Gasoline Prices in the United States remained unchanged at 0.81 USD/Liter in November. This dataset provides the latest reported value for - United States Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

A structured data that describes how much of my 2021 annual salary was spent each month on; - Accomodating/Housing/Rent (how much I pay as rent every month) - Health/Fitness (how much I spent on gym subscriptions and dietary products and medicines) - Personal expenses (other social and personal expenses) - Utilities/Energy (how much I spent on gas and electricity and water and internet) - Automobiles/Transport (how much I spent on repairs of my car and fuel) - Food/Dining (cost of grocery and dinners I had with friends and family) I made sure I collected receipts and stamps for each item that was purchased and each bill that was paid and everything was totaled at the end of the month. For items that I could not get receipts or stamps, the expense was written in my handbook and captured during the data collection stage.

I categorised all the expenses under these 6 headings and created an excel table for its analysis and visualization purposes. I hope to find out if there's a way I can reduce my expenditure and increase savings and also understand how much of my incomes goes into what particular expenditure.

Gasoline Prices in Mexico increased to 1.27 USD/Liter in November from 1.26 USD/Liter in October of 2025. This dataset provides the latest reported value for - Mexico Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset provides a detailed look at the greenhouse gas emissions associated with various sectors of the economy, measured in terms of CO2 equivalent per monetary unit spent (2021 USD).

By offering insights into both direct emissions and those associated with supply chain margins, it allows for an understanding of the environmental impact of economic activities across different industries.

For data scientists and environmental researchers, this dataset is a treasure of information for analyzing and modeling the carbon footprint of economic activities. Additionally, it supports lifecycle assessment studies, policy formulation for sustainability, and the development of low-carbon investment strategies.

With this dataset, a variety of analyses can be performed, such as:

• Sectoral: Identify industries with the highest GHG emissions per dollar spent, highlighting sectors where improvements in sustainability could have the most significant impact.
• Temporal and Scenario: By integrating this dataset with historical or projected economic data, trends in emissions can be analyzed, and future scenarios can be modeled.
• Efficiency Assessments: Evaluate the emission efficiency of industries, comparing how much economic value is generated per unit of GHG emitted.

The datasets comprise greenhouse gas (GHG) emission factors (Factors) for 1,016 U.S. commodities as defined by the 2017 version of the North American Industry Classification System (NAICS). The Factors are based on GHG data for 2022. Factors are given for all NAICS-defined commodities at the 6-digit level except for electricity, government, and households. Each record consists of three factor types as in the previous releases: Supply Chain Emissions without Margins (SEF), Margins of Supply Chain Emissions (MEF), and Supply Chain Emissions with Margins (SEF+MEF). One set of Factors provides kg carbon dioxide equivalents (CO2e) per 2022 U.S. dollar (USD) for all GHGs combined using 100-yr global warming potentials from IPCC 5th report (AR5) to calculate the equivalents. In this dataset there is one SEF, MEF and SEF+MEF per commodity. The other dataset of Factors provides kg of each unique GHG emitted per 2022 dollar per commodity without the CO2e calculation. The dollar in the denominator of all factors uses purchaser prices. See the supporting file 'Aboutv1.3SupplyChainGHGEmissionFactors.docx' for complete documentation of this dataset.

The City and County Energy Profiles lookup table provides modeled electricity and natural gas consumption and expenditures, on-road vehicle fuel consumption, vehicle miles traveled, and associated emissions for each U.S. city and county. Please note this data is modeled and more precise data may be available from regional, state, or other sources. The modeling approach for electricity and natural gas is described in Sector-Specific Methodologies for Subnational Energy Modeling: https://www.nrel.gov/docs/fy19osti/72748.pdf. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and complements the wealth of data, maps, and charts on the State and Local Planning for Energy (SLOPE) platform, available at the "Explore State and Local Energy Data on SLOPE" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

Gasoline Prices in Japan decreased to 1.09 USD/Liter in November from 1.14 USD/Liter in October of 2025. This dataset provides the latest reported value for - Japan Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The U.S. auto industry sold nearly ************* cars in 2024. That year, total car and light truck sales were approximately ************ in the United States. U.S. vehicle sales peaked in 2016 at roughly ************ units. Pandemic impact The COVID-19 pandemic deeply impacted the U.S. automotive market, accelerating the global automotive semiconductor shortage and leading to a drop in demand during the first months of 2020. However, as demand rebounded, new vehicle supply could not keep up with the market. U.S. inventory-to-sales ratio dropped to its lowest point in February 2022, as Russia's war on Ukraine lead to gasoline price hikes. During that same period, inflation also impacted new and used car prices, pricing many U.S. consumers out of a market with increasingly lower car stocks. Focus on fuel economy The U.S. auto industry had one of its worst years in 1982 when customers were beginning to feel the effects of the 1973 oil crisis and the energy crisis of 1979. Since light trucks would often be considered less fuel-efficient, cars accounted for about ** percent of light vehicle sales back then. Thanks to improved fuel economy for light trucks and cheaper gas prices, this picture had completely changed in 2020. That year, prices for Brent oil dropped to just over ** U.S. dollars per barrel. The decline occurred in tandem with lower gasoline prices, which came to about **** U.S. dollars per gallon in 2020 - and cars only accounted for less than one-fourth of light vehicle sales that year. Four years on, prices are dropping again, after being the highest on record since 1990 in 2022.

