Marc Linus Rosales
Aspring Data Professional
I enjoy building machine learning models and extracting meaningful insights from data to solve real-world problems.
About
🎓 Education: Computer Science, Batangas State University (2022-2026) | Consistent Dean’s Lister 🏆
🚀 Goal: Passionate about transforming raw data into insights as a Data Analyst, Engineer, or Scientist.
📊 Interests: Data visualization, SQL optimization, and predictive modeling.
🏀 Fun Fact: I've analyzed everything from bicycle rentals to NBA stats!
Skills
Programming & Frameworks: Python (Pandas, NumPy, Scikit-Learn), C/C++, Java
Databases & Data Analysis: PostgreSQL, MySQL, MariaDB, Google Firebase, SQL; Microsoft Power BI, Microsoft Excel, Matplotlib, Seaborn
Workflow & Version Control: Git, GitHub, Docker, Dagster, Airflow
Personal Projects
Senator Election Prediction Using YouTube Comments
Built a data pipeline to extract, clean, and store YouTube comments in PostgreSQL, following Kimball’s Data Warehousing principles. Applied BERT-based NLP for sentiment analysis and Named Entity Recognition. Used PCA & K-Means to cluster sentiment patterns and predict 12 likely senators. Designed a scalable architecture for real-time political trend analysis.
View on GitHub
Bicycle Rental Business Analysis
Achieved 84.56% accuracy in predicting optimal bicycle rental locations based on temperature ranges. Built a Power BI dashboard and a scalable ETL pipeline with Docker, APIs, Pandas, PostgreSQL, and Dagster, optimizing data collection from 1,000 to 47,000 rows daily. Automated workflows to enhance efficiency and support business expansion.
View on GitHub
Calories Burned Analysis
Revealed high-impact calorie burn from activities like racing and fast-paced running (1.77-1.99 cal/kg). Converted CSV to SQLite database and analyzed data to determine the most efficient calorie-burning exercises. Visualized key results to highlight top calorie-burning activities.
View on GitHub
Household Income of Filipino Families Analysis
Analyzed spending habits and economic differences across regions in the Philippines. Highlighted disparities in food spending, with ARMM dedicating 48.19% of income to food versus NCR's 30.29%. Suggested industries to improve the economic landscape based on regional education and income data.
View on GitHub
NBA Analysis
Identified critical factors, such as +3.3 rebounds and +3.08 assists, contributing to team victories. Processed 25,000 rows of game data for analysis after handling null values and data extraction. Visualized key insights impacting team performance for home and away games.
View on GitHub