Select any CATEGORICAL dataset that contains at least 150 observations with THREE (3) attributes from any reliable source. Choose any THREE (3) of the following classification methods i. Logistic Regression (LR), ii. Nave Bayes (NB), iii. Linear Discriminant Analysis (LDA), iv. K Nearest Neighbors (KNN), v. Support Vector Machines (SVM), to perform detailed analyses of the selected dataset. Use the first 70% of the data to train the model and the remaining 30% to test the accuracy of the model. Explain your choices of attributes 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 and shown. Your analyses shall include the descriptions of your Python codes or any other software outputs to support the analyses.