Selected Analytics & Business Intelligence Projects

This portfolio showcases a curated selection of analytics projects that demonstrate my approach to business intelligence, data analysis, and insight delivery.

While my professional work involves confidential enterprise data, the projects featured here highlight my ability to structure datasets, define meaningful metrics, build clear visualizations, and translate data into actionable insights.

These examples reflect the technical foundation and analytical thinking I apply in my professional role, including work with SQL, Power BI, Tableau, Python, and R. Each project emphasizes problem-solving, clarity, and effective data storytelling.

Please explore the projects below, and feel free to reach out if you’d like to discuss my experience or approach in more detail.

Alumni Donor Predictive Analytics & Segmentation

This project focused on identifying key factors that influence alumni donation behavior and using data-driven methods to inform targeted outreach strategies.

I analyzed a dataset of 100,000+ records across 55+ variables, applying statistical analysis and predictive modeling to uncover patterns in donor engagement. Using R, I developed a predictive model that achieved 95.01% accuracy, enabling the identification of high-probability donor segments.

Based on model outputs, I narrowed recommendations to four priority target groups, demonstrating how analytics can support focused decision-making and resource allocation. Results were communicated through Tableau visualizations, translating complex model outputs into clear, actionable insights for stakeholders.

Tools & Techniques: R, predictive modeling, data preparation, statistical analysis, Tableau, data visualization

Outcome: Identified high-probability donor segments to support targeted outreach and data-informed decision-making.

Interactive Recommendation Engine & Analytics Application

This project involved the design and development of an interactive recommendation application built to surface under-discovered (“hidden gem”) movies using data-driven logic.

I worked with a dataset of 50,000+ records, performing data cleaning, transformation, and feature preparation to support a recommendation framework implemented using R Shiny. The application dynamically generates recommendations based on user-defined inputs, including selected movies, recommendation volume, confidence thresholds, and popularity constraints.

The focus of this project was on building a scalable, user-driven analytics experience, combining backend data processing with an intuitive interface that allows users to explore recommendations in real time. This demonstrates my ability to translate analytical logic into interactive tools that support exploration and decision-making.

Tools & Techniques: R, R Shiny, data cleaning and transformation, recommendation logic, interactive analytics, UI-driven data applications

Outcome: Delivered an interactive data application that enables users to explore personalized recommendations through real-time, parameter-driven analytics.

Airline Operations & Performance Analysis (Tableau)

This project focused on analyzing airline operational data to uncover patterns in flight volume and carrier-level activity.

I prepared and transformed raw airline datasets using R and Excel, then developed a series of Tableau visualizations to explore flight distribution across carriers and identify relative operational scale. The visualizations were designed to highlight differences in carrier activity levels and support quick comparison across airlines.

The resulting dashboards demonstrate my ability to structure data for visualization, select appropriate chart types, and present insights clearly for analytical review. This project emphasizes translating large datasets into intuitive visuals that support exploratory analysis and data-driven discussion.

Tools & Techniques: Tableau, R, Excel, data transformation, exploratory data analysis, data visualization

Insight Example: Visualization revealed clear differences in flight volume concentration across major and regional carriers.

Tourism Demand Forecasting & Time Series Analysis Application

This project focused on analyzing tourism demand trends in Australia using time-series analysis and interactive analytics.

I worked with 50,000+ historical records, segmenting tourism data by region and trip purpose to examine long-term trends, seasonality, and volatility. Using R, I applied time-series techniques including decomposition, autocorrelation analysis, and forecasting models to support insight generation and future-oriented analysis.

The results were delivered through an interactive R Shiny application, allowing users to dynamically explore trends, compare regions, and evaluate patterns across travel purposes. This approach demonstrates my ability to combine statistical analysis with intuitive interfaces that support exploratory analysis and informed decision-making.

Tools & Techniques: R, R Shiny, time-series analysis, forecasting models, data segmentation, interactive analytics

Outcome: Enabled interactive exploration of tourism trends and forecasting patterns across regions to support data-driven planning discussions.