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Analyzing K-Pop Using Machine Learning

This is the last part of the tutorial where I show how to document your work on GitHub and how to host a simple portfolio website using GitHub.

Jaemin Lee
Photo by Saveliy Bobov on Unsplash

You can find the previous tutorials here.

Video Version

Note: You can find the link to the entire GitHub repository at the bottom of this post.

I would like to end this tutorial by documenting it on GitHub and creating a simple portfolio website using GitHub. Special thanks to Ken Jee for the useful video on how to create a simple portfolio website.

- Created a web application that returns the predicted number of hours one listens to K-Pop on a daily basis (MAE ~ 1.2 hours).
- Engineered features from the text of each column.
- Explored the data to analyze the relationships among the features.
- Built five different regression models — linear, lasso, ridge, random forest, and XGBoost.
- Optimized the random forest and the XGBoost models using GridSearchCV to find the optimal parameters.

Create a new repository

Repositories -> New -> Initialize with

I am naming mine as “ds-simple-portfolio”.

I am going to add a description for the K-Pop Analysis project as below.

I am also going to add some images here — an image for the web application and some images for the exploratory data analysis.

Once you finished adding content in the file, go to “Settings”.

Scroll down to “GitHub Pages” and turn on the “Master Branch” under “Source”.

Once you click it, it will give you a URL which is your working portfolio website!

You can also choose the theme of your taste.

Final Portfolio Website

Thank you for tuning in this tutorial! Kudos to all of you who made it to this final part. I personally enjoyed this journey as I learned technologies that I haven’t used before. I learned how to use GridSearchCV to find optimal parameters and I learned how to deploy a model and create a web app.

Finished Simple Portfolio Website

My GitHub Repository is here.