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             Final Submission for AQM             

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                   Larissa Haas                   
                  MatrNr 1417669                  
           [email protected]           
         
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This Submission contains: 
- Final Paper
- R-File (local)
- R-File (server)
- 2 data sets

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Before running the (local) R-File: Please make 
sure that you have enough working storage, because
the code constructs several Random Forests and 
Neural Networks, which may occupy some space.

Also make sure that you configure the 4 paths to
the data files correctly (as R only works with 
absolute and not with relative paths).

Please: Do not run the (server) R-File, besides
you really want/have to! It takes quite some time,
even if you had some GB working storage. (The 
important stuff happens in the local R-File, 
though.)

The local code contains many out-commented parts,
these are mostly plots and stuff which I had to
calculate to plot the graphics. These parts are 
not necessary to get the basic results from the
models.

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The code used for this paper was written with the 
help of the tutorial held by Marcel Neunhoeffer 
(provided by the chair of Quantitative Methods in 
the Social Sciences) to support the lecture 
Advanced Quantitative Methods, as well as with the 
provided R code by "Practical Machine Learning 
with H2O", "R Deep Learning Essentials", and 
"Machine Learning with R".

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