The unemployment rate in fiscal year 2204 rose to 3.9 percent. The unemployment rate of the United States which has been steadily decreasing since the 2008 financial crisis, spiked to 8.1 percent in 2020 due to the COVID-19 pandemic. The annual unemployment rate of the U.S. since 1990 can be found here. Falling unemployment The unemployment rate, or the part of the U.S. labor force that is without a job, fell again in 2022 after peaking at 8.1 percent in 2020 - a rate that has not been seen since the years following the 2008 financial crisis. The financial crash caused unemployment in the U.S. to soar from 4.6 percent in 2007 to 9.6 percent in 2010. Since 2010, the unemployment rate had been steadily falling, meaning that more and more people are finding work, whether that be through full-time employment or part-time employment. However, the affects of the COVID-19 pandemic created a spike in unemployment across the country. U.S. unemployment in comparison Compared to unemployment rates in the European Union, U.S. unemployment is relatively low. Greece was hit particularly hard by the 2008 financial crisis and faced a government debt crisis that sent the Greek economy into a tailspin. Due to this crisis, and the added impact of the pandemic, Greece still has the highest unemployment rate in the European Union.

Graph and download economic data for Longer Run FOMC Summary of Economic Projections for the Civilian Unemployment Rate, Central Tendency, Midpoint (UNRATECTMLR) from 2009-02-18 to 2025-06-18 about projection, civilian, unemployment, rate, and USA.

Context

This data is related to HackerEarth's Customer Churn Rate Prediction Challenge

Content

Contains 3 Files. For more info regarding data click on it

Acknowledgements

HackerEarth

Inflation Rate in Canada increased to 1.90 percent in June from 1.70 percent in May of 2025. This dataset provides - Canada Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Bank Lending Rate in Austria decreased to 4.01 percent in June from 4.05 percent in May of 2025. This dataset provides - Austria Prime Lending Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Uruguay BCU Forecast: Exchange Rate against US$: Average: End of Month data was reported at 32.700 UYU/USD in Nov 2018. This records a decrease from the previous number of 33.070 UYU/USD for Oct 2018. Uruguay BCU Forecast: Exchange Rate against US$: Average: End of Month data is updated monthly, averaging 23.645 UYU/USD from Jun 2005 (Median) to Nov 2018, with 162 observations. The data reached an all-time high of 33.200 UYU/USD in Sep 2018 and a record low of -2.350 UYU/USD in Apr 2013. Uruguay BCU Forecast: Exchange Rate against US$: Average: End of Month data remains active status in CEIC and is reported by Central Bank of Uruguay. The data is categorized under Global Database’s Uruguay – Table UY.M006: Foreign Exchange Rate: Forecast: Central Bank of Uruguay.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SHL Telemedicine Ltd American Depositary Shares is assigned short-term B1 & long-term Ba3 estimated rating.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Measurable Data on 2025-08-20. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Assisterr AI on 2025-07-26. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for MemeCore on 2025-08-11. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

LON:EMIS Stock Forecast: A Hold For The Next 3 Month

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Singapore MAS Forecast: Exchange Rate: Maximum data was reported at 1.900 SGD/USD in Dec 2018. This records an increase from the previous number of 1.390 SGD/USD for Sep 2018. Singapore MAS Forecast: Exchange Rate: Maximum data is updated quarterly, averaging 1.500 SGD/USD from Dec 1999 (Median) to Dec 2018, with 77 observations. The data reached an all-time high of 2.000 SGD/USD in Mar 2001 and a record low of 1.200 SGD/USD in Mar 2013. Singapore MAS Forecast: Exchange Rate: Maximum data remains active status in CEIC and is reported by Monetary Authority of Singapore. The data is categorized under Global Database’s Singapore – Table SG.M007: Foreign Exchange Rate: Forecast: Monetary Authority of Singapore: Survey of Professional Forecasters.

Recent modeling studies of ammonia oxidation, which are motivated by the prospective role of ammonia as a zero-carbon fuel, have indicated significant discrepancies among the existing literature mechanisms. In this study, high-level theoretical kinetics predictions have been obtained for reactions on the NH2O potential energy surface, including the NH2 + O, HNO + H, and NH + OH reactions. These reactions have previously been highlighted as important reactions in NH3 oxidation with high sensitivity and high uncertainty. The potential energy surface is explored with coupled cluster calculations, including large basis sets and high-level corrections to yield high-accuracy (∼0.2 kcal/mol 2σ uncertainty) estimates of the stationary point energies. Variational transition state theory is used to predict the microcanonical rate constants, which are then incorporated in master equation treatments of the temperature- and pressure-dependent kinetics. For radical–radical channels, the microcanonical rates are obtained from variable reaction coordinate transition state theory implementing directly evaluated multireference electronic energies. The analysis yields predictions for the total rate constants as well as the branching ratios. We find that the NO + H2 channel contributes 10% of the total NH2 + O flux at combustion temperatures, although this channel is not included in modern NH3 oxidation mechanisms. Modeling is used to illustrate the ramifications of these rate predictions on the kinetics of NH3 oxidation and NOx formation. The present results for NH2 + O are important for predicting the chain branching and formation of NO in the oxidation of NH3 and thermal DeNOx.

The population share with mobile internet access in Finland was forecast to continuously increase between 2024 and 2029 by in total *** percentage points. After the tenth consecutive increasing year, the mobile internet penetration is estimated to reach ***** percent and therefore a new peak in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).