Apply naive base algorithm on the dataset given above in C++ or java?

it must perform the accuracy test as well.

The code should be in any of the language given above

Please do not attempt this question if you are not able to provide correct answer and code

!!! I need code and explanation.

WPS Office File Edit View Insert Format Tools Slide Show Workspace Window Help A ))) 89% Mon 11:15 PM = Cource: Artificial Intellinancov I Home Machine Learning 2.pptx + Guest = Menu 6 2 = Home Insert Design Animation Slide Show Review View Tools Q Find Cut Reset - AAE 14- HAB = = Paste L Copy Format Play From New Section BIUS X? X2 A - 5 X X2 ak Current Slide Slide You can use 6 days without signing in Sign in to continue using for free and enjoy more services. Free Use after Login A= Picture Shape Fill Text Box Shapes - Arrange Shapes Arrange Shape Outline CA Replace Se X Outline Slides X An example: data (loan application) 45 The data and the goal Data: A set of data recorda (also called examples, Instances or cases desored by . attributes: A A...A . a class Esch example is labeled with a pre- defined class Goal To leam a classification model from the data that can be used to predict the classes of new cases/instances ID r not Age young young young VAN Has Job false false true 46 compro CCC application) rra TER PRO E 200 + - ram 5 6 7 8 9 10 11 12 13 14 15 HDD Own_House Credit_Rating false fair false good false good True false fair false fair false good true good true excellent true excellent true excellent true good false good false excellent false fair young middle middle middle middle middle old old old old old TY Class No No Yes Yes No No No Yes Yes Yes Yes Yes Yes Yes No false false false true false false false false true true false 47 . We first estimate prior probabilities for the status oss labels . P-7/and PM-5/12 Class Young midde old yes 217 3/7 2/7 ng 3/5 2/5 0 48 1. middle,true,false,excellent 2. old,false,false,excellent P has joilla TV Class false true 4/7 3/7 no 5/5 0 Click to add notes Slide 4 JAN 10 Ctv A g W WPS Office File Edit View Insert Format Tools Slide Show Workspace Window Help A > 0 89% Mon 11:15 PM faheem a = Cource: Artificial Intellinancov I Home Machine Learning 2.pptx + Guest = Menu 6 2 = Home Insert Design Animation Slide Show Review View Tools Q Find Cut Reset - AAE 14- HAB = = Paste L Copy Format Play From New Section BIUS X? X2 A - 5 X X2 ak Current Slide Slide You can use 6 days without signing in Sign in to continue using for free and enjoy more services. Free Use after Login A= Picture Shape Fill Text Box Shapes - Arrange Shapes Arrange Shape Outline CA Replace Se X Outline Slides X An example: data (loan application) 45 The data and the goal Data: A set of data recorda (also called examples, Instances or cases desored by . attributes: A A...A . a class Esch example is labeled with a pre- defined class Goal To leam a classification model from the data that can be used to predict the classes of new cases/instances ID r not Age young young young VAN Has Job false false true 46 compro CCC application) rra TER PRO E 200 + - ram 5 6 7 8 9 10 11 12 13 14 15 HDD Own_House Credit_Rating false fair false good false good True false fair false fair false good true good true excellent true excellent true excellent true good false good false excellent false fair young middle middle middle middle middle old old old old old TY Class No No Yes Yes No No No Yes Yes Yes Yes Yes Yes Yes No false false false true false false false false true true false 47 . We first estimate prior probabilities for the status oss labels . P-7/and PM-5/12 Class Young midde old yes 217 3/7 2/7 ng 3/5 2/5 0 48 1. middle,true,false,excellent 2. old,false,false,excellent P has joilla TV Class false true 4/7 3/7 no 5/5 0 Click to add notes Slide 4 JAN 10 Ctv A g W