Data Cleaning

Data cleaning involves examining a dataset for quality problems and correcting them where appropriate. This can include removing duplicate records, handling missing values, correcting formatting inconsistencies, fixing inaccurate entries, and identifying outliers. The goal is to create a more accurate and consistent dataset for downstream processing and model training.

Machine learning models depend heavily on the quality of their training data. Poor-quality data can produce inaccurate predictions, introduce bias, and reduce model reliability. Effective data cleaning helps improve data quality, model performance, and the accuracy of analytical results.