Python notebook describing the entire process of developing a prediction model including: Exploratory Data Analysis, Data Preprocessing, and Model Development

Use PySpark to predict the success of a terrorist attack using different machine learning approaches

This project uses an enhanced WGAN with GCN layers to tackle class imbalance in fraud detection. By converting data into graph structures, it generates realistic synthetic samples, improving balance and accuracy compared to traditional methods like SMOTE.