Artificial intelligence (AI) and data science (DS) are
receiving
a lot of attention in various fields. In the educational field, the
need for education utilizing AI and DS is also being emerged. In this
context, we have created an AI/DS integrating program that generates
a compound classification/regression model using characteristics of
compounds and predicts classification and boiling points of compounds
from an unknown dataset. Students have experienced data collection
and preprocessing, exploratory data analysis, modeling, and prediction.
The No-Code-Low-Code Orange3 tool has been used for the process of
modeling and prediction so that even beginners can easily perform
Machine Learning (ML) analysis. The raw dataset containing 24 characteristics
for 277,569 compounds went through data preprocessing process and
became a well-refined dataset. The Random Forest model accurately
predicted whether the type of compound in the unknown dataset was
hydrocarbons, alcohols, or amines and predicted the boiling points
of the some arbitrary compounds within the average error range of
4.49K. This activity will provide meaningful implications for how
AI/DS technology could be integrated into each domain.