Data Cleaning

Data cleaning is the process of identifying and correcting inaccuracies or inconsistencies in data to improve its quality. This process involves removing duplicate entries, correcting errors, and filling in missing values to ensure that the dataset is accurate and reliable. Data cleaning is crucial for effective data analysis and machine learning, as poor quality data can lead to misleading results and conclusions. Common use cases include preparing datasets for analysis, enhancing the performance of machine learning models, and ensuring compliance with data quality standards.