This project was designed to delve into the intricacies of K-pop music by
extracting song features from a range of K-pop artists to construct a Content-Based Recommendation System.
Utilizing the Spotify API, a robust data pipeline was developed to gather the necessary information, forming a comprehensive dataset.
The recommendation engine was then crafted, applying principles of linear algebra to generate and present top song
suggestions to users based on their song preferences.
This project focused on conducting a multifaceted analysis and creating dynamic visualizations of NBA data, accessed through an NBA API.
The project involved meticulous data wrangling, in-depth exploration, and the strategic use of tools including SQL, Python, and Looker.
A significant component was the integration of machine learning, where an LSTM model was developed and trained using PyTorch to predict upcoming NBA season averages,
drawing on four decades of historical data.
Wrangled COVID-19 data, used SQL to perform
exploratory data analysis, and created an interactive Tableau dashboard that visualizes the insights discovered.