In this assignment, you will do a series of tutorials followed by a data mining task. You will apply the CRISP-DM data mining technique to your selected domain

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Data Pre-processing and Decision Trees

In this assignment, you will do a series of tutorials followed by a data mining task. You will apply the CRISP-DM data mining technique to your selected domain. Specifically, gain insights into the data, formulate a set of hypothesizes, preprocess your data, and build a decision tree model. This assignment is graded on a scale of 100. For reference, when discussing the data mining process please reference CRISP-DM (shown below). You should submit the final Rapid Miner models. You will also include an analysis document

Step 1: Data preprocessing tutorials

 

Analysis Task: After you have installed RapidMiner, please complete the following data preprocessing tutorials (please note, the datasets you need for the tutorials are included in the RapidMiner download).

 

https://rapidminer.com/data-prep-data-exploration/

 

https://rapidminer.com/many-tools-data-prep-data-quality/

 

Upon completing each tutorial, briefly summarize what you did and discuss how it relates to what we have discussed in class.

 

Step 2: Decision Tree tutorial

 

Analysis Task: Read the following documentation and complete the example at the end of this document: https://docs.rapidminer.com/studio/operators/modeling/predictive/trees/parallel_decision_tree.html

Again, briefly summarize what you did and discuss how it relates to what we have discussed in class.

 

Step 3: Data Understanding

 

Analysis Task: Download and open the Supermarket Transactions dataset. You should go through the dataset and think about each attribute. Then in your analysis document, for each attribute you should give its name, a description, its type (numeric or categorical), if you notice any outliers or missing values, and any other interesting things you notice. You should also address data reduction & attribute reduction. You should also describe the class labels and explain what they mean.

 

 

Step 3: Building your Model – Preprocessing

 

RapidMiner Task: Import your data into Rapid Miner. For each attribute, you should perform appropriate data pre-processing.

 

Analysis Question: For each attribute, describe what sort of data preprocessing that you performed. For each attribute, you should describe and justify your decisions on handling missing data, anomalies, and normalization. (Feel free to explore other operators not demoed in class.) You should also mention if you chose to perform any data reduction. You should include screenshots in your explanation.

 

 

Step 4: Modeling

 

RapidMiner Task: Select and build at least 2 models (e.g. variations of a decision tree). You can build different decision trees by modifying operators/parameters (e.g. changing the splitting criterion, applying or not applying pruning, etc. While it is up to you to decide how you want to build your models, you need to demonstrate that you went through a non-trivial modeling process to receive full credit.

 

Analysis Question: Identify and explain all the work you did in the modeling task. You should justify your decisions. Be sure to include screenshots.

 

Step 6: Reflection

Analysis Question: What did you learn from doing this assignment? Did it impact your perception of how BI techniques can be applied to data? If faced with a similar challenge in the future, how would you change your approach?

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