Data from Fortune 500's 2023 ranking.
Includes data on top 1000 companies w/ additional info (Stock symbol/*ticker*, CEO name).

Update (New dataset): 2024 Fortune 1000 Companies

What Is the Fortune 1000?

From Investopedia:

The Fortune 1000 is an annual list of the 1000 largest American companies maintained by the popular magazine Fortune Fortune ranks the eligible companies by revenue generated from core operations, discounted operations, and consolidated subsidiaries Since revenue is the basis for inclusion, every company is authorized to operate in the United States and files a 10-K or comparable financial statement with a government agency -- .

Project Background

Fortune magazine publishes this list every year and some lists can be found from different sources. From looking at this year's available datasets, some features were missing or could not be found. This was built from scraping the standard features as well as what's included on Company Info (such as CEO, Ticker and website) from the Fortune magazine website. Details on how the data was generated can be found on this notebook where a few of the features were also visualized.

The source code from the 2023 fortune 500 Ranking includes 1000 companies. A reference page (slug) to additional info is included for each companies which were also scrapped to complete the dataset.

The Dataset

Available formats: csv, parquet

Features are follows:

[Note: References to datatypes are relevant when using the parquet file; Labels refer to the original website names]

• Rank
    dtype: int64; Label: Rank
• Company
    dtype: object; Label: Company
• Ticker
    dtype: object; Label: Ticker
• Sector
    dtype: category; Label: Sector
• Industry
    dtype: category; Label: Industry
• Profitable
    dtype: category; Label: Profitable
• Founder_is_CEO
    dtype: category; Label: Founder is CEO
• FemaleCEO
    dtype: category; Label: Female CEO
• Growth_in_Jobs
    dtype: category; Label: Growth in Jobs
• Change_in_Rank
    dtype: float64; Label: Change in Rank (Full 1000)
• Gained_in_Rank
    dtype: category; Label: Gained in Rank
• Dropped_in_Rank
    dtype: category; Label: Dropped in Rank
• Newcomer_to_the_Fortune500
    dtype: category; Label: Newcomer to the Fortune 500
• Global500
    dtype: category; Label: Global 500
• Best_Companies
    dtype: category; Label: Best Companies
• Number_of_employees
    dtype: int64; Label: Employees
• MarketCap_March31_M
    dtype: float64; Label: Market Value — as of March 31, 2023 ($M)
• Revenues_M
    dtype: int64; Label: Revenues ($M)
• RevenuePercentChange
    dtype: float64; Label: Revenue Percent Change
• Profits_M
    dtype: int64; Label: Profits ($M)
• ProfitsPercentChange
    dtype: float64; Label: Profits Percent Change
• Assets_M
    dtype: int64; Label: Assets ($M)
• CEO
    dtype: object; Label: CEO
• Country
    dtype: category; Label: Country
• HeadquartersCity
    dtype: object; Label: Headquarters City
• HeadquartersState
    dtype: category; Label: Headquarters State
• Website
    dtype: object; Label: Website
• CompanyType
    dtype: category; Label: Company type
• Footnote
    dtype: object; Label: Footnote
• MarketCap_Updated_M
    dtype: float64; Label: Market value ($M)
• Updated
    dtype: datetime64[ns]; Label: Updated Click to add a cell.

Small business transactions and revenue data aggregated from several credit card processors, collected by Womply and compiled by Opportunity Insights. Transactions and revenue are reported based on the ZIP code where the business is located. Data provided for CT (FIPS code 9), MA (25), NJ (34), NY (36), and RI (44). Data notes from Opportunity Insights: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division. Small businesses are defined as those with annual revenue below the Small Business Administration’s thresholds. Thresholds vary by 6 digit NAICS code ranging from a maximum number of employees between 100 to 1500 to be considered a small business depending on the industry. County-level and metro-level data and breakdowns by High/Middle/Low income ZIP codes have been temporarily removed since the August 21st 2020 update due to revisions in the structure of the raw data we receive. We hope to add them back to the OI Economic Tracker soon. More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

Oracle’s cloud services and license support division is the company’s most profitable business segment, bringing in over ** billion U.S. dollars in its 2024 fiscal year. In that year, Oracle brought in annual revenue of close to ** billion U.S. dollars, its highest revenue figure to date. Oracle Corporation Oracle was founded by Larry Ellison in 1977 as a tech company primarily focused on relational databases. Today, Oracle ranks among the largest companies in the world in terms of market value and serves as the world’s most popular database management system provider. Oracle’s success is not only reflected in its booming sales figures, but also in its growing number of employees: between fiscal year 2008 and 2021, Oracle’s total employee number has grown substantially, increasing from around ****** to *******. Database market The global database market reached a size of ** billion U.S. dollars in 2020. Database Management Systems (DBMSs) provide a platform through which developers can organize, update, and control large databases, with products like Oracle, MySQL, and Microsoft SQL Server being the most widely used in the market.

