Explanation of CPI and the Dataset:

What is CPI?

CPI (Consumer Price Index) measures the average change in prices over time that consumers pay for a basket of goods and services. It is a key indicator of inflation and is used by governments and central banks to monitor price stability and for inflation targeting. Components: The construction of CPI involves two main components: Weighting Diagrams: These represent the consumption patterns of households. Price Data: This is collected at regular intervals to track changes in prices.

Role of the Central Statistics Office (CSO):

The CSO, under the Ministry of Statistics and Programme Implementation, is responsible for releasing CPI data. The indices are released for Rural, Urban, and Combined sectors for all-India and individual States/UTs.

Dataset Alignment:

Sectors: The dataset includes a "Sector" column that categorizes data into "Rural," "Urban," and "Rural+Urban," aligning with the CPI data released by the CSO. Time Period: The "Year" and "Name" (which appears to represent months) columns in the dataset track the data over time, consistent with the monthly release schedule by the CSO starting from January 2011. State/UT Data: Each column corresponding to a state or union territory likely represents the CPI values for that region. The numeric values under each state/UT column represent the CPI index values, with a base of 2010=100. Purpose: This data can be used to analyze inflation trends, price stability, and the impact on economic policies, such as adjustments to dearness allowance for employees. Practical Use of This Data: Inflation Analysis: By examining the changes in CPI values across different states, analysts can study regional inflation trends and compare them to the national average. Policy Making: Governments and central banks can use this data to design and adjust policies aimed at controlling inflation, targeting specific regions or sectors that are experiencing higher inflation. Wage Indexation: Companies and governments can use CPI data to adjust wages and allowances in line with inflation, ensuring that purchasing power is maintained.

Consumer Price Index CPI in the United States increased to 322.56 points in June from 321.46 points in May 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.

Analysis of ‘🚊 Consumer Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/consumer-price-indexe on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

9The Consumer Price Index for All Urban Consumers: All Items (CPIAUCSL) is a measure of the average monthly change in the price for goods and services paid by urban consumers between any two time periods.(1) It can also represent the buying habits of urban consumers. This particular index includes roughly 88 percent of the total population, accounting for wage earners, clerical workers, technical workers, self-employed, short-term workers, unemployed, retirees, and those not in the labor force.(1)

The CPIs are based on prices for food, clothing, shelter, and fuels; transportation fares; service fees (e.g., water and sewer service); and sales taxes. Prices are collected monthly from about 4,000 housing units and approximately 26,000 retail establishments across 87 urban areas.(1) To calculate the index, price changes are averaged with weights representing their importance in the spending of the particular group. The index measures price changes (as a percent change) from a predetermined reference date.(1) In addition to the original unadjusted index distributed, the Bureau of Labor Statistics also releases a seasonally adjusted index. The unadjusted series reflects all factors that may influence a change in prices. However, it can be very useful to look at the seasonally adjusted CPI, which removes the effects of seasonal changes, such as weather, school year, production cycles, and holidays.(1)

The CPI can be used to recognize periods of inflation and deflation. Significant increases in the CPI within a short time frame might indicate a period of inflation, and significant decreases in CPI within a short time frame might indicate a period of deflation. However, because the CPI includes volatile food and oil prices, it might not be a reliable measure of inflationary and deflationary periods. For a more accurate detection, the core CPI (Consumer Price Index for All Urban Consumers: All Items Less Food & Energy [CPILFESL]) is often used. When using the CPI, please note that it is not applicable to all consumers and should not be used to determine relative living costs.(1) Additionally, the CPI is a statistical measure vulnerable to sampling error since it is based on a sample of prices and not the complete average.(1)

Attribution: US. Bureau of Labor Statistics from The Federal Reserve Bank of St. Louis

For more information on the consumer price indexes, see:

• (1) Bureau of Economic Analysis. “CPI Detailed Report.” 2013
• (2) Handbook of Methods
• (3) Understanding the CPI: Frequently Asked Questions

This dataset was created by Finance and contains around 900 samples along with Consumer Price Index For All Urban Consumers: All Items, Title:, technical information and other features such as: - Consumer Price Index For All Urban Consumers: All Items - Title: - and more.

