Analysis Steps: Follow these steps to create a RapidMiner process (Hint: You could make a copy of the sample process "ToyotaCorolla Prediction Model Example v2.rmp" and modify it or you can create a process from scratch in a similar manner) 1. Import the data 2. Change "wine type" to a numeric data type. Use the red wine as the comparison group for dummy coding. Use underscore in the names created. 3. Set the role of "quality" as label. You will be predicting the wine quality (numeric) in this dataset by building a multiple linear regression model. 4. Conduct a Split Validation using a 70% training and 30% testing split ratio. 5. Build a multiple linear regression model using the Linear Regression operator. 6. Use the correct Performance operator to generate the model along with following metrics for the validation data: root mean squared error (RMSE), absolute error (MAE), relative error (MAPE), squared error (MSE), squared correlation (R2). 7. With the Generate Attributes operator, create two new attributes "Error" and "Percent Error" similar to that shown in the sample process. Upon running the process, the Statistics tab will show you the "Average" of these attributes