Data Cleaning

Data cleaning, also known as data cleansing, is a crucial step in data preparation. It involves identifying and rectifying errors, inconsistencies, and inaccuracies within a dataset to enhance its quality and reliability. This process includes detecting and correcting corrupt records, removing duplicate entries, and filling in missing values. Effective data cleaning ensures that the data is accurate, consistent, and ready for analysis. By eliminating noise and errors, data cleaning helps in obtaining more reliable insights and making informed decisions. It is an essential practice in various fields, including data science, business intelligence, and machine learning, where high-quality data is paramount for achieving accurate and meaningful results.