Reference no: EM133570490

Question 1:

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 prepare a report which include the followings:

1. Explain the process of building each classifier using the training dataset (add the screenshots).

2. 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.

Question 2:

a DASHBOARD. For creating a dashboard, the group can use the same Loan Prediction Train dataset (See the attachment in LMS). The group has to prepare a report which include the followings:

1. an introduction about the fields used in the dataset.

2. at least four figures(different graphs) and add them to dashboard.

3. Add Screenshot of each of the steps.

4. Describe the figures in the dashboard.