I come from a third world country and it hasn't been long since we got introduced to the internet. The internet came with a lot of good stuff, like ecommerce sites but it also came with some bad stuff like cybercriminals who steal the user's information. My dad was a victim of such schemes where his credit card info was stolen and used to make extravagant purchases. Had a strong fraud detection system been put in place that wouldn't have happened. That was when I decided to build a very powerful one.
Throughout the financial sector, machine learning algorithms are being developed to detect fraudulent transactions. In this project, that is exactly what we are going to be doing as well. Using a dataset of of nearly 28,500 credit card transactions and multiple unsupervised anomaly detection algorithms, we are going to identify transactions with a high probability of being credit card fraud. In this project, I will built and deployed the following two machine learning algorithms:
Local Outlier Factor (LOF) Isolation Forest Algorithm Furthermore, using metrics such as precision, recall, and F1-scores, we will investigate why the classification accuracy for these algorithms can be misleading.
In addition, we will explore the use of data visualization techniques common in data science, such as parameter histograms and correlation matrices, to gain a better understanding of the underlying distribution of data in our data set.
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