The Purpose of this project is to familiarize with the Naïve Bayes and K Nearest Neighbor classification methods.
Algorithms related to these two methods were implemented and then trained and tested on the Abalone dataset.
Classification has been done on two features of the given dataset (sex and rings)
The scikit-learn KNeighborsClassifier has been used for classification
10-Fold Cross validation has been used in order to train and evaluate the categories
The results for each round of training and testing, including precision and recall for each class, as well as overall accuracy has been reported