这是indexloc提供的服务,不要输入任何密码
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calvinhoang203/README.md

👋 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! 🚀


🛠 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:


💻 Skills

Skills


📊 GitHub Stats:

GitHub Stats
GitHub Streak
Top Languages


📫 Connect with Me:

Pinned Loading

  1. Aggie-Reminder Aggie-Reminder Public

    JavaScript 1

  2. Exploring-the-Impact-of-Stroke-Heart-Disease-and-Diabetes-on-Mobility-Challenges Exploring-the-Impact-of-Stroke-Heart-Disease-and-Diabetes-on-Mobility-Challenges Public

    Jupyter Notebook 1

  3. GDSC-UCD-Cohort-24-25/MealBuddy GDSC-UCD-Cohort-24-25/MealBuddy Public

    JavaScript 2 1

  4. lJulietl/Pantry-Tracking-Website lJulietl/Pantry-Tracking-Website Public

    Python

  5. Analysis-of-Amazon-Sales-Trend Analysis-of-Amazon-Sales-Trend Public

    Jupyter Notebook

  6. rohith4444/Oprina_AVAFG rohith4444/Oprina_AVAFG Public

    Oprina is an AI-powered conversational agent with real-time avatar streaming, featuring multimodal Gemini 2.0 Flash integration, Gmail/Calendar automation, voice controls, and enterprise-grade dep…

    Python 1