The price of emissions allowances (EUA) traded on the European Union's Emissions Trading Scheme (ETS) exceed 100 euros per metric ton of CO₂ for the first time in February 2023. Although average annual EUA prices have increased significantly since the 2018 reform of the EU-ETS, they fell ** percent year-on-year in 2024 to ** euros. What is the EU-ETS? The EU-ETS became the world’s first carbon market in 2005. The scheme was introduced as a way of limiting GHG emissions from polluting installations by putting a price on carbon, thus incentivizing entities to reduce their emissions. A fixed number of emissions allowances are put on the market each year, which can be traded between companies. The number of available allowances is reduced each year. The EU-ETS is now in its fourth phase (2021 to 2030). Carbon price comparisons The EU ETS has one of the highest average annual carbon prices worldwide, averaging ** U.S. dollars as of April 2025. In comparison, prices for UK ETS carbon credits averaged 57 U.S. dollars during same period, while those under the Regional Greenhouse Gas Initiative (RGGI) in the United States averaged just ** U.S. dollars.

Prices for EU Carbon Permits including live quotes, historical charts and news. EU Carbon Permits was last updated by Trading Economics this December 2 of 2025.

The average closing spot price of European Emission Allowances (EUAs) has increased notably since reforms were made to the EU ETS in 2018. In 2022, the average closing spot price of CO₂ EUAs increased by roughly ** percent to **** euros per metric ton of CO₂.

The average annual price of European Union Emissions Trading System (EU ETS) allowances fell ** percent year-on-year in 2024, to ** euros. Still, EU ETS carbon allowances are forecast to rise to almost *** euros by the end of the decade. Each EU ETS emissions allowance (EUA) gives the holder the right to emit one metric ton of carbon dioxide equivalent.

An accurate prediction of carbon pricing is essential in carbon emission management, and also provides an important role for governments to formulate corresponding policies. However, due to the inherent complexity and dynamics of carbon price sequence, the effectiveness of different decomposition algorithms for carbon price remains to be tested. In addition, existing studies lack a systematic framework to explore the organic integration of external factors and secondary decomposition technology, and the feature processing of complex external factors still needs to be improved. In order to overcome the shortcomings of existing research, This paper presents a Variational Modal Decomposition(VMD) algorithm and a Complete Ensemble Empirical Mode Decomposition with Adaptive Second decomposition technology of Noise(CEEMDAN) decomposition algorithm, and extract the features of external factors by Extreme Gradient Boosting (XGBoost) algorithm. The HI-VMD-PE-CEEMDAN-XGBoost-Transformer model for predicting carbon price is constructed by the combined Transformer algorithm. Specifically, first, we use Hampel identifer(HI) to detect and rectify the anomalies in the original sequence. After applying Variational Mode Decomposition(VMD) decomposition algorithm, Permutation Entropy(PE) is utilized to reassemble the decomposed component. Quadratic Decomposition is performed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN) algorithm. Then, the XGBoost algorithm is employed to extract features of external factors and screen key factors as predictive input variables. Finally, Transformer, which has stronger capability of large-scale data parallel processing, is selected as the prediction model to achieve a more scientific and effective carbon price prediction. The empirical analysis results based on EU carbon market data verify the validity and superiority of the proposed model in different forecasting scenarios.

This dataset contains product prices from Amazon USA, with a focus on price prediction. With a good amount of data on what price points sell the most, you can train machine learning models to predict the optimal price for a product based on its features and product name.

If you find this dataset useful, make sure to show your appreciation by upvoting! ❤️✨

Inspirations

This dataset is a superset of my Amazon USA product price dataset. Another inspiration is this competition that awareded 100K Prize Money

What To Do?

• Your objective is to create a prediction model that will assist sellers in pricing their products within the optimal price range to generate the most sales.
• The dataset includes various data points, such as the number of reviews, rating, best seller status, and items sold last month.
• You can select specific factors (e.g., over 100 reviews = optimal price for the product) and then divide the dataset into products priced optimally vs products priced unoptimally.
• By utilizing techniques like vectorizing product names and features, you can train a model to provide the optimal price for a product, which sellers or businesses might find valuable.

How to know if a product sells?

• I would prefer to use the number of reviews as a metric to determine if a product sells. More reviews = more sales, right?
• According to one source only 1-2% of buyers leave a review
• So if we multiply the reviews for a product by 50x, then we would get a good understanding how many units has sold.
• If we then multiple the product price by number of units sold, we'd get the total revenue generated by the product

How is this useful?

• Sellers and businesses can leverage your model to determine the optimal price for their products, thereby maximizing sales.
• Businesses can assess the profitability of a product and plan their supply chain accordingly.

🚗 2025 Used Car Market Dataset 🚗 This dataset is carefully prepared for data scientists, analysts, and researchers who want to analyze the 2025 used car market. With approximately 2,500 rows and 13 different features, this dataset serves as a powerful resource for exploring pricing trends, brand-model preferences, and vehicle history.

