As a continuation of my previous post, in this post, I dive into the modeling stages of building out my music genre classifier. I divided my modeling approach into two stages. For the first stage, I experimented with various classification models such as Logistic Regression, KNN, Random Forest, SVM, Gradient Boost, and XGBoost. For the second stage, I experimented with deep learning models such as Convolutional Neural Network and Convolutional Recurrent Neural Network.

For the first stage of my modeling process, instead of diving into each classification models individually, I’ll lay out the general framework that I used and highlight…


For my capstone project, I decided to explore the wonderful world of music genre classification through machine learning. I purposely chose this topic because I wanted to take this opportunity to challenge myself to learn something new and to demonstrate how data science is truly an inter-disciplinary field.

As someone who casually enjoys listening to music and plays a few instruments here and there, learning all the fundamentals of audio analysis was a challenge in and of itself. …

Whenever I think of feature engineering, the first image that pops up to my mind is a hot dog with mustard. I blame one of my General Assembly instructors for this. During class, he used the hot dog and mustard imagery to demonstrate how feature engineering works.

In case you are curious, the concept behind this imagery is that no one really likes mustard by itself. Think about it. when was the last time you chugged a bottle of mustard for enjoyment? Chances are most likely never (apologies if you are a mustard lover). …

I recently dove head first into the world of data science. Without much prior experience in programming language, learning Python was a challenge in and of itself. In fact, my fear of learning Python is what actually delayed my exploration of data science. Even after starting my data science journey, it took me a while to REALLY buy into Python. After all, why go through all the pain of learning Python when I can just do everything I need to do in Excel?

I come from a traditional finance background where most of my work was done through a complicated…

Young Park

Data Scientist | MBA | Ex-McKinsey

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