Visualizing credit card fraud info using t-SNE

I used a machine learning technique called t-distributed stochastic neighbor embedding (t-SNE) in order to visualize high (30) dimensional data points in 2 and 3 dimensions, and analyze the results.

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What it does

My hack uses t-SNE to reduce the number of dimensions of the original data to a level that is visualizable, while maintaining degrees of separability of points in the original dimension.

How we built it

I used Python, including the sklearn library for the t-SNE algorithm.

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