Over the past decade, Oracle Corporation’s annual revenue has grown from around 22 billion U.S. dollars to almost 53 billion, with fiscal year 2024 marking one of the company’s highest revenue figures to date. The company’s cloud services and license support segment is its biggest earner, accounting for more than half of its overall revenues. Oracle Corporation Oracle was founded by Larry Ellison in 1977, as a tech company primarily focused on relational databases. Today Oracle ranks among the largest companies in the world in terms of market value, and serves as the world’s most popular database management system provider. Oracle’s database products have remained popular throughout the years, and the company has more recently widened its focus to include cloud computing resources as well. Cloud computing Like Oracle, many of the world’s largest technology companies have begun to dedicate significant portions of their resources towards the development of cloud computing platforms and services. Cloud computing allows customers to make use of storage and computing resources without the need for physical server equipment. The public cloud computing market brings in hundreds of billions of dollars’ worth of revenue each year, and being a relatively new technology, shows no signs of slowing down. The fiscal year end of the company is May, 31st.

Corporate Profits in the United States decreased to 3203.60 USD Billion in the first quarter of 2025 from 3312 USD Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Analysis of ‘Fortune 1000 companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ramjasmaurya/fortune-1000-companieslatest on 28 January 2022.

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

The Fortune Global 500, also known as Global 500, is an annual ranking of the top 500 corporations worldwide as measured by revenue. The list is compiled and published annually by Fortune magazine.

Until 1989, it listed only non-United States industrial corporations under the title "International 500" while the Fortune 500 contained and still contains exclusively United States corporations. In 1990, United States companies were added to compile a truly global list of top industrial corporations as ranked by sales. Since 1995, the list has had its current form, the listing also tops financial corporations and service providers by revenue.

Several inconsistencies exist in Fortune's ranking of cities with the most Fortune 500 headquarters. On June 3, 2011, the Atlanta Business Chronicle stated examples of Fortune including regional headquarters for some cities, excluding regional headquarters for other cities, and in some cases excluding headquarters that are physically located inside a city limit.

BUT THIS IS THE Extended version of The Fortune Global 500

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

• The dataset contains sales data of a Turkey-based company that sells tobacco products.
• The data covers a period of 2 years from 2020 to 2022.
• The company has several product categories, including cigarettes, cigars, and pipe tobacco, among others.
• The dataset contains information on sales volume, revenue, and profit for each product category and year.
• The company's total revenue and profit have been increasing steadily over the years, with a peak in 2020.
• Cigarettes are the most popular product category, accounting for more than 60% of the company's sales volume and revenue.
• However, the sales volume and revenue of cigarettes have been declining slightly over the years, while the sales of other product categories such as cigars and pipe tobacco have been increasing.
• The dataset also contains information on the average selling price and cost per unit for each product category.
• The average selling price and cost per unit vary significantly among product categories, with cigars having the highest average selling price and pipe tobacco having the lowest.
• The dataset could be used to identify trends and patterns in the company's sales, as well as to perform forecasting and optimization analyses.

Profit Margin 0.33 if you need

Analysis of ‘Womply State-level Business Revenue’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/de408fa1-0d08-420d-b877-2109891047d9 on 27 January 2022.

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

Small business transactions and revenue data aggregated from several credit card processors, collected by Womply and compiled by Opportunity Insights. Transactions and revenue are reported based on the ZIP code where the business is located.

Data provided for CT (FIPS code 9), MA (25), NJ (34), NY (36), and RI (44).

Data notes from Opportunity Insights: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division.

Small businesses are defined as those with annual revenue below the Small Business Administration’s thresholds. Thresholds vary by 6 digit NAICS code ranging from a maximum number of employees between 100 to 1500 to be considered a small business depending on the industry.

County-level and metro-level data and breakdowns by High/Middle/Low income ZIP codes have been temporarily removed since the August 21st 2020 update due to revisions in the structure of the raw data we receive. We hope to add them back to the OI Economic Tracker soon.