How to use this dataset

• Analyze Consumer Price Index For All Urban Consumers: All Items in relation to Title:
• Study the influence of Consumer Price Index For All Urban Consumers: All Items on Title:
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

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--- Original source retains full ownership of the source dataset ---

Description from the Bureau of Labor and Statistics:

Average consumer prices are calculated for household fuel, motor fuel, and food items from prices collected for the Consumer Price Index (CPI). Average prices are best used to measure the price level in a particular month, not to measure price change over time. It is more appropriate to use CPI index values for the particular item categories to measure price change.

Prices, except for electricity, are collected monthly by BLS representatives in the 75 urban areas priced for the CPI. Electricity prices are collected for the BLS for the same 75 areas on a monthly basis by the Department of Energy using mail questionnaires. All fuel prices include applicable Federal, State, and local taxes; prices for natural gas and electricity also include fuel and purchased gas adjustments.

Analysis of ‘U.S. Inflation Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/varpit94/us-inflation-data-updated-till-may-2021 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset provides monthly data of US Consumer Price Index (CPI). Average of CPI for all US cities, in a given month, is provided. Details of columns: * Yearmon - Year-month in date format. Day is provided as 1 for each month. * CPI - Consumer price index

--- Original source retains full ownership of the source dataset ---

This dataset provides monthly data of US Consumer Price Index (CPI). Average of CPI for all US cities, in a given month, is provided. Details of columns: * Yearmon - key variable, combines year and month (numeric 1-12). First 4 digits represent year and last two represent the month. A value of 201203 means year 2012 and month of March (03) * CPI - Consumer price index

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 happens to gold if CPI increases?

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

Inflation Rate in India decreased to 1.55 percent in July from 2.10 percent in June of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Analysis of ‘Walmart Dataset (Retail)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rutuspatel/walmart-dataset-retail on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Dataset Description :

This is the historical data that covers sales from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales. Within this file you will find the following fields:

Store - the store number

Date - the week of sales

Weekly_Sales - sales for the given store

Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week

Temperature - Temperature on the day of sale

Fuel_Price - Cost of fuel in the region

CPI – Prevailing consumer price index

Unemployment - Prevailing unemployment rate

Holiday Events Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

Analysis Tasks

Basic Statistics tasks

1) Which store has maximum sales

2) Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation

3) Which store/s has good quarterly growth rate in Q3’2012

4) Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together

5) Provide a monthly and semester view of sales in units and give insights

Statistical Model

For Store 1 – Build prediction models to forecast demand

Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.

Change dates into days by creating new variable.

Select the model which gives best accuracy.

--- Original source retains full ownership of the source dataset ---

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 is cpi?

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.

What is the relationship between CPI and the stock market?

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

Consumer price indexes. Also includes average prices paid for commodities, utilities and fuels, CPI for older Americans, chained CPI, department store inventory price indexes and CPI research series Data

Consumer Price Index CPI in India increased to 196 points in July from 194.20 points in June of 2025. This dataset provides - India Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Historical sales data for 45 Walmart stores located in different regions are available. There are certain events and holidays which impact sales on each day. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to inappropriate machine learning algorithm. Walmart would like to predict the sales and demand accurately. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc. The objective is to determine the factors affecting the sales and to analyze the impact of markdowns around holidays on the sales.

Holiday Events Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

Analysis Tasks

Basic Statistics tasks 1) Which store has maximum sales

2) Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation

3) Which store/s has good quarterly growth rate in Q3’2012

4) Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together

5) Provide a monthly and semester view of sales in units and give insights

Statistical Model For Store 1 – Build prediction models to forecast demand (Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.) Change dates into days by creating new variable. Select the model which gives best accuracy.

Analysis of ‘US Minimum Wage by State from 1968 to 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lislejoem/us-minimum-wage-by-state-from-1968-to-2017 on 12 November 2021.

