Context

The Federal Reserve sets interest rates to promote conditions that achieve the mandate set by the Congress — high employment, low and stable inflation, sustainable economic growth, and moderate long-term interest rates. Interest rates set by the Fed directly influence the cost of borrowing money. Lower interest rates encourage more people to obtain a mortgage for a new home or to borrow money for an automobile or for home improvement. Lower rates encourage businesses to borrow funds to invest in expansion such as purchasing new equipment, updating plants, or hiring more workers. Higher interest rates restrain such borrowing by consumers and businesses.

Content

This dataset includes data on the economic conditions in the United States on a monthly basis since 1954. The federal funds rate is the interest rate at which depository institutions trade federal funds (balances held at Federal Reserve Banks) with each other overnight. The rate that the borrowing institution pays to the lending institution is determined between the two banks; the weighted average rate for all of these types of negotiations is called the effective federal funds rate. The effective federal funds rate is determined by the market but is influenced by the Federal Reserve through open market operations to reach the federal funds rate target. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds target rate; the target rate transitioned to a target range with an upper and lower limit in December 2008. The real gross domestic product is calculated as the seasonally adjusted quarterly rate of change in the gross domestic product based on chained 2009 dollars. The unemployment rate represents the number of unemployed as a seasonally adjusted percentage of the labor force. The inflation rate reflects the monthly change in the Consumer Price Index of products excluding food and energy.

Acknowledgements

The interest rate data was published by the Federal Reserve Bank of St. Louis' economic data portal. The gross domestic product data was provided by the US Bureau of Economic Analysis; the unemployment and consumer price index data was provided by the US Bureau of Labor Statistics.

Inspiration

How does economic growth, unemployment, and inflation impact the Federal Reserve's interest rates decisions? How has the interest rate policy changed over time? Can you predict the Federal Reserve's next decision? Will the target range set in March 2017 be increased, decreased, or remain the same?

The benchmark interest rate in the United States was last recorded at 4 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The interest rate set by the Federal Reserve is a crucial tool for promoting economic conditions that meet the mandate established by the United States Congress, which includes high employment, low and stable inflation, sustainable economic growth, and the moderation of long-term interest rates. The interest rates determined by the Fed directly influence the cost of credit, making financing either more accessible or more restrictive. When interest rates are low, there is a greater incentive for consumers to purchase homes through mortgages, finance automobiles, or undertake home renovations. Additionally, businesses are encouraged to invest in expanding their operations, whether by purchasing new equipment, modernizing facilities, or hiring more workers. Conversely, higher interest rates tend to curb such activity, discouraging borrowing and slowing economic expansion.

The dataset analyzed contains information on the economic conditions in the United States on a monthly basis since 1954, including the federal funds rate, which represents the percentage at which financial institutions trade reserves held at the Federal Reserve with each other in the interbank market overnight. This rate is determined by the market but is directly influenced by the Federal Reserve through open market operations to reach the established target. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds rate target, which has been defined within a range with upper and lower limits since December 2008.

Furthermore, real Gross Domestic Product (GDP) is calculated based on the seasonally adjusted quarterly rate of change in the economy, using chained 2009 dollars as a reference. The unemployment rate represents the seasonally adjusted percentage of the labor force that is unemployed. Meanwhile, the inflation rate is determined by the monthly change in the Consumer Price Index, excluding food and energy prices for a more stable analysis of core inflation.

The interest rate data was sourced from the Federal Reserve Bank of St. Louis' economic data portal, while GDP information was provided by the U.S. Bureau of Economic Analysis, and unemployment and inflation data were made available by the U.S. Bureau of Labor Statistics.

The analysis of this data helps to understand how economic growth, the unemployment rate, and inflation influence the Federal Reserve’s monetary policy decisions. Additionally, it allows for a study of the evolution of interest rate policies over time and raises the question of how predictable the Fed’s future decisions may be. Based on observed trends, it is possible to speculate whether the target range set in March 2017 will be maintained, lowered, or increased, considering the prevailing economic context and the challenges faced in conducting U.S. monetary policy.

