Credit Card Fraud Detection

About Credit Fraud Dataset :

$ cd ..  
$ mkdir data  
$ cd data  
$ kaggle datasets download -d mlg-ulb/creditcardfraud 
$ unzip creditcardfraud.zip
$ rm creditcardfraud.zip

Input variables
- 28 numerical input variables V which are the result of a PCA transformation
- 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. (172792.0 seconds = about 48 hours = about 2 days)
- 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning
Target Variable
- 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

Name		Name	Last commit message	Last commit date
Latest commit History 60 Commits
.ipynb_checkpoints		.ipynb_checkpoints
img		img
prototype		prototype
slides		slides
Anomaly Detection.ipynb		Anomaly Detection.ipynb
DBSCAN.ipynb		DBSCAN.ipynb
EDA.ipynb		EDA.ipynb
KNN.ipynb		KNN.ipynb
Novelty_detection_with_VAE_hj.ipynb		Novelty_detection_with_VAE_hj.ipynb
README.md		README.md
SVM-2.ipynb		SVM-2.ipynb
VAE_Novelty_Detection.ipynb		VAE_Novelty_Detection.ipynb
about-novelty-detection.ipynb		about-novelty-detection.ipynb
creditcard_vae.ipynb		creditcard_vae.ipynb
isoforest.ipynb		isoforest.ipynb
loss_function.py		loss_function.py
mnist_vae.ipynb		mnist_vae.ipynb
novelty_detection_pytorch.ipynb		novelty_detection_pytorch.ipynb
vae.py		vae.py