Statistics on student debt, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of graduates with debt who had paid it off at the time of the interview, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

Data on the average amount of OSAP debt owed by students. The data is specific to those who attended programs with typical durations. Data is for: * 4-year undergraduate university students * 2-year college diploma students * 1-year private career college students The data fields are: * academic year of completion * postsecondary sector (university, publicly-assisted college, or private career college) * program duration (1 year, 2 years or 4 years) * average repayable debt after loan forgiveness applied through the Ontario Student Opportunity Grant Debt is in nominal dollars with no adjustment for inflation. *[OSAP]: Ontario Student Assistance Program

Statistics on postsecondary graduates who owed money for their education to government-sponsored student loans at graduation, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of debt paid off at the time of the interview, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

The Survey of Consumer Finances (SCF) dataset, provided by the Federal Reserve, offers comprehensive insights into the financial condition of U.S. households. This dataset is invaluable for researchers, policymakers, and analysts interested in understanding consumer behavior, wealth distribution, and economic trends in the United States.

The SCF dataset includes detailed information on household income, assets, liabilities, and various demographic characteristics. It is collected every three years and serves as a crucial resource for analyzing the financial well-being of American families.

Key Features: Income Data: Information on various sources of income, including wages, investments, and government assistance. Asset Ownership: Detailed accounts of household assets, such as real estate, retirement accounts, stocks, and other investments. Liabilities:Comprehensive details on household debts, including mortgages, credit card debts, and student loans. Demographics: Data covering age, education, race, and family structure, allowing for nuanced analysis of financial trends across different segments of the population.

Use Cases: Economic research and analysis, Policy formulation and assessment, Understanding wealth inequality, Consumer behavior studies

Citing the Dataset:

When using this dataset in your research, please ensure to cite the Federal Reserve Board and the SCF as the original source.

Note: The dataset is intended for educational and research purposes. Users are encouraged to adhere to ethical guidelines when analyzing and interpreting the data.

The State Loan Repayment Program helps HRSA provide grant funding for states and territories to operate their own loan repayment programs. Through SLRP each state and territory can design programs that address the most pressing health care needs of their residents. Primary medical, mental/behavioral, and dental clinicians who receive awards through SLRP-funded programs pay off their student debt in exchange for working in areas with provider shortages.HRSA programs provide equitable health care to people who are geographically isolated and economically or medically vulnerable. This includes programs that deliver health services to people with HIV, pregnant people, mothers and their families, those with low incomes, residents of rural areas, American Indians and Alaska Natives, and those otherwise unable to access high-quality health care. HRSA programs also support health infrastructure, including through training of health professionals and distributing them to areas where they are needed most, providing financial support to health care providers, and advancing telehealth. Location and data was provided by the Health Resources and Services Administration in October 2022. Update Frequency: Annual

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.

what percentage of your gross salary does the consumer financial protection bureau suggest your student loan payment be in order to be affordable and limit your risk of delinquency and default?

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

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.

Student Loan Meltdown? (SLM)

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