This dataset consists of 101 animals from a zoo. There are 16 variables with various traits to describe the animals. The 7 Class Types are: Mammal, Bird, Reptile, Fish, Amphibian, Bug and Invertebrate The purpose for this dataset is to be able to predict the classification of the animals, based upon the variables. It is the perfect dataset for those who are new to learning Machine Learning. # zoo.csv Attribute Information: (name of attribute and type of value domain) 1. animal_name: Unique for each instance 2. hair Boolean 3. feathers Boolean 4. eggs Boolean 5. milk Boolean 6. airborne Boolean 7. aquatic Boolean 8. predator Boolean 9. toothed Boolean 10. backbone Boolean 11. breathes Boolean 12. venomous Boolean 13. fins Boolean 14. legs Numeric (set of values: {0,2,4,5,6,8}) 15. tail Boolean 16. domestic Boolean 17. catsize Boolean 18. class_type Numeric (integer values in range [1,7]) # class.csv This csv describes the dataset 1. Class_Number Numeric (integer values in range [1,7]) 2. Number_Of_Animal_Species_In_Class Numeric 3. Class_Type character -- The actual word description of the class 4. Animal_Names character -- The list of the animals that fall in the category of the class # Acknowledgements UCI Machine Learning: https://archive.ics.uci.edu/ml/datasets/Zoo Source Information -- Creator: Richard Forsyth -- Donor: Richard S. Forsyth 8 Grosvenor Avenue Mapperley Park Nottingham NG3 5DX 0602-621676 -- Date: 5/15/1990 # Inspiration What are the best machine learning ensembles/methods for classifying these animals based upon the variables given?