I have a background in data science, mathematics, robotics, and applied engineering, with hands-on experience in Python, SQL, C++, machine learning, data visualization, ROS 2, and simulation.

My GitHub focuses on practical data and engineering projects, including used-car price modeling, autonomous racing simulation, computer vision detection, API-based analytics, biostatistical modeling, and C++ object-oriented programming.

A machine learning project for analyzing used-car depreciation patterns and estimating resale prices from listing-level vehicle attributes.

This project builds a practical valuation workflow, including data cleaning, exploratory analysis, feature engineering, LightGBM modeling, benchmark comparison, segment-level error analysis, and a Streamlit app for interactive vehicle price estimation.

Highlights:

• Cleaned and processed large-scale used-car listing data
• Engineered depreciation-related features such as vehicle age, mileage transforms, and engine displacement
• Trained a LightGBM regression model for resale price prediction
• Benchmarked model performance against simpler baseline models
• Built a Streamlit app to demonstrate price estimation and depreciation curves

Tech Stack: Python, Pandas, LightGBM, Scikit-learn, Streamlit, Matplotlib, Regression Modeling

A ROS 2 autonomous racing simulation project using LiDAR-based local planning in the F1TENTH simulator.

This project combines wall following, follow-the-gap, local trajectory planning, predictive speed control, racing-line biasing, and ML-assisted tuning. It demonstrates how robotics simulation and data-driven parameter tuning can be used to improve autonomous vehicle behavior.

Highlights:

• Built a LiDAR-based local racing planner for the F1TENTH simulator
• Combined wall following, follow-the-gap, and local planning logic
• Added predictive speed control based on curvature, clearance, braking distance, and steering smoothness
• Used parameter tuning and ML-assisted adjustment to improve simulated driving performance
• Organized configs, scripts, models, and test results for reproducible evaluation

Tech Stack: ROS 2, Python, F1TENTH, LiDAR, NumPy, YAML, Simulation

A computer vision project for traffic sign detection using YOLOv8 and custom lightweight architecture variants.

This project compares a standard YOLOv8 baseline with attention and feature-fusion extensions such as CBAM, LCFE, IMCMD, and YOLO-TS-style fusion. The goal is to improve small-object traffic sign detection and evaluate model robustness across day and night driving conditions.

Highlights:

• Trained and evaluated YOLOv8-based traffic sign detection models
• Compared baseline, attention-based, and feature-fusion model variants
• Evaluated mAP, parameter efficiency, and day/night robustness
• Organized experiment configs, scripts, reports, and result summaries for reproducible analysis

Tech Stack: Python, PyTorch, Ultralytics YOLOv8, OpenCV, Object Detection, Computer Vision

A Python data analysis project comparing 2020 and 2021 year-end music trends using Spotify API data, SQLite, SQL queries, and Matplotlib visualizations.

Highlights:

• Collected playlist data from the Spotify Web API
• Stored artist and track records in SQLite
• Used SQL joins to compare artists across years
• Identified artists appearing in both years and artists unique to each year
• Generated visualizations for artist overlap and trend comparison

Tech Stack: Python, Spotify API, SQLite, SQL, Matplotlib, JSON

A Python-based mathematical biology project that simulates HIV infection dynamics and immune response using ordinary differential equations.

Highlights:

• Modeled interactions between healthy CD4+ T-cells, infected cells, virions, and immune response cells
• Used SciPy ODE solvers for numerical simulation
• Compared immune response behavior under different parameter settings
• Visualized CTLp and CTLe trends over time
• Compiled results into a research report and presentation

Tech Stack: Python, NumPy, SciPy, Matplotlib, ODE Modeling, Biostatistics

A semester-long C++ programming portfolio covering console applications, games, ciphers, object-oriented design, and simulation.

Highlights:

• Built projects including a focaccia calculator, Rock-Paper-Scissors game, cipher program, Battleship game, and elevator simulation
• Practiced C++ fundamentals, functions, loops, strings, file input, and class design
• Developed larger multi-file programs using object-oriented programming

Tech Stack: C++, Object-Oriented Programming, File I/O, Console Applications

Programming: Python, SQL, C++
Data Science & Analytics: Pandas, Scikit-learn, LightGBM, SQLite, Matplotlib, Streamlit, API Data Collection, Data Cleaning
Machine Learning: Regression Modeling, Model Evaluation, Feature Engineering, Benchmarking, Error Analysis
Computer Vision: PyTorch, Ultralytics YOLOv8, OpenCV, Object Detection
Robotics & Simulation: ROS 2, F1TENTH, LiDAR, Odometry, Ackermann Drive, Simulation
Tools: Git, GitHub, Ubuntu, WSL2, VS Code

I am currently building projects at the intersection of data science, business analytics, machine learning, automotive systems, and robotics simulation. My strongest interests are data analytics, business intelligence, applied machine learning, data-driven engineering, and practical software tools for solving real-world problems.