📊 Dataset Contents:

price → Vehicle price brand → Brand model → Model year → Manufacturing year mileage → Mileage information title_status → Vehicle title status (Clean, Salvage, etc.) color → Color information vin, lot → Vehicle identification details 🎯 Use Cases: ✔️ Machine learning projects – Price prediction, regression models ✔️ Data analysis & visualization – Analyzing market trends ✔️ Used car market research

🔹 This dataset is clean, well-structured, and ready for use—start your analysis right away! We’d love to hear feedback from the Kaggle community. 🚀

👉 Let’s explore this data and uncover valuable insights together! 💡

Graph and download economic data for Import Price Index by Origin (NAICS): All Industries for European Union (EECTOT) from Dec 1990 to Aug 2025 about imports, commodities, price index, indexes, price, and USA.

Explore the Redfin USA Properties Dataset, available in CSV format. This extensive dataset provides valuable insights into the U.S. real estate market, including detailed property listings, prices, property types, and more across various states and cities. Perfect for those looking to conduct in-depth market analysis, real estate investment research, or financial forecasting.

Key Features:

• Comprehensive Property Data: Includes essential details such as listing prices, property types, square footage, and the number of bedrooms and bathrooms.
• Geographic Coverage: Encompasses a wide range of U.S. states and cities, providing a broad view of the national real estate market.
• Historical Trends: Analyze past market data to understand price movements, regional differences, and market trends over time.
• Geo-Location Details: Enables spatial analysis and mapping by including precise geographical coordinates of properties.

Who Can Benefit From This Dataset:

• Real Estate Investors: Identify lucrative opportunities by analyzing property values, market trends, and regional price variations.
• Market Analysts: Gain a deeper understanding of the U.S. housing market dynamics to inform research and reporting.
• Data Scientists and Researchers: Leverage detailed real estate data for modeling, urban studies, or economic analysis.
• Financial Analysts: Utilize the dataset for financial modeling, helping to predict market behavior and assess investment risks.

Download the Redfin USA Properties Dataset to access essential information on the U.S. housing market, ideal for professionals in real estate, finance, and data analytics. Unlock key insights to make informed decisions in a dynamic market environment.

Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
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Dataset

This dataset was created by Omkar Suryawanshi

Released under CC0: Public Domain

Contents

This dataset contains the predicted prices of the asset TIKTOK USA over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This dataset contains the predicted prices of the asset USA TRIP day #1 over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Context

This data gives different sales prices with respect to type of houses in USA

Content

There are 72 Variables gives house property and predicted variable is in last Sales price of the house

Acknowledgements

Please compare all the variable with respect to sales price and try to create different model, come up with the solution for Sales price predictions of the house

Inspiration

business probes is predicting sales price

Corn rose to 433.53 USd/BU on December 2, 2025, up 0.01% from the previous day. Over the past month, Corn's price has fallen 0.17%, but it is still 2.43% higher than a year ago, 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 December of 2025.

Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q3 2025 about appraisers, HPI, housing, price index, indexes, price, and USA.

This dataset provides monthly, quarterly and annual average regular or premium unleaded gasoline pump prices, taxes and ex-tax pump prices in Canada, USA, France, Germany, Britain and Japan, all converted to Canadian cents per litre.

To view charts and current fuel price data you can also "https://www.ontario.ca/page/motor-fuel-prices">visit the motor fuel prices page.

*[USA]: United States of America

This dataset contains USA Daily RBOB Regular Gasoline Spot Price form 2003. Data from US Energy Information Administration. Follow datasource.kapsarc.org for timely data to advance energy economics research.Notes:RBOB: "Reformulated Gasoline Blendstock for Oxygenate Blending" is motor gasoline blending components intended for blending with oxygenates to produce finished reformulated gasoline.Regular Gasoline: Gasoline having an antiknock index (average of the research octane rating and the motor octane number) greater than or equal to 85 and less than 88. Note: Octane requirements may vary by altitude. Los Angeles Reformulated RBOB Regular Gasoline Spot Price (Dollars per Gallon)

This dataset contains the predicted prices of the asset Trump USA over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Sugar prices, USA in , October, 2025 For that commodity indicator, we provide data from January 1960 to October 2025. The average value during that period was 0.45 USD per kilogram with a minimum of 0.12 USD per kilogram in January 1960 and a maximum of 1.26 USD per kilogram in November 1974. | TheGlobalEconomy.com

House price index in the USA, June, 2025 The most recent value is 233.5 index points as of Q2 2025, a decline compared to the previous value of 234.03 index points. Historically, the average for the USA from Q1 1990 to Q2 2025 is 113.54 index points. The minimum of 54.35 index points was recorded in Q1 1991, while the maximum of 234.03 index points was reached in Q1 2025. | TheGlobalEconomy.com