Machine learning algorithm to detect fraudulent credit card transactions.
The dataset of of nearly 28,500 credit card transactions was used to identify transactions with a high probability of being credit card fraud. The dataset can be downloaded from kaggle
The following two machine learning algorithms are deployed for outlier detection.
Local Outlier Factor (LOF) The anomaly score of each sample is called Local Outlier Factor. It measures the local deviation of density of a given sample with respect to its neighbors. It is local in that the anomaly score depends on how isolated the object is with respect to the surrounding neighborhood.
Isolation Forest Algorithm The IsolationForest ‘isolates’ observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. Since recursive partitioning can be represented by a tree structure, the number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node. This path length, averaged over a forest of such random trees, is a measure of normality and our decision function. Random partitioning produces noticeably shorter paths for anomalies. Hence, when a forest of random trees collectively produce shorter path lengths for particular samples, they are highly likely to be anomalies.
To run this python code on your machine, run jupyter notebook credit-card-fraud.ipynb in terminal or command prompt.
This will open the project in Jupyter notebook