GitHub - jaycross1/Machine-Learning-Animal-Classification: We used various techniques to critically evaluate and practice a range of machine learning algorithms, tools and frameworks for developing AI solutions. We choice the Zoo dataset, this is a dataset that 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. Our goal was to predict the classification of animals (type), based upon the variables. We had to test our dataset and split it into 3 groups: training, validation and test data, modelling the data was next. Followed by validating then model, we then had to test and use the model. Lastly, we had to tune the model (change parameter to optimise our results). We used four different machine learning models Decision Tree Algorithm, K-Nearest Neighbours Algorithm, Artificial Neural Network Algorithms and Support Vector Machine Algorithm.

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We used various techniques to critically evaluate and practice a range of machine learning algorithms, tools and frameworks for developing AI solutions. We choice the Zoo dataset, this is a dataset that 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, Rep…