View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Last Update - 9th FEB 2025

Disclaimer!!! Data uploaded here are collected from the internet and some google drive. The sole purposes of uploading these data are to provide this Kaggle community with a good source of data for analysis and research. I don't own these datasets and am also not responsible for them legally by any means. I am not charging anything (either money or any favor) for this dataset. RESEARCH PURPOSE ONLY

THIS IS THE LARGEST DATASET ON NIFTY 100 STOCKS WITH EACH MINUTES AND DAILY DATA (2015 to 2025)

The NIFTY 50 is a benchmark Indian stock market index that represents the weighted average of 50 of the largest Indian companies listed on the National Stock Exchange. It is one of the two main stock indices used in India, the other being the BSE SENSEX.

Nifty 50 is owned and managed by NSE Indices (previously known as India Index Services & Products Limited), which is a wholly-owned subsidiary of the NSE Strategic Investment Corporation Limited.NSE Indices had a marketing and licensing agreement with Standard & Poor's for co-branding equity indices until 2013. The Nifty 50 index was launched on 22 April 1996, and is one of the many stock indices of Nifty.

The NIFTY 50 index is a free-float market capitalization-weighted index. The index was initially calculated on a full market capitalization methodology. On 26 June 2009, the computation was changed to a free-float methodology. The base period for the NIFTY 50 index is 3 November 1995, which marked the completion of one year of operations of the National Stock Exchange Equity Market Segment. The base value of the index has been set at 1000 and a base capital of ₹ 2.06 trillion.

Content This dataset contains Nifty 100 historical daily prices. The historical data are retrieved from the NSE India website. Each stock in this Nifty 500 and are of 1 minute itraday data.

Every dataset contains the following fields. Open - Open price of the stock High - High price of the stock Low - Low price of the stock Close - Close price of the stock Volume - Volume traded of the stock in this time frame

Inspiration

• Data is uploaded for Research and Educational purposes.
• The data scientists and researchers can download and can build EDA, find Correlations, and perform Regression analysis on it.
• Quant researchers can build strategies and backtest their strategies with this dataset.

Stock Names

| ACC | ADANIENT | ADANIGREEN | ADANIPORTS | AMBUJACEM | | -- | -- | -- | -- | -- | | APOLLOHOSP | ASIANPAINT | AUROPHARMA | AXISBANK | BAJAJ-AUTO | | BAJAJFINSV | BAJAJHLDNG | BAJFINANCE | BANDHANBNK | BANKBARODA | | BERGEPAINT | BHARTIARTL | BIOCON | BOSCHLTD | BPCL | | BRITANNIA | CADILAHC | CHOLAFIN | CIPLA | COALINDIA | | COLPAL | DABUR | DIVISLAB | DLF | DMART | | DRREDDY | EICHERMOT | GAIL | GLAND | GODREJCP | | GRASIM | HAVELLS | HCLTECH | HDFC | HDFCAMC | | HDFCBANK | HDFCLIFE | HEROMOTOCO | HINDALCO | HINDPETRO | | HINDUNILVR | ICICIBANK | ICICIGI | ICICIPRULI | IGL | | INDIGO | INDUSINDBK | INDUSTOWER | INFY | IOC | | ITC | JINDALSTEL | JSWSTEEL | JUBLFOOD | KOTAKBANK | | LICI | LT | LTI | LUPIN | M&M | | MARICO | MARUTI | MCDOWELL-N | MUTHOOTFIN | NAUKRI | | NESTLEIND | NIFTY 50 | NIFTY BANK | NMDC | NTPC | | ONGC | PEL | PGHH | PIDILITIND | PIIND | | PNB | POWERGRID | RELIANCE | SAIL | SBICARD | | SBILIFE | SBIN | SHREECEM | SIEMENS | SUNPHARMA | | TATACONSUM | TATAMOTORS | TATASTEEL | TCS | TECHM | | TITAN | TORNTPHARM | ULTRACEMCO | UPL | VEDL | | WIPRO | YESBANK | | | |

Context

Amazon.com, Inc. is an American online retailer with a wide range of products. According to its own information, Amazon, as the market leader in Internet trade, has the world's largest selection of books, CDs and videos. Via the integrated sales platform Marketplace, private individuals or other companies can also offer new and used products as part of online trading. The Amazon Kindle is sold under its own brand as a reader for electronic books, the Amazon Fire HD tablet computer, the Fire TV set-top box, the Fire TV Stick HDMI stick and the Echo speech recognition system.