More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

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

Apple annual/quarterly revenue history and growth rate from 1980 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services. Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income. Apple revenue for the quarter ending June 30, 2022 was $82.959B, a 1.87% increase year-over-year. Apple revenue for the twelve months ending June 30, 2022 was $387.542B, a 11.63% increase year-over-year. Apple annual revenue for 2021 was $365.817B, a 33.26% increase from 2020. Apple annual revenue for 2020 was $274.515B, a 5.51% increase from 2019. Apple annual revenue for 2019 was $260.174B, a 2.04% decline from 2018.

Walmart Inc. is an American multinational retail corporation that operates a chain of hypermarkets (also called supercenters), discount department stores, and grocery stores in the United States, headquartered in Bentonville, Arkansas. The company was founded by Sam Walton in nearby Rogers, Arkansas in 1962 and incorporated under Delaware General Corporation Law on October 31, 1969. It also owns and operates Sam's Club retail warehouses. In India, Walmart operates under the name of Flipkart Wholesale.

As of July 31, 2022, Walmart has 10,585 stores and clubs in 24 countries, operating under 46 different names. Out of which we have chosen 45 stores for basic analysis.

Walmart is the world's largest company by revenue, with about US$570 billion in annual revenue, according to the Fortune Global 500 list in May 2022.

How Walmart uses Big Data?

• Improving Store Checkout: By using Predictive Analysis, the stores can anticipate demand at a certain week and determine how many Sales Representatives / Employees are needed.
• Managing the Steps of Supply Chain: The company optimizes the routes to the shipping dock and tracks the number of times the product is accessed before it reaches the Customer's destination. Also, it uses the data to analyze transportation lanes and routes for the company's trucks. These data help Walmart keep transportation costs down and schedule an appropriate time for drivers.
• Optimizing Product Assortment: By analyzing customer preferences and shopping patterns, Walmart accelerates the decision-making on how to maintain stocks. Big Data provides insights on new items and discontinued products.
• Personalizing Shopping Experience: With Big Data, Walmart analyzes the shopping preferences of the customers to develop a consistent and delightful shopping experience. and much more...

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

With Oxylabs Datasets, you can count on:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

Company Intelligence Name and Websites - Company Website and Alternative Domains.
Address - Standardized headquarter Address, City, Region, Zip Code, and Country LAT / LONG - Used for Geo Location Locations - Additional office locations of the business Phone - Standardized headquarter phone with country code Social Profiles - LinkedIn, CrunchBase, Facebook, Twitter, Yelp, Instagram Type - Headquarters, Branch, Local Only Description - detailed overview of the company business model and pursuit. Industry - Standardized Industries to segment companies by their most notable contributions Sector - 20 industry groupings Specialties - Non industry details shared by the company to better understand what they do SIC Code - 839 industry classifications and their definitions Revenue - Annual revenue from 1M to over 1B Employee - Number of Employees at the company

Similar Companies - used to identify competitors Funding - for start up data IP Address - from the hosted website Affiliated Companies - company hierarchy

Analysis of ‘Germany Largest Companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/germany-largest-companiese on 13 February 2022.

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

About this dataset

From the Forbes Global 2000 list​ last updated on May 2013. Forbes publishes an annual list of the world's 2000 largest publicly listed corporations. ​The Forbes Global 2000 weigh​s​ sales, profits, assets and market value​ equally​ so companies can be ranked by size. Figures for all companies are in US dollars.

​Source: Economy Watch

This dataset was created by Finance and contains around 0 samples along with Profits ($billion), Assets ($billion), technical information and other features such as: - Sales ($billion) - Market Value ($billion) - and more.

How to use this dataset

• Analyze Global Rank in relation to Profits ($billion)
• Study the influence of Assets ($billion) on Sales ($billion)
• More datasets

Acknowledgements

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

Start A New Notebook!

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

From 2004 to 2024, the net revenue of Amazon e-commerce and service sales has increased tremendously. In the fiscal year ending December 31, the multinational e-commerce company's net revenue was almost *** billion U.S. dollars, up from *** billion U.S. dollars in 2023.Amazon.com, a U.S. e-commerce company originally founded in 1994, is the world’s largest online retailer of books, clothing, electronics, music, and many more goods. As of 2024, the company generates the majority of it's net revenues through online retail product sales, followed by third-party retail seller services, cloud computing services, and retail subscription services including Amazon Prime. From seller to digital environment Through Amazon, consumers are able to purchase goods at a rather discounted price from both small and large companies as well as from other users. Both new and used goods are sold on the website. Due to the wide variety of goods available at prices which often undercut local brick-and-mortar retail offerings, Amazon has dominated the retailer market. As of 2024, Amazon’s brand worth amounts to over *** billion U.S. dollars, topping the likes of companies such as Walmart, Ikea, as well as digital competitors Alibaba and eBay. One of Amazon's first forays into the world of hardware was its e-reader Kindle, one of the most popular e-book readers worldwide. More recently, Amazon has also released several series of own-branded products and a voice-controlled virtual assistant, Alexa. Headquartered in North America Due to its location, Amazon offers more services in North America than worldwide. As a result, the majority of the company’s net revenue in 2023 was actually earned in the United States, Canada, and Mexico. In 2023, approximately *** billion U.S. dollars was earned in North America compared to only roughly *** billion U.S. dollars internationally.

