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Statistically Insignificant

Trying, Failing, and Sometimes Succeeding at Data Science

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Hello!

My name is Chi Wang and I’m an aspiring data nerd from Alberta, Canada! I’m a consultant at Deloitte Canada by day, and a complete mess by night usually experimenting with music, playing basketball, or tinkering with machine learning. This blog aims to sharpen my data science skills by trying, failing, and sometimes succeeding!

Projects

  • 1. All-NBA Predict (32)
  • 2. Music Genre Clustering (7)
  • 3. Edmonton Property Assessment (5)
  • 4. Chi / Larissa Face Detection (18)
  • 5. NYPD Crime (20)

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Category: 2. Music Genre Clustering

Music Genre Clustering #7 – Gradient Boosting (Part II)

Troubleshooting Our Model So we saw that our first GBT approach in the last post was, to quote myself, a “steaming pile of hot garbage”. In reality, that’s probably and understatement because it’s barely even a model. I could’ve gotten the results with literally no model and straight guesses. Disappointing to say the least. Of […]

Read More Music Genre Clustering #7 – Gradient Boosting (Part II)

Music Genre Clustering #6 – Gradient Boosting (Part I)

Selecting Model Man, we’ve made it so far haha. It’s felt like quite a journey so far. This is good. The bright light at the end of the tunnel is ahead. Let’s hope, when we reach the other side, the bright light comes from a blazing sun in a bright blue sky with clouds that […]

Read More Music Genre Clustering #6 – Gradient Boosting (Part I)

Music Genre Clustering #5 – Extracting MFCCs from My iTunes Library

Training & Classification Last post, I referenced a great MIR resource that already had outlined a process to do what I’m currently trying to do. To recap some of the methods I learned, each training song is broken up into frames and the MFCCs of each frame became a training sample. Each song yielded around […]

Read More Music Genre Clustering #5 – Extracting MFCCs from My iTunes Library

Music Genre Clustering #4 – Mel-Frequency Cepstral Coefficients

Mel-Frequency Cep-WHAA?? Uhh yeah, pretty much what the title says lol. Okay, let’s back up about 3 days. I’ve been doing a bit of light research on how to meaningfully extract features out of a song. In fact, this can probably be generalized to sounds in general because I’m by no means an expert on […]

Read More Music Genre Clustering #4 – Mel-Frequency Cepstral Coefficients

Music Genre Clustering #3 – Analyzing Music Genres

Review Okay, so the last post was great and all, but what exactly did I get out of it that will help me identify something to analyze? I saw librosa’s spectrogram, chromagram, and tempogram capabilities which help us somewhat identify the instruments, key, and tempo respectively. These will absolutely change and evolve as we listen […]

Read More Music Genre Clustering #3 – Analyzing Music Genres

Music Genre Clustering #2 – Exploring Librosa and its Visualizations

Librosa I learned about LibROSA while watching a scipy video: Seems pretty cool, the guy seems like a huge music nerd (in the senses of a nerd about music and just a nerd in general), he seems to get who I am and what I want to do, so why not give it a try. […]

Read More Music Genre Clustering #2 – Exploring Librosa and its Visualizations

Music Genre Clustering #1 – Intro

Yello Yello. My name is Chi, and this is my second set of posts on this blog. In my first post about basketball, I go into a bit of an intro of myself. I won’t rehash everything, but maybe a small intro will help again. First of all, I work as an IT consultant by […]

Read More Music Genre Clustering #1 – Intro
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