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 →
Source: https://x.com/Pokemon/status/1106326121228857344/photo/1 This project started with a simple question:"Is there a relationship between a Pokémon’s type and its base stats?" That’s how most data projects begin: with curiosity and a CSV. I loaded the full Pokédex dataset expecting to run some basic distributions: maybe compare average Attack across types, or see if Dragon-types really are... Continue Reading →
If you’ve ever started a data project from scratch, you know the truth: finding a good dataset is often the hardest part. While Kaggle and GitHub are great, most of the time, real-world projects don’t come with tidy CSV files ready to go. You have to get your hands dirty and fetch the data yourself.... Continue Reading →
When I started digging into the UFO Sightings dataset, I wasn't looking for little green men. I was curious about the patterns hiding behind decades of reports. What people saw, when they saw it, and where they were when it happened. As someone passionate about data storytelling, this project gave me the perfect opportunity to... Continue Reading →
When the semester ended and winter break began, I saw it as the perfect opportunity to focus on skill-building. I spent time diving deep into data manipulation with Pandas and experimenting with machine learning models. It was a challenging yet rewarding experience that left me feeling more confident and equipped for future projects. Here’s what... 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 →
Data Science became my passion in a very unexpected and simple way. It was through an assignment in my Advanced Python class, working with a simple dataset. The dataset contained over a century's average high temperatures in New York City for January. The goal was to predict future temperature trends, but I learned much more... Continue Reading →
Embarking on the journey of Data Science can be both exciting and overwhelming. There’s so much to learn—statistics, machine learning algorithms, data manipulation, and more. However, a few essential Python libraries can simplify the process and get you up to speed. In this post, I’ll briefly walk you through two fundamental libraries—pandas, NumPy, Scikit-learn, and... Continue Reading →
Learning with Data 