PREDICTIVE MODELwith RapidMiner

Wanida Saetang Ph.D (candidate)King Mongkut's University of Technology North Bangkok

THE PROGRESSION OF ANALYTICS

CRISP-DM

http://mlwiki.org/index.php/CRISP-DM

DATA MINING TECHNIQUES

Decision TreeNaive BayesNeural NetworkSupport Vector Machines (SVM)

K-Means DBSCANEM Clustering using GMMs.Agglomerative Hierarchical

Apriori algorithmEclat algorithmFP-growth algorithm

DECISION TREE

http://dataminingtrend.com/2014/data-mining-techniques/ensemble-model/

http://dataminingtrend.com/2014/data-mining-techniques/ensemble-model/

APPLYMODEL

VALIDATE MODEL

Self Consistency Test

Split-validation

Cross-validation

http://dataminingtrend.com/2014/data-mining-techniques/cross-validation/

http://dataminingtrend.com/2014/data-mining-techniques/cross-validation/

http://dataminingtrend.com/2014/data-mining-techniques/cross-validation/

http://dataminingtrend.com/2014/data-mining-techniques/cross-validation/

http://dataminingtrend.com/2014/data-mining-techniques/cross-validation/

http://dataminingtrend.com/2014/data-mining-techniques/cross-validation/

Presenter

Presentation Notes

RapidMiner Studio combines data analysis and preparation with custom business deployment. This code-optional software lets you automate reporting based on time intervals or have events trigger changes in your visualizations. Import your own data sets and export to other programs thanks to the platform’s 60+ native integrations. Extensions give you greater flexibility (such as anomaly detection, text processing, and web mining), but may fall outside of the basic subscription price. Although RapidMiner is unapologetically built for data scientists, it’s easy to install and get started. You can even download a basic, free version of the product directly from the site (limited to one logical processor; no customer support).

Work Shop RapidMiner

• Lab1 Decision Tree

• Lab2 Apply Model

• Lab3 Test Model

• Lab4 Validate Model