Turning data into knowledge, One step at a time
One of my favorite parts of the SURE Program was experimenting with different machine learning models. Going in, I thought the whole goal was to find the model that performs best. But what I discovered is that each model looks at the problem differently, and sometimes the real value comes from comparing their perspectives. The... Continue Reading →
This past summer, I had the chance to take part in the Summer Undergraduate Research Experience (SURE) at Lewis University, and honestly, it was one of the coolest things I have ever done as a student. I had done plenty of projects before, like analyzing Pokémon stats, cleaning up UFO sighting reports, or even building... Continue Reading →
https://realpython.com/pycon-guide/ As I was flying back to Chicago, I found myself wondering, Should I write something about PyCon this year? The answer came to me instantly—and loudly: YES! All caps. With a huge exclamation mark. This was my second time attending PyCon, and honestly, the experience was just as exciting as the first. In a... Continue Reading →
When working with machine learning models, raw data often isn't enough. Features in your dataset may need transformation to make them understandable for algorithms, especially when dealing with categorical data. That’s where encoding techniques like One-Hot Encoding and Ordinal Encoding come into play. What Are One-Hot Encoding and Ordinal Encoding? One-Hot Encoding One-hot encoding is... Continue Reading →
Learning with Data 