Throughout the financial sector, machine learning algorithms are being developed to detect fraudulent transactions. In this project, that is exactly what I am going to be doing as well. Using a dataset of of nearly 2,84,807 credit card transactions and multiple unsupervised anomaly detection algorithms, I am going to identify transactions with a high probability of being credit card fraud. In this project, I will build and deploy the following two machine learning algorithms:
- Local Outlier Factor (LOF)
- Isolation Forest Algorithm
Furthermore, using metrics such as precision, recall, and F1-scores, I will be investigating why the classification accuracy for these algorithms can be misleading.
In addition, I will be exploring 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.
Competition Link: https://www.kaggle.com/mlg-ulb/creditcardfraud
Dataset Link: https://www.kaggle.com/mlg-ulb/creditcardfraud/downloads/creditcardfraud.zip/3