absolutely necessary for you. [ID325, ID326, ID327, ID328, ID329, ID330, ID331, ID332, ID333, ID334, ID335, ID336, ID337, ID338, ID339, ID340, ID341, ID133, ID242]. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary. [ID251, ID256, ID257, ID292, ID293, ID294, ID295, ID296, ID297, ID298, ID299, ID301, ID302, ID303, ID304, ID305, ID306, ID250, ID251]. Priority service provision in case of full black-outs. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary. [ID141, ID5, ID147]. Used for statements that best represent survey responder Section 2 - Determinants (factors). Questions used to rate (from 0 to 100) factors that may influence the decision-making process contributing to make an ideal scenario possible. ID100 Risk profile ID101 Added value ID102 Self-Satisfaction ID103 Technical Fit ID104 Own competence ID105 Knowledge ID106 Cost-Efficiency ID107 Safety ID108 Trust ID109 Autarky ID110 Legal ID111 Climate Protection ID112 Wellbeing ID113 Coziness ID114 Rights and Duties ID115 Peer-Pressure ID116 Socialising ID117 Support ID118 Agreement ID119 Brag ID120 Fun ID121 Novelty ID122 Trends ID123 Authority ID124 Own Significance ID125 Poseur ID2 Frugality ID3 Environmental concerns ID31 Adherence ID52 Commitment ID97 Profits ID99 Credit Score Section 3 - âSocio-economicâ description. Questions about the socio-economic information of the survey respondents for data stratification. The indentation represents the dependency of questions and whether this data was asked ID164 Understanding of questions ID300 Country of residence ID137 Age ID178 Highest level of education ID136 Willingness to provide data on the investment decision (respond apply for -Investment decision section) Section 4 - Investment decision. Questions about specific prices of potential purchases-decisions related to four scenarios (respondent's lifestyle) Appliances ID42 Affordable cost of a Regular refrigerator ID45 Energy efficient refrigerator costs ID50 Willingness to purchase an energy efficient refrigerator ID65 Why no ID66 affordable cost of an energy efficient option ID67 Years to amortize an efficient option Insulation ID47 Affordable cost of updating to a state of the art insulation on the facade ID56 Willingness for paying/invest ID74 Why no? ID20 affordable cost of an energy efficient option ID34 Years to amortize an energy efficient option Energy Generation ID68 Affordable cost of a solar photovoltaic system ID76 Willingness for paying/invest ID84 Why no? ID132 Affordable cost of a photovoltaic system ID138 Years that amortize a photovoltaic system Energy Storage ID142 Affordable cost of an energy storage system ID146 Willingness for paying/invest ID181 Why no? ID182 Affordable cost of an energy storage system ID183 Years that amortize an energy storage systems Heating ID140 Affordable cost of a gas boiler ID209 Affordable cost of an energy efficient heating system ID217 Willingness for paying/invest ID238 Why no? ID239 Affordable cost of a energy efficient option ID241 Years that amortize a heat pumps Mobility ID41 Average kilometers traveled a typical day ID51 Usual travel option ID264 Affordable cost of a diesel or gasoline mid-range brand new car ID265 Affordable cost of a mid-range brand new electric car ID281 Willingness to buy an electric car ID289 Why no? ID290 Affordable price of an electric car ID291 Years that amortize an electric car Section 5 - Household characterization ID127 Selecting an asked value ID189 Type of living area ID202 Gender identity ID1 Those living in the house ID32 Number of inhabitants ID220 Average neat yearly income ID229 Average monthly saving ID240 Type of housing ID249 Owner / co-owner ID255 Usable area of the property (m²) ID263 Insulation level ID270 Climate zone ID86 Level of self-awareness about climate change. On scale of 0-10, where 0 is âclimate change does not existâ and 10 is âI am a climate change expert/activistâ ID87 Level of awareness of climate change among your peers or relatives, On a scale of 0-10, where 0 is âclimate change does not existâ and 10 is âThey are climate change experts/activistsâ ID88 Level of self-awareness about energy transition. On a scale of 0-10, where 0 is âIt is the first time I hear about itâ and 10 is âI am an expert or activistâ ID89 Level of awareness of energy transition among your peers or relatives On a scale of 0-10, where 0 is âIt is the first time they hear about itâ and 10 is âThey are experts or activistsâ ID190 feedback about survey 5 star: âââ Preprocessing steps: anonymization, data fusion, imputation of gaps. Reuse: NA Update policy: No more updates are planned Ethics and legal aspects: Spanish electric cooperative data contains the CUPS (Meter Point Administration Number), which is personal data. A pre-processing step has
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Money Supply M2 in the United States increased to 21942 USD Billion in May from 21862.40 USD Billion in April of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.
This data came from a survey of students. The purpose of the survey was to identify attitudes and habits regarding food consumption at school and outside of school.
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Government Debt in the United States decreased to 36211469 USD Million in June from 36215818 USD Million in May of 2025. This dataset provides - United States Government Debt- actual values, historical data, forecast, chart, statistics, economic calendar and news.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Tourism Revenues in the United States increased to 21584 USD Million in April from 20071 USD Million in March of 2025. This dataset provides - United States Tourism Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.
As of April 2024, Facebook had an addressable ad audience reach 131.1 percent in Libya, followed by the United Arab Emirates with 120.5 percent and Mongolia with 116 percent. Additionally, the Philippines and Qatar had addressable ad audiences of 114.5 percent and 111.7 percent.
As of January 2024, #love was the most used hashtag on Instagram, being included in over two billion posts on the social media platform. #Instagood and #instagram were used over one billion times as of early 2024.
As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platformâs global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.
Instagramâs Global Audience
As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.
Who is winning over the generations?
Even though Instagramâs audience is almost twice the size of TikTokâs on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.
U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Marylandâs high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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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.