Credit-Card-Fraud-Detection-using-autoencoders-and-ml-classification-algorithms

I have made this credit card fraud detection project using the dataset available on kaggle. From my observations from dataset, I found that this dataset is highly imbalanced dataset where fraud class contribute to only around 0.01 % and rest above are Legal class. I have used the machine learning classification algorithms like Naive Bayes, Logistic Regression and Random Forest for predicting the class and finding the similarities between different dataset. I have also tried to used autoencoder for better understanding the similarites and finding hidden relations between dataset.