✅ Precision: 1.00 — zero false positives; no legitimate messages flagged as spam
📉 Recall: 0.72 — catches most spam but misses a few, ensuring cautious filtering
⚖️ F1 Score: 0.84 — well-balanced model with emphasis on precision
Why it matters:
The model is optimized for high-precision scenarios, making it ideal for systems where false spam flags must be avoided, such as in business-critical communications.
WeatherAQI is a Jupyter Notebook project that fetches and compares real-time weather and air quality (AQI) data for different cities using APIs and data visualization.
🔧 Built with:
Python, Jupyter Notebook
OpenWeatherMap API
Seaborn, Matplotlib, Pandas
Optional: AQI API + BeautifulSoup
📊 Features:
🔄 Live integration with OpenWeatherMap & AQI APIs
Auto-generated visual comparisons for multiple cities
Includes auto-labeled bar plots with units (°C, %, AQI)
🗃️ Converts live CSV data to a local SQLite database (weather_aqi.db)
📥 Supports SQL queries for filtering and historical analysis
A lightweight Python tool that detects potentially suspicious credit card transactions using rule-based AML (Anti-Money Laundering) checks.
🚨 Flags high-value transactions and unusual activity
📊 Processes CSV files with clear, reviewable output
🔧 Easily extendable for more compliance logic or machine learning
I’m currently working on automation for complex GRC and TPRM programs’ change management and workflows with Gen AI and Python.
I’m learning Rego (OPA) to implement policy as code for IAM risk management and map different frameworks and regulations e.g. NIST, HIPAA, PCI DSS, etc. so that it’s easier for software engineers to update change management and add it to their CI/CD pipeline.
I’m looking to collaborate on anything related to cybersecurity.
Ask me about GRC Compliance and TPRM program management with workflow and risk scoring methodology automation.
How to reach me:
Languages and Tools:
Language Statistics...
A little more about me…
const cat = {
pronouns: "She" | "Her" | "Hers",
code: ["Javascript", "HTML", "CSS", "Python", "Ruby", "FEEL"],
askMeAbout: ["web dev", "tech", "app dev", "startup", "baking"],
technologies: {
webApp: ["Python App"],
frontEnd: {
js: ["React", "Context"],
python: ["FastAPI"]
css: ["material ui", "ant design", "bootstrap", "Sass", "Less"]
},
backEnd: {
js: ["node", "express"],
python: ["flask"]
},
devOps: ["AWS", "Heroku", "Docker🐳", "K8"],
databases: ["postgreSQL", "MySql", "sqlite"],
misc: ["DMN", "selenium", "postman"]
},
architecture: ["serverless architecture", "progressive web applications", "single page applications", "microservices", "event-driven", "design system pattern"],
techCommunities: {
member: "Py-Lambda",
member: "Women Techmakers",
member: "freeCodeCamp",
},
currentProject: "I am building an interactive Github Dashboard and REST APIs with Flask and Python",
funFact: "Let your code brew overnight and magic will happen the next morning"
};
I love connecting with people with different backgrounds so if you want to say hi, I’ll be happy to meet you! 😊
🕑︎ Time Zone: America/Los_Angeles
💬 Programming Languages:
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🔥 Editors:
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🐱💻 Projects:
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Hola
, I'm Catherine Kim!
🚀 Featured Projects
📦 Spam Detector
Machine learning model to classify SMS messages as ‘spam’ or ‘ham’ using text preprocessing, TF-IDF vectorization, and scikit-learn classifiers.
🔍 Accuracy: 96% — overall strong classification performance
✅ Precision: 1.00 — zero false positives; no legitimate messages flagged as spam
📉 Recall: 0.72 — catches most spam but misses a few, ensuring cautious filtering
⚖️ F1 Score: 0.84 — well-balanced model with emphasis on precision
Why it matters: The model is optimized for high-precision scenarios, making it ideal for systems where false spam flags must be avoided, such as in business-critical communications.
➡️ View project: Spam Detector on GitHub →
🌦️ WeatherAQI
WeatherAQI is a Jupyter Notebook project that fetches and compares real-time weather and air quality (AQI) data for different cities using APIs and data visualization.
🔧 Built with:
📊 Features:
weather_aqi.db)🔗 View Project: WeatherAQI on GitHub →
🤖 AML Checker
🚧
A lightweight Python tool that detects potentially suspicious credit card transactions using rule-based AML (Anti-Money Laundering) checks.
Languages and Tools:
Language Statistics...
🐱 My GitHub Data
I’m an Early 🐤
📅 I’m Most Productive on Tuesday
📊 This Week I Spent My Time On
I Mostly Code in JavaScript
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Last Updated on 24/03/2026 20:08:54 UTC
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