Dataset

This dataset was created by Moses Moncy

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Dataset

This dataset was created by Emmanuel Arias

Released under Database: Open Database, Contents: Database Contents

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Dataset

This dataset was created by Somya Agarwal

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Dataset

This dataset was created by TeseRact

Released under CC0: Public Domain

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The dataset contains randomly generated persons' data. It is created to be used in explaining data science. It currently contains the following columns: - Name - Gender - Skin Color - Height(cm) - Weight(m) - Date of Birth

Source:

Oresti Banos, Department of Computer Architecture and Computer Technology, University of Granada Rafael Garcia, Department of Computer Architecture and Computer Technology, University of Granada Alejandro Saez, Department of Computer Architecture and Computer Technology, University of Granada

Email to whom correspondence should be addressed: oresti '@' ugr.es (oresti.bl '@' gmail.com)

Data Set Information:

The MHEALTH (Mobile HEALTH) dataset comprises body motion and vital signs recordings for ten volunteers of the diverse profile while performing several physical activities. Sensors placed on the subject's chest, right wrist, and left ankle are used to measure the motion experienced by diverse body parts, namely, acceleration, rate of turn, and magnetic field orientation. The sensor positioned on the chest also provides 2-lead ECG measurements, which can be potentially used for basic heart monitoring, checking for various arrhythmias, or looking at the effects of exercise on the ECG.

DATASET SUMMARY:

• Activities: 12
• Sensor devices: 3
• Subjects: 10

EXPERIMENTAL SETUP

The collected dataset comprises body motion and vital signs recordings for ten volunteers of the diverse profile while performing 12 physical activities (Table 1). Shimmer2 [BUR10] wearable sensors were used for the recordings. The sensors were respectively placed on the subject's chest, right wrist, and left ankle and attached by using elastic straps (as shown in the figure in the attachment). The use of multiple sensors permits us to measure the motion experienced by diverse body parts, namely, the acceleration, the rate of turn, and the magnetic field orientation, thus better capturing the body dynamics. The sensor positioned on the chest also provides 2-lead ECG measurements which are not used for the development of the recognition model but rather collected for future work purposes. This information can be used, for example, for basic heart monitoring, checking for various arrhythmias, or looking at the effects of exercise on the ECG. All sensing modalities are recorded at a sampling rate of 50 Hz, which is considered sufficient for capturing human activity. Each session was recorded using a video camera. This dataset is found to generalize to common activities of daily living, given the diversity of body parts involved in each one (e.g., the frontal elevation of arms vs. knees bending), the intensity of the actions (e.g., cycling vs. sitting and relaxing) and their execution speed or dynamicity (e.g., running vs. standing still). The activities were collected in an out-of-lab environment with no constraints on the way these must be executed, with the exception that the subject should try their best when executing them.

ACTIVITY SET

The activity set is listed in the following: L1: Standing still (1 min) L2: Sitting and relaxing (1 min) L3: Lying down (1 min) L4: Walking (1 min) L5: Climbing stairs (1 min) L6: Waist bends forward (20x) L7: Frontal elevation of arms (20x) L8: Knees bending (crouching) (20x) L9: Cycling (1 min) L10: Jogging (1 min) L11: Running (1 min) L12: Jump front & back (20x) NOTE: In brackets are the number of repetitions (Nx) or the duration of the exercises (min).

A complete and illustrated description (including table of activities, sensor setup, etc.) of the dataset is provided in the papers presented in the section “Citation Requests†.

Attribute Information:

The data collected for each subject is stored in a different log file: 'mHealth_subject.log'. Each file contains the samples (by rows) recorded for all sensors (by columns). The labels used to identify the activities are similar to the abovementioned (e.g., the label for walking is '4').

The meaning of each column is detailed next: Column 1: acceleration from the chest sensor (X-axis) Column 2: acceleration from the chest sensor (Y axis) Column 3: acceleration from the chest sensor (Z axis) Column 4: electrocardiogram signal (lead 1) Column 5: electrocardiogram signal (lead 2) Column 6: acceleration from the left-ankle sensor (X-axis) Column 7: acceleration from the left-ankle sensor (Y axis) Column 8: acceleration from the left-ankle sensor (Z axis) Column 9: gyro from the left-ankle sensor (X-axis) Column 10: gyro from the left-ankle sensor (Y axis) Column 11: gyro from the left-ankle sensor (Z axis) Column 13: magnetometer from the left-ankle sensor (X-axis) Column 13: magnetometer from the left-ankle sensor (Y axis) Column 14: magnetometer from the left-ankle sensor (Z axis) Column 15: acceleration from the right-lower-arm sensor (X-axis) Column 16: acceleration from the right-lower-arm sensor (Y axis) Column 17: acceleration from the right-lower-arm sensor (Z axis) Column 18: gyro from the right-lower-arm sensor (X-axis) Column 19: gyro from the right-lower-arm sensor (Y axis) Column 20: gyro fro...