--- Dataset description provided by original source is as follows ---

US Minimum Wage by State from 1968 to 2020

The Basics

• What is this? In the United States, states and the federal government set minimum hourly pay ("minimum wage") that workers can receive to ensure that citizens experience a minimum quality of life. This dataset provides the minimum wage data set by each state and the federal government from 1968 to 2020.

• Why did you put this together? While looking online for a clean dataset for minimum wage data by state, I was having trouble finding one. I decided to create one myself and provide it to the community.

• Who do we thank for this data? The United States Department of Labor compiles a table of this data on their website. I took the time to clean it up and provide it here for you. :) The GitHub repository (with R Code for the cleaning process) can be found here!

Content

This is a cleaned dataset of US state and federal minimum wages from 1968 to 2020 (including 2020 equivalency values). The data was scraped from the United States Department of Labor's table of minimum wage by state.

Description of Data

The values in the dataset are as follows: - Year: The year of the data. All minimum wage values are as of January 1 except 1968 and 1969, which are as of February 1. - State: The state or territory of the data. - State.Minimum.Wage: The actual State's minimum wage on January 1 of Year. - State.Minimum.Wage.2020.Dollars: The State.Minimum.Wage in 2020 dollars. - Federal.Minimum.Wage: The federal minimum wage on January 1 of Year. - Federal.Minimum.Wage.2020.Dollars: The Federal.Minimum.Wage in 2020 dollars. - Effective.Minimum.Wage: The minimum wage that is enforced in State on January 1 of Year. Because the federal minimum wage takes effect if the State's minimum wage is lower than the federal minimum wage, this is the higher of the two. - Effective.Minimum.Wage.2020.Dollars: The Effective.Minimum.Wage in 2020 dollars. - CPI.Average: The average value of the Consumer Price Index in Year. When I pulled the data from the Bureau of Labor Statistics, I selected the dataset with "all items in U.S. city average, all urban consumers, not seasonally adjusted". - Department.Of.Labor.Uncleaned.Data: The unclean, scraped value from the Department of Labor's website. - Department.Of.Labor.Cleaned.Low.Value: The State's lowest enforced minimum wage on January 1 of Year. If there is only one minimum wage, this and the value for Department.Of.Labor.Cleaned.High.Value are identical. (Some states enforce different minimum wage laws depending on the size of the business. In states where this is the case, generally, smaller businesses have slightly lower minimum wage requirements.) - Department.Of.Labor.Cleaned.Low.Value.2020.Dollars: The Department.Of.Labor.Cleaned.Low.Value in 2020 dollars. - Department.Of.Labor.Cleaned.High.Value: The State's higher enforced minimum wage on January 1 of Year. If there is only one minimum wage, this and the value for Department.Of.Labor.Cleaned.Low.Value are identical. - Department.Of.Labor.Cleaned.High.Value.2020.Dollars: The Department.Of.Labor.Cleaned.High.Value in 2020 dollars. - Footnote: The footnote provided on the Department of Labor's website. See more below.

Data Footnotes

As laws differ significantly from territory to territory, especially relating to whom is protected by minimum wage laws, the following footnotes are located throughout the data in Footnote to add more context to the minimum wage. The original footnotes can be found here.

--- Original source retains full ownership of the source dataset ---

The dataset contains several macroeconomic time-series regarding the Russian economy. The time-series were collected from the Russian Federal State Statistics Service, the Bank of Russia and Federal Reserve Economic Data. The time-series included in the dataset are: 1. Time: 1-Jan-2005 = 1, every successive step in time represents one quarter 2. Date: Quarterly dates from 1-Jan-2005 to 1-Oct-2021 5. GDP: Quarterly nominal GDP in 2016 prices, excluding seasonal factor (bln RUB) 6. GDPgr: Nominal GDP growth rate (Quarterly, %) 7. M0: Base or high-powered money (bln RUB) 8. M0gr: M0 growth rate (Quarterly, %) 9. BM: M2 measure of money supply (bln RUB) 10. BMgr: M2 growth rate (Quarterly, %) 11. Interest: 90-day interbank rate (APR, %) 12. USDRUB: USD/RUB exchange rate (RUB) 12. EURRUB: EUR/RUB exchange rate (RUB) 13. Unemployment: Unemployment rate (%) 14. PPI: Domestic producer price index (index: 2015=100) 15. PPIgr: Growth rate of producer price index (Quarterly, %) 16. OIL: Spot prices of Brent per barrel (USD) 17. OILgr: Growth rate of Brent prices (Quarterly, %) 18. WAGE: Average monthly nominal wage rate (RUB) 19. WAGEgr: Changes in nominal wage rate (Quarterly, %) 3. CPI: Change in CPI as a ratio (End of quarter to end of previous quarter, %) 4. Inflation: Percentage change in CPI, calculated as Relative CPI - 100 (Quarterly, %)

