Last week I mentioned I would be working through the new IEEE-CIS Fraud Detection Kaggle Competition as a mechanism for exploring some Data Science concepts. The first video walked you through setting up a Python environment and version control for an ML project.
This week, I have a new video that dives into the data and builds the first model using the Gradient Boosting library, Catboost.
Zak Jost
A builder and Machine Learning Scientist
The goal is to present useful, long-form technical content in a way that's privacy-conscious. With the exception of basic analytics, I have made an effort to remove all forms of tracking–please notify me if you find otherwise.
By hitting the Subscribe button, you can subscribe to a MailChimp e-mail list, which e-mails new blog content. If you wish to subscribe to the content without using MailChimp, you can find the RSS feed here.
I am currently a Research Scientist at Amazon Web Services, where I apply Machine Learning to solve business problems related to fraud. I am a father of two girls and a husband. My educational background is in physics and I started my career in the semiconductor industry. After a few years as a Process Engineer and then Materials Scientist, I discovered my passion for programming and statistical analysis, which led to a transition into Data Science. I currently enjoy coffee, reading, and mountains while residing in Seattle, WA.