The benchmark interest rate in Sweden was last recorded at 1.75 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Source is Federal Reserve Bank of St. Louis. Retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/"NAME OF MEASURE" Column names are "Name of Measure" from FRED's catalog.

Group 1: Yield Curve Indicators These focus on the shape of the Treasury yield curve, comparing longer-term to shorter-term rates. They are primarily used to: Signal Economic Expectations: A normal curve (longer-term rates higher) suggests expectations of growth and possibly inflation. A flattening or inverted curve (short-term rates near or above long-term) could signal a potential slowdown or recession.

Group 2: Monetary Policy and Market Expectations These spreads look at the difference between Treasury yields and the Federal Funds Rate, the primary tool of monetary policy. They indicate: Market vs. Fed Outlook: Widening spreads could suggest the market expects faster rate hikes or higher long-term inflation than the Fed is signaling. Narrowing spreads could mean the opposite. Risk-Taking: When these spreads widen, it can be a sign of investors moving from safe Treasuries to riskier assets in search of yield.

Group 3: Credit Risk and Market Sentiment These spreads focus on corporate bond yields relative to Treasuries, highlighting the added compensation investors require for holding riskier corporate debt. They signal: Credit Conditions: Widening spreads suggest deteriorating credit conditions or lower risk tolerance among investors. Narrowing spreads suggest the opposite. Economic Confidence: Investors often demand higher premiums for corporate bonds during economic uncertainty, widening these spreads.

Group 4: Breakeven Inflation Rates The breakeven inflation rate represents a measure of expected inflation derived from 30-Year Treasury Constant Maturity Securities (BC_30YEAR) and 30-Year Treasury Inflation-Indexed Constant Maturity Securities (TC_30YEAR). The latest value implies what market participants expect inflation to be in the next 30 years, on average.

The benchmark interest rate in the United Kingdom was last recorded at 4 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

30 Year Mortgage Rate in the United States decreased to 6.23 percent in November 26 from 6.26 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

Dataset Description

This dataset contains the actual and predicted federal funds target rate for the United States from 1990 to 2023. The federal funds target rate is the interest rate at which depository institutions lend their excess reserves to each other overnight. It is set by the Federal Open Market Committee (FOMC) and is a key tool used by the Federal Reserve to influence the economy.

The dataset includes the following five columns:

Release Date: The date on which the data was released by the Federal Reserve. Time: The time of day at which the data was released. Actual: The actual federal funds target rate. Predicted: The predicted federal funds target rate. Forecast: The forecast federal funds target rate.

Data Usage

This dataset can be used for a variety of purposes, including: - Analyzing trends in the federal funds target rate over time. - Forecasting the future path of the federal funds target rate. - Assessing the effectiveness of monetary policy. - Data Quality

The data for this dataset is of high quality. The Federal Reserve is a reputable source of data and the data is updated regularly.

Data Limitations

The data for this dataset is limited to the United States. Additionally, the data does not include information on the factors that influenced the Federal Open Market Committee's decision to set the federal funds target rate.

This dataset provides values for INTEREST RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Monthly and long-term United States Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

A dataset of mentions, growth rate, and total volume of the keyphrase 'High Interest Rates' over time.

U.S. Economic and Financial Dataset

Dataset Description

This dataset combines historical U.S. economic and financial indicators, spanning the last 50 years, to facilitate time series analysis and uncover patterns in macroeconomic trends. It is designed for exploring relationships between interest rates, inflation, economic growth, stock market performance, and industrial production.

Key Features

• Frequency: Monthly
• Time Period: Last 50 years from Nov-24
• Sources:
  • Federal Reserve Economic Data (FRED)
  • Yahoo Finance

Dataset Feature Description

1. Interest Rate (Interest_Rate):

   • The effective federal funds rate, representing the interest rate at which depository institutions trade federal funds overnight.

2. Inflation (Inflation):

   • The Consumer Price Index for All Urban Consumers, an indicator of inflation trends.

3. GDP (GDP):

   • Real GDP measures the inflation-adjusted value of goods and services produced in the U.S.

4. Unemployment Rate (Unemployment):

   • The percentage of the labor force that is unemployed and actively seeking work.

