The biggest challenge I ran into was optimizing our classification model. The dataset contains 50+ variables that needed to align with the mission of this tool as well as providing the most accurate prediction possible. Throughout this process, four different classification models were used to find the highest accuracy. After determining the best classification method, simplifying the model and reducing the variables in order to achieve similar accuracy rates needed to be done. The trial and error part of this project resulted in numerous failed attempts in implementing the best model possible. However, through the trial and error of identifying the best variables and ML algorithm pairing, an optimal predictive model was discovered.

Another challenge I ran into was using the taipy library to create a GUI. Personally, I have never heard or used this library and wanted to see its capabilities. This library has very little documentation online and lead to many challenges in implementing the ideal design features. Through many re-runs of this GUI, a final product was created and has allowed me to learn the amazing capabilities taipy can provide users.