Select any dataset that contains at least 200 observations and at least 5 attributes. Choose ONE (1) target variable among the available variables. Given the following 2 groups of classification methods: Group A: Logistic Regression (LR), Nave Bayes (NB), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). Group B: k Nearest Neighbors (kNN), Support Vector Machines (SVM) and Decision Trees (DT).

Perform detailed analyses of the selected dataset by using TWO (2) methods from Group A and TWO (2) methods from Group B. Explain your choices and discuss your results.

NOTES: The link to the selected dataset should be provided and the dataset should not have been used in the lectures or labs of the course.

Any preprocessing method (e.g. removal or filling of empty cells) performed on the original data needs to be fully described.

Your analyses shall include the descriptions of your Python codes. [30 marks]