Day 293 of 365: Model Comparison 🚀📚✏️🚀

Hey, Model!

Welcome to Day 293 of our #365DaysOfDataScience Journey! 🎉

This day will feel like a collaborative learning effort as you dive deeper into comparing models, which is a key part of building robust machine learning systems. After completing this task, you can share your results with the community for feedback and discuss insights with others.

🔑 What We’ll Be Exploring Today:

  - Compare multiple machine learning models (e.g., Random Forest, Gradient Boosting, XGBoost).

  - Analyze the strengths and weaknesses of each model.

📚 Learning Resources:

  - Read: "Model Comparison and Selection" in Hands-On Machine Learning by Aurélien Géron.

✏️ Today’s Task:

  - Train and compare at least two machine learning models. Document their performances and analyze which performs better on your dataset. Consider metrics like accuracy, precision, recall, or RMSE, depending on your task. This will help solidify understanding of the trade-offs between models and which might be more appropriate for certain problems.

Happy Learning & See You Soon!

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