The annual Retail store data CD-ROM is an easy-to-use tool for quickly discovering retail trade patterns and trends. The current product presents results from the 1999 and 2000 Annual Retail Store and Annual Retail Chain surveys. This product contains numerous cross-classified data tables using the North American Industry Classification System (NAICS). The data tables provide access to a wide range of financial variables, such as revenues, expenses, inventory, sales per square footage (chain stores only) and the number of stores. Most data tables contain detailed information on industry (as low as 5-digit NAICS codes), geography (Canada, provinces and territories) and store type (chains, independents, franchises). The electronic product also contains survey metadata, questionnaires, information on industry codes and definitions, and the list of retail chain store respondents.

Analysis of ‘China Largest Companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/china-largest-companiese on 28 January 2022.

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

About this dataset

From the Forbes Global 2000 list​ last updated on May 2013. Forbes publishes an annual list of the world's 2000 largest publicly listed corporations. ​The Forbes Global 2000 weigh​s​ sales, profits, assets and market value​ equally​ so companies can be ranked by size. Figures for all companies are in US dollars.

​Source: Economy Watch

This dataset was created by Finance and contains around 100 samples along with Profits ($billion), Market Value ($billion), technical information and other features such as: - Sales ($billion) - Assets ($billion) - and more.

How to use this dataset

• Analyze Global Rank in relation to Profits ($billion)
• Study the influence of Market Value ($billion) on Sales ($billion)
• More datasets

Acknowledgements

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

Start A New Notebook!

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

📊 Dataset Features This dataset includes 5,000 startups from 10 countries and contains 15 key features: Startup Name: Name of the startup Founded Year: Year the startup was founded Country: Country where the startup is based Industry: Industry category (Tech, FinTech, AI, etc.) Funding Stage: Stage of investment (Seed, Series A, etc.) Total Funding ($M): Total funding received (in million $) Number of Employees: Number of employees in the startup Annual Revenue ($M): Annual revenue in million dollars Valuation ($B): Startup's valuation in billion dollars Success Score: Score from 1 to 10 based on growth Acquired?: Whether the startup was acquired (Yes/No) IPO?: Did the startup go public? (Yes/No) Customer Base (Millions): Number of active customers Tech Stack: Technologies used by the startup Social Media Followers: Total followers on social platforms Analysis Ideas 📈 What Can You Do with This Dataset? Here are some exciting analyses you can perform:

Predict Startup Success: Train a machine learning model to predict the success score. Industry Trends: Analyze which industries get the most funding. **Valuation vs. Funding: **Explore the correlation between funding and valuation. Acquisition Analysis: Investigate the factors that contribute to startups being acquired.

Yahoo Finance Business Information dataset to access comprehensive details on companies, including financial data and business profiles. Popular use cases include market analysis, investment research, and competitive benchmarking.

Use our Yahoo Finance Business Information dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape.

