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Machine Learing in General Chemistry - Welcome to Cobberland!

Welcome to Lab 2 of our Machine Learning in General Chemistry series! In this lab, we take our first steps into Cobberland—a fictional world where kernels (the building blocks of matter) have three properties: EARitability, aMAIZEingness, and STOCKiness. You will use machine learning to predict STOCKiness based on the other two properties, introducing you to the fundamentals of predictive modeling. Before jumping into real-world chemistry, this exercise allows us to focus on key machine learning concepts in a simplified context. To setup the software needed for this lab please watch the following video.    • Getting Started with Machine Learning...   Later this year, we will perform real machine learning experiments. You now know what to look for! 🔬 Lab Overview: In this experiment, we’ll explore several machine learning models, including Linear Regression, Decision Trees, Random Forests, Support Vector Machines (SVMs), and k-Nearest Neighbors (k-NN). You'll learn how to choose the best model, evaluate performance using both graphical and numerical tools, and balance accuracy with computational efficiency. 🌟 Key Takeaways: Understanding how to use machine learning models for property prediction Learning the strengths and weaknesses of different models Interpreting quantitative metrics like MAE, MSE, and R² Visualizing model performance through Actual vs. Predicted and Residuals plots Exploring the effect of training/test splits on model performance 📚 Educational Value: This lab will provide you with practical experience in selecting machine learning models and evaluating their performance. You'll learn how to recognize overfitting, underfitting, and how to improve generalization to unseen data. 💡 Hands-On Skills: You’ll practice: Loading datasets and choosing machine learning algorithms Analyzing model performance with key metrics and visual plots Making STOCKiness predictions for new kernels based on EARitability and aMAIZEingness 🔗 Useful Resources: Course Website: Machine Learning in General Chemistry Instructor's Website: www.darinulness.com Concordia College Chemistry Department: Concordia Chemistry GitHub Repository: Machine Learning General Chemistry 👍 Stay Connected: If you're enjoying the journey into machine learning for chemistry, don’t forget to like, share, and subscribe! Your support helps us continue creating in-depth educational content. #MachineLearning #GeneralChemistry #ChemistryEducation #PredictiveModels #ConcordiaCollege #GNLProject