For the purposes of this assignment you will develop two different classification predictive models to classify

different types of dry beans. 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 figures will also not be considered if these tables and figures are not accompanied by your own explanation

of what these tables and figures portray.

Complete the assignment in the following steps:

$1.$ Download the DryBeanDataSet$774.$xlsx dataset. The dataset contains $13611$ instances, $20$ descriptive

features, and the class feature Class in column U$.$

$2.$ Without changing anything in the provided dataset, provide an analytics base table wherein you characterize all of the features of the dataset. $(10)$

$3.$ You now have to very carefully explore the dataset to identify data quality issues. For this part of your

report, only identify the data quality issues and provide justifications for these issues. $(30)$

$4.$ Based on your analysis above, decide on two different machine learning approaches that you will employ

to construct a predictive model for this problem. Give justifications for why you have selected these two

approaches for this problem. $(20)$

$5.$ For each of the machine learning approaches, discuss the data$-$preprocessing steps that you have implemented to optimally transform the dataset for that specific machine learning approach and to correct

data quality issues. Note: do not do unnecessary data transformations. Carefully think about the data

transformations needed for your selected machine learning algorithms. Provide justifications for each of

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

When you pre$-$process the dataset, make sure that you do not change the order of the instances in the

dataset. $(80)$

$6.$ Develop the two predictive models and evaluate the performance of the two models. Make sure to construct

optimal configurations of your chosen models both with respect to architecture and values for control

parameters. Describe the process that you have followed to produce an optimal configuration for each

model. For this purpose, carefully decide on the performance metrics that you will use. Conclude on which

one of the two approaches is best for this problem, and support your conclusion with justifications. For

the purposes of this assignment, make sure to report the performance based on a k$-$fold cross$-$validation.

Decide on the number of folds with a justification