Led the implementation of machine learning classification techniques for financial fraud detection, focusing on anti-money laundering transaction detection. ● Successfully addressed the challenges of class imbalance and shortage of labelled data by employing advanced sampling methods and semi-supervised learning approaches, resulting in enhanced fraud detection accuracy.

Managed data collection, gathering, and preparation from diverse sources of large-volume data, ensuring data quality and reliability for credit risk assessment. Engineered data pipelines to automate these processes, reducing manual efforts. Developed and deployed cutting-edge machine learning models for credit risk assessment using Amazon Web Services (AWS)

- **Automated Pricing Model:** - Develop Python scripts for geodata-based real estate project pricing. - Integrate automation into Excel-based models for seamless data processing. - **Optimized Pricing Model:** - Improve assumptions for competitive offers and effective risk management. - Enhance model logic in line with company goals. - **Interactive Dashboard Tool:** - Create a Dash (Plotly) dashboard for senior management with key metrics. - Aid decision-making through a user-friendly interface, ensuring clarity on pricing strategies.