To predict whether a given banknote is authentic given a number of measures taken from a photograph.
Dataset used - Banknote Authentication Data Set
It is binary classification problem. The dataset consists of 5 columns as follows:
Attributes:
- Variance of Wavelet Transformed image (continuous).
- Skewness of Wavelet Transformed image (continuous).
- Kurtosis of Wavelet Transformed image (continuous).
- Entropy of image (continuous).
Labels(Target)
- Output (0 for authentic, 1 for inauthentic).
Load the dataset
Load dataset into a pandas dataframe from the csv file.
Analyse the data
Using scatter matrix.
Prepare the data
Break the data (labels and attributes) into two subsets: a test set and a training set.
Create the model
Using Linear Regression.
Train the model
Train the classifier using training set.
Evaluate the model
Evaluate using test set.
Generate confusion matrix for varying threshold values.
Calculate misclassification rate.
Calculate area under ROC.
Dependence of Misclassification Error on Decision Threshold
In-sample ROC for banknote classifier
Out-of-sample ROC for banknote classifier
The entire code to this problem can be found on my github profile-
Code on GitHub - github.com/penthaapatel/BankNoteAuthentication
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