Tuberculosis (TB) Prediction(Top 75 Countries)

About Dataset

This dataset includes 400,000 records with 22 variables that capture demographic, health, and socioeconomic factors influencing tuberculosis incidence across 70 countries. The data is designed to resemble real-world patterns observed in tuberculosis prevalence and healthcare indicators. It can be used for tasks such as denoscriptive analysis, machine learning, and public health research.

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Tuberculosis (TB) Prediction(Top 75 Countries)

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STL-10 Image Recognition Dataset

Train models to recognize different animals and vehicles

Context

STL-10 is an image recognition dataset inspired by CIFAR-10 dataset with some improvements. With a corpus of 100,000 unlabeled images and 500 training images, this dataset is best for developing unsupervised feature learning, deep learning, self-taught learning algorithms. Unlike CIFAR-10, the dataset has a higher resolution which makes it a challenging benchmark for developing more scalable unsupervised learning methods.
Content

Data overview:

There are three files: train_image.zips, test_images.zip and unlabeled_images.zip
10 classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck
Images are 96x96 pixels, color
500 training images (10 pre-defined folds), 800 test images per class
100,000 unlabeled images for unsupervised learning. These examples are extracted from a similar but broader distribution of images. For instance, it contains other types of animals (bears, rabbits, etc.) and vehicles (trains, buses, etc.) in addition to the ones in the labeled set
Images were acquired from labeled examples on ImageNet

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STL-10 Image Recognition Dataset

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Skin Cancer MNIST: HAM10000

a large collection of multi-source dermatoscopic images of pigmented lesions

Overview
Another more interesting than digit classification dataset to use to get biology and medicine students more excited about machine learning and image processing.

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The California Wildfire Data 🔥🔥🔥🔥

Structures Impacted by Wildland Fires in California!

Column Denoscriptions:
OBJECTID: A unique identifier for each record in the dataset.
DAMAGE: Indicates the level of fire damage to the structure (e.g., "No Damage", "Affected (1-9%)").
STREETNUMBER: The street number of the impacted structure.
STREETNAME: The name of the street where the impacted structure is located.
STREETTYPE: The type of street (e.g., "Road", "Lane").
STREETSUFFIX: Additional address information, such as apartment or building numbers (if applicable).
CITY: The city where the impacted structure is located.
STATE: The state abbreviation (e.g., "CA" for California).
ZIPCODE: The postal code of the impacted structure.
CALFIREUNIT: The CAL FIRE unit responsible for the area.
COUNTY: The county where the impacted structure is located.
COMMUNITY: The community or neighborhood of the structure.
INCIDENTNAME: The name of the fire incident that impacted the structure.
APN: The Assessor’s Parcel Number (APN) of the property.
ASSESSEDIMPROVEDVALUE: The assessed value of the improved property (e.g., structures, not just land).
YEARBUILT: The year the structure was built.
SITEADDRESS: The full address of the property, including city, state, and ZIP code.
GLOBALID: A globally unique identifier for each record.
Latitude: The latitude coordinate of the structure’s location.
Longitude: The longitude coordinate of the structure’s location.
UTILITYMISCSTRUCTUREDISTANCE: The distance between the main structure and any utility or miscellaneous structures (if recorded).
FIRENAME: An alternative or secondary name for the fire incident.
geometry: A geospatial representation of the location in a point format (e.g., "POINT (-13585927.697 4646740.750)").

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The California Wildfire Data 🔥🔥🔥🔥

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Stress Non stress Images

Emotion-Based Stress Classification: Categorization of Stress and Non-Stress Sta

The dataset contains images categorized based on a person's emotional state, classified into the following groups:

Non-Stress: Includes emotions such as happy and neutral.
Stress: Includes emotions such as sad and angry.
This categorization facilitates the analysis of emotional states in relation to stress levels.
Originally these datasets are available at official website of CK+ and TFEID

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Stress Non stress Images

Emotion-Based Stress #Classification: Categorization of Stress and Non-Stress Sta

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Crime Data

Crime Data from 2020 to Present

Denoscription:
This dataset contains detailed records of crimes reported across various regions from 2020 to the present. It provides valuable insights into crime trends, patterns, and changes in crime rates over time. The data is suitable for researchers, data analysts, law enforcement agencies, and policymakers looking to analyze crime dynamics or develop predictive models to enhance public safety measures.

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Crime Data

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CICIDS2017: Cleaned & Preprocessed

Cleaned and Preprocessed CICIDS2017 Data for Machine Learning

Cleaned and Preprocessed CICIDS2017 Data for Machine Learning

This dataset provides a cleaned and preprocessed version of the original CICIDS2017 network intrusion detection dataset, prepared for machine learning. It includes the following CSV file:

cicids2017_cleaned.csv: Contains the raw, unscaled feature values after cleaning and preprocessing, ready for further treatment (such as scaling and sampling) after train/test split.

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CICIDS2017: Cleaned & Preprocessed

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Global Terrorism Database

More than 180,000 terrorist attacks worldwide, 1970-2017

Context

Information on more than 180,000 Terrorist Attacks

The Global Terrorism Database (GTD) is an open-source database including information on terrorist attacks around the world from 1970 through 2017. The GTD includes systematic data on domestic as well as international terrorist incidents that have occurred during this time period and now includes more than 180,000 attacks. The database is maintained by researchers at the National Consortium for the Study of Terrorism and Responses to Terrorism (START), headquartered at the University of Maryland.
More Information

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Global Terrorism Database

#Datasets #Kaggle #MachineLearning #Python #ML #LLM #NLP #ComputerVision #GPT4

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