Example Concept: Improving model accuracy often involves steps such as data cleaning, feature engineering, model selection, hyperparameter tuning, and cross-validation. Data cleaning ensures that the dataset is free from errors and inconsistencies. Feature engineering involves creating new features or modifying existing ones to better capture the underlying patterns. Model selection is about choosing the right algorithm that fits the problem. Hyperparameter tuning involves adjusting the parameters that govern the training process to find the best configuration. Finally, cross-validation helps in assessing the model's performance more reliably by using different subsets of the data for training and testing.