Data (A6DATA.csv) were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extract features from images.

Attribute Information:

V1: variance of Wavelet Transformed image (continuous)

V2: skewness of Wavelet Transformed image (continuous)

V3: kurtosis of Wavelet Transformed image (continuous)

V4: entropy of image (continuous)

V5: class (0-forged, 1-genuine)

PART 1

Read the A6DATA.csv data file into RStudio. Run set.seed(222) for partitioning of the dataset into training (50%) andtesting (50%). Report on the number of forged and genuine banknote-like specimens in the training and testing data.