Dataset Hub

The student performance dataset, sourced from Kaggle, contains students' mathematics, writing, and reading exam scores. The task is binary classification to determine whether a student passed the mathematics, writing, or reading exam. Features include gender, ethnicity, parental education level, whether the student receives standard lunch, whether a preparation test was completed, and the reading, writing, and mathematics scores.

This dataset contains attributes A1 to A16, including continuous and categorical data, with missing values represented by "?". The target variable is binary classification, appearing as "+" or "-" in attribute A16.

The TwoNorm dataset originates from the OpenML repository, primarily used for binary classification tasks. It includes two configurations: 8hr and 1hr.

The Ionosphere dataset, sourced from the UCI Machine Learning Repository, is used for binary classification tasks aiming to determine whether received radar signals indicate the presence of electrons in the ionosphere. The dataset includes individual feature information and ionospheric threshold values.

This dataset is intended for training and supporting a Support Vector Machine (SVM) model to classify images of cats and dogs. It contains images of cats and dogs suitable for binary classification tasks.

This dataset involves predicting whether a given banknote is genuine based on several measurements extracted from photographs. It is a binary classification problem with imbalanced class distributions.