5. Stock Market Performance (S&P500):

   • Monthly average of the adjusted close price, representing stock market trends.

6. Industrial Production (Ind_Prod):

   • A measure of real output in the industrial sector, including manufacturing, mining, and utilities.

Dataset Statistics

1. Total Entries: 599
2. Columns: 6
3. Memory usage: 37.54 kB
4. Data types: float64

Feature Overview

• Columns:
  • Interest_Rate: Monthly Federal Funds Rate (%)
  • Inflation: CPI (All Urban Consumers, Index)
  • GDP: Real GDP (Billions of Chained 2012 Dollars)
  • Unemployment: Unemployment Rate (%)
  • Ind_Prod: Industrial Production Index (2017=100)
  • S&P500: Monthly Average of S&P 500 Adjusted Close Prices

Executive Summary

This project explores the interconnected dynamics of key macroeconomic indicators and financial market trends over the past 50 years, leveraging data from the Federal Reserve Economic Data (FRED) and Yahoo Finance. The dataset integrates critical variables such as the Federal Funds Rate, Inflation (CPI), Real GDP, Unemployment Rate, Industrial Production, and the S&P 500 Index, providing a holistic view of the U.S. economy and financial markets.

The analysis focuses on uncovering relationships between these variables through time-series visualization, correlation analysis, and trend decomposition. Key findings are included in the Insights section. This project serves as a robust resource for understanding long-term economic trends, policy impacts, and market behavior. It is particularly valuable for students, researchers, policymakers, and financial analysts seeking to connect macroeconomic theory with real-world data.

Potential Use Cases

• Economic Analysis: Examine relationships between interest rates, inflation, GDP, and unemployment.
• Stock Market Prediction: Study how macroeconomic indicators influence stock market trends.
• Time Series Modeling: Perform ARIMA, VAR, or other models to forecast economic trends.
• Cyclic Pattern Analysis: Identify how economic shocks and recoveries impact key indicators.

Snap of Power Analysis

https://github.com/user-attachments/assets/1b40e0ca-7d2e-4fbc-8cfd-df3f09e4fdb8">

To ensure sufficient power, the dataset covers last 50 years of monthly data i.e., around 600 entries.

Key Insights derived through EDA, time-series visualization, correlation analysis, and trend decomposition

• Interest Rate and Inflation Dynamics: The interest Rate and inflation exhibit an inverse relationship, especially during periods of aggressive monetary tightening by the Federal Reserve.
• Economic Growth and Market Performance: GDP growth and the S&P 500 Index show a positive correlation, reflecting how market performance often aligns with overall economic health.
• Labor Market and Industrial Output: Unemployment and industrial production demonstrate a strong inverse relationship. Higher industrial output is typically associated with lower unemployment
• Market Behavior During Economic Shocks: The S&P 500 experienced sharp declines during significant crises, such as the 2008 financial crash and the COVID-19 pandemic in 2020. These events also triggered increased unemployment and contractions in GDP, highlighting the interplay between markets and the broader economy.
• Correlation Highlights: S&P 500 and GDP have a strong positive correlation. Interest rates negatively correlate with GDP and inflation, reflecting monetary policy impacts. Unemployment is negatively correlated with industrial production but positively correlated with interest rates.

Link to GitHub Repo

https:/...

Monthly and long-term Argentina Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

The benchmark interest rate in Russia was last recorded at 16.50 percent. This dataset provides the latest reported value for - Russia Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Interest is charged if payment is not received by the due date. Remember: if the due date falls on a weekend or holiday, your payment is due the next working day.

The Ministry of Finance also applies interest to amounts the ministry owes to individuals and corporations.

Tax interest is compounded daily and interest rates are reset every 3 months.

Note: Provincial land tax interest rates are not reset every three months. Provincial land tax interest rates are summarized on the "https://www.ontario.ca/document/provincial-land-tax">provincial land tax webpage.

Note: Interest rates do not apply to the Estate Administration Tax Act, 1998.

Current interest rates (October 1, 2025 to December 31, 2025):

• 8% on taxes you owe to the ministry
• 2% on taxes you overpaid
• 5% on taxes or refunds you are eligible for as a result of a successful appeal or objection
• 6% on late International Fuel Tax Agreement payments
• 6% on International Fuel Tax Agreement refunds the ministry has not paid you within 90 days

You can download the dataset to view the historical tax interest rates.

