Here’s a collection of personal projects I’ve worked on — some started out of curiosity, others from a desire to build something useful. All of them helped me learn along the way.

Hinge Data Analysis

A project that analyzes and visualizes personal data exports provided by the dating app Hinge.

By examining the user’s profile, dating preferences, and interactions with other users, the project aims to reveal patterns, trends, and meaningful statistics that enhance the understanding of how users engage with Hinge and make decisions based on their preferences.

  • Technologies: Python, Plotly, pandas, Docker
  • Data Source: Personal data exports from Hinge
  • GitHub: View Repository
  • Key Features: Analyzes user profile presentation, dating preferences, messaging patterns, response times, and match durations

BRFSS 2021 Mental Health Analysis

Predicting mental health outcomes using CDC survey data.

This project analyzes data from the 2021 Behavioral Risk Factor Surveillance System (BRFSS), a large-scale health survey conducted by the CDC. Using linear regression models, the notebook explores how factors like employment status, e-cigarette usage, and receiving the flu shot relate to reported mental health outcomes.

  • Technologies: R, Jupyter, tidyverse, lm.beta
  • Dataset: 2021 BRFSS (Behavioral Risk Factor Surveillance System)
  • Focus: Mental health prediction using linear regression
  • GitHub: View Repository

Scooby-Doo Episode Analysis

Unmasking decades of cartoon mysteries through data.

This project analyzes episodes and monster encounters from the long-running Scooby-Doo franchise using a comprehensive dataset from Kaggle with data from over 600 episodes and movies. It explores how different themes, characters, and catchphrases show up over time, and highlights the show’s trends through data visualization.

Job Search Sankey Visualization

A visual exploration of job application progress through interview stages using Sankey diagrams.

In this notebook, I process and visualize detailed job application data tracking how candidates move through various interview stages over time. By mapping transitions such as “Applied” to “Recruiter Inquiry,” then to “Technical Interview” or “Offer,” across multiple companies and application cycles, this project provides insights into common hiring workflows, bottlenecks where candidates drop out, and patterns that lead to successful offers. Using Sankey diagrams, the project reveals common paths, drop-off points, and outcomes to better understand the flow and challenges of navigating job applications.

  • Technologies: Python, Jupyter, pandas, Plotly
  • Dataset: Personal job application tracking data in .csv format across companies and interview stages
  • Focus: Visualizing job search interview stages with Sankey diagrams
  • GitHub: View Repository

Palmer Penguins Data Storytelling

An exploratory analysis using the palmerpenguins dataset, a collection of data about penguins from the Palmer Archipelago in Antarctica.

In this notebook, I leverage data analysis and visualization techniques using ggplot2, and I explore and uncover insights from the palmerpenguins dataset through compelling visualizations.

  • Technologies: R, Jupyter, ggplot2, dplyr, tidyr
  • Focus: Reproducible visual insights from real-world ecological data
  • GitHub: View Repository