Construct a machine learning based model for classification using Python for the following UCI datasets (can be loaded from the package itself):

a. Iris plants dataset: https://archive.ics.uci.edu/ml/datasets/Iris/

b. Diabetes dataset: https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html

c. Wisconsin Breast Cancer Dataset: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

• Employ Naive Bayes (Gaussian, Multinomial & Bernoulli) classifier and show classification results (Accuracy, Precision, Recall, F-score, confusion matrix).

• Use Decision Tree classifier for all the three datasets and show classification results (Accuracy, Precision, Recall, F-score, confusion matrix). Generate the decision tree images for all cases highlighting information like Gini and Entropy. Tune the parameters such that the maximum possible performance is achieved (90%<=performance<=100%)