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# I need to build a prediction model than contain all these methods SOM, DBSCAN, Linear Regression....

I need to build a prediction model than contain all these methods SOM, DBSCAN, Linear Regression, and LIME. I know there are some errors. I need to get a suggestion that has all my methods. (I have Dengue Fever Disease data + Weather Data). if anyone can draw a model to me it will be helpful and appreciate it.

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## Answers

72Contributor II3".568UnicornLet's see.

- Retrieve your data.
- Convert your data from nominal to numerical if needed.
- SOM is Self-Organizing-Map, a dimensionality reduction algorithm. You should reduce your dimensions before clustering. (Like, why clustering if we still have some dimensions/attributes that we don't need?)
- DBSCAN is for clustering, so you should generate your clusters now. Clustering helps you obtaining a little more accuracy in scoring when using a sample, depending on where you put it.
- Now you should generate a sample to apply Linear Regression. This is the important part: you never use dimensionality reduction before the supervised algorithm. But you don't train your algorithm before LIME, you do it inside LIME. That is: put the LIME super operator, inside LIME you should use a Cross Validation or Split Validation to actually obtain the performance, and then inside your validation, you should put your Linear Regression on your training part and the classic Apply Model / Performance on the testing part.

Self-Organizing Map is part of RapidMiner, DBSCAN too. For LIME, you have to install the Operator Toolbox extension.All the best,

Rod.