This project explores the relationship between model complexity and generalization performance, using polynomial regression models of varying degrees. It involves feature engineering, model training, and performance analysis using R2 scores.
- Generation of synthetic data points.
- Feature engineering by adding polynomial features of different degrees.
- Training Linear Regression models with polynomial features.
- Evaluating model performance using R2 score.
- Visualizing training, test data, and model predictions.
- Analysis of overfitting, well-generalization, and underfitting based on R2 scores.
