What to submit? $-$ Submit a R Markdown file that contains R codes, output and responses as required below. The file submitted should be Word or PDF$.$

Max points: $100$

In this analysis you will develop logistic regression model based on the data set provided to predict whether or not the specimens are genuine.

Data Set Information: Data $($A$6$DATA.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 $400$x $400$ 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:

V$1$: variance of Wavelet Transformed image $($continuous$)$

V$2$: skewness of Wavelet Transformed image $($continuous$)$

V$3$: kurtosis of Wavelet Transformed image $($continuous$)$

V$4$: entropy of image $($continuous$)$

V$5$: class $(0-$forged, $1-$genuine$)$

Read the A$6$DATA.csv data file into RStudio. Run set.seed$(681)$ for partitioning of the dataset into training $(60\%)$ and testing $(40\%).$ Report on the number of forged and genuine banknote$-$like specimens in the training and testing data. To

split data in to train and test, use following for consistency in results:

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 discuss performance of the model.