You should submit a Report describing your work. The Report may include the following parts:Problem Definition$($describing the problem at hand and the aim of the work$)$

Solution Methodology $($a description of your machine learning models,why you choose them, how you apply them, the results of your experiments, including numbers, visualizations, and interpretations as appropriate$)$

Comparison of the models $($describing how you measure goodness of the models and the final decision of this comparison$)$

Conclusion$($summarizing your work with a result$)$

# Follow the steps to generate your personal data set.

# Then apply at least two machine learning methods covered in the lectures.

# The data has features ID$,$ Race, Age, Sex, Your, IQ and Height with

# class variable Died. The aim is to predict whether or not the# patient in the hospital will Died$($TRUE$)$ or not $($FALSE$).$

# r$\_$data$()$function in wakefield package is used to the generate

# random data set.

$>$ install.packages$($wakefield$)$

$>$ library$($wakefield$)$

$>$ set.seed$(*******)$ # Plug your $7-$digits student ID in $*$s$.$

$>$ data $=$ r$\_$data$(500)$ # Generate a dataset consisting of $500$ instances# Follow the steps to generate your personal data set.

# Then apply at least two machine learning methods covered in the lectures.

# The data has features ID$,$ Race, Age, Sex, Hour, IQ and Height with

# class variable Died. The aim is to predict whether or not the

# patient in the hospital will Died $($TRUE$)$ or not $($FALSE$).$

# r$\_$data$()$ function in wakefield package is used to the generate

# random data set.

$>$ install.packages $("$wakefield$")$

$>$ library $($wakefield$)$

$>$ set.seed $(**************)$ # Plug your $7-$digits student ID in $*\text{'}$s$.$

$>$ data $=$ r$\_$data$(500)$ # Generate a data set consisting of $500$ instances

INE$4933$ HOMEWORK

Homework should be performed individually. First, follow the steps given in Data Set Generation

file to generate your personal data set. Then, apply the ML algorithms that have been learned in the

lectures by using the generated data set. It has two parts: submitting a report and presenting your work

in the class.

REPORT

You should submit a Report describing your work. The Report may include the following parts:

Problem Definition $($describing the problem at hand and the aim of the work$)$

Solution Methodology $($a description of your machine learning models, why you choose them, how

you apply them, the results of your experiments, including numbers, visualizations, and interpretations

as appropriate$)$

Comparison of the models $($describing how you measure goodness of the models and the final

decision of this comparison$)$

Conclusion $($summarizing your work with a result$)$

PRESENTATION

You should present your work in the class with a $5-$minute talk.