Task 1.1 Exploratory data analysis and date preparation Conduct an exploratory data analysis and data preparation of card.csv data set using RapidMiner to understand the characteristics of each variable and relationship of each variable to other variables. Summarise the findings of your exploratory data analysis and data preparation in terms of describing key characteristics of each variable in the card.csv data set such as maximum, minimum values, average, standard deviation, most frequent values (mode), missing values and invalid values etc and relationships with other variables, transformation of existing variables, creation of new variables in a table named Task 1.1 Results of Exploratory Data Analysis and Data Preparation.
Hint: Statistics Tab and Chart Tab in RapidMiner provide a lot of descriptive statistical information and useful charts like Barcharts, Scatterplots required for Task 1.1 etc. You might also like to look at running some correlations and/or chi square tests depending on whether a variable is a categorical variable or a numeric variable. Indicate in Table 1.1 which variables which contribute most to predicting whether a credit card transaction is likely to be fraudulent or not. You could also consider transforming some variables and creating new variables and converting target/label variable into a binominal variable to facilitate analysis in Tasks 1.2, 1.3 and 1.4.
Briefly discuss the key findings of your exploratory data analysis and data preparation and justification for variables most likely to predict whether a credit card transaction is likely to be fraudulent or not (10 marks 500 words).
Task 1.2 Decision Tree Model Build a Decision Tree model for predicting whether a credit card transaction is likely to be fraudulent or not based on the card.csv data set using RapidMiner and a set of data mining operators in part determined by your exploratory data analysis in Task 1.1. Provide these outputs from RapidMiner (1) Final Decision Tree Model process, (2) Final Decision Tree diagram and (3) Decision tree rules. Briefly explain your final Decision Tree Model Process and discuss the results of the Final Decision Tree Model drawing on key outputs (Decision Tree Diagram, Decision Tree Rules) for predicting whether a credit card transaction is likely to be fraudulent or not based on key contributing variables and relevant supporting literature on interpretation of decision trees (10 marks 150 words).
Task 1.3 Logistic Regression Model Build a Logistic Regression model for predicting whether a credit card transaction is likely to be fraudulent or not, using RapidMiner and an appropriate set of data mining operators and card.csv data set determined in part by your exploratory data analysis in Task 1.1. Provide these outputs from RapidMiner (1) Final Logistic Regression Model process (2) Key outputs from Logistic Regression Model. Hint for Task 1.3 Logistic Regression Model you may need to change data types of some variables. Briefly explain your final Logistic Regression Model Process and discuss the results of the Final Logistic Regression Model drawing on the key outputs (Coefficients, Standardised Coefficients, Odds Ratios, Standardised Error, Z Values, P Values etc) for predicting whether a credit card transaction is likely to be fraudulent or not based on key contributing variables and relevant supporting literature on interpretation of logistic regression models (10 marks 150 words).
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