Dataset

This dataset was created by chirag singh chaudhary

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About this file The Kaggle Global Superstore dataset is a comprehensive dataset containing information about sales and orders in a global superstore. It is a valuable resource for data analysis and visualization tasks. This dataset has been processed and transformed from its original format (txt) to CSV using the R programming language. The original dataset is available here, and the transformed CSV file used in this analysis can be found here.

Here is a description of the columns in the dataset:

category: The category of products sold in the superstore.

city: The city where the order was placed.

country: The country in which the superstore is located.

customer_id: A unique identifier for each customer.

customer_name: The name of the customer who placed the order.

discount: The discount applied to the order.

market: The market or region where the superstore operates.

ji_lu_shu: An unknown or unspecified column.

order_date: The date when the order was placed.

order_id: A unique identifier for each order.

order_priority: The priority level of the order.

product_id: A unique identifier for each product.

product_name: The name of the product.

profit: The profit generated from the order.

quantity: The quantity of products ordered.

region: The region where the order was placed.

row_id: A unique identifier for each row in the dataset.

sales: The total sales amount for the order.

segment: The customer segment (e.g., consumer, corporate, or home office).

ship_date: The date when the order was shipped.

ship_mode: The shipping mode used for the order.

shipping_cost: The cost of shipping for the order.

state: The state or region within the country.

sub_category: The sub-category of products within the main category.

year: The year in which the order was placed.

market2: Another column related to market information.

weeknum: The week number when the order was placed.

This dataset can be used for various data analysis tasks, including understanding sales patterns, customer behavior, and profitability in the context of a global superstore.

Dataset

This dataset was created by Parth Chokhra

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Dataset

This dataset was created by Md Nizam Sapiee

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The dataset contains https://www.kaggle.com/competitions/icr-identify-age-related-conditions competition dataset transformed into integerized data. The common denominator is found for each column. Distribution of even/odd numbers were performed to identify if some values should be a fraction.

Columns 'FL' and 'GL' were untouched, probably float by nature.

Please refer to notebook for exact transformations: https://www.kaggle.com/code/raddar/convert-icr-data-to-integers

This dataset, named "state_trends.csv," contains information about different U.S. states. Let's break down the attributes and understand what each column represents:

1. state: The name of the U.S. state.
2. state_code: The two-letter postal code abbreviation for the state.
3. population: The population of the state.
4. sq_miles: The total land area of the state in square miles.
5. pop_density: Population density, which is the number of people per square mile.
6. region: The geographical region of the United States to which the state belongs (e.g., South, West).
7. psych_region: A description of the psychological region based on personality traits.
8. psy_reg: A shortened version of the psychological region.
9. extraversion: A measure of the state's population tendency toward extraversion.
10. agreeableness: A measure of the state's population tendency toward agreeableness.
11. conscientiousness: A measure of the state's population tendency toward conscientiousness.
12. neuroticism: A measure of the state's population tendency toward neuroticism.
13. openness: A measure of the state's population tendency toward openness.
14. data_science: A score related to the state's interest or proficiency in the field of data science.
15. artificial_intelligence: A score related to the state's interest or proficiency in artificial intelligence.
16. machine_learning: A score related to the state's interest or proficiency in machine learning.
17. data_analysis: A score related to the state's interest or proficiency in data analysis.
18. business_intelligence: A score related to the state's interest or proficiency in business intelligence.
19. spreadsheet: A score related to the state's interest or proficiency in spreadsheet usage.
20. statistics: A score related to the state's interest or proficiency in statistics.
21. art: A score related to the state's interest or involvement in the field of art.
22. dance: A score related to the state's interest or involvement in dance.
23. museum: A score related to the state's interest or presence of museums.
24. basketball: A score related to the state's interest or involvement in basketball.
25. football: A score related to the state's interest or involvement in football.
26. baseball: A score related to the state's interest or involvement in baseball.
27. soccer: A score related to the state's interest or involvement in soccer.
28. hockey: A score related to the state's interest or involvement in hockey.
29. has_nba: Indicates whether the state has a National Basketball Association (NBA) team (Yes/No).
30. has_nfl: Indicates whether the state has a National Football League (NFL) team (Yes/No).
31. has_mlb: Indicates whether the state has a Major League Baseball (MLB) team (Yes/No).
32. has_mls: Indicates whether the state has a Major League Soccer (MLS) team (Yes/No).
33. has_nhl: Indicates whether the state has a National Hockey League (NHL) team (Yes/No).
34. has_any: Indicates whether the state has any of the mentioned professional sports teams (Yes/No).

