Building a Basic Machine Learning Model in Python

By now, all of us have seen the results of various basic machine learning (ML) models. The internet is rife with images, videos, and articles showing off how a computer identifies, correctly or not, various animals. While we have moved towards more intricate machine learning models, such as ones that generate or upscale images, those basic ones still form the foundation of those efforts. Mastering the basics can become a launchpad for much greater future endeavors. So, I decided to revisit the basics myself and build a basic machine learning model with several caveats -- it must be somewhat useful, as simplistic as possible, and return reasonably accurate results. Unlike many other tutorials on the internet, however, I want to present my entire thought process from beginning to end. As such, the coding part will begin quite a bit later as problem selection in both the theoretical and practical realm is equally important. In the end, I believe that understanding why will go further than how to. Although machine learning can solve a great deal of challenges, it's not a one-size-fits-all approach. Even if we were to temporarily forget about the financial, temporal, and other resource costs, ML models would still be great at some things and terrible at others. Categorization is a great example of where machine learning may shine.