Okay, so today I wanna talk about this little project I messed around with called “chelsea astrid”. It wasn’t anything crazy, just a fun dive into something I was curious about. Let me walk you through what I did.
First off, I started by gathering my resources. I had this old dataset I found lying around, it was a mess of different things – text snippets, some images, and even a little bit of audio. Thought it would be a good challenge to wrangle it all together. The goal? Well, I didn’t really have one, to be honest. It was more about seeing what I could do with this pile of digital junk.
Next up, I decided to focus on the text data first. It was the easiest to handle. I fired up Python and started cleaning things up using Pandas. You know, the usual: removing duplicates, handling missing values (there were a ton), and standardizing the text format. It was tedious, but necessary. After that, I threw the cleaned data into a basic NLP pipeline. Nothing fancy, just tokenization, stop word removal, and stemming. I used NLTK for that, it’s quick and gets the job done.
Then things got a little trickier. The images were all over the place – different sizes, formats, resolutions. I figured I’d try to do some basic image classification. I used TensorFlow and Keras to build a simple CNN. I struggled a bit with getting the data into the right format for the model, but eventually, I got it working. The accuracy wasn’t great, but it was enough to at least categorize the images into a few broad categories. I then moved onto the audio, which honestly, I barely touched. I tried using Librosa to extract some features, like MFCCs, but I didn’t really know what to do with them. So, I ended up just visualizing the waveforms and spectrograms, which was kinda cool, visually speaking.
After all that, I wanted to see if I could tie everything together somehow. I explored some basic machine learning models. I wanted to predict something related to the text data based on what I had from the image and audio. It was tough because the features I extracted from the images and audio weren’t super informative. In the end, I managed to create a simple model that could predict a general sentiment score for the text, based on the image category and some audio features. Again, nothing groundbreaking, but it was a cool proof of concept.
Finally, I threw everything together in a simple Streamlit app. It was just a basic interface where you could input some text, and it would show the predicted sentiment, along with a relevant image and audio visualization. It was rough around the edges, but it worked. I had to install some packages, like streamlit, pandas, tensorflow, keras, nltk, librosa. It took a while, and I ran into dependency issues.
Looking back, “chelsea astrid” wasn’t a huge success in terms of solving a real-world problem. But I learned a ton about data wrangling, NLP, image processing, and audio analysis. And that, to me, is worth more than anything. Plus, it was just fun to mess around and see what I could create. Maybe I’ll revisit this project someday and try to take it to the next level. But for now, it’s a done deal.
- Data collection: Scraped data from various online sources
- Data cleaning: Removed duplicates, handled missing values
- NLP: Tokenization, stop word removal, stemming
- Image processing: Basic image classification using CNN
- Audio analysis: Feature extraction using Librosa
- Model building: Predicting sentiment based on image and audio features
- App development: Created a Streamlit app to showcase the results
