Cost of food in the United States increased 2.90 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The provided dataset, titled "product_price_dataset.csv," contains information about various products across different categories. It can be used for a project titled "Dynamic Product Price Adjustment Using Machine Learning." The dataset includes the following columns:

1) ProductID: A unique identifier for each product. 2) ProductName: The name of the product. 3) Brand: The brand or company that manufactures the product. 4) Category: The category to which the product belongs (e.g., Laptops, Mobile Phones, Wearable Tech, Home Appliances, etc.). 5) Weight: The weight of the product, typically in kilograms. 6) Dimensions: The dimensions of the product, specified as length x width x height. 7) Material: The primary material used in the construction of the product. 8) Color: The color of the product. 9) Rating: The average rating of the product based on customer reviews, usually on a scale of 1 to 5. 10) NumReviews: The number of customer reviews for the product. 11) Price: The current price of the product.

This dataset contains information about 120 different products spanning various categories such as electronics, home appliances, fitness and health, outdoor and sports equipment, and more. The dataset includes products like laptops, smartphones, headphones, smartwatches, gaming consoles, tablets, cameras, drones, fitness trackers, wireless mice, external hard drives, and many others. With this comprehensive dataset, machine learning techniques can be applied to analyze the relationships between product features (such as brand, category, weight, dimensions, material, color, rating, and number of reviews) and the price. The goal would be to develop a dynamic pricing model that can adjust product prices based on these features, potentially helping businesses optimize their pricing strategies and increase profitability. Additionally, the dataset can be used for other tasks such as product recommendation systems, market segmentation, and demand forecasting, among others.

Value chain disruptions are expected to trigger sudden price changes and increase in price volatility. This is data from the FAO Daily Prices pages which monitors consumer prices of 14 main food products in all countries and compiles the average price change for each product since 14 February 2020.

Natural gas rose to 3.68 USD/MMBtu on June 27, 2025, up 4.25% from the previous day. Over the past month, Natural gas's price has risen 3.34%, and is up 41.32% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Natural gas - values, historical data, forecasts and news - updated on June of 2025.

We construct daily house price indices for 10 major US metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the methodology of the popular monthly Case-Shiller house price indices. Our new daily house price indices exhibit dynamic features similar to those of other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity of the corresponding daily returns. A relatively simple multivariate time series model for the daily house price index returns, explicitly allowing for commonalities across cities and GARCH effects, produces forecasts of longer-run monthly house price changes that are superior to various alternative forecast procedures based on lower-frequency data.

This data package includes the underlying data files to replicate the data, tables, and charts presented in Why Trump’s tariff proposals would harm working Americans, PIIE Policy Brief 24-1.

If you use the data, please cite as: Clausing, Kimberly, and Mary E. Lovely. 2024. Why Trump’s tariff proposals would harm working Americans. PIIE Policy Brief 24-1. Washington, DC: Peterson Institute for International Economics.

This table includes the average increase of rent paid for dwellings in the Netherlands. The rent increase is set per 1 July. Data available from: 1959 Status of the figures: The provisional figures are published in August and relate to the rent increase as implemented in July. The figures become definitive upon publication in September. Disparities between provisional and definitive figures are caused by new source material. Changes as of 4 September 2024: Definitive figures of 2024 have been published. When will new figures be published? Provisional figures of 2025 will be published in August 2025.

Gasoline fell to 2.09 USD/Gal on June 27, 2025, down 0.67% from the previous day. Over the past month, Gasoline's price has fallen 0.26%, and is down 16.73% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on June of 2025.

This table includes the average increase of rent paid for dwellings in the Netherlands. It shows a breakdown regarding the rent change in- and excluding rent harmonisation. Another breakdown is for the commercial and non-commercial rent movements of dwellings. The rent change is given on an annual basis and is significant input for the housing price movements in the consumer price index.

Data available from: 2009

Status of the figures: All values are definite.

Frequency: Discontinued on 10 October 2011.

We offer three easy-to-understand equity data packages to fit your business needs. Visit intrinio.com/pricing to compare packages.

Bronze

The Bronze package is ideal for developing your idea and prototyping your platform with high-quality EOD equity pricing data, standardized financial statement data, and supplementary fundamental datasets.

When you’re ready for launch, it’s a seamless transition to our Silver package for additional data sets, 15-minute delayed equity pricing data, expanded history, and more.

• Historical EOD equity prices & technicals (10 years history)
• Security reference data
• Standardized & as-reported financial statements (5 years history)
• 7 supplementary fundamental data sets

Bronze Benefits:

• Web API access
• 300 API calls/minute limit
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Silver

The Silver package is ideal for startups that are in development, testing, or in the beta launch phase. Hit the ground running with 15-minute delayed and historical intraday and EOD equity prices, plus our standardized and as-reported financial statement data with nine supplementary data sets, including insider transactions and institutional ownership.

