Conduct an exploratory data analysis of the training data set loan-delinq.csv using RapidMiner Studio data mining tool.
(i) A screen capture of your final EDA process and briefly describe your final EDA process
(ii) Summarise the key results of your exploratory data analysis in a table named Table Results of Exploratory Data Analysis for loan-delinq.csv
(iii) Discuss the key results of your exploratory data analysis and provide a rationale for selecting your top 5 variables for predicting loan delinquency as the outcome based on the results of your exploratory data analysis and a review of the relevant literature on key factors contributing to a loan delinquency
Build a Decision Tree model for predicting loan delinquency based on the data set loan-delinq.csv using RapidMiner and an appropriate set of data mining operators and a reduced set of variables from loan-delinq.csv determined by your exploratory data analysis
(i) (1) Final Decision Tree Model process, (2) Final Decision Tree diagram, and (3) Decision tree rules.
(ii) Briefly explain your final Decision Tree Model Process, and discuss the results of the Final Decision Tree Model drawing on the key outputs (Decision Tree Diagram, Decision Tree Rules) for predicting loan delinquency. This discussion should be based on the contribution of each of the top five variables to the Final Decision Tree Model and relevant supporting literature on the interpretation of decision trees
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received 72 marks this is first assignment which i have received above 70 i have given so many assignment after this i received a good marks. but expected is 75 above as i had paid too much money.