Neural Net Project

Let's wrap up this course by taking a a quick look at the effectiveness of Neural Nets!

We'll use the Bank Authentication Data Set from the UCI repository.

The data consists of 5 columns:

  • variance of Wavelet Transformed image (continuous)
  • skewness of Wavelet Transformed image (continuous)
  • curtosis of Wavelet Transformed image (continuous)
  • entropy of image (continuous)
  • class (integer)

Where class indicates whether or not a Bank Note was authentic.

Get the Data

Use read.csv to read the bank_note_data.csv file.

In [122]:






















Check the head of the data frame and its structure.













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Out[123]:







Image.VarImage.SkewImage.CurtEntropyClass


	
13.62168.6661-2.8073-0.446990

	
24.54598.1674-2.4586-1.46210

	
33.866-2.63831.92420.106450

	
43.45669.5228-4.0112-3.59440

	
50.32924-4.45524.5718-0.98880

	
64.36849.6718-3.9606-3.16250























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'data.frame':	1372 obs. of  5 variables:
 $ Image.Var : num  3.622 4.546 3.866 3.457 0.329 ...
 $ Image.Skew: num  8.67 8.17 -2.64 9.52 -4.46 ...
 $ Image.Curt: num  -2.81 -2.46 1.92 -4.01 4.57 ...
 $ Entropy   : num  -0.447 -1.462 0.106 -3.594 -0.989 ...
 $ Class     : int  0 0 0 0 0 0 0 0 0 0 ...

























EDA
Create whatever visualizations you are interested in. We'll skip this step for the solutions notebook/video because the data isn't easily interpretable since its just statistical info on images.



















Train Test Split
Use the caTools library to split the data into training and testing sets.













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Check the structure of the train data and note that Class is still an int data type. We won't convert it to a factor for now because the neural net requires all numeric information.













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'data.frame':	960 obs. of  5 variables:
 $ Image.Var : num  3.622 4.546 3.457 0.329 4.368 ...
 $ Image.Skew: num  8.67 8.17 9.52 -4.46 9.67 ...
 $ Image.Curt: num  -2.81 -2.46 -4.01 4.57 -3.96 ...
 $ Entropy   : num  -0.447 -1.462 -3.594 -0.989 -3.163 ...
 $ Class     : int  0 0 0 0 0 0 0 0 0 0 ...

























Building the Neural Net
 Call the neuralnet library













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 Browse through the documentation of neuralnet 













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 Use the neuralnet function to train a neural net, set linear.output=FALSe and choose 10 hidden neurons (hidden=10) 













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Predictions
 Use compute() to grab predictions useing your nn model on the test set. Reference the lecture on how to do this.













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 Check the head of the predicted values. You should notice that they are still probabilities. 













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Out[143]:







	
30.002193454319

	
110.00003652277413

	
120.001859463551

	
130.00000632594144

	
140.00000656587257

	
170.0001286774258





























 Apply the round function to the predicted values so you only 0s and 1s as your predicted classes.













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  1. 0
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  2. 0
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  3. 0
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  4. 0
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  5. 0
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  6. 0
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Use table() to create a confusion matrix of your predictions versus the real values













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Out[146]:






           
predictions   0   1
          0 229   0
          1   0 183

























 You should have noticed that you did very well! Almost suspiciously well! Let's check our results against a randomForest model!


Comparing Models
Call the randomForest library













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 Run the Code below to set the Class column of the data as a factor (randomForest needs it to be a factor, not an int like neural nets did. Then re-do the train/test split













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 Create a randomForest model with the new adjusted training data.













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Use predict() to get the predicted values from your rf model.













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 Use table() to create the confusion matrix.













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Out[151]:






       
rf.pred   0   1
      0 227   1
      1   2 182

























How did the models compare?


Great Job!