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Predict Students' Dropout and Academic Success
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 dataset includes information known at the time of student enrollment (academic path, demographics, and social-economic factors) and the students' academic performance at the end of the first and second semesters. The data is used to build classification models to predict students' dropout and academic sucess. The problem is formulated as a three category classification task, in which there is a strong imbalance towards one of the classes.
Diabetes
This diabetes dataset is from AIM '94
Air Quality
Contains the responses of a gas multisensor device deployed on the field in an Italian city. Hourly responses averages are recorded along with gas concentrations references from a certified analyzer.
Automobile
From 1985 Ward's Automotive Yearbook
Mushroom
From Audobon Society Field Guide; mushrooms described in terms of physical characteristics; classification: poisonous or edible
Abalone
Predict the age of abalone from physical measurements
Estimation of Obesity Levels Based On Eating Habits and Physical Condition
This dataset include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition.
Default of Credit Card Clients
This research aimed at the case of customers' default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods.
Human Activity Recognition Using Smartphones
Human Activity Recognition database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors.
Statlog (German Credit Data)
This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix
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