When you’re ready to scale, easily move up to the Gold package for our full range of data sets and full history, real-time equity pricing data, premium support options, and much more.

• 15-minute delayed & historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (10 years history)
• 9 supplementary fundamental data sets

Silver Benefits:

• Web API access
• 2,000 API calls/minute limit
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Gold

The Gold package is ideal for funded companies that are in the growth or scaling stage, as well as institutions that are innovating within the fintech space. This full-service solution offers our complete collection of equity pricing data feeds, from real-time to historical EOD, plus standardized financial statement data and nine supplementary feeds.

You’ll also have access to our wide range of modern access methods, third-party data via Intrinio’s API with licensing assistance, support from our team of expert engineers, custom delivery architectures, and much more.

• Real-time equity prices
• Historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (full history)
• 9 supplementary fundamental data sets

Gold Benefits:

• No exchange fees
• No user reporting or variable per-user exchange fees
• High liquidity (6%+)
• Web API & WebSocket access
• 2,000 API calls/minute limit
• Customizable access methods (Snowflake, FTP, etc.)
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support
• Access to engineering team
• Concierge customer success team
• Comarketing & promotional initiatives

Platinum

Don’t see a package that fits your needs? Our team can design premium custom packages for institutions.

Dataset

This dataset was created by Shashin Kumar Sachan

Contents

It contains the following files:

It has long been suspected, given the salience of gasoline prices, that fluctuations in gasoline prices shift households' 1-year inflation expectations. Assessing this view empirically requires the use of dynamic structural models to quantify the cumulative effect of gasoline price shocks on household inflation expectations at each point in time. We find that, on average, gasoline price shocks account for 42% of the variation in these expectations. The cumulative increase in household inflation expectations from early 2009 to early 2013, in particular, is almost entirely explained by unexpectedly rising gasoline prices. However, there is no support for the view that the improved fit of the Phillips curve augmented by household inflation expectations during 2009 2013 is mainly explained by rising gasoline prices.

--- DATASET OVERVIEW --- This dataset provides critical insights into market pricing dynamics, availability patterns, and booking trends with AI-enhanced forecasting capabilities for vacation rental properties across global markets. With daily updates and extensive coverage, it provides a detailed view of pricing strategies, demand patterns, and market positioning for properties across different segments and regions.

The data is sourced directly from major OTA platforms using advanced collection methodologies that ensure high accuracy and comprehensive coverage. Our proprietary algorithms enhance the raw data with AI and machine learning driven booking probability predictions, enabling users to anticipate future booking patterns and occupancy levels with increased precision.

--- KEY DATA ELEMENTS --- Our dataset includes the following core performance metrics for each property: - Property Identifiers: Unique identifiers for each property with OTA-specific IDs - Geographic Information: Location data including neighborhood, city, region, and country - Property Characteristics: Property type, bedroom count, bathroom count, and capacity - Quoted Rates: Price points for each available date - Minimum Stay Requirements: Minimum night requirements for different booking periods - Availability Status: Available/unavailable including guest stay detection for each calendar date - Key Pricing Patterns: Price variations across different seasons and months as well as event driven and other high-demand periods. - Price Positioning: Relative price positioning compared to similar properties in the same area - Historical Price Trends: Price changes over time for the same property and dates

--- USE CASES --- Revenue Management Optimization: Property managers and revenue specialists can leverage this dataset to develop sophisticated dynamic pricing strategies. By analyzing how similar properties adjust pricing based on seasonality, day of week, and market demand, managers can optimize their own pricing to maximize revenue without sacrificing occupancy. The AI-detected guest bookings provide the best context for expected demand, allowing for more precise rate adjustments during different booking windows.

Demand Forecasting and Trend Analysis: Market analysts and tourism organizations can use this dataset to forecast demand patterns across different destinations. The comprehensive availability data, coupled with AI-detected guest bookings, enables accurate prediction of occupancy trends, booking pace, and seasonal fluctuations. These insights support capacity planning, marketing timing, and resource allocation decisions.

Competitive Benchmarking: Property owners and managers can benchmark their pricing and availability strategies against competitors in the same market. The dataset allows for detailed comparison of pricing strategies, minimum stay requirements, and booking pace across similar properties. This competitive intelligence helps identify opportunities for market positioning adjustments and pricing optimization.

Investment Decision Support Real estate investors focused on the vacation rental sector can analyze pricing and occupancy patterns across different markets to identify investment opportunities. The dataset provides insights into rate potential, seasonal demand variations, and overall market performance, supporting data-driven acquisition and portfolio expansion decisions.

