Briefly describe how your dataset will be used in predictive modeling

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Nov 18, 2022


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This assignment is meant to demonstrate your expertise in building a predictive model using RapidMiner. Using the dataset pre-processed in assignment 1,
Briefly describe how your dataset will be used in predictive modeling
Hint: Make sure you describe your target variable
Import the dataset
Hint: You may follow the steps on the “Importing Data” training video.
Create a prediction model using a Decision Tree:
Provide a screenshot of your process design.
Provide a screenshot of the decision tree model
In 1-2 paragraphs, explain your decision tree model (interpret at least 2 complete rules from the decision tree).
Hint: You may need to rename your attributes, set role, or change the type of attributes.
Assignments Rubric (1)
Assignments Rubric (1)
Criteria Ratings Pts
This criterion is linked to a Learning Outcome Data Adequacy/Descriiption
30 to >28.0 pts
• The data selected is appropriate for analytics and includes novel data types/measures. • The submission includes a detailed descriiption of the variables to be used for analysis.
28 to >24.0 pts
• The data selected is appropriate for analytics. • The post includes a detailed descriiption of the variables to be used for analysis.
24 to >0 pts
• The data selected is not appropriate for analytics. • The post doesn’t include a detailed descriiption of the variables to be used for analysis.
30 pts
This criterion is linked to a Learning Outcome Data Processing – Rapidminer
40 to >38.0 pts
• The process created includes additional modules not described in the tutorial videos. • The submission includes screenshots of additional modules and associate results.
38 to >34.0 pts
• The process created includes all required modules. • The submission includes screenshots of the data process and associate results.
34 to >0 pts
• The process created doesn’t include all required modules. • The submission doesn’t include screenshots of the data process and associate results.
40 pts
This criterion is linked to a Learning Outcome Results’ Interpretations
30 to >28.0 pts
Submission includes descriiption of how the results can be used in business to improve decision making. • Rules of grammar, usage, and punctuation are followed; spelling is correct. • Language is clear and precise; sentences display consistently strong.
28 to >24.0 pts
• Includes solid interpretations of results. • Submission contains a few grammatical, punctuation and spelling errors. • Spell check, punctuation errors do not interfere with meaning.
24 to >0 pts
• Lacks solid interpretation of results. • Submission contains numerous grammatical, punctuation, and spelling errors.
30 pts
Total Points: 100


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