This dataset simulates realistic financial transaction patterns and generated by using python code. For the purpose of developing and testing fraud detection models. The dataset was generated to mimic a wide range of transactional scenarios across multiple categories, including retail, grocery, dining, travel, and more, making it ideal for exploring patterns that distinguish legitimate transactions from fraudulent ones.

Financial fraud is an increasingly prevalent issue, with organizations constantly seeking advanced solutions to detect and prevent suspicious activity. This dataset was inspired by real-world transaction data but was generated synthetically to avoid privacy concerns. It includes key features that play a critical role in fraud detection, such as transaction amounts, device types, geographic locations, currency, card type, and a "fraud" label indicating whether a transaction is suspicious.

Comprehensive Transaction Categories: Transactions span categories like retail (online and in-store), groceries, restaurants (fast food to premium), entertainment (streaming, gaming, events), healthcare, education, gas, and travel.

Geographic and Demographic Variety: The dataset includes diverse geographic data (countries, cities) and currency types, allowing for analysis on a global scale with varying risk profiles.

Detailed Customer Profiles: Each transaction is linked to a customer profile that includes characteristics like account age, preferred devices, typical spending range, and fraud-protection features.

Feature-Rich Data for ML and Fraud Analysis: Features like transaction velocity, merchant risk, card presence, and device fingerprints provide an enriched environment for machine learning models to detect anomalies and suspicious patterns.

Use Cases:

This dataset is designed for data scientists, analysts, and machine learning practitioners interested in: Building and training fraud detection models. Exploring financial transaction patterns and consumer behaviors. Developing and testing machine learning algorithms for anomaly detection. With this dataset, users can delve into advanced topics like feature engineering, model evaluation, and performance optimization, especially relevant to fraud detection applications in finance and e-commerce.

This table shows regional figures on the average consumption of energy (natural gas and electricity) of private dwellings broken down by type of dwelling and ownership for Nederland, group of provinces, provinces and municipalities. Besides, for total dwellings only, the share of heat distribution (district heating) has been added, because this is relevant for the interpretation of the height of the average consumption of natural gas.

Data available from: 2010

Status of the figures: All figures from 2010 - 2021 are definite. Figures of 2022 and 2023 are revised provisional. Figures for 2024 are provisional.

Changes as of September 2025: Figures added for 2024. Figures for 2022 and 2023 have been revised based on smart-meter data. These figures are more accurate than figures based on standard yearly consumption data.

Changes as of October 2023: Provisional figures of 2022 have been added. Figures of 2021 have been updated. The category “Average consumption of electricity” is replaced by “Average supply of electricity” and a category “Average net supply of electricity” has been added.

When will new figures be published? A revision to the method of this statistic is currently underway, causing the table to be delayed. New figures will come in the 3rd quarter of the folowing year.

Gasoline Prices in Philippines remained unchanged at 0.96 USD/Liter in November. This dataset provides the latest reported value for - Philippines Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This Alberta Official Statistic describes investment (capital expenditures) in Alberta’s Oil and Gas Industry. Oil and Gas Industry data is obtained from Statistics Canada. Statistics Canada reports this data under the North American Industry Classification System (NAICS) sub-sector 211 (Oil and Gas Extraction). The “Oil and Gas Extraction” sub-sector consists of “Conventional Oil and Gas Extraction” national industry (NAICS 211113) and “Non-Conventional Oil Extraction” (or oil sands) national industry (NAICS 211114).

Comprehensive dataset on Russia's energy sector, economy, and socio-economic metrics from post-Soviet era to future projections.

This dataset provides a historical and projected overview of key economic, energy, and social indicators for Russia spanning from 1991 (post-Soviet dissolution) to 2025 (including forecasts). It focuses on the oil and gas sector, which has been a cornerstone of Russia's economy, alongside broader macroeconomic and demographic metrics. The data is useful for analyzing trends in energy production, exports, fiscal dependencies, inflation, and social inequality during periods of economic transformation, crises (e.g., 1998 ruble crisis, 2014 sanctions), and recent geopolitical events. Key Features:

Time Coverage: Annual data from 1991 to 2025 (with projections for 2024-2025 based on estimates). Rows: 35 (one per year). Columns: 29, covering energy production, prices, exports, fiscal indicators, demographics, and more. File Format: CSV (UTF-8 encoded for compatibility with special characters like en-dash in tax rates). Data Sources: Compiled from public sources including Rosstat, World Bank, IMF, EIA (U.S. Energy Information Administration), and Russian Central Bank reports. Projections for 2024-2025 are estimates based on trends and may require updates. Missing Values: Some fields (e.g., early years for FDI or import volumes) are blank due to data unavailability; handle with imputation if needed.

Column Descriptions

Githab rep https://github.com/AsDo001/Forecasting-of-revenues-to-the-budget-of-the-Russian-Federation

This table contains 165 series, with data for years 2011-2019 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia; ...) ; Energy type (4 items: Total, all energy types; Electricity; Natural gas; Heating oil) ; Energy consumption (4 items: Gigajoules; Gigajoules per household; Proportion of total energy; Number of households).

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

Gasoline Prices in New Zealand increased to 1.48 USD/Liter in November from 1.46 USD/Liter in October of 2025. This dataset provides - New Zealand Gasoline Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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