Bank Note Fraud Detection with SVMs in Python with Scikit-Learn - Exploratory Data Analysis

Exploratory Data Analysis

David Landup
Cássia Sampaio

Following the example given in the introduction, we will use a dataset that has measurements of real and forged bank notes images.

When looking at two notes, our eyes usually scan them from left to right and check where there might be similarities or dissimilarities. We look for a black dot coming before a green dot, or a shiny mark that is above an illustration. This means that there is an order in which we look at the notes. If we knew there were greens and black dots, but not if the green dot was coming before the black, or if the black was coming before the green, it would be harder to discriminate between notes.

There is a similar method to what we have just described than can be applied to the bank notes images. In general terms, this method consists in translating the image's pixels into a signal, then taking into consideration the order in which each different signal happens in the image by transforming it into little waves, or wavelets. After obtaining the wavelets, there is a way to figure out the order in which some signal happens before another, or the time of occurrence, but not exactly what signal. To know that, the image's frequencies need to be obtained. They are obtained by a method that performs the decomposition of each signal, called the Fourier method.

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