Taiwanese Bankruptcy Predictor

Photo by Melinda Gimpel on Unsplash

We live in a time where decentralized finance has been on the rise and more businesses have been closing than ever before. My last post analyzed posts from a group dedicated to the downfall of rich hedge fund investors. Now I will be doing a different type of analysis.

I created a model to predict the likelihood of a business going bankrupt in Taiwan. My data set came from Kaggle. I did the usual imports of essential libraries for EDA and modeling. Fortunately my dataset has no errors. After doublechecking that the data had no issues, I did some exploratory data analysis to identify any potential trends of collinearity between features. There were no significant correlations or signs of collinearity. I then began developing a model. I used a handy method from SKLearn called “feature selection”. It algorithmically selects the most useful features out of a set of data. I chose to stick with the top 50 features for my model to train from. I will add more analysis to this project in my next post.

An innovative, content creator and young CEO who uses the languages of Music and Data to help the world become a better place.