Which method is commonly used for predictive modeling in data science?

Machine learning is a fundamental method used for predictive modeling in data science. It involves algorithms that learn patterns from historical data and make predictions or classifications based on new data inputs. This approach is highly effective because it can generalize from examples, identifying relationships within data that might not be immediately obvious.

Predictive modeling aims to forecast future outcomes based on past data, and machine learning techniques, such as regression, decision trees, and neural networks, directly contribute to this process. These models can handle large datasets and complex interactions among variables, enabling high-stakes predictions in various fields, including finance, healthcare, and marketing.

While descriptive analysis provides insights into past occurrences, sentiment analysis focuses on understanding emotions from textual data and is not primarily aimed at predicting future outcomes. Data cleaning, on the other hand, is a critical preprocessing step that ensures the quality and integrity of the data used in modeling. However, it does not in itself serve as a predictive technique. Therefore, machine learning stands out as the key approach for successfully implementing predictive modeling in the context of data science.