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?