Alright, so check it out, today I’m gonna walk you through my dive into something I was messing around with called “jessica delp”. Don’t ask me why the name, it just stuck.
First things first, I had to figure out what I even wanted to do with it. I knew I wanted to play with some data, maybe visualize something cool, but I didn’t have a specific project in mind. So, step one was just gathering some data. I started by looking for any publicly available datasets that seemed interesting. Scraped a couple of websites, nothing too fancy.
Next up, cleaning that data. Ugh, this part always sucks. Found a bunch of missing values, weird formatting issues, the whole nine yards. Used Python with Pandas to wrangle it all into shape. Honestly, half the battle is just making sure your data isn’t a complete mess.
Okay, so with the data somewhat cleaned, I started exploring. I wanted to see if there were any interesting relationships or patterns hiding in there. Threw together some quick charts using Matplotlib and Seaborn. Scatter plots, histograms, the usual suspects. Didn’t find anything earth-shattering, but it gave me a better feel for the data.
Then, I thought, “Why not try to build a simple predictive model?” I had this one variable I wanted to try and forecast. So, I split the data into training and testing sets. Tried a few different algorithms: linear regression, decision trees, even a random forest. The random forest actually performed surprisingly well.
- Data Acquisition (Scraping, open source)
- Data Cleaning (Pandas, Python)
- Exploratory Data Analysis (EDA with visualization)
- Model Building (sklearn, Python)
But here’s the thing: the model wasn’t really useful. It was more of a proof of concept. I realized that to make it truly valuable, I’d need to incorporate more features, maybe get some external data sources, and definitely spend more time fine-tuning the parameters.
So, what did I learn from this “jessica delp” adventure? Well, data projects are messy, iterative processes. You start with an idea, you gather data, you clean it, you explore it, you build a model, and then you realize you need to go back and do everything all over again. But that’s part of the fun, right?
Final Thoughts
Even though the project didn’t result in some groundbreaking discovery, it was a great learning experience. I got more comfortable with data manipulation, visualization, and machine learning. And who knows, maybe I’ll revisit it someday and turn it into something truly awesome.
