Credit Card Fraud Detection Model
This repository contains a credit card fraud detection model that uses machine learning algorithms to detect fraudulent transactions. The model was built using the Python programming language and scikit-learn library.
The dataset used for training and testing the model is the Credit Card Fraud Detection dataset from Kaggle. The dataset contains credit card transactions a total of 284315 transactions, of which 492 (0.17%) are fraudulent.
Here is the link to the dataset: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
The model uses a supervised learning algorithm called Random Forest Classifier to classify transactions as either fraudulent or non-fraudulent. SMOTE is used to handle imbalanced datasets like the credit card fraud dataset.
The model achieved an accuracy of 99.98%, precision of 99.97%, and recall score was 1 on the test set.
Deployment
Inorder to deploy the fraud detection model, I have used streamlit.
Put all the V1-V28 features along with the amount(Normalized value) and predict whether it is a fraudulent or a legitimate transactions.
V1-V28: may be result of a PCA Dimensionality reduction to protect user identities and sensitive features.
Acknowledgements
The dataset used in this project was obtained from Kaggle. The code for this project was inspired by the Credit Card Fraud Detection project by Data Flair.
https://www.youtube.com/watch?v=239TaYSQI-s&t=1440s
Feel free to contribute to the project by opening pull requests or creating issues if you encounter any problems.