Table 1.2 , below, shews the values of these variables for the first veveral fecoeds in the ease: The conseguenees of misclassification have bon assessod as follows the costs of a falve posteve saying an applicant is a bad crobit nik) bya factor of five. This can be sammariad in the following bet. Table 13 Opporsuning Cest Table in deutch Marks) The oppontuily oest tahle was detived from the average nes peofit per laun as shown below: Table 1.1 Variakles for the Gierman Chodit data Table 1.2, below, shews the values of these variables for the first several rooves in the case Tahle 1.30poseturity Cost Table (in deukth Marks) The oppintanify cust eable was derined from the average net profie per laan as shown helow: Talle 1.4 Averape Net Profit Lef us use this bble in asessiog the performasce of the various models bocause it is simgler so explain to Assieterisent 1. Review the predictur variables and guess frum their definition at what their role might be in a credt: decisian. Ane these any sarpries in the data? models using the following data mining lechniques in XI.Mfiner: - Logistic regressioa - Clacsificatios tross - Neurat networks - Discriminant Analysia. ("vuscess" =1 ) of 0 5. Which scherique gives the mest ext profit on the validation data? initial classification of cveryene's credie status, let's use the "prcdicted probability of vaccese" is logitic repression as a basis for selesting the best credit risks first, followed by prons nik applicants b. Foe each vahtation case, calculase the acoual contzais of eviending grodit. c. Add another column foe cumulative net peofit. perecetile or roundod bo deciles.) should be wod ie catending crodin? (700 eavest of 'bul credir" (300 canes). New appicints for crebit can also be evahused os itese 30 "prodictor" variables. We wast to develop. based en vabies for ose or more of the predicter vanables. Alt the variables are explained in Twele 1.1. lato a series of binary variables so that they can be appropriancly handled by XI.Miner. Several oedered caleponical variables have been left as is; to be ireated by XI. Miner as mumstical. The duta has beet