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.
-
Notifications
You must be signed in to change notification settings - Fork 0
omololevy/Detecting-Fraudulent-Credit-Card-Transactions
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
anomaly detection algorithm which to predict fraudulent credit card transactions
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published
Languages
- Jupyter Notebook 100.0%