Market Entry Analysis Companies considering entering new vacation rental markets can utilize this dataset to understand pricing dynamics, seasonality impacts, and demand patterns before committing resources. The comprehensive pricing and availability data reduces market entry risk by providing clear visibility into potential revenue opportunities and competitive positioning requirements.

Economic Impact Studies Researchers and economic development organizations can leverage this dataset to analyze the economic impact of vacation rentals on local communities. By tracking pricing trends, occupancy patterns, and overall inventory utilization, researchers can quantify the contribution of the vacation rental sector to local economies and tax bases.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: daily | weekly | monthly • Delivery Method: scheduled file deliveries • File Formats: csv | parquet • Large File Format: partitioned parquet • Delivery Channels: Google Cloud | Amazon S3 | Azure Blob • Data Refreshes: daily

Dataset Options: • Coverage: Global (most countries) • Historic Data: Available (2021 for most areas) • Future Looking Data: Available (Current date + 180 days+) • Point-in-Time: Not Available • Aggregation and Filtering Options: • Area/Market • Time Scales (daily, weekly) • Listing Source • Property Characteristics (property types, bedroom counts, amenities, etc.) • Management Practices (professionally managed, by o...

About this dataset

Context

The dataset tabulates the Price population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Price across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Price was 8,261, a 0.12% increase year-by-year from 2022. Previously, in 2022, Price population was 8,251, an increase of 0.87% compared to a population of 8,180 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Price decreased by 244. In this period, the peak population was 8,716 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Price is shown in this column.
• Year on Year Change: This column displays the change in Price population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Price Population by Year. You can refer the same here

Prices of energy activated to maintain a balance in Elia’s control area, as well as the strategic reserve activated to ensure adequacy in the Elia control area. The cumulative prices per minute are indicated for every product category (if the product was actually used). Only regulation-related measures requested by Elia with a view to offsetting imbalances in the control area are included.At the specified time, the most recent available data are collected and displayed as quickly as technically possible. All published values are non-validated values and can therefore only be used for information purposes. Contains the historical data and is refreshed daily.This dataset contains data until 21/05/2024 (before MARI local go-live).

Dataset

This dataset was created by Vivek Prajapati

Released under CC0: Public Domain

Contents

It contains the following files:

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available. Prices for the goods and services used to calculate the CPI are collected in 75 urban areas throughout the country and from about 23,000 retail and service establishments. Data on rents are collected from about 43,000 landlords or tenants. More information and details about the data provided can be found at http://www.bls.gov/cpi

System imbalance prices applied if an imbalance is found between injections and offtakes in a balance responsible parties (BRPs) balance area. When imbalance prices are published on an one minute basis, the published prices have not yet been validated and can therefore only be used as an indication of the imbalance price.Only after the published prices have been validated can they be used for invoicing purposes. Contains the historical data and is refreshed daily.This dataset contains data until 21/05/2024 (before MARI local go-live).

Bitcoin Historical Data (2018-2024) - 15M, 1H, 4H, and 1D Timeframes

Dataset Overview

This dataset contains historical price data for Bitcoin (BTC/USDT) from January 1, 2018, to the present. The data is sourced using the Binance API, providing granular candlestick data in four timeframes: - 15-minute (15M) - 1-hour (1H) - 4-hour (4H) - 1-day (1D)

This dataset includes the following fields for each timeframe: - Open time: The timestamp for when the interval began. - Open: The price of Bitcoin at the beginning of the interval. - High: The highest price during the interval. - Low: The lowest price during the interval. - Close: The price of Bitcoin at the end of the interval. - Volume: The trading volume during the interval. - Close time: The timestamp for when the interval closed. - Quote asset volume: The total quote asset volume traded during the interval. - Number of trades: The number of trades executed within the interval. - Taker buy base asset volume: The volume of the base asset bought by takers. - Taker buy quote asset volume: The volume of the quote asset spent by takers. - Ignore: A placeholder column from Binance API, not used in analysis.

Data Sources

Binance API: Used for retrieving 15-minute, 1-hour, 4-hour, and 1-day candlestick data from 2018 to the present.

File Contents

1. btc_15m_data_2018_to_present.csv: 15-minute interval data from 2018 to the present.
2. btc_1h_data_2018_to_present.csv: 1-hour interval data from 2018 to the present.
3. btc_4h_data_2018_to_present.csv: 4-hour interval data from 2018 to the present.
4. btc_1d_data_2018_to_present.csv: 1-day interval data from 2018 to the present.

Automated Daily Updates

This dataset is automatically updated every day using a custom Python program.
The source code for the update script is available on GitHub:
🔗 Bitcoin Dataset Kaggle Auto Updater

Licensing

This dataset is provided under the CC0 Public Domain Dedication. It is free to use for any purpose, with no restrictions on usage or redistribution.