The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

United Kingdom's main stock market index, the GB100, fell to 9690 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has declined 0.12%, though it remains 15.91% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on December of 2025.

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.

Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on December of 2025.

Hong Kong's main stock market index, the HK50, rose to 26095 points on December 2, 2025, gaining 0.24% from the previous session. Over the past month, the index has declined 0.24%, though it remains 32.15% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on December of 2025.

Sweden's main stock market index, the Stockholm 30, fell to 2782 points on December 2, 2025, losing 0.11% from the previous session. Over the past month, the index has climbed 0.95% and is up 8.08% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Sweden. Sweden Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

Context

NIFTY 500 is India’s first broad-based stock market index of the Indian stock market. It contains the top 500 listed companies on the NSE. The NIFTY 500 index represents about 96.1% of free-float market capitalization and 96.5% of the total turnover on the National Stock Exchange (NSE).

NIFTY 500 companies are disaggregated into 72 industry indices. Industry weights in the index reflect industry weights in the market. For example, if the banking sector has a 5% weight in the universe of stocks traded on the NSE, banking stocks in the index would also have an approximate representation of 5% in the index. NIFTY 500 can be used for a variety of purposes such as benchmarking fund portfolios, launching index funds, ETFs, and other structured products.

• Other Notable Indices -
  • NIFTY 50: Top 50 listed companies on the NSE. A diversified 50-stock index accounting for 13 sectors of the Indian economy.
  • NIFTY Next 50: Also called NIFTY Juniors. Represents 50 companies from NIFTY 100 after excluding the NIFTY 50 companies.
  • NIFTY 100: Diversified 100 stock index representing major sectors of the economy. NIFTY 100 represents the top 100 companies based on full market capitalization from NIFTY 500.
  • NIFTY 200: Designed to reflect the behavior and performance of large and mid-market capitalization companies.

Content

The dataset comprises various parameters and features for each of the NIFTY 500 Stocks, including Company Name, Symbol, Industry, Series, Open, High, Low, Previous Close, Last Traded Price, Change, Percentage Change, Share Volume, Value in Indian Rupee, 52 Week High, 52 Week Low, 365 Day Percentage Change, and 30 Day Percentage Change.

Dataset Glossary (Column-Wise)

Company Name: Name of the Company.

Symbol: A stock symbol is a unique series of letters assigned to a security for trading purposes.

Industry: Name of the industry to which the stock belongs.

Series: EQ stands for Equity. In this series intraday trading is possible in addition to delivery and BE stands for Book Entry. Shares falling in the Trade-to-Trade or T-segment are traded in this series and no intraday is allowed. This means trades can only be settled by accepting or giving the delivery of shares.

Open: It is the price at which the financial security opens in the market when trading begins. It may or may not be different from the previous day's closing price. The security may open at a higher price than the closing price due to excess demand for the security.

High: It is the highest price at which a stock is traded during the course of the trading day and is typically higher than the closing or equal to the opening price.

Low: Today's low is a security's intraday low trading price. Today's low is the lowest price at which a stock trades over the course of a trading day.

Previous Close: The previous close almost always refers to the prior day's final price of a security when the market officially closes for the day. It can apply to a stock, bond, commodity, futures or option co-contract, market index, or any other security.

Last Traded Price: The last traded price (LTP) usually differs from the closing price of the day. This is because the closing price of the day on NSE is the weighted average price of the last 30 mins of trading. The last traded price of the day is the actual last traded price.

Change: For a stock or bond quote, change is the difference between the current price and the last trade of the previous day. For interest rates, change is benchmarked against a major market rate (e.g., LIBOR) and may only be updated as infrequently as once a quarter.

Percentage Change: Take the selling price and subtract the initial purchase price. The result is the gain or loss. Take the gain or loss from the investment and divide it by the original amount or purchase price of the investment. Finally, multiply the result by 100 to arrive at the percentage change in the investment.

Share Volume: Volume is an indicator that means the total number of shares that have been bought or sold in a specific period of time or during the trading day. It will also involve the buying and selling of every share during a specific time period.

Value (Indian Rupee): Market value—also known as market cap—is calculated by multiplying a company's outstanding shares by its current market price.

52-Week High: A 52-week high is the highest share price that a stock has traded at during a passing year. Many market aficionados view the 52-week high as an important factor in determining a stock's current value and predicting future price movement. 52-week High prices are adjusted for Bonus, Split & Rights Corporate actions.

