Data quality can significantly impact the performance of the chosen algorithm. High-quality data with minimal missing values and noise is ideal. However, in real-world scenarios, data might be incomplete or noisy. Algorithms like random forests and gradient boosting are known for their robustness to imperfect data. It is also crucial to consider data preprocessing techniques to clean and prepare the data for analysis.
