While linear regression is a powerful tool, it has its limitations: - Assumes Linear Relationship: It assumes a linear relationship between the dependent and independent variables, which may not always be the case. - Sensitive to Outliers: Outliers can significantly affect the model's accuracy. - Overfitting: Including too many variables can lead to overfitting, where the model performs well on historical data but poorly on new data.