Dataset Features

• name: Represents the company name.
• company_id: Unique identifier assigned to each company.
• entity_type: Denotes the type/category of the business entity.
• summary: A brief description or summary of the company.
• stock_ticker: The ticker symbol used for trading on stock exchanges.
• currency: The currency in which financial values are expressed.
• earnings_date: The date for the reported earnings.
• exchange: The stock exchange on which the company is listed.
• closing_price: The final stock price at the end of the trading day.
• previous_close: The stock price at the close of the previous trading day.
• open: The price at which the stock opened for the trading day.
• bid: The current highest price that a buyer is willing to pay for the stock.
• ask: The current lowest price that a seller is willing to accept.
• day_range: The range between the lowest and highest prices during the trading day.
• week_range: A broader price range over the past week.
• volume: Number of shares that traded in the session.
• avg_volume: Average daily share volume over a specific period.
• market_cap: Total market capitalization of the company.
• beta: A measure of the stock's volatility in comparison to the market.
• pe_ratio: Price-to-earnings ratio for valuation.
• eps: Earnings per share.
• dividend_yield: Dividend yield percentage.
• ex_dividend_date: The date on which the stock trades without the right to the declared dividend.
• target_est: The analyst's target price estimate.
• url: The URL to more detailed company information.
• people_also_watch: Companies frequently watched alongside this company.
• similar: Other companies with similar profiles.
• risk_score: A quantified risk score.
• risk_score_text: A textual interpretation of the risk score.
• risk_score_percentile: The risk score expressed in percentile terms.
• recommendation_rating: Analyst recommendation ratings.
• analyst_price_target: Analyst provided stock price target.
• company_profile_address: Company address from the profile.
• company_profile_website: URL for the company’s website.
• company_profile_phone: Contact phone number.
• company_profile_sector: The sector in which the company operates.
• company_profile_industry: Industry classification of the company.
• company_profile_employees: Number of employees in the company.
• company_profile_description: A detailed profile description of the company.
• valuation_measures: Contains key valuation ratios and metrics such as enterprise value, price-to-book, and price-to-sales ratios.
• Financial_highlights: Offers summary financial statistics including EPS, profit margin, revenue, and cash flow indicators.
• financials: This column appears to provide financial statement data.
• financials_quarterly: Similar to the previous field but intended to capture quarterly financial figures.
• earnings_estimate: Contains consensus earnings estimates including average, high, and low estimates along with the number of analysts involved.
• revenue_estimate: Provides revenue estimates with details such as average estimate, high and low values, and sales growth factors.
• earnings_history: This field tracks historical earnings and surprises by comparing actual EPS with estimates.
• eps_trend: Contains information on how the EPS has trended over various recent time intervals.
• eps_revisions: Captures recent changes in EPS forecasts.
• growth_estimates: Offers projections related to growth prospects over different time horizons.
• top_analysts: Intended to list the top analysts covering the company.
• upgrades_and_downgrades: This field shows recent analyst upgrades or downgrades.
• recent_news: Meant to contain recent news articles related to the company.
• fanacials_currency: Appears to indicate the currency used for financial reporting or valuation in the dataset.
• **company_profile_he

Problem Statement: Sales management has gained importance to meet increasing competition and the need for improved methods of distribution to reduce cost and to increase profits. Sales management today is the most important function in a commercial and business enterprise. We need to extract all the Amazon sales datasets, transform them using data cleaning and data preprocessing and then finally loading it for analysis. We need to visualize sales trend month-wise, year-wise and yearly-month wise. Moreover, we need to find key metrics and factors and show meaningful relationships between attributes.

Approach The main goal of the project is to find key metrics and factors and then show meaningful relationships between them based on different features available in the dataset.

Data Collection : Imported data from various datasets available in the project using Pandas library.

Data Cleaning : Removed missing values and created new features as per insights.

Data Preprocessing : Modified the structure of data in order to make it more understandable and suitable and convenient for statistical analysis.

Data Analysis : I started analyzing dataset using Pandas,Numpy,Matplotlib and Seaborn.

Data Visualization : Plotted graphs to get insights about dependent and independent variables. Also used Tableau and PowerBI for data visulization.

A. SUMMARY This dataset contains sales tax collected in San Francisco for calendar years 2018 through 2023 (CY 2018 to 2023). Sales tax is aggregated, or summed, at the census block level. However, some census blocks have been combined to maintain the anonymity of businesses based on Taxation Code Section 7056. See “How to use this dataset” below for more details on how the data has been aggregated. Sales tax is collected by businesses on many types of transactions and regulated by the California Department of Tax and Fee Administration. B. HOW THE DATASET IS CREATED Data is collected by HDL. The data is then aggregated based on the criteria outlined in the "How to use this dataset" section. C. UPDATE PROCESS This dataset will be updated annually. D. HOW TO USE THIS DATASET This dataset can be used to analyze sales tax data over time across census blocks in San Francisco. Due to data privacy protection regulations for businesses, sales tax data is not available for all census blocks. Census blocks where there are less than 4 businesses paying sales tax or a single business that pays 80% or more of the total sales tax have been combined with neighboring Census Blocks to protect the confidentiality of affected businesses. Because of this aggregation, some Census Block groups in this dataset may change in future years as the number of businesses in a particular Census Block changes. The historical data changes based on audit findings and amended returns. If census block groupings change, it will happen when the dataset is updated - on an annual basis. These new blocks will be backfilled to previous years. Additionally, business payers with multiple locations (for example chain stores) are excluded because sales tax cannot be tied back to the location where it was collected. Finally, census blocks in the area field are from 2010 (GEOID10) and not from 2020. A map of this dataset can be viewed here.