The Loan Prediction Model can be viewed as a problem of classification. The data set you will be using for this problem is the Loan prediction dataset which you can use to build a yes/no loan approval model (See the attachment in LMS). Your goal is to use different classifiers to build a training model based on training data points and then test its performance on test data points. Students must use the following classifiers. The selection of the classifiers depends upon the members of the group, e.g., if the group has four members, then they will use the four classifiers from the following five classifiers. 1. Neural network 2. Support vector machine 3. Nearest Neighbour algorithm 4. Decision tree 5. Naive Bayes

The group must report which include the followings: 1. Explain the process of building each classifier using the training dataset (add the screenshots). 2. Create the confusion matrix based on training/ testing. 3. Explain how you evaluated the classifier. 4. Predict the category of the values in table used for Testing set. 5. Compare the results between the different classifiers and discuss which one is the best and why.