Prediction Model using Rapid Miner (Assignment 3)

RapidMiner Studio is a powerful visual programming environment for rapidly building complete predictive analytic workflows. This all-in-one tool features hundreds of pre-defined data preparation and machine learning algorithms to efficiently support all your data science needs. It is can be used for business and commercial applications as well as for research, education, training, rapid prototyping, and application development and supports all steps of the data mining process including data preparation, results visualisation, validation and optimization. I use RapidMiner for my tools to finish my assignment about create a prediction model using data training on data pemilu that given by my lecture.

In this case, we have to predict weather the legistative candidates is elected or not using using the following
algorithm :

1. Decision Tree (C4.5)
2. Naïve Bayes (NB)
3. K-Nearest Neighbor (K-NN)

And for th evaluation / accuracy testing, we are asked to use 10-fold X Validation.

This diagram shown the flow of my activity from inputting the data, setting the role and creating the visualization.

The steps are:

1. Open Rapidminer and click new process and open with the blank space.
2. Then you can start it.
3. In operators menu search ‘Read Excel’, ‘Set Role’, and ‘Validations’ then drag and drop to the Process window.
4. Double click on ‘Read Excel’ then input ‘datapemilukpu.xls’.
5. Click on ‘Set Role’ and edit on parameters box, input “TERPILIH ATAU TIDAK” and change Target Role become Label.
6. Connect the operators dot to dot.
7. Double click on ‘Validation’ to open the Training and Testing.
8. In operator box, search ‘Decision Tree’/’K-NN’/’Naive Bayes’ then drag and drop in the Training space.
9. In operator box, search ‘Apply Model’ and ‘Performance’ then drag and drop in Testing space.
10. 10. Then connect the operators.

• Decision Tree

• Naive Bayes

• K-NN

• Refrences

https://rapidminer.com/products/studio/

https://kevinbwstudenttelkomuniversity.wordpress.com/2016/10/23/big-data-task-evaluation-prediction-elektabilitas-caleg/