Mix Retriever: A Hip-Hop Playlist Generator
I love discovering new music, especially when it comes to hip-hop. Today, music streaming platforms offer thousands of dynamic playlists for finding new music that cover topics such as specific artists, genres, time of year, and many more. But what if we can’t find a playlist built around a particular song that we like? For the fifth and final project at Metis, we were given the freedom to solve any problem we were interested using any of the data science techniques and technologies we’ve learned from the bootcamp. My favorite topics covered were NLP and recommendation systems, and I wanted to solve this problem with techniques from these topics.
Would Reddit Like My Comment?
In today’s age of information, social media has become the most popular medium for sharing ideas and expressing one’s opinion, whether that be on politics or what the best genre of music is. But are all opinions treated equally on the internet? The fourth project at Metis focused on utilizing unsupervised and supervised learning techniques on datasets of our choice. One of my favorite social media platforms is Reddit. For this project, I was specifically interested in using Natural Language Processing and classification techniques to see what information I could draw from looking at Reddit comments and their content.
Introduction to Graph Databases with Neo4j
Relational database management systems (RDBMS) have been the go-to data storage system for many years. The strong theoretical foundation of relational databases and frequent use has led to the availability of many stable and standardized products. While relational databases fit well into many projects, there are trade-offs. I’ll be going over some of these trade-offs and discuss why a graph database management system, such as Neo4j, might be better suited for the job. I’ll also give a quick tutorial on how to set up and use Neo4j on your local machine!
Supervised Learning and Online Gaming
In recent years, the professional video game scene has seen huge growth in both revenue and online stream numbers. Teams are competing for huge cash prizes, and with so much money on the line, these teams are now starting to use data science in order to provide themselves with the best win conditions for the respective game they’re playing. The third project at Metis concentrated on exploring multiple supervised learning techniques to see what we could learn from datasets of our choice.