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If you want a more complex analysis, you could try **Polynomial Regression**. This method allows you to capture non-linear relationships by including polynomial terms (e.g., squared or cubed features). This can improve your model's ability to fit the data if the relationship between features and housing prices is curved rather than straight. Then, we have **Regularization Techniques**, such as Ridge and Lasso regression. These are fantastic for preventing overfitting, a common problem where your model performs well on the training data but poorly on new data. They work by adding a penalty term to the model, which discourages overly complex models. Finally, you can try **Ensemble Methods**, such as Random Forests or Gradient Boosting. These combine multiple decision trees to create a powerful predictive model. They're often very accurate but can be more complex to interpret.
