Welcome to the UC Irvine Machine Learning Repository
We currently maintain 668 datasets as a service to the machine learning community. Here, you can donate and find datasets used by millions of people all around the world!
Popular Datasets
Iris
A small classic dataset from Fisher, 1936. One of the earliest known datasets used for evaluating classification methods.
Dry Bean
Images of 13,611 grains of 7 different registered dry beans were taken with a high-resolution camera. A total of 16 features; 12 dimensions and 4 shape forms, were obtained from the grains.
Heart Disease
4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach
Rice (Cammeo and Osmancik)
A total of 3810 rice grain's images were taken for the two species, processed and feature inferences were made. 7 morphological features were obtained for each grain of rice.
Raisin
Images of the Kecimen and Besni raisin varieties were obtained with CVS. A total of 900 raisins were used, including 450 from both varieties, and 7 morphological features were extracted.
Adult
Predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset.
New Datasets
Dataset for Assessing Mathematics Learning in Higher Education
MathE is a mathematical platform developed under the MathE project (mathe.pixel-online.org). The dataset has 9546 answers to questions in the Mathematical topics taught in higher education. The file has eight features, named: Student ID, Student Country, Question ID, Type of answer (correct or incorrect), Question level (basic or advanced), Math Topic, Math Subtopic, and Question Keywords. The question level was associated with the professor who submitted the question. The data was obtained from February 2019 until December 2023.
Micro Gas Turbine Electrical Energy Prediction
This dataset consists of measurements of electrical power corresponding to an input control signal over time, collected from a 3-kilowatt commercial micro gas turbine.
Printed Circuit Board Processed Image
This CSV dataset, originally used for test-pad coordinate retrieval from PCB images, presents potential applications like classification (e.g., Grey test pad detection), anomaly detection (e.g., fake test pads), or clustering for grey test pads discovery. The dataset includes X and Y representing pixel positions, and R, G, B values determining pixel color (minmax normalized from 0-255). A 'Grey' field indicates approximate grey pixels. This dataset was originally used for a 2-stage discovery of high number of test pad clusters (>100) in a dataset presented in: @article{Tan2016FastRO, title={Fast retrievals of test-pad coordinates from photo images of printed circuit boards}, author={Swee Chuan Tan and Schumann Tong Wei Kit}, journal={2016 International Conference on Advanced Mechatronic Systems (ICAMechS)}, year={2016}, pages={464-467}, url={https://api.semanticscholar.org/CorpusID:38544897} } More pixels here than that in the paper due to different extraction method.
PhiUSIIL Phishing URL (Website)
PhiUSIIL Phishing URL Dataset is a substantial dataset comprising 134,850 legitimate and 100,945 phishing URLs. Most of the URLs we analyzed, while constructing the dataset, are the latest URLs. Features are extracted from the source code of the webpage and URL. Features such as CharContinuationRate, URLTitleMatchScore, URLCharProb, and TLDLegitimateProb are derived from existing features.
UR3 CobotOps
The UR3 CobotOps Dataset is an essential collection of multi-dimensional time-series data from the UR3 cobot, offering insights into operational parameters and faults for machine learning in robotics and automation. It features electrical currents, temperatures, speeds across joints (J0-J5), gripper current, operation cycle count, protective stops, and grip losses, collected via MODBUS and RTDE protocols. This dataset supports research in fault detection, predictive maintenance, and operational optimization, providing a detailed operational snapshot of a leading cobot model for industrial applications
RT-IoT2022
The RT-IoT2022, a proprietary dataset derived from a real-time IoT infrastructure, is introduced as a comprehensive resource integrating a diverse range of IoT devices and sophisticated network attack methodologies. This dataset encompasses both normal and adversarial network behaviours, providing a general representation of real-world scenarios. Incorporating data from IoT devices such as ThingSpeak-LED, Wipro-Bulb, and MQTT-Temp, as well as simulated attack scenarios involving Brute-Force SSH attacks, DDoS attacks using Hping and Slowloris, and Nmap patterns, RT-IoT2022 offers a detailed perspective on the complex nature of network traffic. The bidirectional attributes of network traffic are meticulously captured using the Zeek network monitoring tool and the Flowmeter plugin. Researchers can leverage the RT-IoT2022 dataset to advance the capabilities of Intrusion Detection Systems (IDS), fostering the development of robust and adaptive security solutions for real-time IoT networks.