Kaggle Data Hub – Telegram
Kaggle Data Hub
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Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.

Admin: @HusseinSheikho || @Hussein_Sheikho
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🔒 Dataset Name: Web Network Traffic

🚦Network traffic for classification of good or bad request
   
🚀    This dataset contains network traffic logs captured by Burp-Suite, aimed at classifying web requests as either good or bad based on their characteristics. The dataset is designed for the task of predicting whether incoming requests are legitimate (good) or malicious (bad), aiding in the detection and prevention of web-based attacks.

🎲 From: Kaggle      
      
🤖 Size: 112.3 KB       
   
🔄 https://news.1rj.ru/str/datasets1
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Web Network Traffic.zip
112.3 KB
Datasets Name: Web Network Traffic

https://news.1rj.ru/str/datasets1 ❤️
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🔒 Dataset Name: Amazon Phone Data: Prices, Ratings & Sales Insight

♦️Real-time data on phone prices, ratings, and sales trends for analysis
   
🚀 This dataset provides comprehensive real-time information on 340 phone products from Amazon, collected using the "Real-Time Amazon Data" API. The data covers various attributes such as product noscripts, prices, ratings, availability, and sales volume, offering a valuable resource for e-commerce analysis, machine learning projects, and consumer behavior studies focused on mobile phones.

🎲 From: Kaggle      
      
🤖 Size: 30.4 kB       
   
🔄 https://news.1rj.ru/str/datasets1
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Amazon Phone Data.zip
30.4 KB
Datasets Name: Amazon Phone Data: Prices, Ratings & Sales Insight

https://news.1rj.ru/str/datasets1 ❤️
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Cat Dataset 😻

Over 9,000 images of cats with annotated facial features

Context:
The CAT dataset includes over 9,000 cat images. For each image, there are annotations of the head of cat with nine points, two for eyes, one for mouth, and six for ears.

🎲 From: Kaggle      
      
🤖 Size: 4.04 GB       
   
🔃 https://news.1rj.ru/str/datasets1
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archive.zip.002
133.3 MB
Cat Dataset 😻       
   
🔃 https://news.1rj.ru/str/datasets1
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@codeprogrammer Helpful Cheat Sheets Compilation.zip
5.2 MB
🤩 Helpful Cheat Sheets Compilation

Including Pandas, NumPy, Matplotlib, Seaborn, and others

Is it useful to you

📂 Tags: #ML #AI #Python

http://t.me/codeprogrammer ⭐️
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WHAT YOU NEED TO WORK:
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Access is available at the link below
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https://news.1rj.ru/str/+NhwYZAXFlT8yZDIx
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Daily_Dose_Of_Data_Science_Full_Archive.pdf
88.3 MB
Here’s the 2024 edition of the Daily Dose of Data Science archive.

Is it useful to you
, Like 👍

📂 Tags: #DataScience #Python #ML

http://t.me/codeprogrammer ⭐️
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🟠 Bitcoin Network Transactional Metadata

Data set containing bitcoin transactions graph metadata (2011-2013)

🎶 Information:

😶 tx_hash: Hash of the bitcoin transaction.

😶 indegree: Number of transactions that are inputs of tx_hash

😶 outdegree: Number of transactions that are outputs of tx_hash.

😶 in_btc: Number of bitcoins on each incoming edge to tx_hash.

😶 out_btc: Number of bitcoins on each outgoing edge from tx_hash.

😶 total_btc: Net number of bitcoins flowing in and out from tx_hash.

😶 mean_in_btc: Average number of bitcoins flowing in for tx_hash.

😶 mean_out_btc: Average number of bitcoins flowing out for tx_hash.

😶 in-malicious: Will be 1 if the tx_hash is an input of a malicious transaction.

😶 out-malicious: Will be 1 if the tx_hash is an output of a malicious transaction.

😶 is-malicious: Will be 1 if the tx_hash is a malicious transaction.

😶 out_and_tx_malicious: Will be 1 if the tx_hash is a malicious transaction or an output of a malicious transaction.

😶 all_malicious: Will be 1 if the tx_hash is a malicious transaction or an output of a malicious transaction or input of a malicious transaction.

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archive.zip
1.8 GB
🟠 Bitcoin Network Transactional Metadata

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Medical Deepfakes: Lung Cancer.

CT scans tampered with cancer added or removed. Can you find them?

The dataset consists 100 CT scans of two sets (80 scans and 20 scans). The first 80 were used in a blind trial with the radiologists (they weren't told they were tampered), and the 20 scans were used in an open trial with the radiologists (they were told the truth and asked to identify them).

For each experiment there is a csv table containing the ground truth. Each row in the csv indicates where a real, fake, or removed cancer is located (x, y, and z [slice#]) and its classification. There are four classes:

Class Acronym Denoscription
True-Benign TB: A location that actually has no cancer

True-Malicious TM: A location that has real cancer

False-Benign FB: A location that has real cancer, but it was removed.

False-Malicious FM: A location that does not have cancer, but fake cancer was injected there.
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archive.zip.002
2.1 GB
Medical Deepfakes: Lung Cancer.

https://news.1rj.ru/str/datasets1 ⭕️
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BraTS2020 Dataset (Training + Validation)

Brain Tumor Segmentation 2020 Dataset

Size: 42 GB

https://news.1rj.ru/str/datasets1 🌑
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archive.zip.002
261.6 MB
BraTS2020 Dataset (Training + Validation)

https://news.1rj.ru/str/datasets1 🌑
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BraTS 2023: Adult Glioma

https://news.1rj.ru/str/datasets1 🌑
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