Exploratory Data Analysis, Decision Tree, and Linear Regression Model Analysis

Task1Exploratory Data Analysis, Decision Tree and Linear Regression model Analysis Now-a-days Kiwi commercial banks are using data mining techniques, machine learning, statistics and artificial intelligence for analysing the credit worthiness of a borrower.Assumeyou are a credit analyst of ASB bank and now you are required to complete tasks 2.1 to 2.3 before approving credit decisions to each new loan applicant by using ASB bank’s creditdata.csv data set provided inuPortal UUIS8008 Assignment-1 URL link and Data Dictionary in Appendix-A.Task 1.1):Using RapidMiner Studio data mining tool conduct an in-depth exploratory data analysis of the creditdata.csv data set. Summarise the key findings of your exploratory data analysis -in terms of describing key characteristics of each of the variables in the creditdata.csv data set such as maximum, minimum values, average, standard deviation, most frequent values (mode), missing values, inconsistency and relationships with othe