Learning from past mistakes and using ChatGPT to build better machine learning models for Food Industry Companies
The journey I am about to take you on is important for two reasons.
- It will show you how you can use ChatGPT to help support companies working in the food industry.
- Arguably the most important reason, I am going to walk through a post I made almost two years ago, point out the problems with that article, and attempt the fix them.
Yes, I argue that the second reason is more important. Why? Looking back past ways and processes you analyze data is important because it allows you to learn how to fix your failures which ultimately leads to success. I am in no way perfect, and I personally look for the wrong things I have done in the past in the hopes of learning from my mistakes and developing stronger models for the clients I support.
I first published “Machine Learning is Not Just for Big Tech” in July of 2021.
The purpose of the article was to show how a company in the food industry could be supported by the various uses of machine learning (ML). I used Natural Language Processing (NLP) techniques to work with reviews across the internet about the company. Some of the methods I used from NLP were Topic Modeling Analysis to gain a better understanding of what customers were talking about and Sentiment Analysis to create a model that could help predict the sentiment of future reviews and provide feedback to the company. The analysis showed both methods were capable of being performed on a small corpus of data.
AH! The big mistake.
My data was not great. Not only was the dataset small, but it was also biased toward positive reviews. This led to models almost always predicting a review to be positive…