Non-Resident Speculation Tax (NRST)

(1) Interest on tax you overpaid begins to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid.

(2) On refunds you are eligible for as a result of a successful appeal or objection of a NRST refund/rebate disallowance, the interest rate is the same rate as though you had overpaid and will begin to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid. Refunds as a result of a successful appeal or objection of NRST that was paid pursuant to a Notice of Assessment, interest will accrue at the higher appeals/objection rate, beginning to accrue from the date of payment to the date the rebate or refund is paid.

🏦 Synthetic Loan Approval Dataset

A Realistic, High-Quality Dataset for Credit Risk Modelling

🎯 Why This Dataset?

Most loan datasets on Kaggle have unrealistic patterns where:

1. ❌ Credit scores don't matter
2. ❌ Approval logic is backwards
3. ❌ Models learn nonsense patterns

Unlike most loan datasets available online, this one is built on real banking criteria from US and Canadian financial institutions. Drawing from 3 years of hands-on finance industry experience, the dataset incorporates realistic correlations and business logic that reflect how actual lending decisions are made. This makes it perfect for data scientists looking to build portfolio projects that showcase not just coding ability, but genuine understanding of credit risk modelling.

📊 Dataset Overview

🔑 Key Features

Identifier:

-Customer ID (unique identifier for each application)

Demographics:

-Age, Occupation Status, Years Employed

Financial Profile:

-Annual Income, Credit Score, Credit History Length -Savings/Assets, Current Debt

Credit Behaviour:

-Defaults on File, Delinquencies, Derogatory Marks

Loan Request:

-Product Type, Loan Intent, Loan Amount, Interest Rate

Calculated Ratios:

-Debt-to-Income, Loan-to-Income, Payment-to-Income

💡 What Makes This Dataset Special?

1️⃣ Real-World Approval Logic The dataset implements actual banking criteria: - DTI ratio > 50% = automatic rejection - Defaults on file = instant reject - Credit score bands match real lending thresholds - Employment verification for loans ≥$20K

2️⃣ Realistic Correlations - Higher income → Better credit scores - Older applicants → Longer credit history - Students → Lower income, special treatment for small loans - Loan intent affects approval (Education best, Debt Consolidation worst)

3️⃣ Product-Specific Rules - Credit Cards: More lenient, higher limits - Personal Loans: Standard criteria, up to $100K - Line of Credit: Capped at $50K, manual review for high amounts

4️⃣ Edge Cases Included - Young applicants (age 18) building first credit - Students with thin credit files - Self-employed with variable income - High debt-to-income ratios - Multiple delinquencies

🎓 Perfect For - Machine Learning Practice: Binary classification with real patterns - Credit Risk Modelling: Learn actual lending criteria - Portfolio Projects: Build impressive, explainable models - Feature Engineering: Rich dataset with meaningful relationships - Business Analytics: Understand financial decision-making

📈 Quick Stats

Approval Rates by Product - Credit Card: 60.4% more lenient) - Personal Loan: 46.9 (standard) - Line of Credit: 52.6% (moderate)

Loan Intent (Best → Worst Approval Odds) 1. Education (63% approved) 2. Personal (58% approved) 3. Medical/Home (52% approved) 4. Business (48% approved) 5. Debt Consolidation (40% approved)

Credit Score Distribution - Mean: 644 - Range: 300-850 - Realistic bell curve around 600-700

Income Distribution - Mean: $50,063 - Median: $41,608 - Range: $15K - $250K

🎯 Expected Model Performance

With proper feature engineering and tuning: - Accuracy: 75-85% - ROC-AUC: 0.80-0.90 - F1-Score: 0.75-0.85

Important: Feature importance should show: 1. Credit Score (most important) 2. Debt-to-Income Ratio 3. Delinquencies 4. Loan Amount 5. Income

If your model shows different patterns, something's wrong!