With sales of $280 billion in 2019, a profit of $11.6 billion, and a market value of $1.32 trillion (June 2020), it was the third most valuable after Apple and Microsoft, and even before Google United States company.

Market cap

Market capitalization of Amazon (AMZN)

Market cap: $2.362 Trillion USD

As of February 2025 Amazon has a market cap of $2.362 Trillion USD. This makes Amazon the world's 4th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Revenue

Revenue for Amazon (AMZN)

Revenue in 2024 (TTM): $637.95 Billion USD

According to Amazon's latest financial reports the company's current revenue (TTM ) is $637.95 Billion USD. an increase over the revenue in the year 2023 that were of $574.78 Billion USD. The revenue is the total amount of income that a company generates by the sale of goods or services. Unlike with the earnings no expenses are subtracted.

Earnings

Earnings for Amazon (AMZN)

Earnings in 2024 (TTM): $71.02 Billion USD

According to Amazon's latest financial reports the company's current earnings are $637.95 Billion USD. , an increase over its 2023 earnings that were of $40.73 Billion USD. The earnings displayed on this page is the company's Pretax Income.

End of Day market cap according to different sources

On Feb 20th, 2025 the market cap of Amazon was reported to be:

$2.362 Trillion USD by Yahoo Finance

$2.362 Trillion USD by CompaniesMarketCap

$2.362 Trillion USD by Nasdaq

Content

Geography: USA

Time period: May 1997- February 2025

Unit of analysis: Amazon Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2F0653d1b767520d0894074168b97e961b%2FScreenshot%202025-02-21%20174540.png?generation=1740142461604504&alt=media" alt="">

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List of companies in the NASDAQ exchanges.

Data and documentation are available on NASDAQ's official webpage. Data is updated regularly on the FTP site.

The file used in this repository:

Notes:

...

The number of active monthly users of the commission-free trading app Robinhood grew steadily since 2014, even though the app did not officially launch until mid-2015. The number of users grew from ************** in 2014 up to **** million, reaching a peak in 2021. While the number of active monthly users now rests at under ** million, the average revenue per user (ARPU) has increased. The app’s net revenue did also grow steadily since its official launch, reaching *** million U.S. dollars as of 2023. Robinhood and the GameStop story Robinhood was a key player in the GameStop story in January 2021, when they restricted the trading of GameStop stocks for a few days. The platform with its commission-free trading is known to be “for the young and poor," and their trading halt caused a lot of anger among its users, who called it market manipulation and claimed the company was helping the hedge funds. Did the GameStop story affect the number of downloads? The number of downloads of the Robinhood app increased markedly in April 2021. The number of downloads grew by almost **** times compared to the previous month, reaching around **** million downloads from the Google Play Store and Apple App Store in April 2021. This increase shows that the app had a central role for the GameStop stocks, where young investors saw an opportunity to make money with commission-free trades.

This dataset includes data that the Employment and Training Administration's Office of Foreign Labor Certification (OFLC) collected from Labor Condition Applications for Nonimmigrant Workers (LCAs) during previous fiscal years. It includes information on employers, geography, and job details for participants in the LCA program. Historical LCA public disclosure data is available on the OFLC website in the Performance Data section. Data is available as Excel files in aggregate form at https://www.dol.gov/agencies/eta/foreign-labor/performance.

List of companies in the NYSE, and other exchanges.

Data and documentation are available on NASDAQ's official webpage. Data is updated regularly on the FTP site.

The file used in this repository: ...

Key Stock Trading StatisticsTop Stock Trading AppsFinance App Market LandscapeStock Trading App RevenueStock Trading Revenue by AppStock Trading App UsersStock Trading Users by AppStock Trading App...

