Cars Data.zip
1.2 MB
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eCommerce Customer Service Satisfaction.zip
6.3 MB
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Eye-dataset.zip
100.5 MB
The image dataset consists of four folder and comprising a total of 14.5k images.
The images in the dataset are regularized and augmented so that we can achieve more than 96% accuracy on the first training session.
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Don't you like the image data set, why is the reaction so low?
Anonymous Poll
91%
We like
9%
No, we don't like it
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Anemia Dataset.zip
4.6 KB
Gender: 0 - male, 1 - female
Hemoglobin: Hemoglobin is a protein in your red blood cells that carries oxygen to your body's organs and tissues and transports carbon dioxide from your organs and tissues back to your lungs
MCH: MCH is short for "mean corpuscular hemoglobin." It's the average amount in each of your red blood cells of a protein called hemoglobin, which carries oxygen around your body.
MCHC: MCHC stands for mean corpuscular hemoglobin concentration. It's a measure of the average concentration of hemoglobin inside a single red blood cell.
MCV: MCV stands for mean corpuscular volume. An MCV blood test measures the average size of your red blood cells.
Results: 0- not anemic, 1-anemic
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tfl-lu-spad-2011-2012.csv
16.7 KB
The number of signals passed at danger are reported, with details of the line, location, date of occurrence, the delay (minutes) incurred to the service and a brief description of the incident.
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PayPal - Payeer - Crypto - udst
MasterCard - Credit Card
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Economic Disparity .zip
130.8 KB
Data from the World Inequality Database pertains to inequality prior to taxes and benefits.
Data from the World Bank pertains to either income post taxes and benefits or consumption, contingent on the country and year.
For additional details regarding the definitions and methodologies underlying this data, refer to the accompanying article below, where you can also delve into and juxtapose a broader spectrum of indicators from various sources.
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Bitcoin Price Trends.zip
476.4 KB
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Digikala comments & products (1).zip
323.9 MB
This dataset encapsulates the diverse array of products available on Digikala's platform. Each product entry is accompanied by detailed information such as product name, category, price, and specifications, enabling researchers and analysts to explore trends, preferences, and market dynamics.
Furthermore, the dataset includes a wealth of customer comments, providing valuable insights into consumer sentiments, satisfaction levels, and potential areas for improvement.
With its extensive coverage and rich content, this dataset serves as a valuable resource for market analysis, sentiment analysis, and machine learning applications in the e-commerce domain.
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Online Retail Dataset.zip
21.8 MB
Online Retail Dataset: Exploring E-commerce Transactions and Customer Behavior
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Jobs and Salaries.zip
127.3 KB
job_title: The specific title of the job role, like 'Data Scientist', 'Data Engineer', or 'Data Analyst'. This column is crucial for understanding the salary distribution across various specialized roles within the data field.
job_category: A classification of the job role into broader categories for easier analysis. This might include areas like 'Data Analysis', 'Machine Learning', 'Data Engineering', etc.
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If the topic of your post fits our channel, we will publish it with pleasure.
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Global News.zip
260.9 KB
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Biological Data Of Human.zip
323.1 KB
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Global Surface Temperature.zip
24 KB
The CSV file can be used to do a wide variety of Machine Learning tools from Regression, Classification, Clustering, Time Series and much more.
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This channels is for Programmers, Coders, Software Engineers.
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Twitter Sentiment Analysis.zip
2 MB
📦 Datasets name: Twitter Sentiment Analysis
🌹This is an entity-level sentiment analysis dataset of twitter. Given a message and an entity, the task is to judge the sentiment of the message about the entity. There are three classes in this dataset: Positive, Negative and Neutral. We regard messages that are not relevant to the entity (i.e. Irrelevant) as Neutral
🌐 Format: CSV file
🔒 From: Kaggle
🟢 https://t.me/datasets1
🌹This is an entity-level sentiment analysis dataset of twitter. Given a message and an entity, the task is to judge the sentiment of the message about the entity. There are three classes in this dataset: Positive, Negative and Neutral. We regard messages that are not relevant to the entity (i.e. Irrelevant) as Neutral
🌐 Format: CSV file
🔒 From: Kaggle
🟢 https://t.me/datasets1
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Movie Rating DataSet.zip
1.6 MB
This Data have 20 Columns and 4804 Rows. And In this dataset how was the popularity of a movie and their characters and how was the release date of the movie revenue , status , title , movie language , average vote ,id and more..
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US Colleges and Universities.zip
932.8 KB
📦 Datasets name: US Colleges and Universities
🌹The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories.
🌐 Format: CSV file
🔒 From: Kaggle
🟢 https://t.me/datasets1
🌹The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories.
🌐 Format: CSV file
🔒 From: Kaggle
🟢 https://t.me/datasets1
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