In summary, this dataset provides a variety of information about U.S. states, including demographic data, geographical region, psychological region, personality traits, and scores related to interests or proficiencies in various fields such as data science, art, and sports.

Acknowledgements

i download this dataset on opensourse website.

This data set is all about Real or Fake News or Text dataset. Here are only 4 columns. number: title: text: label: This is all about this dataset.

Datasets containing Paragraphs :4118 Question1: 4118 Question2: 4118 Question3: 4118 Answer1: 4118 Answer2: 4118 Answer3 : 4118 were collected during data collection. It includes paragraphs consisting of related question-answer pairs. Each paragraph will have 3 questions and 3 answers. The dataset is stored as a Comma-Separated Values file (.csv). The dataset has been collected manually and subsequently cleaned and filtered. This laborious and time-consuming process was undertaken with the utmost care and dedication to craft a high-quality dataset specifically designed for generating extractive subjective questions and answers from the provided input paragraphs.

Sales Data Description This dataset represents synthetic sales data generated for practice purposes only. It is not real-time or based on actual business operations, and should be used solely for educational or testing purposes. The dataset contains information that simulates sales transactions across different products, regions, and customers. Each row represents an individual sale event with various details associated with it.

Columns in the Dataset

1. Product_ID: Unique identifier for each product sold. Randomly generated for practice purposes.
2. Sale_Date: The date when the sale occurred. Randomly selected from the year 2023.
3. Sales_Rep: The sales representative responsible for the transaction. The dataset includes five random sales representatives (Alice, Bob, Charlie, David, Eve).
4. Region: The region where the sale took place. The possible regions are North, South, East, and West.
5. Sales_Amount: The total sales amount for the transaction, including discounts if any. Values range from 100 to 10,000 (in currency units).
6. Quantity_Sold: The number of units sold in that transaction, randomly generated between 1 and 50.
7. Product_Category: The category of the product sold. Categories include Electronics, Furniture, Clothing, and Food.
8. Unit_Cost: The cost per unit of the product sold, randomly generated between 50 and 5000 currency units.
9. Unit_Price: The selling price per unit of the product, calculated to be higher than the unit cost.
10. Customer_Type: Indicates whether the customer is a New or Returning customer.
11. Discount: The discount applied to the sale, randomly chosen between 0% and 30%.
12. Payment_Method: The method of payment used by the customer (e.g., Credit Card, Cash, Bank Transfer).
13. Sales_Channel: The channel through which the sale occurred. Either Online or Retail.
14. Region_and_Sales_Rep: A combined column that pairs the region and sales representative for easier tracking.

Disclaimer

Please note: This data was randomly generated and is intended solely for practice, learning, or testing. It does not reflect real-world sales, customers, or businesses, and should not be considered reliable for any real-time analysis or decision-making.

Dataset

This dataset was created by Moses Moncy

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Dataset

This dataset was created by Vlad Marascu

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Dataset

This dataset was created by YuLinHsu

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The dataset includes sample data of 1000 startup companies operating cost and their profit. Well-formatted dataset for building ML regression pipelines. Includes R&D Spend float64 Administration float64 Marketing Spend float64 State object Profit float64

This is a dataset downloaded off excelbianalytics.com created off of random VBA logic. I recently performed an extensive exploratory data analysis on it and I included new columns to it, namely: Unit margin, Order year, Order month, Order weekday and Order_Ship_Days which I think can help with analysis on the data. I shared it because I thought it was a great dataset to practice analytical processes on for newbies like myself.