Forecasting Stock Prices And Diabetes Using Ml Models.

This project from the Digital Fabrication workshop focused on using machine learning (ML) models to predict stock market trends. I explored ML models' potential to enhance decision-making in complex environments. While focused on the stock market, the project's implications extend to broader areas and questions about speculating and preparing for various scenarios. The final outcome was a metric evaluating the model's accuracy in predicting stock prices.

I chose this project because it aligned with my interest in technology and exploring the future of tech. The ML model served as a tool to challenge the idea that predictions are mere guesses. By testing the limits of our knowledge and showcasing technology's potential, the project was a starting point for further inquiry.

Initially, I focussed on the model's accuracy, but later considered its ethical implications more deeply. Because I relied on Twitter data, I realised that machine learning models, being created by humans and trained on human data, are biassed. Achieving neutrality is nearly impossible. This made me realise how data, not just in the stock market, can have significant real-world impacts. It made me more critical of how data is used in areas like the media, questioning the fairness of it.