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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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 aStandardScaler
(scaler.pkl
), capture user inputs, perform real-time feature engineering, and display predictions. - Built interactive tabs:
- Input Habits: Numeric inputs for six daily activities, interactive time-allocation progress bar, and “Critical” warnings for unrealistic inputs.
- Progress: Displays latest predicted grade, stress level, time-to-graduation estimate, plus a line chart and table of habit history.
- 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:
- Inspiration:
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- 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:
- Inspiration:
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- 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:
- Inspiration:
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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:
- Project Overview:
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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:
- Project Overview:
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- 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:
- Project Overview:
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- 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:
- Overview:
- GitHub: calvinhoang203
- LinkedIn: Hieu Hoang