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

Graph and download economic data for Real-time Sahm Rule Recession Indicator (SAHMREALTIME) from Dec 1959 to Sep 2025 about recession indicators, academic data, and USA.

This data is downloaded from the official Bombay Stock Exchange Website (BSE). This file contains the last 10 years of Historical Stock Price (By Security & Period) Security Name - Nestle India Ltd. Period - Daily Start Date - 2nd January 2012 End Date - 21st April 2022. This is one of the Best datasets for Regression Supervised Machine Learning. You can Perform SImple as well as Multiple Linear Regression on this Dataset.

Context

With the sole mission to democratize financial data, Finnhub is excited to release the new Filings metadata dataset for bulk download. The data is cleaned and sourced directly from SEC filings from 1994-2020.

If you don't need bulk download, you can query this data for free on our website: https://finnhub.io/docs/api#filings. We also provide various type of financial data such as global fundamentals, deep historical tick data, estimates and alternative data.

Finnhub Stock API

Dataset: Online Shopping Dataset;

CustomerID

Description: Unique identifier for each customer. Data Type: Numeric;

Gender:

Description: Gender of the customer (e.g., Male, Female). Data Type: Categorical;

Location:

Description: Location or address information of the customer. Data Type: Text;

Tenure_Months:

Description: Number of months the customer has been associated with the platform. Data Type: Numeric;

Transaction_ID:

Description: Unique identifier for each transaction. Data Type: Numeric;

Transaction_Date:

Description: Date of the transaction. Data Type: Date;

Product_SKU:

Description: Stock Keeping Unit (SKU) identifier for the product. Data Type: Text;

Product_Description:

Description: Description of the product. Data Type: Text;

Product_Category:

Description: Category to which the product belongs. Data Type: Categorical;

Quantity:

Description: Quantity of the product purchased in the transaction. Data Type: Numeric;

Avg_Price:

Description: Average price of the product. Data Type: Numeric;

Delivery_Charges:

Description: Charges associated with the delivery of the product. Data Type: Numeric;

Coupon_Status:

Description: Status of the coupon associated with the transaction. Data Type: Categorical;

GST:

Description: Goods and Services Tax associated with the transaction. Data Type: Numeric;

Date:

Description: Date of the transaction (potentially redundant with Transaction_Date). Data Type: Date;

Offline_Spend:

Description: Amount spent offline by the customer. Data Type: Numeric;

Online_Spend:

Description: Amount spent online by the customer. Data Type: Numeric;

Month:

Description: Month of the transaction. Data Type: Categorical;

Coupon_Code:

Description: Code associated with a coupon, if applicable. Data Type: Text;

Discount_pct:

Description: Percentage of discount applied to the transaction. Data Type: Numeric;

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8734253%2F84d76e9b100f0eaee4b913d74c0aab45%2FScreenshot%202024-03-10%20at%203.46.10PM.png?generation=1710099996246003&alt=media" alt=""> Caption: Visualization of Microsoft (MSFT) ESG Data

Dataset Overview

This dataset contains ESG (Environmental, Social, and Governance) scores and ratings for a large number of publicly traded companies across various industries. The data is provided at a company level, with each row representing one company.

The key fields include: - Basic company information: ticker symbol, company name, currency, exchange, industry, logo URL, website URL - Environmental scores and rating: environment_score, environment_grade, environment_level - Social scores and rating: social_score, social_grade, social_level - Governance scores and rating: governance_score, governance_grade, governance_level - Overall ESG scores and rating: total_score, total_grade, total_level - Last processing date of the ESG data - CIK identifier

The environmental, social, governance and total scores are numeric values, while the corresponding grades are letter ratings (like AAA, BB etc.) and levels are categorical (like High, Medium, Low).

This dataset can be analyzed to understand the distribution of ESG scores and ratings across different companies, sectors and industries. It could be combined with financial datasets to explore relationships between ESG performance and key metrics like profitability, valuation, and stock returns. The data can provide valuable insights for investors, asset managers, financial analysts, corporate strategists, policymakers and sustainability researchers.

