Context

PromptCloud extracted 30K records of sample data from Walmart to find out product reviews, pricing, and categories of products available on Walmart.

The Full dataset can be downloaded from here.

Content

We saw that there was a large need for datasets of different types of Products across the largest eCommerce websites around the globe. Although Amazon is the largest in the world and is the most searched in the globe. Walmart makes a strong case for itself in the field of eCommerce as it has been around for years now catering to the average American families daily. We thought that this data and information could be helpful to customers, clients and various other people interested in data of different types to use this to come up with a case study or an article or anything that can be used by the common man and competitors to position themselves strongly in the market. Data is wealth in today's modern era and data is the forefront of the world helping in machine learning and NLP. This is the reason why we wanted to come up with this particular dataset.

Given Below are the fields: - Unique ID - Crawl TimeStamp - Product URL - Product Name - Description - List Price - Sale Price - Brand - Item Number

Inspiration

There is a need for high-quality data that can be used by many people across the globe for various research and project purposes. We want to deliver the highest quality of data in the cleanest and easiest format that can be used by everybody.

Fertilizer Use and Price

1960-2012 data on fertilizer consumption in the United States by plant nutrient

By Agriculture [source]

About this dataset

This product brings together an in-depth analysis of fertilizer consumption and prices in the United States across more than half a century (1960-2012). We provide valuable insights into how fertilizer use per crop area and for specific nutrients varies between major producing states, as well as offer data on mixed fertilizers, secondary nutrients, and micronutrients. Furthermore, our dataset includes farm prices for fertilizers, indices of wholesale fertilizer price through 2013 that allows us to compare changes in fertilizer costs. With this data set you can get a better understanding of how fertilizer use has evolved over time, what crops are being benefited from its availability the most and at what point does cost becomes a deciding factor. Get ready to explore U.S Fertilizer Consumption and Price Trends!

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How to use the dataset

This dataset is a comprehensive resource for investigating US Fertilizer Consumption and Price patterns between 1960 and 2013. In the dataset, you'll find data on fertilizer consumption in the United States by plant nutrient and major selected product, as well as consumption of mixed fertilizers, secondary nutrients, and micronutrients. There is also information about fertilizer use per receiving acre for several states that produce corn, cotton, soybeans, or wheat. The dataset provides information about farm prices for fertilizer along with indices of wholesale fertilizer price since 2013

Using this dataset can be useful to identify market trends in the US Fertilizer Market over the past half-decade. Additionally it can be used to identify state-level differences in production or usage that could provide insight into regional agricultural strategies.

To get started using this dataset: - Read up on its contents: Start by thoroughly reading up on what exactly this data contains so you are familiar with things like nutrient types mentioned or state producing crops mentioned in order to better understand how to interpret/use them correctly when analyzing these data points
- Identify key elements: As you read through each column of the data set think about which columns are most relevant to your research question/interest
- Organize & Analyze Data: now that you have identified key elements begin organizing them accordingly (separate out columns not needed) then start analyzing whatever questions/themes have presented themselves while doing research

• After following these steps your research process will be much more streamlined & organized making analysis simpler & results more accurate when interpreting this particular Unlocking US Fertilizer Consumption and Price Patterns Kaggle Dataset from 1960-2013

Research Ideas

• Analyzing the effect of fertilizer use on crop yields and prices over time to inform environmental policy decisions.
• Investigating regional differences in fertilizer prices, consumption and crop yield, to gain more insight into agriculture output variability across the US.
• Comparing fertilizer use per acre planted with crop yields to evaluate farmers’ return on investment by region and nutrient type used

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: fertilizeruse.csv | Column name | Description | |:--------------|:-----------------------------------| | Year | Year of the data sample. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Agriculture.

Dataset Name: Cymbal Investments Trade Capture Report

Description: This dataset, derived from the bigquery-public-data.cymbal_investments.trade_capture_report in BigQuery, provides a comprehensive view of trade capture reports for financial transactions. The data is presented in CSV format with various columns capturing essential information about each trade.