🏆 Use Cases & Projects

Beginner - Binary classification with XGBoost/Random Forest - EDA and visualization practice - Feature importance analysis

Intermediate - Custom threshold optimization (profit maximization) - Cost-sensitive learning (false positive vs false negative) - Ensemble methods and stacking

Advanced - Explainable AI (SHAP, LIME) - Fairness analysis across demographics - Production-ready API with FastAPI/Flask - Streamlit deployment with business rules

⚠️ Important Notes

This is SYNTHETIC Data - Generated based on real banking criteria - No real customer data was used - Safe for public sharing and portfolio use

Limitations - Simplified approval logic (real banks use 100+ factors) - No temporal component (no time series) - Single country/currency assumed (USD) - No external factors (economy, market conditions)

Educational Purpose This dataset is designed for: - Learning credit risk modeling - Portfolio projects - ML practice - Understanding lending criteria

NOT for: - Actual lending decisions - Financial advice - Production use without validation

🤝 Contributing

Found an issue? Have suggestions? - Open an issue on GitHub - Suggest i...

The benchmark interest rate in Turkey was last recorded at 39.50 percent. This dataset provides the latest reported value for - Turkey Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States Interest Rates: 12 Months Expectation: Lower data was reported at 21.400 % in Apr 2025. This records a decrease from the previous number of 23.300 % for Mar 2025. United States Interest Rates: 12 Months Expectation: Lower data is updated monthly, averaging 12.100 % from Jun 1987 (Median) to Apr 2025, with 455 observations. The data reached an all-time high of 45.800 % in Jan 1991 and a record low of 5.200 % in Jun 2018. United States Interest Rates: 12 Months Expectation: Lower data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H051: Consumer Confidence Index: Interest Rate Expectation. [COVID-19-IMPACT]

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The raw data that is used in this dataset is the basic OHLC time series dataset for a gold market of the last 20 years collected and verified from different exchanges. This dataset contains over 8677 daily candle prices (rows) and in order to make it wealthy, extra datasets were merged with it to provide more details to each data frame. The sub-datasets contain historical economic information such as interest rates, inflation rates, and others that are highly related and affecting the gold market movement.

Raw dataset:

Time Range: 1988-08-01 to 2023-11-10 Number of data entries: 4050 Number of features: 4 (open, high, low, close OHLC daily candle price)

What are done to prepare this dataset : 1. Starting Exploratory Data Analysis (EDA) for all the raw datasets. 2. Find and fill in missing days. 3. Merge all the datasets into one master dataset based on the time index. 4. Verify the merge process. 5. Check and remove Duplicates. 6. Check and fill in missing values. 7. Including the basic technical indicators and price moving averages. 8. Outliers Inspection and treatment by different methods. 9. Adding targets. 10. Feature Analysis to identify the importance of each feature. 11. Final check.

After data preparation and feature engineering:

Time Range: 1999-12-30 to 2023-10-01

Number of data entries: 8677

Number of featuers: 28

Features list: open, high, low, close (OHLC daily candle price) dxy_open, dxy_close, dxy_high, dxy_low, fred_fedfunds, usintr, usiryy (Ecnomic inducators) RSI, MACD, MACD_signal, MACD_hist, ADX, CCI (Technical indicators) ROC SMA_10, SMA_20, EMA_10, EMA_20, SMA_50, EMA_50, SMA_100, SMA_200, EMA_100, EMA_200 (Moving avrages)

Targets List: next_1_day_price next_3_day_price next_7_day_price next_30_day_price next_1_day_Price_Change next_3_day_Price_Change next_7_day_Price_Change next_30_day_Price_Change next_30_day_Price_Change next_1_day_price_direction( Up, Same ,Down) next_3_day_price_direction( Up, Same ,Down) next_7_day_price_direction( Up, Same ,Down) next_30_day_price_direction( Up, Same ,Down)

Abbreviations of Features: dxy = US Dollar Index fred_fedfunds= Effective Federal Funds Rate usintr= US Interest Rate usiryy= US Inflation Rate YOY RSI= Relative Strength Index MACD= Moving Average Convergence Divergence ADX= Avrerage Directional Index CCI=Commodity Channel Index ROC= Rate of Change SMA= Simple Moving Average EMA= Exponential Moving Average