52-Week Low: A 52-week low is the lowest ...

This dataset provides a fascinating look into the ever-changing landscape of the S&P 500 by tracking the top 30 companies by market capitalization from 2010 to 2024. Explore which giants consistently held their ground, which rising stars broke into the ranks, and which former leaders faded from the top.

With columns for 'Year', 'Ticker', 'Weight', '1Y_P' (1-year historical return), '1Y_F' (1-year forward return), 'Industry', and 'Sector', this dataset is a goldmine for analyzing:

Market Concentration: How has the weight of the top companies changed over time? Sector Shifts: Which sectors have dominated the top ranks, and how has this evolved? Performance of Leaders: How did the top companies perform in the year they were included and the year after? Turnover Analysis: Identify which companies were added and removed from the top 30 each year and investigate potential reasons. Whether you're a seasoned quantitative analyst, a student of market trends, or simply curious about the companies shaping the US economy, this dataset offers a unique perspective on market dynamics and the forces driving the S&P 500.

Potential Use Cases:

Time Series Analysis of Market Cap Concentration. Studying sector rotation and dominance. Backtesting strategies based on top-performing companies. Analyzing the characteristics of companies entering or leaving the top 30. Educational purposes for understanding market structure. Dive in and uncover the stories behind the numbers in the S&P 500's top tier!

The “Tesla Stock Price Data (Last One Year)” dataset is a comprehensive collection of historical stock market information, focusing on Tesla Inc. (TSLA) for the past year. This dataset serves as a valuable resource for financial analysts, investors, researchers, and data enthusiasts who are interested in studying the trends, patterns, and performance of Tesla’s stock in the financial markets.It consists of 9 columns referring to date, high and low prices, open and closing value, volume, cumulative open and of course changing of price.At a first glance in order to better understand the data we should plot the time series of each attribute.The cumulative Open Interest(OI) is the total open contracts that are being held in a particular Future or Call or Put contracts on the Exchange. We can see that the biggest drop of the stock happened in January of 2023 and after 5 to 6 months it regained its stock value round the summer of the same year with opening and closing price around 300.As a next step we are going to plot some more plots in order ro better understand the relation between our target column(change price) with every other attribute. In order to interpret the results:

Linear Regression:

Mean Absolute Error (MAE): 6.28 This model, on average, predicts the “Price Change” within approximately 6.28 units of the true value. Mean Squared Error (MSE): 52.97 MSE measures the average of squared differences, and this value suggests some variability in prediction errors. Root Mean Squared Error (RMSE): 7.28 RMSE is the square root of MSE and is in the same units as the target variable. An RMSE of 7.28 indicates the typical prediction error. R-squared (R2): 0.0868 R-squared represents the proportion of the variance in the target variable explained by the model. An R2 of 0.0868 suggests that the model explains only a small portion of the variance, indicating limited predictive power. Decision Tree Regression:

Mean Absolute Error (MAE): 9.21 This model, on average, predicts the “Price Change” within approximately 9.21 units of the true value, which is higher than the Linear Regression model. Mean Squared Error (MSE): 150.69 The MSE is relatively high, indicating larger prediction errors and more variability. Root Mean Squared Error (RMSE): 12.28 RMSE of 12.28 is notably higher, suggesting that this model has larger prediction errors. R-squared (R2): -1.598 The negative R-squared value indicates that the model performs worse than a horizontal line as a predictor, indicating a poor fit. Random Forest Regression:

Mean Absolute Error (MAE): 6.99 This model, on average, predicts the “Price Change” within approximately 6.99 units of the true value, similar to Linear Regression. Mean Squared Error (MSE): 62.79 MSE is lower than the Decision Tree model but higher than Linear Regression, suggesting intermediate prediction accuracy Root Mean Squared Error (RMSE): 7.92 RMSE is also intermediate, indicating moderate prediction errors. R-squared (R2): -0.0824 The negative R-squared suggests that the Random Forest model does not perform well and has limited predictive power.

Context

This dataset was created to make the project "AI Learn to invest" for SaturdaysAI - Euskadi 1st edition. The project can be found in https://github.com/ImanolR87/AI-Learn-to-invest

Content

More than 400.000 random investments were created with the data from the last 10 years from the NYSE market. Finantial ratios and volatilities were calculated and added to the random investments dataset.

