This project uses machine learning models like Logistic Regression, Random Forest, and XGBoost to detect fraudulent credit card transactions. It handles class imbalance using SMOTE and visualizes key fraud patterns through an interactive Power BI dashboard.

Code to detect credit card fraud detecton

A robust end-to-end machine learning pipeline for credit card fraud detection using Python, scikit-learn, and Streamlit. Includes data preprocessing, feature selection, model training & evaluation, saving the best model, and an interactive Streamlit app for predictions.

Machine learning model for credit card fraud detection, which is a binary classification task. The model's primary goal is to classify transactions into one of two classes: "fraudulent" or "legitimate," using the provided dataset.