I am a data scientist with a strong foundation in machine learning, deep learning, and artificial intelligence. My expertise spans statistical modeling, data visualization, and end-to-end data analysis. I am passionate about leveraging data-driven insights to drive innovation and solve real-world problems. I am actively seeking opportunities to collaborate on impactful projects in data science and AI research.

• Email: [email protected]
• LinkedIn

Programming Languages

• Python
• R
• SQL
• Java
• JavaScript
• HTML, CSS
• Swift

Technologies & Frameworks

• Tableau
• Spreadsheets
• Bootstrap

University of Melbourne
Master of Information Technology, Major in Artificial Intelligence (2020–2023)

University of Melbourne
Bachelor of Commerce, Major in Finance and Accounting (2017–2020)

• Google Data Analytics Professional Certificate

• Awarded: Endeavour Discipline Award – Computing and Information Systems, Semester 1, 2023
• Developed an iOS application to streamline geothermal systems design for engineers, architects, and contractors.
• Emphasized usability and cost-effectiveness to provide accessible, real-world solutions for sustainable building practices.

• Conducted comprehensive marketing analysis for Bellabeat, utilizing smart device fitness data to generate actionable marketing insights.
• Provided strategic recommendations based on behavioral analysis, usage patterns, and preferences derived from large-scale survey datasets.

• Implemented a hybrid recommendation engine combining collaborative filtering (SVD) and content-based filtering (TF-IDF, cosine similarity).
• Utilized the MovieLens 25M dataset to deliver customized recommendations.

• Applied multiple machine learning models (Logistic Regression, Random Forest, KNN, XGBoost, LightGBM) to detect fraudulent transactions.
• Addressed class imbalance with SMOTE and identified Random Forest as the most effective model.