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anomaly detection algorithm which to predict fraudulent credit card transactions
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Trained a Isolation Forest anomaly detection algorithm which gave 96.5% accuracy in predicting fraudulent transactions on test set. The dataset was a higly skewed dataset of 284,807 transactions in which only 492 transcations were fradulent (0.172%). Developed in Python using Sklearn's Imbalanced learn , Seaborn for data visualization and pandas for data manipulation.

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