👋 Hi! I'm Hieu (Calvin) Hoang.

I’m a data science enthusiast with a strong foundation in machine learning and AI and a passion for leveraging data to solve real-world problems.

• 🎓 Senior at UC Davis, pursuing a B.S. in Statistical Data Science with a Minor in Technology Management.
• 📊 Data Science Coordinator @ ASUCD Pantry, optimizing food inventory with predictive modeling.
• 🌍 Youth Advisory Council Member @ JFF, working to enhance career navigation tools for young adults.
• 💡 Currently learning about AI Agents and Generative AI to explore their potential in automation and decision-making.
• 🔍 Interested in machine learning, data visualization, and applied AI in healthcare, business, and technology.

🌟 Always open to connecting and collaborating—feel free to reach out! 🚀

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🛠 Projects:

• BrainBoost: Academic Success Coach

  • Inspiration:
    • Provide students with a personalized, data-driven “coach” to track daily habits and predict academic performance.
  • What it does:
    • Allows users to input daily metrics—study hours, sleep hours, social activities, physical activity, extracurriculars, and screen time.
    • Predicts letter-grade category and stress level using a Gradient Boosting model (0.9087 overall accuracy) trained on 2,000+ student records.
    • Visualizes progress over time in an interactive Streamlit dashboard, showing habit history alongside predicted outcomes.
    • Generates tailored recommendations in three categories—“Study Strategy,” “Wellness,” and “Balance”—based on predicted grade gaps and stress levels.
    • Includes a simulation tab where users adjust habit sliders to see potential effects on predicted GPA and stress.
  • Key Outcomes:
    • Gradient Boosting model achieved 0.8175 letter-grade accuracy and 1.0000 stress-level accuracy, for an overall 0.9087 accuracy.
    • Empowered students to identify habit changes likely to improve GPA trajectories and manage stress effectively.
  • How we built it:
    • Preprocessed the Student Lifestyle Dataset (2,000 records) in Pandas; engineered features such as Study–Sleep interaction, Social–Study ratio, Total Activity, Study Efficiency, and Life Balance.
    • Trained multiple classifiers (Logistic Regression, Random Forest, XGBoost, Decision Tree, Gradient Boosting) in scikit-learn and saved the best-performing pipeline in stacked_multioutput_predictor.pkl.
    • Developed a Streamlit app (app.py) to load the model and a StandardScaler (scaler.pkl), capture user inputs, perform real-time feature engineering, and display predictions.
    • Built interactive tabs:
      1. Input Habits: Numeric inputs for six daily activities, interactive time-allocation progress bar, and “Critical” warnings for unrealistic inputs.
      2. Progress: Displays latest predicted grade, stress level, time-to-graduation estimate, plus a line chart and table of habit history.
      3. Recommendations: Provides personalized tips for improving study habits, wellness, and work-life balance, and includes interactive sliders so users can adjust daily habits and immediately see how those changes might impact their predicted GPA and stress levels.
  • Technologies used:

    

• SHIPSmart

  • Inspiration:
    • Help UC SHIP students avoid surprise medical bills by estimating healthcare costs up front.
  • What it does:
    • Provides a real-time cost estimation and claims automation system.
    • Allows users to input plan details and claim data to calculate expected reimbursements.
    • Automates rebate processing to expedite refunds.
  • How we built it:
    • Co-developed during HackDavis 2025 with SwiftUI on iOS.
    • Integrated Python back-end logic and the Cerebras API for machine learning calculations.
    • Leveraged OpenAI/Gemini and SQL to process and analyze insurance data in real time.
  • Technologies used:

    

• Aggie Reminder

  • Inspiration:
    • Enhance volunteer management and communication at Aggie House.
  • What it does:
    • Provides an admin portal to monitor volunteer work hours.
    • Sends automated email reminders via the SendGrid API.
    • Implements an automatic reminder feature using JavaScript in a Google Sheets App Script.
  • How we built it:
    • Developed with Node.js for server-side logic.
    • Built with HTML, CSS, and JavaScript for a responsive frontend.
  • Technologies used:

