
Microsoft Certified Data Analyst with expertise in SQL and Power BI, delivering impactful dashboards that enhance decision-making and operational efficiency. Achievements include a 20% increase in issue resolution speed through effective data-driven strategies. Strong collaboration and communication skills facilitate insight sharing across teams.
Crime prediction model, University of Wollongong, 06/01/22, 12/01/23
Developed a machine learning-based crime prediction model using historical crime data for South Australia, utilized algorithms like Random Forest, KNN, Ridge Regression, Linear SVC, and Gaussian Naive Bayes to forecast crime rates and identify high-risk areas, incorporated anti-crime measures and demographic features to enhance prediction accuracy, conducted data preprocessing, feature engineering, and model evaluation using metrics such as accuracy and F1-score, the project supported evidence-based decision-making for resource allocation and proactive crime prevention strategies, implemented a web-based dashboard for the crime prediction model that visualized forecasted data with interactive maps and charts, facilitating stakeholder engagement and enabling real-time analytics for strategic planning.