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 yeso 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.

Monthly Sell Loan_10 Gender Married Dependents Education Applicant Loan Credit Coapplicant Properly Amount Employed Amount HHtory Income () Term COOL015 Make 0 Graduate Na 5720 110300 360 1 Urban 0001032 Male YE 1 Graduate No 3075 1500 126000 360 1 Urban COP1031 Make 2 Graduate No 1809 208000 300 1 Urban COOL015 Male 2 Graduate No 35-45 100808 160 Urban 0001051 Male Na O Not Graduate No 3276 O 78300 360 1 Urban 0001054 Male Q Not Graduate Yes 2165 152000 300 1 Urban Coptoss Female No 1 Not Graduate No 1 Jemlurban CO01056 Make 2 Not Graduate No JARI 147600 160 0 Rural 10 0001059 Make YH 2 Graduate 13633 280000 240 1 Urban 11 0001047 Male No 0 Not Graduate No 2403 2400 121000 160 1 Semiurban 12 0031078 Make Na O Not Graduate No 3001 10300 1 Urban 13 0001043 Make YH 1 Graduate 2185 1516 163000 360 1 Semiurban 14 0001083 Make No Graduate No 40900 180 Urban 15 COOLOM Male Yei 2 Graduate 17171 0 166000 160 0 Semiurban 16 0001036 Female Na 0 Graduate Na 4605 0 124000 360 1 Semiurban 17 0001039 Male No 1 Graduate No 5067 131000 300 1 Urban 18 COOL103 Make 1 Graduate No 200000 1 Urban 19 0001107 Make Graduate Na 136000 160 1 Semiurban 20 0001103 Make YH 0 Graduate No 7916 300000 360 1 Urban 21 CO01215 Male No Q Graduate No 1309 1470 100200 1 Semiurban 32 0031131 Make 1 Not Graduate No 1430 1 Urban Train Cuta Test DataLoan Applicant Coapplicant Loan Property Loan 10 Gender Married Dependents Education Employs Amount Loan Status Income Income Amount HHtorY Area Term CO0102 Male No O Graduate No 112000 160 1 Urban 0001003 Male 1 Graduate No 4641 1508 128000 160 1 Rural COOL095 Male Yes Q Graduate Yes GLOOD 160 1 Urban COPLONG Male Q Not Gradual No 1441 3158 120800 140 1 Urban 6 COOLOCA Make Na 0 Graduate Ha 6OSE 141000 160 1 Urban 0001011 Male 2 Graduate Yes 5475 4196 267000 340 1 Urban CO01013 Male Yet Q Not Gradual No 2391 1416 91000 160 1 Urban 430 0002349 Male 0 Graduate No 3038 16.1300084 160 1 Rural 431 0092370 Male Q Not Gradual No 2775 GOOOD 180 1 Urban 4312 0092377 Female No 1 Graduate You 0 150900 160 1 Semiurban 433 0002379 Male No 0 Graduate No 6558 105000 160 O Rural 134 0002386 Male O Graduate 17934 0 405000 360 1 Semiurban 435 0092147 Male Q Graduate No 2140 143000 140 1 Semiurban 436 COPM0 Male Na O Graduate No 0 100800 1 Urban 437 0002393 Female Graduate No 10105 240 1 Semiurban 138 0092394 Make No Q Graduate 1984 1851 50000 1 Semiurban 439 0030401 Male O Graduate No 3271 1135 160 1 Urban 140 0002403 Male No 0 Graduate Yes 10474 187000 360 O Urban 441 0092407 Female Q Not Gradual Ves 7200 138000 1 Rural 443 00 348 Make No O Graduate No 1718 18 7000 1 Jemiurban 443 0003430 Make O Graduate Ha 7959 1833 180000 160 1 Rural 4-44 0002418 Male No Not Gradual No 4765 1993 148000 360 1 Semiurban Train Data Tew Owa