Shavin Armoogum

Third-year Data Science student at the University of Melbourne with hands-on experience in Python, machine learning, SQL, and statistical analysis. Skilled in building predictive models, cleaning and analyzing large datasets, and implementing AI algorithms. Passionate about turning data into actionable insights and contributing to innovative, data-driven projects. Detail-oriented, motivated, and eager to grow within a professional data team.

• Scam SMS Detection: Developed a text-based Naïve Bayes classifier to identify fraudulent SMS messages; improved classification performance through semi-supervised learning and delivered clear analysis of model behaviour, predictive features, and confidence levels.
• Traffic Sign Classification (Machine Learning): Developed and evaluated multiple supervised ML models using image-based features; engineered features, compared classifier performance, and submitted results to a Kaggle competition.
• Victorian Vibes (Data Analysis): Cleaned, merged, and analysed large Victorian community datasets; conducted EDA, correlation studies, and built predictive models to identify trends supporting local planning decisions.
• Conference Track-r (SQL Project): Designed a relational database for managing conferences, papers, sessions, and researcher relationships; wrote efficient SQL queries to extract insights and validate data integrity.
• Impassable Gate Solver (C / Algorithms): Implemented an AI solver using Uniform Cost Search and Iterated Width Search; optimised memory usage and evaluated algorithmic runtime and space complexity.

• Bilingual International Baccalaureate Diploma — Awarded Nov 2022
• International General Certificate of Education (IGCSE), Distinction — Cambridge International Education, Awarded Nov 2020