Data (A6DATA.csv) can be found here: https://www.dropbox.com/s/gxa0yf2j7mnp1x4/A6DATA.csv?dl=0

Please note the following:

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%) and testing (50%). Report on the number of forged and genuine banknote-like specimens in the training and testing data.

PART 2:

Develop a logistic regression model using the training data. Provide final logistic regression model (with only significant variables), equation for calculating probability that specimen is genuine, confusion matrix for both training & testing data, misclassification error for both training & testing data, and comment on the performance of the model.

PART 3 :

Develop logistic regression models with 60%/40%, 70%/30%, and 80%/20% partitioning into training and testing data sets using set.seed(222). Summarize training and testing accuracy, sensitivity and specificity for each and compare with 50%/50% performance using the table below. Recommend and comment on the best model for future use.

Partitioning Accuracy % Sensitivity % Specificity % Training - 50% Testing - 50% ing - 60% Training Testing - 40% Training - 70% Testing - 30% Training - 80% Testing - 20%