I hold a M.S in Analytics (Data Science) from the University of Chicago and a B.S in Information Systems and Marketing from the University of Illinois Urbana - Champaign. With a background in business and analytics, I leverage data-driven insights to tell compelling stories and drive strategic decisions, transforming complex data into actionable information to enhance business outcomes.

Feel free to take a look at my projects below 🙂

GoodReads: Dynamic Tableau dashboards of Goodsread publications, providing analysis on factors that influence sales, and insights into authors/publishers and seasonal/historical trends. Provides actionable insights for publishers to improve their publication success.

Telecom Customer Churn: Tableau visualization using Telecom Customer Churn data to explore characteristics of high-risk churn customers and their associated behaviors / preferences. Provides recommendations to enhance customer retention strategies.

Disneyland Reviews: Analyzes reviews of three Disneyland parks over several decades. Use Tableau to examine sentiment analysis and trends that influence vistior experiences, providing recommendations that enhance user experience and improve ratings across the parks.

Video Game: Use Tableau to analyzes video game sales over the decades. Explores peak sales periods, platforms on which publishers released their games, and the distribution of sales across regions. Provides recommendations for video game publishers to maximize sales.

Chicago Crime: This project utilizes MySQL Workbench for relational modeling, Python for data cleaning and transformation, Google Cloud Storage for dataset storage, Google BigQuery for querying, and Tableau for reporting and dashboard creation, analyzing Chicago Crime rates in relation to socioeconomic factors to provide actionable insights and recommendations for reducing crime rates.

Yelp Recommendation System: A machine learning project that analyzed the Yelp dataset to train models for recommendations using Alternating Least Squares (ALS) and to generate user rating predictions.

Ethereum Fraud Detection Model: Machine learning project to identify fraudulent transactions on the ethereum blockchain to reduce false positives.

Steel Defect: A machine learning project focused on detecting steel defects using Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and CNNs with transfer learning.