In this assignment, your goal is to implement and compare machine learning classifiers in the context of three prediction tasks, reporting on your findings and drawing general insights from these findings. In particular, you will need to:

Implement two classifiers from scratch $($Naive Bayes and K$-$Nearest$-$Neighbour$)$

Implement $10-$fold stratified cross$-$validation $($to evaluate your classifiers$)$

Evaluate your classifiers' performances on two different datasets $($relating to diabetes and room occupancy$)$

Evaluate additional classifiers on the same datasets using Weka $($ZeroR$,$ OneR, decision trees, random forest, support vector machines and neural networks$)$ along with Weka's versions of the same classifiers $($Naive Bayes and KNN$)$

Write a report $($in the style of a scientific paper$),$ which critically discusses your findings