$1.$ Download the DryBean.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.

$3.$ Decide on the bagging and boosting ensemble learning model that you will use. Give justifications for why you have selected these ensemble learning models.

$4.$ 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 justifications for each of these pre$-$processing steps. Should you decide not to address a data quality issue, justify this decision.

$5.$ 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 find best values for each, and then list the best values obtained.

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

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

$8.$ 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.