For the purposes of this assignment you will develop both a bagging and boosting ensemble learning model of

your choice to produce two dry beans classication models. You will then compare the performance of the chosen

ensemble learning models with one another, and to the performance a single machine learning model instance

of the machine learning approach used as the individual members of the bagging ensemble. As an example, if

you decide to implement a random forest, then the performance of the bagging and boosting ensembles will also

be compared to that of an individual classication tree.

You have to write a report wherein you provide responses in clear narrative on the aspects enumerated below,

under appropriate section headings. Note that code will not be evaluated. Tables and gures will also not be

considered if these tables and gures are not accompanied by your own explanation of what these tables and

gures portray.

Complete the assignment in the following steps:

$1.$ Download the DryBeanPBA$3.$xlsx dataset. The dataset contains $13611$ instances, $16$ descriptive features,

and the class feature Class in column Q$.$

$2.$ You now have to very carefully explore the dataset to identify any issues with in this dataset. Identify

the issues and explain how you have addressed these issues. $(20)$

$3.$ Decide on the bagging and boosting ensemble learning model that you will use. Give justications for

why you have selected these ensemble learning models. $(20)$

$4.$ Discuss the data$-$preprocessing steps that you have implemented to optimally transform the dataset for the

chosen ensemble learning approaches. Note: do not do unnecessary data transformations. Carefully think

about the data transformations needed for the selected ensemble learning approach. Provide justications

for each of these pre$-$processing steps. Should you decide not to address a data quality issue, justify this

decision. $(20)$

$5.$ Make sure to tune the hyperparameters of the ensemble learning models. For each ensemble learning

model, describe each of the hyperparameters, the process followed to nd best values for each, and then

list the best values obtained. $(40)$

$6.$ Now do the same for the individual machine learning model. $(25)$

$7.$ Discuss the empirical process that you have followed to evaluate the performance of each model, and to

compare these two models. $(20)$

$8.$ Now present and discuss the results of the two models and conclude on which of the two approaches are

best. Provide your opinions on why the one model will perform better than the other.