By sharing this data publicly, the provider likely aims to bring greater transparency to corporate ESG practices, enable better integration of ESG considerations into investment decisions, and create incentives for companies to improve their ESG performance over time. Wide availability of robust ESG data is critical to driving progress on major societal goals like combating climate change and enhancing social equity.

See ESG Compare (http://esgcompare.org) for an interactive demo!

E-Commerce Sales Dataset

Analyzing and Maximizing Online Business Performance

By ANil [source]

About this dataset

This dataset provides an in-depth look at the profitability of e-commerce sales. It contains data on a variety of sales channels, including Shiprocket and INCREFF, as well as financial information on related expenses and profits. The columns contain data such as SKU codes, design numbers, stock levels, product categories, sizes and colors. In addition to this we have included the MRPs across multiple stores like Ajio MRP , Amazon MRP , Amazon FBA MRP , Flipkart MRP , Limeroad MRP Myntra MRP and PaytmMRP along with other key parameters like amount paid by customer for the purchase , rate per piece for every individual transaction Also we have added transactional parameters like Date of sale months category fulfilledby B2b Status Qty Currency Gross amt . This is a must-have dataset for anyone trying to uncover the profitability of e-commerce sales in today's marketplace

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a comprehensive overview of e-commerce sales data from different channels covering a variety of products. Using this dataset, retailers and digital marketers can measure the performance of their campaigns more accurately and efficiently.

The following steps help users make the most out of this dataset: - Analyze the general sales trends by examining info such as month, category, currency, stock level, and customer for each sale. This will give you an idea about how your e-commerce business is performing in each channel.
- Review the Shiprocket and INCREF data to compare and analyze profitability via different fulfilment methods. This comparison would enable you to make better decisions towards maximizing profit while minimizing costs associated with each method’s referral fees and fulfillment rates.
- Compare prices between various channels such as Amazon FBA MRP, Myntra MRP, Ajio MRP etc using the corresponding columns for each store (Amazon MRP etc). You can judge which stores are offering more profitable margins without compromising on quality by analyzing these pricing points in combination with other information related to product sales (TP1/TP2 - cost per piece).
- Look at customer specific data such as TP 1/TP 2 combination wise Gross Amount or Rate info in terms price per piece or total gross amount generated by any SKU dispersed over multiple customers with relevant dates associated to track individual item performance relative to others within its category over time periods shortlisted/filtered appropriately.. Have an eye on items commonly utilized against offers or promotional discounts offered hence crafting strategies towards inventory optimization leading up-selling operations.?
- Finally Use Overall ‘Stock’ details along all the P & L Data including Yearly Expenses_IIGF information record for takeaways which might be aimed towards essential cost cutting measures like switching amongst delivery options carefully chosen out of Shiprocket & INCREFF leadings away from manual inspections catering savings under support personnel outsourcing structures.?

By employing a comprehensive understanding on how our internal subsidiaries perform globally unless attached respective audits may provide us remarkably lower operational costs servicing confidence; costing far lesser than being incurred taking into account entire pallet shipments tracking sheets representing current level supply chains efficiencies achieved internally., then one may finally scale profits exponentially increases cut down unseen losses followed up introducing newer marketing campaigns necessarily tailored according playing around multiple goods based spectrums due powerful backing suitable transportation boundaries set carefully

Research Ideas

• Analysing the difference in profitability between sales made through Shiprocket and INCREFF. This data can be used to see where the biggest profit margins lie, and strategize accordingly.
• Examining the Complete Cost structure of a product with all its components and their contribution towards revenue or profitability, i.e., TP 1 & 2, MRP Old & Final MRP Old together with Platform based MRP - Amazon, Myntra and Paytm etc., Currency based Profit Margin etc.
• Building a predictive model using Machine Learning by leveraging historical data to predict future sales volume and profits for e-commerce products across multiple categories/devices/platforms such as Amazon, Flipkart, Myntra etc as well providing m...