The data was used to in time-series regression modelling to explain the factors affecting inflation in Russia. Some other modelling ideas for the dataset are: 1. Shift the focus from factor analysis to predicting future inflation 2. Perform factor analyses of other key macroeconomic variables, such as the GDP growth rate, the unemployment rate or the interest rate

Due to the low number of available observations because of quarterly sampling, this dataset is probably better suited to time-series econometric analysis rather than more modern machine learning methods.

Analysis of ‘Community Parks Initiative Zone Boundaries’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a78a8d15-9dd1-4cb4-90d8-13f831ec2f8b on 26 January 2022.

--- Dataset description provided by original source is as follows ---

CPI Zones are neighborhoods where NYC Parks will pursue a combination of capital investment and/or targeted physical improvements, enhanced programming, and public outreach efforts through the Community Parks Initiative launched in 2014. CPI Zones are boundaries based on the NYC Department of City Planning's Neighborhood Tabulation Areas. These NTAs were selected as they capture neighborhoods that met several criteria for the initiative. They are densely populated and growing neighborhoods where there are higher-than-average concentrations of poverty as measured through the 2010 US Census, and they contain parks that are suitable for recreational redevelopment and received less than $250,000 in capital investment from 1992-2013.

--- Original source retains full ownership of the source dataset ---

This dataset titled "Nigeria PMS Pump Price" encompasses crucial economic indicators of Nigeria, including the GDP growth rate, PMS (Premium Motor Spirit) pump prices, CPI (Consumer Price Index), and Average Crude Oil prices. The PMS pump price data was meticulously collected from the National Bureau of Statistics (NBS), while the GDP, Crude Oil Price, and CPI data were sourced from the esteemed World Bank.

This comprehensive dataset serves as a valuable resource for researchers, economists, and data analysts interested in examining the relationship between PMS pump prices and other economic factors in Nigeria. It offers a significant opportunity for in-depth analysis, trend identification, and policy evaluation related to the country's petroleum industry and overall economic performance.

By exploring this dataset, users can gain valuable insights into the dynamics and fluctuations of Nigeria's PMS pump prices, along with its correlation with GDP growth, CPI, and Crude Oil prices over time.

Disclaimer: The dataset has been compiled with utmost care and accuracy, drawing information from reliable sources. However, users are encouraged to exercise due diligence and verify the data's authenticity and relevance for their specific research or analytical purposes.

We invite researchers and analysts to leverage this dataset for various studies, forecasting models, and policy-making initiatives related to Nigeria's petroleum sector.

Note: The dataset will be periodically updated to ensure its relevance and incorporate the latest available data from the respective sources.

Please feel free to contact us for any further inquiries or assistance related to this dataset.

1 The original Corruption Perceptions index (CPI) was re-coded, so that high values mean high corruption.2 High Values mean high inequality.3 Gay-Rights Index: 1 = homosexual relationships are illegal, 2 = homosexual relationships are not legally sanctioned, but not legalized, 3 = gay-partnerships are legally recognized, but are not equal to marriage, 4 = gay marriage is fully legally recognized.Summary Statistics of the Variables of the Analysis.

Inflation Rate in Nigeria decreased to 22.22 percent in June from 22.97 percent in May of 2025. This dataset provides - Nigeria Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.