1. Robot Execution Failures: This dataset contains force and torque measurements on a robot after failure detection. Each failure is characterized by 15 force/torque samples collected at regular time intervals
2. Chronic_Kidney_Disease: This dataset can be used to predict the chronic kidney disease and it can be collected from the hospital nearly 2 months of period.
3. Autistic Spectrum Disorder Screening Data for Children : Children screening data for autism suitable for classification and predictive tasks
4. Autistic Spectrum Disorder Screening Data for Adolescent : Autistic Spectrum Disorder Screening Data for Adolescent. This dataset is related to classification and predictive tasks.
5. Behavior of the urban traffic of the city of Sao Paulo in Brazil: The database was created with records of behavior of the urban traffic of the city of Sao Paulo in Brazil.
6. Thoracic Surgery Data: The data is dedicated to classification problem related to the post-operative life expectancy in the lung cancer patients: class 1 - death within one year after surgery, class 2 - survival.
7. Wheat kernels: Measurements of morphological descriptors of wheat kernels from Punjab State. A machine Learning based technique was used to extract 15 features, all are real valued attributes
8. GPS Trajectories: The dataset has been feed by Android app called Go!Track. It is available at Goolge Play Store(https://play.google.com/store/apps/details?id=com.go.router).
9. CSM (Conventional and Social Media Movies) Dataset 2014 and 2015: 12 features categorized as conventional and social media features. Both conventional features, collected from movies databases on Web as well as social media features(YouTube,Twitter).
10. ILPD (Indian Liver Patient Dataset): This data set contains 10 variables that are age, gender, total Bilirubin, direct Bilirubin, total proteins, albumin, A/G ratio, SGPT, SGOT and Alkphos.
11. Fertility: 100 volunteers provide a semen sample analyzed according to the WHO 2010 criteria. Sperm concentration are related to socio-demographic data, environmental factors, health status, and life habits