Finantial ratios included: - ESG Ranking - ROA - ROE - Net Yearly Income - PB - PE - PS - EPS - Sharpe

Acknowledgements

I thank SaturdaysAI to push me falling in love with data science.

Inspiration

Our inspiration was to find an answer to why young people doesn't invest more on Stock-Exchange markets.

If you try your hand at stock picking, you risk returning less than the market. Long term Pfizer Inc. (NYSE:PFE) shareholders have had that experience, with the share price dropping 30% in three years, versus a market return of about 24%.

The columns of the dataset are:

Date — The date of the record. Open — The opening price of the day (when trading starts). High — The highest trade price during the day. Low — The lowest trade price during the day. Close — The closing price for the day (when trading is finished). Volume — The number of shares traded. Adj Close — The daily closing price, adjusted retroactively to include any corporate actions.

This table contains 25 series, with data for years 1956 - present (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Toronto Stock Exchange Statistics (25 items: Standard and Poor's/Toronto Stock Exchange Composite Index; high; Standard and Poor's/Toronto Stock Exchange Composite Index; close; Toronto Stock Exchange; oil and gas; closing quotations; Standard and Poor's/Toronto Stock Exchange Composite Index; low ...).

The reference for the dataset and the dashboard was Youtube Channel codebasics. I have used a fictitious company called Atlix where the Sales Director want the sales data to be in a proper format which can help in decision making.

We have a total of 5 tables namely customers, products, markets, date & transactions. The data is exported from Mysql to Tableau.

In tableau , inner joins were used.

In the transactions table, we notice that sum sales amount figures are either negative or zero while the sales qty is either 1 or more. This cannot be right. Therefore, we filter the sales amount table in Tableau by having the least sales amount as minimum 1.

When currency column from transactions table was grouped in MySql, we could see ‘USD’ and ‘INR’ showing up. We cannot have a sales data showing two currencies. This was rectified by converting the USD sales amount into INR by taking the latest exchange rate at Rs.81.

We make the above change in tableau by creating a new calculated field called ‘Normalised Sales Amount’. If [Sales Amount] == ‘USD’ then [Sales Amount] * 81 else [Sales Amount] End.

Conclusion: The dashboard prepared is an interactive dashboard with filters. For eg. By Clicking on Mumbai under “Sales by Markets” we will see the results change in the other charts as well as they Will now show the results pertaining only to Mumbai. This can be done by year , month, customers , products etc. Parameter with filter has also been created for top customers and top products. This produces a slider which can be used to view the top 10 customers and products and slide it accordingly.

Following information can be passed on to the sales team or director.

Total Sales: from Jun’17 to Feb’20 has been INR 12.83 million. There is a drop of 57% in the sales revenue from 2018 to 2019. The year 2020 has not been considered as it only account for 2 months data. Markets: Mumbai which is the top most performing market and accounts for 51% of the total sales market has seen a drop in sales of almost 64% from 2018 to 2019. Top Customers: Path was on 2nd position in terms of sales in the year 2018. It accounted for 19% of the total sales after Electricalslytical which accounted for 21% of the total sales. But in year 2019, both Electricalslytical and Path were the 2nd and 4th highest customers by sales. By targeting the specific markets and customers through new ideas such as promotions, discounts etc we can look to reverse the trend of decreasing sales.

Poland's main stock market index, the WIG, fell to 110618 points on December 2, 2025, losing 1.16% from the previous session. Over the past month, the index has declined 1.29%, though it remains 36.78% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Poland. Warsaw Stock Exchange WIG Index - values, historical data, forecasts and news - updated on December of 2025.

