Small Knime task

Task 1 Read [login to view URL] data set (attached) :  exclude rows where marital-status is missing.  On the remaining rows:  extract rows where marital-status = "Never-married"  extract rows where marital-status = "Divorced" AND 20 <= age <= 40 AND workclass starts with "S"  remove column "marital-status  keep only column "marital-status"  keep only String columns using a Column Filter node and then only column "marital-status" using a Reference Column Filter node Task2 Read the [login to view URL] dataset. ([login to view URL]) • Using the Rule Engine node, create a new column “currency” with value “USD” for the orders from the USA, and “EUR” for the orders from Germany. • Using the Rule Engine node, create a new column “conversion” with value 1 if currency is “EUR”, and 0.88 if currency is “USD” (we refer to the exchange rate of Nov-04-2018). • Using the Math Formula node, calculate values in a new column named “amount-in-EUR” by multiplying the value in column “amount” by the value in column “conversion”. The product names are of type “prod_1”. Using the String Manipulation node, change the product names to “1”, “2”, and so on. Task 3 Read the [login to view URL] dataset. (Attached) Train a Logistic Regression Model to predict whether a wine is red or white. • Use the Normalizer(PMML) node to z normalize all numerical columns. • Partition the dataset into a training set (80%) and a test set (20%) using the Partitioning node with the stratified sampling option on the column “Income”. • Use the Logistic Regression Learner Node to train the model on the training set and the Logistic Regression Predictor Node to apply the model to the test set. • Use the Scorer node to evaluate the accuracy of the model.