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Artificial Intelligence, Deep Neural Network, and Machine Learning all seems like the craze lately in data science. After tunnel-visioning into multiple documentation and tutorial for awhile I realized that I needed to take a step back and regather my foundation. This is a summary of quick math recaps I needed before I started building neural network models.

Linear Regression

Color composition analysis with PRAW and ColorThief

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Inspired by a recent article “Average Jeans Color by State, 2020” by Khyatee Desai, I wanted create a color based analysis of photographs. More specifically, I wanted to utilize the ColorThief library to analyze colors of street photography. My goal was simple: collect street photographs and create color palettes of them to quickly analyze and draw any insights on the colors of photos. I decided to gather my data from one of my favorite subreddits: r/streetphotography with Reddit’s PRAW API.


First, to access Reddit through PRAW you will need to get API access from reddit. You can check out this wiki here to register and get more information. After you have your API keys initializing the PRAW library is very easy. …

“Could you recommend a simple coding projects for my sophomore students?”, my friend asked. “Rock-paper-scissors…?”, I answered with fleeting confidence as I barely grasped the scope of said project. The horrors of input validation back in my college C++ course came back and started to haunt me. I quickly realized that this program might be beyond the scope of a complete novice.

Fortunately, with the guide of my friend we were able condense the code to fit the curriculum of young students. It has it’s obvious flaws, but it’s simple enough for novice to complete within few hours while practicing some basic ideas like if statements and built in functions. …

“What the hell was I thinking?” was my first thought when I revisited an old project from my last job. Reading and trying to re-establish my understanding of the logic was a daunting task to say the least. It really got me wondering what I could’ve done better in the past to make the process of refactoring easier, which inspired me to write this post. Here are 4 valuable lessons I learned from refactoring one of my oldest project.

MRW I look at my old code
MRW I look at my old code
MFW I look at my old code

Pasta: Descriptive variable names > Concise variable names

Naming variables always have been one of the hardest task for me. It’s not difficult to use a simple name for a value or function, but what is difficult is creating a name that convey its purpose. Also, I have a tendency to shorthand or initialize my variables, which seems like a great idea when I’m already familiar with the project. However, it’s a terrible thing to come back to when I’m revisiting an old code that is long forgotten. …


Yung Han Jeong

Python Enthusiast, Aspiring Data Scientist, Music and Martial Arts Nerd

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