Recently, the local burger chain Tropical Hut trended on twitter. It is one the oldest local fast food chains in the Philippines. Unfortunately, unlike its competitor Jollibee, and the multi-national Mc Donald’s, they fell off in popularity. They only have a few remaining branches in Metro Manila, that are still surviving despite the competition and of course the pandemic.
The tweet that started the trend was posted on June 12 (Philippine Independence day). On that tweet, the author stated that he dined in at a Tropical Hut branch, and he is their only customer. Despite the pandemic “restrictions”, commercial activity on malls and fast food chains are pretty much back to “normal”, so having only one customer is disheartening. The tweet included a photo of the branch which has shown that it’s aesthetic was really left behind in the 90’s-2000’s era. Most of its competitors have refurbished/modernized the look of their restaurants in the recent years.
I guess the photo really activated the nostalgia of twitter uses who reminisced dining in the fast food joint years ago. For me personally, I think I only dined in there once, when I was a kid. Their branch near our home closed down more than a decade ago. The trend resulted into a surge of orders both for dine in and delivery that really shocked the staff (as stated in the tweets).
I wanted to look into the trend further, so I studied how to scrape tweets using the snscraper library. There are only about 8000 public tweets since Jan 2022 until Jun 2022 (when the trend happened) so the scraping process was quick. My basic analysis can be seen on the embeded jupyter notebook below.
Other Articles
In this article, I fine-tuned a pre-trained object detection model using a small custom dataset.
In this article, I will discuss a way of accessing Google Cloud GPUs to train your Deep Learning projects.
In this project I trained a transformer model to recognize words from audio.
The rate at which unreliable news was spread online in the recent years was unprecedented. In this project, I finetuned some language models to make an unreliable news classifier.
Bayesian Networks are a compact graphical representation of how random variables depend on each other. It can be used to demonstrate the …
In this article we will break down what the Fourier Transform does to a signal, then we will be using Python to compute and visualize the transforms of different waveforms.
Mastering Pipelines: Integrating Feature Engineering into Your Predictive Models
Master predictive modeling with Scikit-Learn pipelines. Learn the importance of feature engineering and how to prevent data leakage.
However, to make it a little bit more scalable, the tables are defined in a separate Google sheet, and imported into the Google Colab notebook. Each node is a separate worksheet, and the columns list the parent nodes, the name of the node, and probability.
Predicting a Fitness Center’s Class Attendance with Machine Learning
In this project I analyzed a fitness center's attendance data to predict attendance rates of its group classes.
Unlocking Data Science: Your Easy Docker Setup Guide
Ready to dive into data science? Learn how to set up your development environment using Docker for a seamless and reproducible workspace. Say goodbye to compatibility issues and hello to data science success!
In this article, I fine-tuned a pre-trained object detection model using a small custom dataset.
In this article, I will discuss a way of accessing Google Cloud GPUs to train your Deep Learning projects.
In this project I trained a transformer model to recognize words from audio.
The rate at which unreliable news was spread online in the recent years was unprecedented. In this project, I finetuned some language models to make an unreliable news classifier.
Bayesian Networks are a compact graphical representation of how random variables depend on each other. It can be used to demonstrate the …
In this article we will break down what the Fourier Transform does to a signal, then we will be using Python to compute and visualize the transforms of different waveforms.
Mastering Pipelines: Integrating Feature Engineering into Your Predictive Models
Master predictive modeling with Scikit-Learn pipelines. Learn the importance of feature engineering and how to prevent data leakage.
However, to make it a little bit more scalable, the tables are defined in a separate Google sheet, and imported into the Google Colab notebook. Each node is a separate worksheet, and the columns list the parent nodes, the name of the node, and probability.
Predicting a Fitness Center’s Class Attendance with Machine Learning
In this project I analyzed a fitness center's attendance data to predict attendance rates of its group classes.
Unlocking Data Science: Your Easy Docker Setup Guide
Ready to dive into data science? Learn how to set up your development environment using Docker for a seamless and reproducible workspace. Say goodbye to compatibility issues and hello to data science success!
In this article, I fine-tuned a pre-trained object detection model using a small custom dataset.
In this article, I will discuss a way of accessing Google Cloud GPUs to train your Deep Learning projects.