Omkar2118/datasets huggingface dataset:
Diverse Tools Image Dataset for Machine Learning

This dataset consists of images of various hand and power tools, specifically designed to aid in training and improving AI-based object recognition systems.

Tool recognition is a crucial component of modern machine learning applications, enabling automated identification in industrial settings, augmented reality, and inventory management. Training machine learning models on diverse tool images can help improve accuracy, especially in real-world environments where lighting conditions, angles, and backgrounds vary significantly.

This dataset contains images of multiple distinct tool types, with each tool category featuring hundreds of labeled samples captured from different perspectives.
Metabolic Syndrome Dataset

The dataset utilized in this study consists of demographic, clinical, and laboratory data for 2,401 individuals. It includes 15 columns: 13 features, a sequence ID, and a response variable indicating the presence or absence of metabolic syndrome. The 13 features encompass age, sex, marital status, income, race, waist circumference, body mass index (BMI), albuminuria (presence of the blood protein albumin in urine), urine albumin-to-creatinine ratio, uric acid, blood glucose, high-density lipoprotein (HDL), and triglycerides. This dataset originates from the National Health and Nutrition Examination Survey (NHANES) database.