About Me

I am a recent Computer Science graduate with a strong interest in data analytics and data science.

EDUCATION 🧑‍🎓

California State University, Long Beach

• 🎓Fall of 2024: B.S in Computer Science
• 🎓Spring of 2027: M.S in Applied Statistics

BIO 📃

I was born on August 12, 2001, in Bellflower, CA. My journey into programming began in middle school, but it wasn't until high school that I truly immersed myself in coding. During my time at CSULB, I developed a strong interest in data science, and I am currently focused on expanding my knowledge in data analytics.

I am planning to graduate with my Master's in Applied Statistics around 2027 and am actively seeking internships to gain hands-on experience in data analysis and data science. I have a passion for tackling challenging problems and am committed to continuously improving my skills while learning something new every day.

Tools I've used 🧑‍💻

• < Python />
• < R />
• < Java />
• < SAS />
• < C++ />
• < SQL />
• < Firebase />
• < MongoDB />
• < JavaScript />
• < Typescript />
• < React Native />
• < HTML />
• < CSS />
• < Scikit-learn />
• < TensorFlow />
• < PyTorch />
• < XGBoost />
• < Pandas />
• < NumPy />
• < Matplotlib />
• < Seaborn />
• < Plotly />
• < Tableau />
• < PostgreSQL />
• < MySQL />
• < Google Cloud />
• < AWS (EC2) />
• < Spark />
• < Docker />
• < Git />
• < Jupyter />
• < Excel />

Projects

Acumen Entries Extractor

Automated daily work hour extraction system using Selenium, eliminating 2+ hours of manual work monthly. Implemented data pipeline with MongoDB and migrated to GitHub Actions, reducing costs from $12 to $0/month.

• Python
• Selenium
• MongoDB
• GitHub Actions
• Pandas
• Google Calendar API

SpaceX First Stage Landing Prediction

Engineered classification models to predict SpaceX first-stage landing success with 94.4% accuracy. Built interactive dashboard with visualizations and automated EDA pipelines for predictive modeling.

• Python
• Scikit-learn
• SQL
• Pandas
• NumPy
• Plotly

Titanic Survival Prediction

Machine learning pipeline to predict passenger survival, achieving a Kaggle public score of 0.77033. Performed data wrangling, feature engineering, and visualized results with Matplotlib and Seaborn.

• Python
• Scikit-learn
• XGBoost
• Jupyter
• Matplotlib
• Seaborn

Trucking Deregulation Analysis

Economic impact analysis of Florida's 1980 trucking deregulation using SAS and R. Built linear models that showed 62-63% reduction in shipping prices with high statistical confidence (p<0.0001).

• SAS
• Linear Regression
• Economic Modeling

Habibeats App

Habibeats is a mobile app for iOS and Android that connects concertgoers by allowing users to create profiles, match with others, and find events tailored to their preferences.

• Typescript
• React Native
• Expo
• Firebase
• Google Console

Contact