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Welcome to the UC Irvine Machine Learning Repository. We currently maintain 670 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!
This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. Classification, Clustering. Multivariate, Sequential, Time-Series. 541.91K Instances. 8 Features.
The AI4I 2020 Predictive Maintenance Dataset is a synthetic dataset that reflects real predictive maintenance data encountered in industry. Classification, Regression, Causal-Discovery. Multivariate, Time-Series. 10K Instances.
Synthetic Circle Data Set. This dataset comprises 10000 two-dimensional points arranged into 100 circles, each containing 100 points. It was designed to evaluate clustering algorithms, such as k-means, by providing a clear and structured clustering challenge.
A dataset created from a higher education institution (acquired from several disjoint databases) related to students enrolled in different undergraduate degrees, such as agronomy, design, education, nursing, journalism, management, social service, and technologies.
The AI4I 2020 Predictive Maintenance Dataset is a synthetic dataset that reflects real predictive maintenance data encountered in industry. Classification, Regression, Causal-Discovery Multivariate, Time-Series
This is one of the earliest datasets used in the literature on classification methods and widely used in statistics and machine learning. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.
Predict whether annual income of an individual exceeds $50K/yr based on census data. Also known as "Census Income" dataset.
Introductory Paper. Comparative analysis of statistical pattern recognition methods in high dimensional settings. By S. Aeberhard, D. Coomans, O. Vel. 1994. Published in Pattern Recognition.
Import the dataset into your code from ucimlrepo import fetch_ucirepo # fetch dataset diabetes = fetch_ucirepo(id=34) # data (as pandas dataframes) X = diabetes.data.features y = diabetes.data.targets # metadata print(diabetes.metadata) # variable information print(diabetes.variables)