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.

Eggs US fell to 2.54 USD/Dozen on June 27, 2025, down 1.44% from the previous day. Over the past month, Eggs US's price has fallen 1.36%, and is down 0.86% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Eggs US.

Inflation Rate in the United States increased to 2.40 percent in May from 2.30 percent in April of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.

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

Context

Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

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New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

Content

Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

Dataset

The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Starter Kernel(s)

• Simple LSTM Regression

Acknowledgements

Mining and updating of this dateset will depend upon Yahoo Finance .

Inspiration

Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

Some Readings

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

*If you download and find the data useful your upvote is an explicit feedback for future works*

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

Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• Close - close price adjusted for splits
• Adj Close - adjusted close price adjusted for both dividends and splits.
• Volume - the number of shares that changed hands during a given day

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

This Cost of International Education dataset compiles detailed financial information for students pursuing higher education abroad. It covers multiple countries, cities, and universities around the world, capturing the full tuition and living expenses spectrum alongside key ancillary costs. With standardized fields such as tuition in USD, living-cost indices, rent, visa fees, insurance, and up-to-date exchange rates, it enables comparative analysis across programs, degree levels, and geographies. Whether you’re a prospective international student mapping out budgets, an educational consultant advising on affordability, or a researcher studying global education economics, this dataset offers a comprehensive foundation for data-driven insights.

Description

Potential Uses

• Budget Planning Prospective students can filter by country, program level, or university to forecast total expenses and compare across destinations.
• Policy Analysis Educational policymakers and NGOs can assess the affordability of international education and design support programs.
• Economic Research Economists can correlate living-cost indices and tuition levels with enrollment rates or student demographics.
• University Benchmarking Institutions can benchmark their fees and ancillary costs against peer universities worldwide.

Notes on Data Collection & Quality

• Currency Conversions All monetary values are unified to USD using contemporaneous exchange rates to facilitate direct comparison.
• Living Cost Index Derived from reputable city-index publications (e.g., Numbeo, Mercer) to standardize disparate cost-of-living metrics.
• Data Currency Exchange rates and fee schedules should be periodically updated to reflect market fluctuations and policy changes.

Feel free to explore, visualize, and extend this dataset for deeper insights into the true cost of studying abroad!

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).

Analysis of ‘Paris Housing Price Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mssmartypants/paris-housing-price-prediction on 28 January 2022.

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

Context

This is a set of data created from imaginary data of house prices in an urban environment - Paris. I recommend using this dataset for educational purposes, for practice and to acquire the necessary knowledge. What I'm trying to do next is to create a classification dataset with same data from this dataset, I'll add a new column for class attribute ofc. Here is a classification dataset ---> classification dataset <---

Content

What's inside is more than just rows and columns. You can see house details listed as column names.

Description

All attributes are numeric variables and they are listed bellow:

• squareMeters
• numberOfRooms
• hasYard
• hasPool
• floors - number of floors
• cityCode - zip code
• cityPartRange - the higher the range, the more exclusive the neighbourhood is
• numPrevOwners - number of prevoious owners
• made - year
• isNewBuilt
• hasStormProtector
• basement - basement square meters
• attic - attic square meteres
• garage - garage size
• hasStorageRoom
• hasGuestRoom - number of guest rooms
• price - predicted value

Inspiration

Idea was to create dataset that is good for regression and that gives adequate results.

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

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 a quarter-hourly 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. The records for month M are validated after the 15th of month M+1. Contains the historical data and is refreshed daily.This dataset contains data until 21/05/2024 (before MARI local go-live).

This Stock Market Dataset is designed for predictive analysis and machine learning applications in financial markets. It includes 13647 records of simulated stock trading data with features commonly used in stock price forecasting.

🔹 Key Features Date – Trading day timestamps (business days only) Open, High, Low, Close – Simulated stock prices Volume – Trading volume per day RSI (Relative Strength Index) – Measures market momentum MACD (Moving Average Convergence Divergence) – Trend-following momentum indicator Sentiment Score – Simulated market sentiment from financial news & social media Target – Binary label (1: Price goes up, 0: Price goes down) for next-day prediction This dataset is useful for training hybrid deep learning models such as LSTM, CNN, and Attention-based networks for stock market forecasting. It enables financial analysts, traders, and AI researchers to experiment with market trends, technical analysis, and sentiment-based predictions.

This table includes figures on year-on-year developments of expenditure categories of the Harmonised consumer price index (HICP). This table also contains the weighting coefficient. The weighting coefficient shows how much consumers in the Netherlands spend on a product group in relation to their total expenditure.

Furthermore, the table shows the contribution and impact of HICP categories. The contributions of the separate groups add up to the total annual rate of change and show the share of price increases. The impact, on the other hand, answers the question how much higher or lower the annual rate of change would have been, if a specific category would not have been taken into account in calculation. The figures are shown for 141 product groups in 2025. Furthermore, 34 combinations of product groups (special aggregates) are displayed.

HICP figures are published every month. In addition, an annual figure is published at the end of the year. The HICP of a calendar year is calculated as the average of the indices of the twelve months of that year.

Data available from: January 2016.

Status of the figures: The HICP figures in this table are in most cases final immediately upon publication. The figures of the HICP are only marked as provisional in the second publication if it is already known at the time of publication that data are still incomplete, a revision is expected in a later month, or in special circumstances such as the corona crisis.

