Anti-Money Laundering Model

In this project, firstly, I preprocessed the data and engineered new key features like transaction volume and geographic diversity. The data then underwent transformations such as one-hot encoding and scaling to prepare for clustering with KMeans, which is optimized through the elbow method to find the ideal cluster count. I created an interactive dashboard using Plotly, showcasing cluster distributions and centroids in interactive plots managed by Streamlit.