Tessa Gonzalez
I turn messy business data into clear decisions.
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I developed a machine learning classification project to predict student exam performance using demographic, academic, and behavioral factors. The project included data exploration and preprocessing, feature analysis, model development, and evaluation of multiple classification algorithms.
I compared four machine learning approaches—Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting—using accuracy, precision, recall, F1 score, and ROC-AUC to evaluate performance.
Gradient Boosting produced the strongest overall results, achieving 96.50% accuracy, a 98.21% F1 score, and a 93.73% ROC-AUC.
The project demonstrates my ability to take a structured dataset through the machine learning workflow, compare competing models, evaluate results using multiple performance metrics, and translate model outputs into meaningful insights about the factors associated with student performance.
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39
Marketing Strategy & Market Opportunity Analysis
Client: Anonymized local home-services business
I conducted a data-driven marketing and market opportunity analysis for a local home-services business that wanted to make better decisions about where to focus its marketing resources and pursue growth.
The engagement combined client-provided business and marketing information with customer research, geographic analysis, U.S. Census and housing data, sales and lead information, and evaluation of existing marketing channels.
I developed a structured opportunity framework that evaluated potential strategies across customer fit, geographic fit, brand and operational fit, financial potential, cost, accessibility, measurability, competition, and strategic value.
Opportunities were then categorized as Prioritize, Test, Monitor, or Defer, allowing the business to distinguish between opportunities worth pursuing immediately and those requiring additional evidence before investment.
Deliverables included:
Customer and target-market analysis
Geographic market analysis
Marketing opportunity inventory
Opportunity prioritization matrix
Marketing-channel evaluation
Financial evaluation framework
Strategic recommendations
Measurement requirements
90-day implementation roadmap The result was a practical, data-informed strategy designed to help the business allocate marketing resources more intentionally and make decisions based on business fit and measurable opportunity rather than marketing activity alone. Client identity and confidential business information have been omitted from this portfolio case study.
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World of Warcraft Raid Performance Analytics
Analyzed combat-log data to evaluate raid progression, boss performance, player performance, mechanics, abilities, and team-level performance. Transformed detailed gameplay data into structured analytical views that make performance trends and areas for improvement easier to identify.
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Executive Retail Sales Dashboard
Transformed retail sales data into an interactive executive dashboard to help business leaders monitor revenue, profitability, orders, customers, returns, regional performance, product categories, and operational trends.
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