The data analytic process $($Final$\_$b$2.$rmp$)$ was developed by your data science team in the previous project to demonstrate and summarize the performance of three different predictive models for the prediction of the credit application result $(+$ and $-$ for approval and disapproval, respectively$)$ in the Credit Approval dataset.

Currently, your team is working on a new project aiming to summarize the model performance of the three models for the prediction of high productivity $($i$.$e$.,$ high or not$)$ of teams in a sample dataset $($gwp$.$csv$)$ collected from daily records of workers in a garment product manufacturing company. The following is the list of input features and target $($label productivity$)$ in the dataset.

$-$ date: Date in MM$-$DD$-$YYYY

$-$ day of week: Day of the Week

$-$ department: Associated department with the instance

$-$ smv: Standard Minute Value, it is the allocated time for a task

$-$ wip: Work in progress. Includes the number of unfinished items for products

$-$ over$\_$time: Represents the amount of overtime by each team in minutes

$-$ incentive: Represents the amount of financial incentive $($in BDT$)$ that enables or motivates a particular course of action.

$-$ idle$\_$time: The amount of time when the production was interrupted due to several reasons

$-$ idle$\_$men: The number of workers who were idle due to production interruption

$-$ no$\_$of$\_$style$\_$change: Number of changes in the style of a particular product

$-$ no$\_$of$\_$workers: Number of workers in the team to which the instance belongs

$-$ label productivity: The class labels for the productivity that was delivered by the workers.

Because the goals of these projects are similar, your team wanted to utilize the previous project's pipeline although some operations in the previous project are not necessary. Also, your team wanted to identify$/$create a subprocess for the operators that can be applied to both the projects, thereby establishing a basis of an efficient BI system with other data in the future.

Modify the original process $($Final$\_$b$2.$rmp$)$ to produce the same types of outcomes using the new sample data $($gwp$.$csv$),$ excluding the date and day of week. If necessary, add or remove operators and adjust their order. Create a single subprocess that incorporates as many "common" operators as possible, which can be reused for predictions in both projects. Ensure that when this subprocess is integrated into the original project, it reproduces the same results as the original process.

Submit your modified RapidMiner process file for the new project, naming it with your name $($e$.$g$.,"$Q$2\_$Last,FirstName.rmp$").$