In March 2024 Bitcoin BTC reached a new all-time high with prices exceeding 73000 USD marking a milestone for the cryptocurrency market This surge was due to the approval of Bitcoin exchange-traded funds ETFs in the United States allowing investors to access Bitcoin without directly holding it This development increased Bitcoin’s credibility and brought fresh demand from institutional investors echoing previous price surges in 2021 when Tesla announced its 15 billion investment in Bitcoin and Coinbase was listed on the Nasdaq By the end of 2022 Bitcoin prices dropped sharply to 15000 USD following the collapse of cryptocurrency exchange FTX and its bankruptcy which caused a loss of confidence in the market By August 2024 Bitcoin rebounded to approximately 64178 USD but remained volatile due to inflation and interest rate hikes Unlike fiat currency like the US dollar Bitcoin’s supply is finite with 21 million coins as its maximum supply By September 2024 over 92 percent of Bitcoin had been mined Bitcoin’s value is tied to its scarcity and its mining process is regulated through halving events which cut the reward for mining every four years making it harder and more energy-intensive to mine The next halving event in 2024 will reduce the reward to 3125 BTC from its current 625 BTC The final Bitcoin is expected to be mined around 2140 The energy required to mine Bitcoin has led to criticisms about its environmental impact with estimates in 2021 suggesting that one Bitcoin transaction used as much energy as Argentina Bitcoin’s future price is difficult to predict due to the influence of large holders known as whales who own about 92 percent of all Bitcoin These whales can cause dramatic market swings by making large trades and many retail investors still dominate the market While institutional interest has grown it remains a small fraction compared to retail Bitcoin is vulnerable to external factors like regulatory changes and economic crises leading some to believe it is in a speculative bubble However others argue that Bitcoin is still in its early stages of adoption and will grow further as more institutions and governments recognize its potential as a hedge against inflation and a store of value 2024 has also seen the rise of Bitcoin Layer 2 technologies like the Lightning Network which improve scalability by enabling faster and cheaper transactions These innovations are crucial for Bitcoin’s wider adoption especially for day-to-day use and cross-border remittances At the same time central bank digital currencies CBDCs are gaining traction as several governments including China and the European Union have accelerated the development of their own state-controlled digital currencies while Bitcoin remains decentralized offering financial sovereignty for those who prefer independence from government control The rise of CBDCs is expected to increase interest in Bitcoin as a hedge against these centralized currencies Bitcoin’s journey in 2024 highlights its growing institutional acceptance alongside its inherent market volatility While the approval of Bitcoin ETFs has significantly boosted interest the market remains sensitive to events like exchange collapses and regulatory decisions With the limited supply of Bitcoin and improvements in its transaction efficiency it is expected to remain a key player in the financial world for years to come Whether Bitcoin is currently in a speculative bubble or on a sustainable path to greater adoption will ultimately be revealed over time.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Elko New Market. The dataset can be utilized to gain insights into gender-based income distribution within the Elko New Market population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Elko New Market, among individuals aged 15 years and older with income, there were 1,599 men and 1,767 women in the workforce. Among them, 1,206 men were engaged in full-time, year-round employment, while 895 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 1.24% fell within the income range of under $24,999, while 1.56% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 46.10% of men in full-time roles earned incomes exceeding $100,000, while 12.85% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Elko New Market median household income by race. You can refer the same here

Background:

Companies' worth or its total market value is called market capitalization or market cap. It is equal to the share price multiplied by the number of shares outstanding. Stock price is a proportional and relative value of companies' growth. Here, analyzing the stock price data will help us to understand a company's growth. An increase in stock price increases the company's market value.

Objective:

We have Collected the latest data of Microsoft Stock price and calculated daily log return which is approximately normally distributed. Let us try to answer some of the questions that will help us to decide roughly whether to invest in the Microsoft shares or not?

a) What is the probability that the stock price will drop over 5% in a day?

b) What is the probability that the stock price will drop over 10% in a day?

c) What is the probability that the stock price will drop over 50% in a year?

d) What is the probability that the stock price will drop over 25% in a year?

e) What is the 50th percentile of the yearly stock price?

Dataset:

MSFT.csv: It contains information about the stock price of Microsoft.

Date: Date of the stock price

Open: The average value of opened price on a particular day

Close: The average value of closed price on a particular day

Low: The lowest price reached on a particular day

High: The highest price reached on a particular day

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within East New Market. The dataset can be utilized to gain insights into gender-based income distribution within the East New Market population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within East New Market, among individuals aged 15 years and older with income, there were 98 men and 129 women in the workforce. Among them, 62 men were engaged in full-time, year-round employment, while 45 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 27.42% fell within the income range of under $24,999, while 22.22% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 14.52% of men in full-time roles earned incomes exceeding $100,000, while 26.67% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for East New Market median household income by race. You can refer the same here

Context

DA_Avocado_PJ is a personal data analysis project, was create based on the original Avocado Prices data from the Hass Avocado Board (an U.S avocado database) posted on Kaggle by Justin Kiggins (2018) and updated to 2020 by TIMOFEI KORNEV. Finally updated to 2022 by me.