The AQS Data Mart is a database containing all of the information from AQS. It has every measured value the EPA has collected via the national ambient air monitoring program. It also includes the associated aggregate values calculated by EPA (8-hour, daily, annual, etc.). The AQS Data Mart is a copy of AQS made once per week and made accessible to the public through web-based applications. The intended users of the Data Mart are air quality data analysts in the regulatory, academic, and health research communities. It is intended for those who need to download large volumes of detailed technical data stored at EPA and does not provide any interactive analytical tools. It serves as the back-end database for several Agency interactive tools that could not fully function without it: AirData, AirCompare, The Remote Sensing Information Gateway, the Map Monitoring Sites KML page, etc.

AQS must maintain constant readiness to accept data and meet high data integrity requirements, thus is limited in the number of users and queries to which it can respond. The Data Mart, as a read only copy, can allow wider access.

The most commonly requested aggregation levels of data (and key metrics in each) are:

Sample Values (2.4 billion values back as far as 1957, national consistency begins in 1980, data for 500 substances routinely collected) The sample value converted to standard units of measure (generally 1-hour averages as reported to EPA, sometimes 24-hour averages) Local Standard Time (LST) and GMT timestamps Measurement method Measurement uncertainty, where known Any exceptional events affecting the data NAAQS Averages NAAQS average values (8-hour averages for ozone and CO, 24-hour averages for PM2.5) Daily Summary Values (each monitor has the following calculated each day) Observation count Observation per cent (of expected observations) Arithmetic mean of observations Max observation and time of max AQI (air quality index) where applicable Number of observations > Standard where applicable Annual Summary Values (each monitor has the following calculated each year) Observation count and per cent Valid days Required observation count Null observation count Exceptional values count Arithmetic Mean and Standard Deviation 1st - 4th maximum (highest) observations Percentiles (99, 98, 95, 90, 75, 50) Number of observations > Standard Site and Monitor Information FIPS State Code (the first 5 items on this list make up the AQS Monitor Identifier) FIPS County Code Site Number (unique within the county) Parameter Code (what is measured) POC (Parameter Occurrence Code) to distinguish from different samplers at the same site Latitude Longitude Measurement method information Owner / operator / data-submitter information Monitoring Network to which the monitor belongs Exemptions from regulatory requirements Operational dates City and CBSA where the monitor is located Quality Assurance Information Various data fields related to the 19 different QA assessments possible

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.epa_historical_air_quality.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Data provided by the US Environmental Protection Agency Air Quality System Data Mart.

Context

This dataset contains historical data of stocks listed on IHSG with time ranges per minutes, hourly, and daily. The source of the dataset is taken from Yahoo Finance's public data and the IDX website which is listed in the metadata tab. This dataset was created with the intention of academic research purposes and not to be commercialized. If you have questions about the dataset, please ask in the discussion tab. Code snippet: https://github.com/muamkh/IHSGstockscraper

Content

Stock minutes data is taken from 1 November 2021 until 6 January 2023. Stock hourly data is taken from 16 April 2020 until 6 January 2023. Stock daily data is taken from 16 April 2001 until 6 January 2023. All of the data is using CSV format. Stock data isnt adjusted with dividend, stock split, and other corporate action.

Stocklist Structure

• Code = Stock code
• Name = Company name
• ListingDate = Listing date of stock on Indonesia Stock Exchange
• Shares = Amount of shares
• ListingBoard = Board category (Main Board, Development Board or Acceleration). More info: https://www.idx.co.id/en-us/products/stocks/
• Sector = Sector Category based on IDX-IC. More info: https://www.idx.co.id/en-us/products/stocks/
• LastPrice = Last stock price
• MarketCap = Market Capitalization.
• MinutesFirstAdded = Date the data first retrieved in minute range
• MinutesLastAdded = Date the data last retrieved in minute range
• HourlyFirstAdded = Date the data first retrieved in hourly range
• HourlyLastAdded = Date the data last retrieved in hourly range
• DailyFirstAdded = Date the data first retrieved in daily range
• DailyLastAdded = Date the data last retrieved in daily range

Struktur Data Saham

• timestamp = Date and time of stock transaction
• open = opening price
• low = lowest price in the timespan
• high = highest price in the timespan
• close = closing price
• volume = Total volume traded in the timespan

The Dairy Goods Sales Dataset provides a detailed and comprehensive collection of data related to dairy farms, dairy products, sales, and inventory management. This dataset encompasses a wide range of information, including farm location, land area, cow population, farm size, production dates, product details, brand information, quantities, pricing, shelf life, storage conditions, expiration dates, sales information, customer locations, sales channels, stock quantities, stock thresholds, and reorder quantities.

