Marketing Analytics Part 2

Are there any useful variables that you can engineer with the given data?
Review a list of the feature names below, from which we can engineer:

The total number of dependents in the home (‘Dependents’) can be engineered from the sum of ‘Kidhome’ and ‘Teenhome’
The year of becoming a customer (‘Year_Customer’) can be engineered from ‘Dt_Customer’
The total amount spent (‘TotalMnt’) can be engineered from the sum of all features containing the keyword ‘Mnt’
The total purchases (‘TotalPurchases’) can be engineered from the sum of all features containing the keyword ‘Purchases’
The total number of campaigns accepted (‘TotalCampaignsAcc’) can be engineered from the sum of all features containing the keywords ‘Cmp’ and ‘Response’ (the latest campaign)

Deriving Some useful Data

%scala

import spark.implicits._
import org.apache.spark.sql.functions._

spark.sql("set spark.sql.legacy.timeParserPolicy=LEGACY")

val derivedDF = Finalmarketingdataframe.select($"ID", $"Income", $"Kidhome" + $"Teenhome" as "Dependents", year(to_timestamp($"Dt_Customer", "MM/dd/yy")) as "Year_Customer", $"MntWines" + $"MntFruits" + $"MntMeatProducts" + $"MntFishProducts" + $"MntSweetProducts" + $"MntGoldProds" as "TotalMnt", $"NumDealsPurchases" + $"NumWebPurchases" + $"NumCatalogPurchases" + $"NumStorePurchases" as "TotalPurchases", $"AcceptedCmp1" + $"AcceptedCmp2" + $"AcceptedCmp3" + $"AcceptedCmp4" + $"AcceptedCmp5" as "TotalCampaignsAcc", $"Country")