In most cases, all requested price information is known to Statistics Netherlands when the results are published and no adjustment is made later. However, sometimes certain price information is not available in time and the outcome can be adjusted later. HICP results can then always be revised together with the CPI results, even if they were not published as provisional in the previous month. CPI results are marked as provisional when the index figures are first published, the figures are final the following month.

Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented.

Changes as of 13 February 2025: Starting in the reporting month of January 2025, a price change is published for expenditure category 103000 Post-secondary non-tertiary education. The base period for this index series is December 2024.

Changes as of 23 January 2025: Starting in the reporting month of January 2024, a price change is published for expenditure category 063000 Hospital Services. The base period for this index series is December 2023. Starting from the reporting month of December 2024 a year-on-year change, contribution and impact can be determined. The figures of 2024 for this category have been added to the table.

Changes as of 9 June 2022: The unit of the contribution to annual rate of change and the impact on the annual rate of change has been adjusted to 'percentage point'. Previously, the unit was incorrectly referred to as 'percent' in the table.

When will new figures be published? New figures will usually be published between the first and second Thursday of the month following on the reporting month.

All CPI and HICP publications are announced on the publication calendar.

This dataset provides historical stock data for SPDR S&P 500 ETF Trust (SPY), a widely traded exchange-traded fund (ETF), up to the most recent date. It includes daily market data such as Open, High, Low, and Close prices, as well as trading Volume. Additionally, it features data on Dividends, Stock Splits, and Capital Gains, making it an extensive resource for financial analysis, investment research, and machine learning applications. Content

The dataset contains the following columns: - Date: The trading date (timestamp format). - Open: The stock's opening price for the trading day. - High: The highest price during the trading day. - Low: The lowest price during the trading day. - Close: The stock's closing price for the trading day. - Volume: The total number of shares traded during the day. - Dividends: Dividend amounts paid per share.

Use Cases

This dataset is ideal for: - Time Series Analysis: Forecasting stock prices using techniques such as ARIMA, LSTM, or Prophet. - Exploratory Data Analysis (EDA): Understanding price movements, dividend trends, and trading activity. - Machine Learning Models: Predicting future stock prices or constructing algorithmic trading strategies.

This table contains consumer prices for electricity and gas. Weighted average monthly prices are published broken down into transport rate, delivery rates and taxes, both including and excluding VAT. These prices are published on a monthly basis. The prices presented in this table were used to compile the CPI up to May 2023. Prices for newly offered contracts were collected. Contract types that are no longer offered, but have been in previous reporting periods, are imputed. The average can therefore diverge from the prices paid for energy contracts by Dutch households. Data available from January 2018 up to May 2023. Status of the figures: The figures are definitive. Changes as of 17 July 2023: This table will no longer be updated. Due to a change in the underlying data and accompanying method for calculcating average energy prices, a new table was created. See paragraph 3. Changes as of 13 February: Average delivery rates are not shown in this table from January 2023 up to May 2023. With the introduction of the price cap, the average energy rates (delivery rates) of fixed and variable energy contracts together remained useful for calculating a development for the CPI. However, as a pricelevel, they are less useful. Average energy prices from January 2023 up to May 2023 are published in a customized table. In this publication, only data concerning new variable contracts are taken into account When will new figures be published? Does not apply.

Gold fell to 3,273.67 USD/t.oz on June 27, 2025, down 1.65% from the previous day. Over the past month, Gold's price has fallen 0.38%, but it is still 40.72% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on June of 2025.

Interactive chart illustrating the performance of the Dow Jones Industrial Average (DJIA) market index over the last ten years. Each point of the stock market graph is represented by the daily closing price for the DJIA. Historical data can be downloaded via the red button on the upper left corner of the chart.

Large white, Grade A chicken eggs, sold in a carton of a dozen. Includes organic, non-organic, cage free, free range, and traditional."

This table shows the average purchase price that has been paid in the reporting period for existing own homes purchased by a private individual. The average purchase price of existing own homes may differ from the price index of existing own homes. The average purchase price is no indicator for price developments of owner-occupied residential property. The average purchase price reflects the average price of dwellings sold in a particular period. The fact that de dwellings sold differs from one period to another is not taken into account. The following instance explains which problems are entailed by the continually changing of the quality of the dwellings sold. Suppose in February of a particular year mainly big houses with extensive gardens beautifully situated alongside canals are sold, whereas in March many small terraced houses are sold. In that case the average purchase price in February will be higher than in March but this does not mean that house prices are increased. See note 3 for a link to the article 'Why the average purchase price is not an indicator'.

Data available from: 1995

Status of the figures: The figures in this table are immediately definitive. The calculation of these figures is based on the number of notary transactions that are registered every month by the Dutch Land Registry Office (Kadaster). A revision of the figures is exceptional and occurs specifically if an error significantly exceeds the acceptable statistical margins. The average purchasing prices of existing owner-occupied sold homes can be calculated by Kadaster at a later date. These figures are usually the same as the publication on Statline, but in some periods they differ. Kadaster calculates the average purchasing prices based on the most recent data. These may have changed since the first publication. Statistics Netherlands uses figures from the first publication in accordance with the revision policy described above.

Changes as of 17 February 2025: Added average purchase prices of the municipalities for the year 2024.

When will new figures be published? New figures are published approximately one to three months after the period under review.

Monthly average retail prices for selected products, for Canada and provinces. Prices are presented for the current month and the previous four months. Prices are based on transaction data from Canadian retailers, and are presented in Canadian current dollars.