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.

The Consumer Sentiment Index in the United States stood at 51 in November 2025. This reflected a drop of 2.6 point from the previous survey. Furthermore, this was its lowest level measured since June 2022. The index is normalized to a value of 100 in December 1964 and based on a monthly survey of consumers, conducted in the continental United States. It consists of about 50 core questions which cover consumers' assessments of their personal financial situation, their buying attitudes and overall economic conditions.

Consumer Price Index CPI in the United States increased to 324.80 points in September from 323.98 points in August of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 2025, the Consumer Price Index (CPI) for medical professional services in the United States was at 432.46, compared to the period from 1982 to 1984 (=100). The CPI for hospital services was at 1,102.12.

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

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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

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Inflation Rate in Poland decreased to 2.40 percent in November from 2.80 percent in October of 2025. This dataset provides the latest reported value for - Poland Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Information about sample sizes, response rates, household characteristics, and expenditure uncertainty metrics for the Living Costs and Food Survey.

Annual indexes for major components and special aggregates of the Consumer Price Index (CPI), for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the last five years. The base year for the index is 2002=100.

Inflation Rate in India decreased to 0.25 percent in October from 1.44 percent in September of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Measures of monthly UK inflation data including CPIH, CPI and RPI. These tables complement the consumer price inflation time series dataset.

Overview

This dataset provides weekly retail prices of selected consumer items in Colombo, Sri Lanka, for the year 2020-2024. It includes prices of various food items, offering insights into price trends and consumer behavior in main markets in the Colombo District (Main markets in Colombo district: Peliyagoda, Maradana, Dematagoda, Grandpass, Kirulapana, Wellawatta, Nugegoda, Rathmalana, Narahenpita, and Rathmalana economic centers). The data is collected and compiled from reliable retail sources and represents a comprehensive snapshot of price variations on a weekly basis.

Data Features

• Week: Indicates the specific week in which the data was collected (e.g., "2024-09-W2" for the second week of September 2024).
• Item: The consumer item for which the price is listed (e.g., Ash Plantain, Brinjal, Rice).
• Unit: The unit of measurement for the item (e.g., per kg, per bunch, per item).
• Avg Price (Rs.): The average retail price of the item in Sri Lankan Rupees for the given week.
• Price Range - Low (Rs.): The minimum observed retail price for the item during that week.
• Price Range - High (Rs.): The maximum observed retail price for the item during that week.

Key Highlights

• Coverage: Weekly price data for over 40 essential consumer items, including vegetables, fruits, grains, dairy products, and other household items.
• Geographic Location: Colombo, Sri Lanka (Main markets in Colombo district: Peliyagoda, Maradana, Dematagoda, Grandpass, Kirulapana, Wellawatta, Nugegoda, Rathmalana, Narahenpita, and Rathmalana economic centers).
• Timeframe: The dataset spans from 2020 to 2024, with weekly updates provided for each year

Potential Uses

• Economic Analysis: Use this dataset to analyze inflation, price fluctuations, and consumer purchasing power trends in Colombo.
• Supply Chain Management: The dataset can help businesses and retailers monitor price trends and optimize their supply chain strategies.
• Market Research: Researchers can gain insights into consumer behavior, pricing strategies, and product availability across the year.
• Data Science Projects: Ideal for time series analysis, forecasting models, and machine learning applications focused on pricing predictions.
• Data Collection Methodology The data is collected from the Department of Census & Statistics Sri Lanka, ensuring accuracy and consistency. Each item's price reflects the average retail price observed in various markets, alongside the lowest and highest prices for the week.

File Information

Format: CSV

Licensing

This dataset is licensed under the CC BY 4.0 License. You are free to use, share, and modify the dataset, but please provide appropriate credit.

Acknowledgments

This dataset was compiled using publicly available data from the Sri Lanka Census and Statistics Department. For any questions or further clarifications, please feel free to contact the dataset creator.

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

Vijayawada House Price Dataset (2020-2024) Predict real estate prices using location, size, and property type

** Key Variables** Area: Neighborhood name (e.g., Benz Circle, Patamata) Region_Type: Premium/Commercial/Residential (shows price tier) BHK: Number of bedrooms (1 to 5+) Size (sqft): Property area (800–5,000 sqft range) Price_Per_Sqft: Cost per sqft (₹2,500–₹7,000) Total_Price: Calculated price (Size × Price_Per_Sqft) Projects You can prepare using this data set : Ideal for real estate price prediction, geospatial analysis, and market trends. Predict house prices using linear regression or XGBoost Explore price trends by region or BHK count Train a classification model to categorize properties into budget/mid-range/premium Create a price heatmap of Vijayawada (if extended with geo-coordinates)

Monthly indexes and percentage changes for all components and special aggregates of the Consumer Price Index (CPI), not seasonally adjusted, for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the corresponding month of the previous year, the previous month and the current month. The base year for the index is 2002=100.

Inflation Rate in Japan increased to 3 percent in October from 2.90 percent in September of 2025. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

1. This data is collected from Yahoo Finance
2. This data is collected from 2020-01-01 to 2024-12-01
3. This data is collected from Apple Inc.

This data have following features:

1. Open: The opening price of the stock on a given day.
2. High: The highest price of the stock on a given day.
3. Low: The lowest price of the stock on a given day.
4. Close: The closing price of the stock on a given day.
5. Volume: The total number of shares traded on a given day.
6. Adj Close: The adjusted closing price of the stock on a given day.
7. Columns 7
8. Rows 1238

Inflation Rate in Indonesia decreased to 2.72 percent in November from 2.86 percent in October of 2025. This dataset provides - Indonesia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Sweden remained unchanged at 0.90 percent in October. This dataset provides - Sweden Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides synthetic daily stock market data for 50 representative U.S.-listed companies across multiple sectors, covering the period 2020–2024.

It includes traditional OHLC (open, high, low, close) data, adjusted close prices, trading volume, and synthetic fundamental indicators such as market capitalization, P/E ratio, and dividend yield. Additionally, daily returns and 30-day rolling volatility are provided to support time-series and risk modeling.

The dataset is fully synthetic, generated via statistical simulations to mimic realistic U.S. stock market behavior across sectors such as Technology, Financials, Healthcare, Energy, Consumer Discretionary, and Industrials. It does not represent real financial data, making it safe for learning, research, and experimentation.

The Regional Price Index contrasts the cost of a common basket of goods and services at a number of regional locations to the Perth metropolitan area. The RPIs were commissioned to assist with the calculation of the Western Australian State Government’s regional district allowance, and it has been used to assist in policy decision-making. Show full description