Data Preprocessing and Cleaning

Raw data often contains noise, missing values, and inconsistencies. Data preprocessing involves normalization, outlier detection, deduplication, and transformation techniques. Common frameworks include Pandas and NumPy in Python for preprocessing, as well as Apache Spark for distributed data cleaning. Techniques like ETL (Extract, Transform, Load) pipelines help automate this process.