In this project, I will conduct an analysis of the avocado market in the US, helping businesses understand the avocado market in the US over the years and development orientation for business in the future by analyzing Price, Volume Sold, Revenue of avocado in U.S.

In this analysis I will solve 3 main problems:

• How is the avocado market in the US from 2015 to 2022?
• What kind of avocado should be sold?
• Where we should sell that avocado?

About data

date: The date of the observation geography: The city or region of the observation total_volume: Total number of avocados sold average_price: The average price of a single avocado _4046,_4225,_4770: Total number of avocados with PLU 4046,4225,4770 sold type : Conventional or organic

Prepare data

First, I need to update this data to 2022. Because the original data is only updated from 2015 to 2020.

After that, I categorize the dataset into 2 types:

• avocado_isUS_2022: Is a dataset representing totals across the United States

• avocado_notUS_2022: Is a dataset showing only cities and regions in the United States

But

After looking through the data, I recognize that the geography column in avocado_notUs_2022 was mixed between region and city,

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midsouth : is a region include many cities but Hartford/Springfield is two big cities in Connecticut

So I decided to separate it.

Then I reviewed and removed some blank, negative values in two final dataset.

And Finally, we have

• avocado_isUS_2022: Overall data on the US, used to analyze the assessment of the avocado market in the US from 2015 - 2022

• avocado_detail: Data only includes cities from 2015 - 2022

Analysis

1. An overview analysis report on the avocado market in the US from 2015 to 2022

(Overview Eng)

(Overview Vie)

The results show that the US avocado market has just gone through a major crisis in 2020 and is showing signs of recovery. This sign of recovery is strongly expressed in Organic avocados, especially in the 4770 type. The analysis also shows that there is a trend towards organic avocado varieties after the crisis, even though they are more expensive. The analysis results show that the best time to sell avocados is from early spring to the end of summer.

1. In-depth analysis of avocado market through cities, focusing on Organic avocado varieties

(Deep Eng)

(Deep Vie)

In this analysis we will only focus on the Organic variety, because of its prominence in the previous analysis. In addition, 2020 will be the base mark for this analysis, to show how the recovery level of each city varies.

Top 5 cities with the highest revenue from Organic avocados in the last 3 years 1. New York 2. Los Angeles 3. San Francisco 4. Seattle 5. Portland

The analysis results show that Seattle is really a potential city for participating in the avocado market in the US, with the dominance in volume as well as the highest selling price in 2022.

In this project I also created a dynamic dashboard by Power BI but sadly is it's in pbix file and hard for me while Microsoft to limit the dashboard to only pbix or pdf so I can't share it 😭😭😭

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Inspiration

If you find this dataset useful, pls drop a like.

Description

Here you can find daily in-depth data about the most liquid US bonds ETFs. I provide prices, volumes, and connected options' data. In my opinion, it's the best ETFs datasets on Kaggle you can find.

The data is presented in CSV format as follows: 1. Date. 2. Close Price. 3. Open Price. 4. Low Price. 5. High Price. 6. Volume - Total number of shares traded on security on the date. 7. Average Bid Ask Spread % - Average of all bid/ask spreads taken as a percentage of the mid price. 8. Total Put Volume - The total amount of put option contracts (all strike prices and all expiration dates) traded during the previous trading day. If there were no trades the previous day, this field will return the latest volume available, if any, from the most recent 10 trading days. 9. Total Call Volume - The total amount of call option contracts (all strike prices and all expiration dates) traded during the previous trading day. If there were no trades the previous day, this field will return the latest volume available, if any, from the most recent 10 trading days. 10. Put Call Open Interest Total - Total number of call and put option contracts (all strike prices and expiration dates) that have not been closed, liquidated, or delivered for the security during the previous trading day. 11. Call Open Interest Total - The total number of call option contracts (all available strikes and expirations) outstanding for a given underlying as of the close of the previous trading day, as reported by the exchange. 12. Short Interest - Total number of shares investors have sold short but have not yet bought back. 13. Short Interest Ratio - Short Interest divided by the average daily trading volume.

P.S. For historical values, the dates represent the end date of the period the data is for, not the date when the data was made publicly available. For example, if the data is for the two-week period ending on October 15 and that data is made publicly available on October 25, the date shown is October 15. For options' columns and bid-ask spread the data available from 2012 year.