The main stock market index of United States, the US500, rose to 6074 points on June 24, 2025, gaining 0.80% from the previous session. Over the past month, the index has climbed 2.57% and is up 11.05% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

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.

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.

South African monthly The FTSE/JSE All Share Index data was procured from Bloomberg and the nominal effective exchange rate (NEER) from South African Reserve Bank (SARB) database, where the data has been seasonally adjusted specifying 2015 as the base year. Volatility measures in these markets are generated through a multivaraite EGARCH model in the WinRATS software. South African monthly consumer price index (CPI) data was procured from the International Monetary Fund’s International Financial Statistics (IFS) database, where the data has been seasonally adjusted, specifying 2010 as the base year. The inflation rate is constructed by taking the year-on-year changes in the monthly CPI figures. Inflation uncertainty was generated through the GARCH model in Eviews software. The following South African macroeconomic variables were procured from the SARB: real industrial production (IP), which is used as a proxy for real GDP, real investment (I), real consumption (C), inflation (CPI), broad money (M3), the 3-month treasury bill rate (TB3) and the policy rate (R), a measure of U.S. EPU developed by Baker et al. (2016) to account for global developments available at http://www.policyuncertainty.com/us_monthly.html.

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.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Gold fell to 3,331.18 USD/t.oz on June 24, 2025, down 1.11% from the previous day. Over the past month, Gold's price has fallen 0.46%, but it is still 43.58% 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.

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.

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

Analysis of ‘California Housing Prices Data (5 new features!)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/fedesoriano/california-housing-prices-data-extra-features on 28 January 2022.

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

Similar Datasets:

Boston House Prices: LINK

Context

This is the dataset is a modified version of the California Housing Data used in the paper Pace, R. Kelley, and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33.3 (1997): 291-297.. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being too toyish and too cumbersome.

The data contains information from the 1990 California census. So although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning.

Modifications with respect to the original data

This dataset includes 5 extra features defined by me: "Distance to coast", "Distance to Los Angeles", "Distance to San Diego", "Distance to San Jose", and "Distance to San Francisco". These extra features try to account for the distance to the nearest coast and the distance to the centre of the largest cities in California.

The distances were calculated using the Haversine formula with the Longitude and Latitude:

https://wikimedia.org/api/rest_v1/media/math/render/svg/a65dbbde43ff45bacd2505fcf32b44fc7dcd8cc0" alt="">

where:

• phi_1 and phi_2 are the Latitudes of point 1 and point 2, respectively
• lambda_1 and lambda_2 are the Longitudes of point 1 and point 2, respectively
• r is the radius of the Earth (6371km)

Content

The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. The columns are as follows, their names are pretty self-explanatory:

1) Median House Value: Median house value for households within a block (measured in US Dollars) [$] 2) Median Income: Median income for households within a block of houses (measured in tens of thousands of US Dollars) [10k$] 3) Median Age: Median age of a house within a block; a lower number is a newer building [years] 4) Total Rooms: Total number of rooms within a block 5) Total Bedrooms: Total number of bedrooms within a block 6) Population: Total number of people residing within a block 7) Households: Total number of households, a group of people residing within a home unit, for a block 8) Latitude: A measure of how far north a house is; a higher value is farther north [°] 9) Longitude: A measure of how far west a house is; a higher value is farther west [°] 10) Distance to coast: Distance to the nearest coast point [m] 11) Distance to Los Angeles: Distance to the centre of Los Angeles [m] 12) Distance to San Diego: Distance to the centre of San Diego [m] 13) Distance to San Jose: Distance to the centre of San Jose [m] 14) Distance to San Francisco: Distance to the centre of San Francisco [m]

Source

This data was entirely modified and cleaned by me. The original data (without the distance features) was initially featured in the following paper: Pace, R. Kelley, and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33.3 (1997): 291-297.

The original dataset can be found under the following link: https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html

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

Wheat fell to 547.64 USd/Bu on June 24, 2025, down 0.92% from the previous day. Over the past month, Wheat's price has risen 0.72%, but it is still 2.29% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Wheat - values, historical data, forecasts and news - updated on June of 2025.

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 ‘Wine Rating & Price’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/budnyak/wine-rating-and-price on 28 January 2022.

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

Context

I was looking for educational wine dataset with understandable features and suitable for creating ML model for my first DS project. I couldn't find anything relevant, so decided to scrap data from Vivino.com

Content

Data contains 4 files for each winestyle: red, white, rose and sparkling. Also there is a file with wine varieties for further analysis. Files has 8 columns with quite obvious names, but maybe I should add that NumberOfRatings is the number of people who rated this wine.

Inspiration

Analyzing data presented on Vivino.com, I noticed that there are no bottles that have less than 25 ratings, apparently because the company considers the rating of such wines is not accurate enough. So, I had an idea to perfom ML model for predicting the rating of bottles with a small number of ratings. I realised this idea in my project, public notebook with which I also upload here.

As it turned out, the problem of reviews distribution is exists in many spheres. It consists in the fact that customers are often afraid of choosing a product or service that no one has ever bought before. Due to this, many businesses lose large amounts of money on the unnormal distribution of customers by product. My idea is to create a model for any such business that predicts the rating based on other features, it can help to increase the demand for new, but promising products.

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

Palm Oil rose to 4,102 MYR/T on June 19, 2025, up 0.02% from the previous day. Over the past month, Palm Oil's price has risen 4.91%, and is up 3.66% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Palm Oil - values, historical data, forecasts and news - updated on June of 2025.

Corn fell to 418.95 USd/BU on June 24, 2025, down 0.07% from the previous day. Over the past month, Corn's price has fallen 8.73%, and is down 5.43% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on June of 2025.

Interactive chart of historical daily platinum prices back to 1985. The price shown is in U.S. Dollars per troy ounce.

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.

Context

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the all_stocks_5yr.csv and corresponding folder) and a smaller version of the dataset (all_stocks_1yr.csv) with only the past year's stock data for those wishing to use something more manageable in size.

The folder individual_stocks_5yr contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_5yr.csv and all_stocks_1yr.csv contain this same data, presented in merged .csv files. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd Open - price of the stock at market open (this is NYSE data so all in USD) High - Highest price reached in the day Low Close - Lowest price reached in the day Volume - Number of shares traded Name - the stock's ticker name

Acknowledgements

I scraped this data from Google finance using the python library 'pandas_datareader'. Special thanks to Kaggle, Github and The Market.

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!