Noisy data is any data that contains errors, inaccuracies, or irrelevant information that can interfere with the quality and performance of machine learning models. Noisy data can arise from various sources, including sensor inaccuracies, human error during data collection, and environmental factors. The presence of noise can lead to incorrect conclusions, reduced model accuracy, and poor generalization to new data. To address this challenge, data preprocessing techniques such as outlier detection, data cleaning, and data augmentation are often employed. By identifying and mitigating noise, data scientists can enhance the robustness and reliability of their models, leading to more accurate predictions and insights.