Features:

1. Location: The geographical location of the dairy farm.
2. Total Land Area (acres): The total land area occupied by the dairy farm.
3. Number of Cows: The number of cows present in the dairy farm.
4. Farm Size: The size of the dairy farm(in sq.km).
5. Date: The date of data recording.
6. Product ID: The unique identifier for each dairy product.
7. Product Name: The name of the dairy product.
8. Brand: The brand associated with the dairy product.
9. Quantity (liters/kg): The quantity of the dairy product available.
10. Price per Unit: The price per unit of the dairy product.
11. Total Value: The total value of the available quantity of the dairy product.
12. Shelf Life (days): The shelf life of the dairy product in days.
13. Storage Condition: The recommended storage condition for the dairy product.
14. Production Date: The date of production for the dairy product.
15. Expiration Date: The date of expiration for the dairy product.
16. Quantity Sold (liters/kg): The quantity of the dairy product sold.
17. Price per Unit (sold): The price per unit at which the dairy product was sold.
18. Approx. Total Revenue (INR): The approximate total revenue generated from the sale of the dairy product.
19. Customer Location: The location of the customer who purchased the dairy product.
20. Sales Channel: The channel through which the dairy product was sold (Retail, Wholesale, Online).
21. Quantity in Stock (liters/kg): The quantity of the dairy product remaining in stock.
22. Minimum Stock Threshold (liters/kg): The minimum stock threshold for the dairy product.
23. Reorder Quantity (liters/kg): The recommended quantity to reorder for the dairy product.

Potential Use-Case:

This dataset can be used by researchers, analysts, and businesses in the dairy industry for various purposes, such as:

1. Analyzing the performance of dairy farms based on location, land area, and cow population.
2. Understanding the sales and distribution patterns of different dairy products across various brands and regions.
3. Studying the impact of storage conditions and shelf life on the quality and availability of dairy products.
4. Analyzing customer preferences and buying behavior based on location and sales channels.
5. Optimizing inventory management by tracking stock quantities, minimum thresholds, and reorder quantities.
6. Conducting market research and trend analysis in the dairy industry.
7. Developing predictive models for demand forecasting and pricing strategies.

Note: This dataset includes data from the period between 2019 and 2022, and it specifically focuses on selected dairy brands operating in specific states and union territories of India. There is an intentional drift highlighted in the dataset's figures due to its opensource and creative license, currently !

Dataset comprising data from five day-ahead electricity markets:

Nord pool: The Nord pool day-ahead electricity market, one of the largest European power market. PJM: The zonal prices of the COMED area in the Pennsylvania-New Jersey-Maryland (PJM) market. EPEX-FR: The French day-ahead electricity market. EPEX-BE: The Belgian day-ahead electricity market. EPEX-DE: The German day-ahead electricity market. Each market contains 6 years of data (we consider a year to be 364 days to have an integer number of weeks). The specific dates are:

Nord pool: 01.01.2013 – 24.12.2018 PJM: 01.01.2013 – 24.12.2018 EPEX-FR: 09.01.2011 – 31.12.2016 EPEX-BE: 09.01.2011 – 31.12.2016 EPEX-DE: 09.01.2012 – 31.12.2017 Each dataset comprises historical prices and two relevant exogenous inputs based on day-ahead forecasts of price drivers. The day--ahead forecast representing other exogenous inputs are market dependent:

Nord pool: System load + Wind power generation. PJM: System load + Zonal load in the COMED area. EPEX-FR: System load + Generation in France EPEX-BE: System load in France + Generation in France EPEX-DE: Zonal load in the TSO Amprion zone + Aggregated Wind and Solar power generation All datasets are given using the local timezone:

Nord pool: Central European Time (CET) PJM: Eastern Time (ET) EPEX-FR: Central European Time (CET) EPEX-BE: Central European Time (CET) EPEX-DE: Central European Time (CET) For all five datasets, the daylight saving times (DST) are pre-processed by interpolating the missing values in Spring and averaging the values corresponding to the duplicated time indices in Autumn.