BigQuery description: Dataset in BigQuery About Cymbal

The Cymbal brand was created to make storytelling consistent across Google Cloud. Datasets are synthetic, and >provided to industry practitioners for the purpose of product discovery, testing, and evaluation. Cymbal Investments

Cymbal Investments is a US-based, global investment and asset manager. Founded in 1925, the boutique investment >banking firm’s mission is to provide meaningful financial opportunity to veterans. After nearly a century of >consistently positive returns and smart bets, it has grown into a global institution and has acquired multiple funds and >smaller institutions. In 1986, Cymbal Investments was acquired by Cymbal Group. Today, the company holds $850B >under management, employs 49K+ people and, in 2019, reported $35B in revenue. Cymbal Investments is digitally transforming legacy financial services institutions.

CSV Columns:

1. SendingTime

   • Type: TIMESTAMP
   • Mode: NULLABLE
   • Description: Time the message was sent.

2. TargetCompID

   • Type: STRING
   • Mode: NULLABLE
   • Description: Assigned value used to identify the firm receiving the message.

3. SenderCompID

   • Type: STRING
   • Mode: NULLABLE
   • Description: Assigned value used to identify the firm sending the message.

4. Symbol

   • Type: STRING
   • Mode: NULLABLE
   • Description: Trading symbol of the asset.

5. Quantity

   • Type: INTEGER
   • Mode: NULLABLE
   • Description: Overall/total quantity (e.g., number of shares).

6. OrderID

   • Type: STRING
   • Mode: NULLABLE
   • Description: Order identifier.

7. TransactTime

   • Type: TIMESTAMP
   • Mode: NULLABLE
   • Description: Time the transaction occurred.

8. StrikePrice

   • Type: FLOAT
   • Mode: NULLABLE
   • Description: Price at which the Contract for Difference (CFD) closed.

9. LastPx

   • Type: FLOAT
   • Mode: NULLABLE
   • Description: Price at which the CFD was entered.

10. MaturityDate

    • Type: TIMESTAMP
    • Mode: NULLABLE
    • Description: Date of contract expiry.

11. TradeReportID

    • Type: STRING
    • Mode: NULLABLE
    • Description: ID of this trade report.

12. TradeDate

    • Type: DATE
    • Mode: NULLABLE
    • Description: Date the trade was executed.

13. CFICode

    • Type: STRING
    • Mode: NULLABLE
    • Description: Financial instrument classification code.

14. OrderID

    • Type: STRING
    • Mode: NULLABLE
    • Description: Order identifier.

15. PartyID

    • Type: STRING
    • Mode: NULLABLE
    • Description: Counterparty identifier.

16. PartyIDSource

    • Type: STRING
    • Mode: NULLABLE
    • Description: Party ID Source

17. PartyRole

    • Type: STRING
    • Mode: NULLABLE
    • Description: Counterparty role.

Potential Analyses: - Trade Pattern Analysis: Explore patterns in trading behavior over time, identifying common trends or anomalies. - Risk Assessment: Evaluate the risk associated with different trades based on quantities, prices, and counterparties. - Market Impact Analysis: Examine how trades impact the market, considering factors like liquidity and price movements. - Time Series Analysis: Analyze the temporal aspects of trade data, identifying seasonality or recurring patterns.

Possible ML Tasks: - Predictive Modeling: Develop models to predict future trade quantities or prices based on historical data. - Anomaly Detection: Implement algorithms to detect unusual trading activities that deviate from the norm. - Counterparty Risk Assessment: Build models to assess the risk associated with specific counterparties in trade transactions. - Market Sentiment Analysis: Utilize natural language processing to analyze textual data related to trades and assess market sentiment.

Note: - The dataset provides a comprehensive view of trade capture reports, including information about the trade itself, the entities involved, and crucial timestamps. - The CSV format facilitates easy integration and analysis using various data analysis tools and platforms.