AKSHAYAA SHANMUGANATHAN

Business Analyst specializing in data analysis and machine learning, utilizing expertise in Python, SQL, and Tableau to enhance business intelligence. Proven track record in developing forecasting models and delivering insights through effective data visualization. Focused on driving continuous improvement and innovative solutions aligned with organizational objectives.

• Standard Chartered Financial Markets Sales Job Simulation, Forage, Jun 2025
• Citi Finance Job Simulation, Forage, Jun 2025
• Deloitte Australia data analytics job simulation, Forage, Jun 2025
• BCG job simulation, Forage, May 2025
• Accenture Australia Strategy Consulting Job Simulation, Forage, Jun 2025

• Accenture Strategy Consulting Simulation, Forage, Jun 2025, client case review, root cause analysis, and solution proposals
• Standard Chartered sales simulation - Forage - Jun 2025 - asset recommendation and market analysis
• Citi Finance Simulation - Forage - Jun 2025 - KPI creation, capital ratio estimations, and executive summary writing
• Deloitte Analytics Simulation, Forage, Jun 2025, Tableau dashboarding, and Excel-based data storytelling

UMAP for dimensionality reduction - Jan 2025 to March 2025

• Applied UMAP to reduce high-dimensional data for visualization and clustering
• Enhanced interpretability of complex datasets and improved model transparency
• Supported better decision-making in classification tasks through visual insights, visualized high-dimensional datasets using UMAP for better interpretability in model analysis
• Visualized high-dimensional datasets using UMAP for better interpretability in model analysis

Smart traffic monitoring system, Oct 2024 to Dec 2024

• Developed an IoT-enabled solution for real-time traffic analytics
• Participated in Agile sprint planning, cloud integration, and documentation

Loan default prediction – research paper project Feb 2025 to Apr 2025

• Conducted an in-depth study on predicting loan default using statistical and machine learning models
• Built and evaluated classification models, including logistic regression and random forest
• Analyzed borrower risk using ROC-AUC, confusion matrix, and precision-recall metrics
• Delivered actionable insights to enhance credit risk assessment strategies