    

• Pantry Tracking Website

  • Description:
    • A web-based dashboard for tracking pantry inventory in real-time.
  • How it works:
    • Built with Python, allowing users to input, update, and visualize inventory data.
    • Efficiently manages distributed products and remaining stock.
  • Technologies used:

    

• Exploring the Impact of Stroke, Heart Disease, and Diabetes on Mobility Challenges

  • Project Overview:
    • Uses the BRFSS 2015 dataset to analyze and predict heart disease indicators.
    • Leverages health metrics such as BMI, smoking habits, physical activity, and healthcare access.
  • Inspiration:
    • Motivated by the alarming prevalence of heart disease and the need for early intervention.
  • Dataset Overview:
    • Based on the Heart Disease Health Indicators from the 2015 BRFSS survey.
    • Consists of 22 columns covering health metrics, demographics, and lifestyle factors.
  • Analysis Details:
    • Objectives: Identify key predictors of heart disease, build and evaluate predictive models, and provide actionable insights.
    • Methods: Exploratory Data Analysis, Feature Engineering, and Model Building using algorithms like Logistic Regression and Random Forest.
    • Results & Learnings: Highlighted significant predictors (e.g., HighBP, HighChol) and gained insights into lifestyle impacts on heart disease risk.
  • Technologies used:

    

• Analysis-of-Amazon-Sales-Trend

  • Project Overview:
    • Conducts an in-depth analysis of customer behavior using the Amazon Sales Dataset.
    • Focuses on product review categories, review lengths, and their impact on product engagement.
  • Key Questions Explored:
    • What information does the dataset provide?
    • Which products are top-rated based on the number of ratings?
    • Is there a correlation between ratings count and average product rating?
    • Which products have the most discounted prices, and how do discounts relate to review counts?
    • What are the top products by click-through rates and by category?
    • How do review characteristics (e.g., length) correlate with product ratings?
  • Technologies used:

    

• Marvel-Universe-Explorer

  • Project Overview:
    • A fun, interactive web project that dives into the Marvel Universe.
    • Retrieves data from the Marvel API to showcase characters, comics, and creators.
  • Inspiration:
    • Sparked by a childhood fascination with Marvel heroes and their incredible stories.
  • Features:
    • Home: Introductory section guiding users through the site.
    • Marvel Characters Gallery: Displays characters with images and descriptions.
    • Marvel Comics Gallery: Lists comics with cover images, titles, and issue numbers.
  • Technologies used:

    

• MealBuddy

  • Overview:
    • An AI-powered meal planning app designed to help users track ingredients, generate personalized meal suggestions, and monitor nutritional intake.
  • Features (In Progress):
    • Ingredient Tracking: Search, add, edit, and delete ingredients with nutritional breakdown (calories, protein, fats, water, sugar).
    • AI-Powered Chatbot: Provides recipe suggestions using Google Gemini AI based on user-provided ingredients, with integrated YouTube video links for cooking instructions.
    • Dynamic Dashboards: Displays nutritional summaries with circular trackers and pie charts for calories, water, protein, carbs, and fats.
    • Profile Management: Manage user data (name, age, gender, height, weight) with authentication through Firebase and Google Sign-In, including password reset and logout functionality.
    • Searchable Fridge Inventory: Filter and manage stored ingredients with real-time updates using Firestore snapshot listeners.
    • Themed UI: Automatically adapts to system light/dark themes using a custom color scheme.
  • Tech Stack:
    • Frontend: React Native, HTML, CSS, JavaScript, TypeScript.
    • Backend: Firebase for authentication and database management, Google Gemini API for AI integration.
    • AI Integration: Google Gemini API for personalized meal recommendations and YouTube Data API for video retrieval.
  • Technologies used:

    

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💻 Skills

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📊 GitHub Stats:

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📫 Connect with Me:

• GitHub: calvinhoang203
• LinkedIn: Hieu Hoang