CIS8008 Assignment 4 Criterion based marking sheet Student name: Student no Component Marks Obtained Comment Task 1.1 Conduct an exploratory data analysis (EDA)of weatherAUS.csv data set using RapidMiner summarise key findings of EDA in a table and discuss key findings in regards to the weatherAUS.csv data set (about 500 words) 10 Excellent EDA of weatherAUS.csv data set with appropriate outputs and discussion of key findings 8 Very good EDA of weatherAUS.csv data set with appropriate outputs and discussion of key findings 7 Good EDA of weatherAUS.csv data set with appropriate outputs and discussion of key findings 5 Average EDA of weatherAUS.csv data set with appropriate outputs and discussion of key findings 4 Poor EDA of weatherAUS.csv data set with appropriate outputs and discussion of key findings 2 Very poor EDA of weatherAUS.csv data set with appropriate outputs and discussion of key findings 0 Not Attempted No attempt made or irrelevant. Task 1.2 Build a Decision Tree model for predicting whether it is likely to rain tomorrow based on today’s weather using the weatherAUS.csv data set and RapidMiner; provide Final Decision Tree model process, Decision Tree Model and Decision Tree Rules and explain final decision tree model process and discuss results of Final Decision Tree Model (about 250 words) 6 Excellent Explanation of Decision Tree process, model, rules and results 5 Very good Explanation of Decision Tree process, model, rules and results 4 Good Explanation of Decision Tree process, model, rules and results 3 Average Explanation of Decision Tree process, model, rules and results 2 Poor Explanation of Decision Tree process, model, rules and results 1 Very poor Explanation of Decision Tree process, model, rules and results 0 Not attempted No attempt made or irrelevant. Task 1.3 Build a Logistic Regression model for predicting whether it is likely to rain tomorrow based on today’s weather using the weatherAUS.csv data set and using RapidMiner; provide Final Logistic Regression model process, and Coefficients and Odds Ratios and explain final logistic regression model process and discuss results of Final /logistic Regression Model (about 250 words) 7 Excellent Explanation of Logistic Regression process, model, coefficients, Odds Ratios results 6 Very good Explanation of Logistic Regression process, model, coefficients, Odds Ratios results 5 Good Explanation of Logistic Regression process, model, coefficients, Odds Ratios results 4 Average Explanation of Logistic Regression process, model, coefficients, Odds Ratios results 3 Poor Explanation of Logistic Regression process, model, coefficients, Odds Ratios results 1 Very poor Explanation of Logistic Regression process, model, coefficients, Odds Ratios results 0 Not attempted No attempt made or irrelevant. i Task 1.4 Comment on the accuracy of Final Decision Tree Model and Final Logistic Regression Model for predicting whether it is likely to rain tomorrow based on today’s weather using the weatherAUS.csv data set and RapidMiner based the results of the confusion matrix, and ROC chart for each final model (about 250 words) 7 Excellent Explanation of accuracy of Final Decision Tree and Logistic Regression models 6 Very good Explanation of accuracy of Final Decision Tree and Logistic Regression models 5 Good Explanation of accuracy of Final Decision Tree and Logistic Regression models 4 Average Explanation of accuracy of Final Decision Tree and Logistic Regression models 3 Poor Explanation of accuracy of Final Decision Tree and Logistic Regression models 1 Very poor Explanation of accuracy of Final Decision Tree and Logistic Regression models 0 Not attempted No attempt made or irrelevant.