For my final project at Metis, I decided to combine my passion for music and data science into one project involving the best band of all time. The overall project involved Natural Language Processing (NLP) with the NLTK libraries, text generation by applying transfer learning to OpenAI’s GPT-2 model, and an application built using Steamlit. The data I used were the lyrics from The Bealtes’ discography from Genius.com and supplementary data from the web about who sang and composed each track. In this blog post, I will discuss the following topics:
This blog post corresponds to my third individual project at Metis. The project itself is an exploration of text data by the use of Natural Language Processing (NLP) and unsupervised learning. My specific project used Tweets with keyword “vegan” from 2016–2020 to understand the vegan conversation over the past five years. In this blog post, I will discuss the following topics:
I’ve been on a plant-based diet for over a year, and…
As a student of the Metis Data Science Bootcamp, I chose to explore fraudulent card transactions for my second individual project. This project had three requirements: query data from a postgreSQL database with SQL, develop a classification model, and create an interactive visualization. I found the postgreSQL and interactive visualization components fairly straight forward, so the bulk of this post discusses the development of a usable model, however, I included some insights and tips for the other two components. Also, check out the Github Repository for all of the code behind the project.
My name is Joe Cowell and I recently enrolled in the Metis Data Science Bootcamp. The 12-week immersive program will turn me from ‘data novice’ into a full-fledged data scientist. I mean, the title of this post includes ‘Supervised Machine Learning’ and I’ve only been in the program for three weeks, so it seems like Metis is holding up their end of the bargain. Anyway, I’ll try to make a post about who I am for those interested, but for now, let’s take a look at how I used supervised machine learning to predict IMDb movie ratings.