DISCLAIMER

We do not own the data, but we simply have gathered it so other researchers can easily test their methods on multiple day-ahead markets. The data has been gathered using the respective websites of each day-ahead market where these data are freely available. The websites we used to gather the data are:

Nord Pool: Nord pool website PJM: PJM website EPEX-FR: ENTSO-E transparency platform + RTE website (French TSO) EPEX-BE: ENTSO-E transparency platform + RTE website (French TSO) + Elia website (Belgian TSO) EPEX-DE: ENTSO-E transparency platform + Amprion TSO website + TenneT website + 50Hertz website

*****Documentation Process***** 1. Data Preparation: - Upload the data into Power Query to assess quality and identify duplicate values, if any. - Verify data quality and types for each column, addressing any miswriting or inconsistencies. 2. Data Management: - Duplicate the original data sheet for future reference and label the new sheet as the "Working File" to preserve the integrity of the original dataset. 3. Understanding Metrics: - Clarify the meaning of column headers, particularly distinguishing between Impressions and Reach, and comprehend how Engagement Rate is calculated. - Engagement Rate formula: Total likes, comments, and shares divided by Reach. 4. Data Integrity Assurance: - Recognize that Impressions should outnumber Reach, reflecting total views versus unique audience size. - Investigate discrepancies between Reach and Impressions to ensure data integrity, identifying and resolving root causes for accurate reporting and analysis. 5. Data Correction: - Collaborate with the relevant team to rectify data inaccuracies, specifically addressing the discrepancy between Impressions and Reach. - Engage with the concerned team to understand the root cause of discrepancies between Impressions and Reach. - Identify instances where Impressions surpass Reach, potentially attributable to data transformation errors. - Following the rectification process, meticulously adjust the dataset to reflect the corrected Impressions and Reach values accurately. - Ensure diligent implementation of the corrections to maintain the integrity and reliability of the data. - Conduct a thorough recalculation of the Engagement Rate post-correction, adhering to rigorous data integrity standards to uphold the credibility of the analysis. 6. Data Enhancement: - Categorize Audience Age into three groups: "Senior Adults" (45+ years), "Mature Adults" (31-45 years), and "Adolescent Adults" (<30 years) within a new column named "Age Group." - Split date and time into separate columns using the text-to-columns option for improved analysis. 7. Temporal Analysis: - Introduce a new column for "Weekend and Weekday," renamed as "Weekday Type," to discern patterns and trends in engagement. - Define time periods by categorizing into "Morning," "Afternoon," "Evening," and "Night" based on time intervals. 8. Sentiment Analysis: - Populate blank cells in the Sentiment column with "Mixed Sentiment," denoting content containing both positive and negative sentiments or ambiguity. 9. Geographical Analysis: - Group countries and obtain additional continent data from an online source (e.g., https://statisticstimes.com/geography/countries-by-continents.php). - Add a new column for "Audience Continent" and utilize XLOOKUP function to retrieve corresponding continent data.

*****Drawing Conclusions and Providing a Summary*****

• The data is equally distributed across different categories, platforms, and over the years.
• Most of our audience comprises senior adults (aged 45 and above).
• Most of our audience exhibit mixed sentiments about our posts. However, an equal portion expresses consistent sentiments.
• The majority of our posts were located in Africa.
• The number of posts increased from the first year to the second year and remained relatively consistent for the third year.
• The optimal time for posting is during the night on weekdays.
• The highest engagement rates were observed in Croatia then Malawi.
• The number of posts targeting senior adults is significantly higher than the other two categories. However, the engagement rates for mature and adolescent adults are also noteworthy, based on the number of targeted posts.