Preprocessing techniques

Preprocessing techniques are steps taken to prepare raw data for analysis or modeling. They involve cleaning data by removing errors or irrelevant parts, transforming data into a consistent format, handling missing values, and normalizing or scaling data to make features comparable. These steps help ensure the data is accurate, complete, and organized, which improves the performance of algorithms that analyze or learn from it. Essentially, preprocessing turns messy or complex data into a clear, structured format suitable for extracting meaningful insights or making predictions.