Day 29: Polynomial Regression — Capturing Non-Linear Relationships

1. 1Apply Polynomial Regression to a dataset using Python's scikit-learn library to capture non-linear relationships
2. 2Build a model with varying degrees of polynomial features to compare results
3. 3Configure the model to handle overfitting by using regularization techniques
4. 4Test the model's performance using metrics such as mean squared error and R-squared
5. 5Compare the results of Polynomial Regression with Linear Regression to determine the best approach for a given problem

Data scientists and analysts can benefit from this topic to improve their regression analysis skills and build more accurate models

💡 Polynomial Regression can effectively model non-linear relationships, but requires careful tuning of hyperparameters to avoid overfitting

📈 Capture non-linear relationships with Polynomial Regression! 💡